satellite based hydrology and modeling

SATELLITE BASED HYDROLOGY AND MODELING

Space Applications Centre, ISRO Ahmedabad- 380015, India

May, 2018

i

SATELLITE BASED HYDROLOGY AND MODELING

Space Applications Centre, ISRO Ahmedabad- 380015, India

May, 2018

ii

Preface Water is most precious resource on earth and responsible for existence of life in any planetary system.

Although water exists in abundance on earth but availability of fresh water is a major concern as the

population is growing over the years. There is need to balance between Industrialization, urbanization,

economic growth and steadily depleting water resources. It requires efficient management strategy to

utilize the available resources in best possible manner.

India is country with 17.1 % of world population and has to manage with 2.4% of world’s area and

limited water resources (Precipitation around 4000 BCM). Large variations of rainfall in different regions

(desert to evergreen forest region) poses challenge in water management. Measurement and knowledge of

availability of water resources in different part of the country helps in management of water. Satellite

based measurements integrated with hydrological modeling are emerging technological field to assess

information on different components of water balance at regional to national scales.

The lecture notes on Satellite based Hydrology and Modeling is aimed to provide updated knowledge on

emerging trends in the field of satellite remotes sensing applications in Hydrological modeling. The

lecture note is prepared as reading material to the participants of the training on Satellite based Hydrology

and Modeling carried out at Space Applications Centre, ISRO Ahmedabad.

I am grateful to Shri Tapan Misra, Director, SAC, Dr Raj Kumar, DD, EPSA, Dr. A S Rajawat, GD

GHCAG, Shri Shashikant Sharma, GH, VEDAS Research Group, Dr. S P Vyas, Head ERTD and Shri

Hiren P Bhatt, Scientist-SG for their encouragements to conduct the training programs in different

themes for students and researchers. I thank all the faculty members for their timely contribution of

lecture notes and their interest in sharing their research work with participants across the country.

I hope this lecture material will provide the valuable information and will be useful addition to currently

available information in this field.

My sincere thanks to all the scientists of LHD for their support in organizing the training program and

Shri V. Pompapathi, Senior Research Fellow, SAC for compilation and help in editing the manuscripts.

R. P. Singh

iii

Contents

1. Basics of Remote Sensing and Applications R. P. Singh

1

2 Satellite Hydrology R.P. Singh

10

3 Remote Sensing of Soil Moisture from Space R. P. Singh

16

4 Hydrological Modeling and Remote Sensing P. K. Gupta

26

5 SACHYDRO: Snow-Melt Runoff Modeling Amit Kumar Dubey

36

6 Ramsar Wetlands: Science and Experience of Nalsarovar T V R Murthy

44

7 Evapotranspiration: Tools and Techniques Rohit Pradhan

55

8 Satellite Altimetry and Its Applications to Hydrology (Surface Water Information over Inland Water Bodies) Shard Chander

65

9 Water Quality Monitoring from Space Ashwin Gujrati

71

10 Adaptation Strategies for Ground Water Sustainability in the Face of Climate Change in India R.C.Jain

78

11 Groundwater Quality in India : Implications and Management R.C.Jain

92

12 Flood Assessment through 1D/2D Coupled Hydrodynamic Modeling Dhruvesh Patel

102

13 National Wetland Inventory and Assessment (NWIA) J G Patel

112

14 Urban Flooding Gaurav Jain

129

15 Remote Sensing of Isotopes for Hydrological Applications Nimisha Singh

136

16 Introduction to Geographical Information System (GIS) R J Bhanderi

143

BASICS OF REMOTE SENSING AND APPLICATIONS

R. P. SINGH

Land Hydrology Division

Geosciences, Hydrology, Cryosphere Sciences and Applications Group (EPSA)

Space Applications Centre, ISRO

Ahmedabad-380015

Remote sensing technique is being currently used for providing solution to many resource

management issues as well as carry out scientific investigations to explore newer dimensions in

field of global carbon cycle, water cycle, improved weather prediction and planetary studies.

Satellite observations in different electromagnetic regions allow detection of various geophysical

parameters of the earth and planetary environment. This article reviews basic concepts involved

in remote sensing as well discusses various earth observation applications in India

Key Words: Remote Sensing, Spectral Signatures, Synthetic Aperture Radar, Passive

Microwave Radiometer, Scatterometry.

1.0 Introduction

Remote Sensing usually refers to the technology

of acquiring information about the earth's surface

(land and ocean) and atmosphere using sensors

onboard airborne (aircraft, balloons) or space

borne (satellites, space shuttles) platforms. In

remote sensing, the sensors are not in direct

contact with the objects or events being observed.

The electromagnetic radiation is normally used as

an information carrier in remote sensing.

Electromagnetic radiation is a self-propagating

wave in space with electric and magnetic

components. These components oscillate at right

angles to each other and to the direction of

propagation. Every object reflects/scatters a

portion of the electromagnetic energy incident on

it depending upon its physical properties. In

addition, objects emit radiation depending on

their temperature and emissivity. If we study the

reflectance/emittance of any object at different

wavelengths, we get a reflectance/emittance

pattern which is characteristic of that object.

Visual perception of objects is the best example

of remote sensing. We see an object by the light

reflected from the object falling on the human

eye. Modern remote sensing is an extension of

this natural phenomenon. However, apart from

visible light, the electromagnetic radiation

extending from the ultraviolet to the far infrared

(IR) and the microwave regions are also used for

remote sensing of the earth resources.

Remote sensing employs passive and/or active

sensors. Passive sensors are those, which sense

natural radiations, either emitted or reflected

from the earth. On the other hand, sensors, which

produce their own electromagnetic radiation, are

called active sensors (LIDAR, SAR). Remote

sensing can also be broadly classified as optical,

thermal and microwave. In optical remote

sensing, sensors detect solar radiation in the

visible and near infrared wavelength regions,

reflected or scattered from the earth, forming

images resembling photographs taken by a

camera located up high in the space. Thermal

remote sensing deals with detection of emitted

thermal radiation (generally in 3- 15 um spectral

1

range). This information is used to estimate,

temperature, humidity, cloud properties, thermal

inertia, surface mineral composition etc.

Microwave remote sensing is done by observing

passive emission or backscattered signal in 1-200

GHz spectral range.

Figure 1. Remote sensing in which solar radiation reflected from different surface features are observed by

satellite sensor.

2.0 Spectral Signature

Different land cover features, such as water, soil,

vegetation, cloud and snow reflect visible and

infrared light in different ways. The interpretation

of optical images requires the knowledge of the

spectral reflectance signatures of the various

materials (natural or man-made) covering the

surface of the earth. Any set of observable

characteristics (such as wavelength wise

variation of target reflectance) which directly or

indirectly leads to the identification of an object

and/or its condition is termed as signature.

Spatial, spectral and temporal variations are

important characteristics of target, which is used

for discrimination. Spectral signature of

vegetation is uniquely characterized by

absorption in the blue and red bands due to

chlorophyll. Generally leaf pigments in visible

region, cell structure in near infrared region

(NIR) and water content in short wave infra red

(SWIR) region are the dominant controlling

factor in leaf/canopy spectra. Infrared sensors

which measure the thermal infrared radiation

emitted from the earth help in estimation of land

or sea surface temperature.

Figure 2. Wavelength wise distribution of

reflectance (spectral signature) of different earth

features superimposed by vertical lines showing

different bands of MOS-B sensor.

2

Satellite sensor measures radiance which is the

radiant flux per unit solid angle leaving an

extended source in a giving direction per unit

projected source area in that direction. Unit of the

radiance is watts per meter square per micron, per

steradian (Wm-²um-1sr-¹). Observed radiances are

converted into reflectance which is the fractional

part of the incident radiation that is reflected by

the surface. Spectral reflectance is the reflectance

measured within a specific wavelength interval.

The reflection from a surface, which follows

Snell’s Law of reflection (angle of incidence =

angle of reflection, both measured from the

surface normal) is called specular reflection.

Here, the direction of the outgoing or reflected

ray is completely determined by the incoming

direction. If the angular distribution of the

reflected ray varies with the surface property and

does not follow Snell’s law then such reflection

is said to be diffuse. The reflection from a

Lambertian surface (whose intensity varies as

cosine of the angle measured from the normal to

the surface) is diffuse in nature. Bidirectional

Reflectance Distribution Function (BRDF)

describes the directional dependence of reflected

optical radiation. It characterizes the radiance

reflected into a specific view direction as a result

of the radiant flux incident upon a surface.

In optical remote sensing of the earth, the optical

sensors are looking through a layer of atmosphere

lying in between the sensors and the Earth's

surface being observed. Hence, it is essential to

understand the effects of atmosphere on the

electromagnetic radiation traveling from the

Earth to the sensor through the atmosphere. The

atmospheric constituents cause wavelength

dependent absorption and scattering of radiation.

These effects degrade the quality of images.

Some of the atmospheric effects can be corrected

before the images are subjected to further

analysis and interpretation. Absorption in the

atmosphere mostly occur when the EM radiation

interact with the atmospheric atoms or molecules

so as to excite the molecule to a higher energy

level. In this process, the incident radiation

transfers all or part of its energy to molecule.

A consequence of atmospheric absorption is that

certain wavelength bands in the electromagnetic

spectrum are strongly absorbed and effectively

blocked by the atmosphere. The wavelength

regions in the electromagnetic spectrum usable

for remote sensing are determined by their ability

to penetrate the atmosphere. These regions are

known as atmospheric transmission windows.

Remote sensing systems are often designed to

operate within one or more of the atmospheric

windows. Atmospheric molecules are

responsible for selective absorption in different

wavelength.

Even in the regions of atmospheric windows, the

scattering by the atmospheric molecules and

aerosols produces spatial redistribution of energy.

The scattered / diffused radiance entering the

field of view of a remote sensor, other than that

from the target of interest, is called path radiance.

Scattering is a multiple reflection of

electromagnetic waves by particles or surfaces.

Energy is not lost to the medium but the radiation

is scattered out to other directions, thereby

reducing the amount of radiation in the original

direction. The sum total of absorption and

scattering is known as attenuation. Broadly there

are three type of scattering process in atmosphere.

1) Molecular or Rayleigh Scattering - This occurs

when the particles causing the scattering are

smaller in size than the wavelengths of radiation

in contact with them. This type of scattering is

therefore wavelength dependent. As the

wavelength decreases, the amount of scattering

increases. It is the Rayleigh scattering that is

responsible for the sky appearing blue.

2) Particle or Mie Scattering - Mie scattering is

caused by pollen, dust, smoke, water droplets,

and other particles in the lower portion of the

3

atmosphere. It occurs when the size of particles

causing the scattering are similar or slightly

larger than the wavelengths of radiation in

contact with them. Turbid appearance of sky is

due to Mie scattering caused by suspended

aerosols.

3) Non-selective Scattering - It occurs in the

lower portion of the atmosphere when the

particles are much larger than the incident

radiation. This type of scattering is not

wavelength dependent. Scattering of optical light

in cloud is associated with non selective

scattering.

Figure 3 Atmospheric transmittance due to absorption of different atmospheric molecules.

3.0 Thermal Remote Sensing

Any object above absolute zero temperature

emits electromagnetic radiation. Thus the objects

we see around, including ourselves are thermal

radiators. An ideal substance is called blackbody

which absorbs the entire radiant energy incident

on it and emits radiant energy at the maximum

possible rate per unit area at each wavelength for

any given temperature. No actual substance is a

true blackbody, although some substances

approach its properties. The radiance being

emitted by a blackbody at given wavelength ( )

and Temperature T is given by Planck’s

Radiation Law.

Mλ = 2лhc2

λ5 [exp(hc/λkT)-1]

Where k Boltzmann’s constant, h is Planck’s

constant, c is velocity of light, and T is the

absolute temperature in Kelvin.

A black body is an ideal surface such that

1. It absorbs all incident radiation

regardless of the wavelength or direction of the

incident radiation;

2. For a given temperature and wavelength,

nobody can emit more energy than a black body;

3. Emission from a black body is

independent of direction, that is, the black body

is a diffuse emitter.

4

The total emission within all the wavelengths

Mtotal can be found out by integrating the Planck’s

equation from λ = 0 to λ = ∞ and works out to be

Mtotal = σT4 Wm-2

Where σ is Stefan –Boltzman constant.

Another useful expression in thermal remote

sensing is Wien’s Displacement Law, which

gives the wavelength λmax at which the exitance is

maximum and is related to the temperature as

λmax T = constant

if λmax is expressed in micrometer and T in 0K,

then the constant is 2897.

The heat energy is converted to radiation at a

maximum rate as per Planck’s law. However, a

real surface does not emit at this maximum rate.

The emission from a real surface is characterized

with respect to a black body. In order to do so, a

term called emissivity is used which compares,

the ‘radiating capability’ of a surface to that of a

black body (an ideal radiator).

Figure 4. The Planck’s radiation distribution of blackbody at different temperatures

Emissivity () defined as the ratio of radiant

exitance of the material of interest (Mm) to the

radiant exitance of a black body (Mb) at the same

temperature.

= Mm / Mb

For a black body, = 1, for all wavelengths. For

a gray body < 1, and can vary with wavelength

and direction.

Brightness temperature (TB) of the surface is the

temperature of a blackbody surface which, when

placed in front of the receiver aperture, would

produce the same received flux within the

spectral band of receiver. Brightness temperature

(TB) is defined a

TB = T

where is emissivity of the target and the T is

absolute physical temperature.

5

Figure 5. Observations of thermal emission on Martian surface showing (a) radiance in W/cm2/um/sr, (b)

Surface temperature in K and (c) surface Emissivity estimated from THEMIS observations from band 3

(7.93 um).

4.0 Microwave Remote Sensing

Microwave remote sensing is highly useful as it

provides the observations of earth‘s surface

regardless of day/night and atmospheric

conditions. Microwave remote sensing makes use

frequency range from 1 to 300 GHz of the

spectrum. The electromagnetic waves in this

range are relatively less affected by the

atmosphere and hence provide useful data in

overcast or turbid environment. The active

sensors in microwave consist of transmitter and

receiver. Scatterometers, Synthetic Aperture

Radars (SAR) and altimeters are some of the

examples of active microwave sensors. The

transmitted energy is reflected and /or scattered

from the target. The signal with a propagation

delay is received and processed to deduce and

understand the target properties. Radar equation

expresses the fundamental relationship between

radar parameters, target characteristics and the

received signal. For monostatic radars, it is given

by

𝑃𝑟 =𝜆2

(4𝜋)3∫𝑃𝑡𝐺

2𝜎0

𝑅4𝑑𝐴

Where Pr is the average power returned to the

radar antenna from the extended target, Pt is the

power transmitted by radar, G is the gain of the

antenna, R is the distance of the antenna from the

target, λ is the wavelength of the radar, o is the

radar scattering coefficient of the target. The

integration is over the illuminated area A. The

backscattering coefficient is defined as the ratio

of the energy received by the sensor, over the

energy that the sensor would have received if the

surface scattered the energy incident upon it in

isotropic fashion. It is represented as Decibels

(dB).

In the active microwave remote sensing,

information about the object’s physical structure

and electrical property is retrieved by analyzing

the backscattering The microwave signature of

the object are governed by sensor parameters

(frequency, polarization, incidence angle) and

(a) (b) (c)

6

physical (surface roughness, feature orientation)

and electrical (dielectric constant) property of the

target. A given surface may appear very rough at

higher frequency compared to lower frequency.

Generally backscattering coefficient increases

with increasing frequency. In addition, the signal

penetration depth increases with wavelength in

microwave region. Use of multi- frequency

allows distinction between roughness types. The

backscattering also depends on the polarization of

the incident wave.

A vegetation canopy consisting of short vertical

scatter over a rough surface can be considered as

short vertical dipoles. In such case, vertically

polarized incident wave interact strongly with

canopy. The multiple scattering and volume

scattering from a complex surface, such as forest

cause depolarization. The radar backscattering

coefficient from a terrain is strongly dependent

on angle of incidence. The angular dependency of

backscattering coefficient is primarily due to

surface roughness. The surface water extent

during flood is detectable on radar backscatter

image due to high contrast between smooth water

and rough land surface.

SAR interferometry is an extremely powerful tool

for mapping the Earth’s land, ice and even the sea

surface topography. The basic idea is that the

position of a point on the Earth’s surface can be

reconstructed from the phase difference

(interferogram) between two complex-valued

SAR images achieved by coherently processing

the backscattered signals (phase) recorded by the

two antennas.

A Passive sensor consists of only a receiver.

Emitted radiation from manmade or natural

targets is received and processed by the

radiometer to infer the target properties. Raleigh-

Jeans law describe the spectral radiance Mλ(T)

from a black body in microwave at a given

temperature through classical arguments. At

microwave frequencies, Planck’s equation gets

approximated to Raleigh-Jeans law as

Mλ = 2лckT

λ4

where K is Boltzmann constant, is emissivity of

the body at absolute temperature T and

wavelength λ. Emission from a body in

microwave region at particular wavelength is

proportional to the brightness temperature.

Brightness temperature observed over a region

which is product of physical temperature and

surface emissivity is used to infer many

geophysical properties.

5.0 Remote Sensing Applications

The output of a remote sensing system is

usually an image representing the scene being

observed. Image analysis and modeling is used

in order to extract useful information from the

image. Remote sensing images are normally in

the form of digital images. There are many

image analysis techniques (image

transformation, enhancements, pattern

recognition, fusion, merging etc) available for

analysis the data. Suitable techniques are

adopted for a given area and land cover

characteristics, depending on the requirements

of the specific problem.

Identification of terrain categories is done by

digital processing of data acquired by

multispectral scanners. Classification is a process

of assigning individual pixels of an image to

categories, generally on the basis of spectral

seperability analysis. Classification is generally

carried out by supervised or unsupervised

7

Figure 6. Spatial variability of different ecosystem as observed by IRS-LISS-III data

Figure 7. Supervised classification of different land cover classes in parts of Madhya Pradesh, India.

technique. Supervised Classification is digital-

information extraction technique in which the

operator provides training-site information that

the computer uses to assign pixel to categories. It

generates the decision rule and assigns the classes

accordingly. Unsupervised classification is

digital information extraction technique in which

the computer assigns pixels to categories through

clustering techniques without a priori field

information of classes. Remote sensing help

mapping, monitoring and management of various

resources like agriculture, forestry, geology,

wetlands, ocean etc. It further enables monitoring

of environment and thereby helping in

Forest Crop WL/Fallow Water SandForest Crop WL/Fallow Water Sand

8

conservation. In the last four decades it has grown

as a major tool for collecting information on

almost every aspect on the earth. Some of the

important projects carried out in the country

include Groundwater Prospects Mapping under

Drinking Water Mission, Forecasting

Agricultural output using Space,

Agrometeorology and Land based observations

(FASAL), Forest Cover/Type Mapping, Wetland

Mapping, Biodiversity Characterization, Snow &

Glacier Studies, Land Use/Cover mapping,

Coastal Studies, Coral and Mangroves Studies,

Wasteland Mapping etc. The information

generated by large number of projects is used by

various departments, industries and others for

different purposes like development planning,

monitoring, conservation etc.

9

SATELLITE HYDROLOGY

R.P. SINGH




Ahmedabad-380015

Scientific basis and techniques of retrieval of various hydro-meteorological parameters (Rainfall,

Soil Moisture, Groundwater, Water level, and Water quality etc.) which are estimated using

advanced satellite based instruments (Altimeter, Radar, Optical and Microwave Radiometers) are

discussed. Remote sensing techniques are explained by showing the variability of various hydro-

meteorological parameters and recent satellite based hydrological observation over India.

Key Words: Remote Sensing, Hydrology, Water resources, Water balance, Soil Moisture,

Evapotranspiration, Synthetic Aperture Radar, Altimeter

1.0 Introduction

Hydrological observations and modeling using

satellite data is important for sustainable

management of water resources over large

region. Remote sensing of water resources

involves generating information ranging from

regular inventory of surface water bodies to

assessment of rainfall, soil moisture,

evapotranspiration, ground water and snow melt

runoff (Singh and Gupta 2016). Availability of

per capita fresh water is major concern in India as

the population continue to increase although the

average annual rainfall including snowfall in

India is 4000 Billion Cubic Meters (Kumar et al.

2005, Mall et al. 2006). Satellite provides an

important platform from where measurements

can be done in any part of electromagnetic

spectrum suitable to detect different phases of

water i.e. solid, liquid and gas over a large region.

2.0 Scientific Basis of Detection

Satellite based sensors employ active as well

passive sensing system. Active systems have

their own source of illumination (Radar,

Scatterometer, Altimeter) whereas passive

systems sense natural radiations, either reflected

or emitted from the earth. In active remote

sensing, generally instruments (Radar,

Scatterometer) measure back scattered signals

from target however Altimeters use the time

delay in propagation of the incident signal to infer

the topography (elevation, water level) of the

surface. In the active microwave remote sensing,

information about the object’s physical structure

and electrical property is retrieved by analyzing

the backscattering signal. Microwave remote

sensing provides the observations of earth‘s

hydrological cycle regardless of day/night and

atmospheric conditions. Water being a polar

molecule has very high sensitivity in microwave

wavelengths due to orientation polarization

property. The microwave signature of the object

is governed by sensor parameters (frequency,

polarization, incidence angle) and physical

(surface roughness, feature orientation) and

electrical (dielectric constant) property of the

target.

The surface water extent is detectable on radar

backscatter image due to high contrast between

10

smooth water and rough land surface (Fig.1 ). A

Passive sensor consists of only a receiver. Any

object above absolute zero temperature emits

electromagnetic radiation. Thus the objects we

see around, including ourselves are thermal

radiators. Emitted radiation from manmade or

natural targets is received and processed by the

radiometer to infer the Brightness temperature of

the target (Fig.2).

Fig. 1 Inland water bodies delineated using

RISAT-1 SAR Data.

Fig. 2 Brightness Temperature of 19 GHz in

Horizontal polarization observed during July

over India.

Information collected by satellite based multi

spectral sensors in visible region allows

discrimination of different land cover types and

their biophysical properties. Information on water

quality in terms of turbidity, chlorophyll and total

suspended sediments can be derived by analysis

the spectral signature of water in different bands.

Clear water generally absorbs higher wavelength

(Red, NIR) in comparison to low wavelength

(Blue and Green) region. Increase in turbidity

increases reflectance characteristics more in

higher wavelength region.

Hydrological remote sensing is carried out using

measurements from various Indian satellite

platforms (Navalgund and Singh 2010) such as

SARAL-Altika Mission (Inland Water level),

RISAT-1 SAR Mission (Surface water spread,

Soil Moisture), Resourcesat-1/2 Missions (Snow

cover, Wetlands, Land use Land cover, Water

quality), Cartosat Missions (DEM), Kalpana,

Megha Tropiques, Scatsat-1 and INSAT-3D

Missions (Rainfall, Solar Radiation etc.). Global

Missions such as Landsat Program, Sentinel

Program, Jason program, SRTM/ASTER

topography missions, MODIS instruments on

Earth Observation Terra and Aqua Missions,

GRACE Mission, Soil Moisture and Ocean

Salinity (SMOS) Mission, Soil Moisture Active

Passive (SMAP) Mission and Tropical Rainfall

Measuring Mission (TRMM) etc. also provide

valuable datasets to model the water fluxes over

India. Brief principles behind retrieval of major

hydrological parameters such as precipitation,

soil moisture, ground water, surface water level,

inland water quality etc. are as follows.

3.0 Rainfall Estimation

Estimation of rainfall is based on the fact that

heavier rainfall condition is associated with cold

cloud top and generally seen as thick cloud in

visible imagery (Fig. 3). There are broadly three

techniques by which rainfall can be estimated

11

using satellite data. First the Cloud top

temperature based technique using Visible and

Infrared Observations. Second Microwave

technique using active radar or Passive

Brightness Temperature measurement and third

is blended technique. The blended technique uses

both optical observations from Geostationary

platform as well as Microwave technique along

with available network rainfall data from field

measurements. In the first approach cloud top

temperature brightness temperature in thermal

Infrared spectrum observed from Geostationary

satellite are correlated with surface rain rate

observations. Microwave technique of rainfall

estimation depend on concept that rain droplets

lead to increase in microwave emission over the

low background emission water surface (ocean)

in low frequencies.

Fig. 3 Thick cloud formation with cold cloud top

temperature observed during flood in Gujarat in

23 July 2017 using MODIS data.

4.0 Soil Moisture Estimation

Soil moisture is detected from visible, thermal

infrared as well microwave techniques. Visible

technique depends on lowering of surface albedo

when the soil is moist in comparison to dry soil.

Soil moisture assessment using thermal infrared

technique depends on lowering of land surface

temperature in moist situation. The scientific

rationale of the microwave applications in

estimation of soil moisture lies in the strong

dependence of radar backscatter on dielectric

constant of soil. The dielectric constant of water

at microwave frequencies is about 80 while dry

soil is about 3. The moisture content in soil is

linearly related with backscattering coefficient

obtained by SAR Imaging. Observations in

passive remote sensing shows lower brightness

temperature in high soil moist condition as

compared to dry soil condition (Singh et al. 2005,

Oza et al. 2006) (Fig. 4).

Fig. 4 Variations in Brightness temperature as a

function of frequency and water fraction studied

during flood in Ganga in August 2016 using

SMAP, AMSR2 and Sentinel-2 observations.

5.0 Ground water Assessment

The remote sensing data along with ground

survey information provides information on the

geology, geomorphology, structural pattern and

recharge conditions which ultimately define the

groundwater regime. Ground water rechargeable

areas are those which have porous lithologies,

maximum fractures, highly weathered region and

associated flood plains. Satellite data along with

field survey knowledge have been used to

generate Ground water prospect maps showing

probable regions. Tracking the Low Earth

Observation satellites and their orbital dynamics

helps to define the earth’s global gravity field.

The time varying gravity field mapping helps in

monitoring of hydrological mass redistribution

through their integrated gravitational effect.

Gravity Recovery And Climate Experiment

(GRACE) Mission sense changes in gravity field

by the twin GRACE satellites, and GPS

networks. Analysis of multiyear observations

12

from GRACE mission (Fig. 5) have provided

important information related with depleting

ground water in various pockets of earth

including parts of northern India.

Fig. 5 Formation flying of Grace satellites

(Courtesy: JPL, NASA)

6.0 Water Level Estimation

Water level is important hydrological quantity

required to budget the fresh water availability.

Satellite altimetry is active remote sensing

technique for systematic monitoring of water

levels of reservoirs, lakes and rivers (Rajkumar et

al. 2017). Satellite altimetry technique was

originally started for assessment of ocean

topography but recent instruments on JASON,

SARAL Altika, Jason-3, Sentinel-3 and future

missions such as Surface Water and Ocean

Topography (SWOT) mission are designed to

study the inland water bodies also. Radar

altimeter onboard ISRO/CNES SARAL –AltiKa

mission provides important information of water

level for rivers and large reservoirs at 35-day

repeat interval (Dubey et al. 2015, Gupta et al.

2015). Satellite altimetry has been used to study

the river stage and its discharge using rating curve

relationship (Fig. 6).

7.0 Water Quality Estimation

Pollutants in water surface changes the spectral

signature of surface water. Remote sensing of

water quality basically aims to measure these

changes in spectral characteristics and related

these measurements with water quality

parameters empirically or analytically. Major

factor affecting water quality in land water bodies

are suspended sediments (turbidity), algae (i.e.

chlorophyll), chemicals (nutrients, pesticides,

metals), dissolved organic matter (DOM).

Suspended sediments increases the radiance

emergent from water surface in visible and Near

Infrared spectrum. Hyperspectral spectrometer

with Narrow spectral channels are preferred for

estimating the water quality parameters (Fig. 7).

Fig. 6 Water level variations in Ganga river using

Satellite Altimeter (JASON-2/3) near Allahabad

during 2016 showing flood conditions (orange

colour).

Remote sensing techniques have been very useful

in dealing with the natural disasters like droughts

and floods which affect crop productivity, water

availability and economy. Rain is a major

causative factor in both the cases. Accurate and

timely information on conditions of drought or

floods helps in better planning in terms of

preparedness, prevention and relief work. Flood

is a devastating hazard which affects mankind.

Heavy rain, snowmelt, dam failures cause floods.

Observations from SAR and Scatterometer help

in monitoring the flood affected region.

Hydrological models are used to forecast the

flood inundation. Satellite data helps in

monitoring the intensity and movement of a

precipitating system. Rainfall, DEM, prevailing

surface wetness condition, water level heights

and land use-land cover derived from satellite

13

data provide important inputs in modeling and

forecasting the flood.

Fig. 7 Natural colour composite showing

variations in water turbidities in Chilika lake,

observed during AVIRIS-NG hyperspectral

airborne campaign.

8.0 Hydrological Applications

Long term data analysis of satellite based ET

estimation over India showed increasing trends

over arid and semi-arid regions and decreasing

trends in dry deciduous forest regions (Goroshi et

al. 2017). Overexploitation of groundwater (GW)

in the recent past is a well-known fact for the

Punjab and Haryana region of India, as reported

by GRACE satellite-based studies (Tiwari et al.

2009). This decline in GW has enforced the

Punjab Preservation of Sub-Soil Water Act 2009,

and resulted in change in rice irrigation practices

over the study region. A shifting pattern of

irrigation practices has been detected during pre-

and post-Water Act using high temporal passive

microwave radiometer and optical data (Singh et

al. 2007). The overall delay in irrigation practices

was observed over Punjab and Haryana in the

pre- and post-Water Act implementation (Singh

et al. 2006, Singh et al. 2017). Multi-temporal

passive microwave radiometry was found to be

useful for observing the dynamic pattern of

irrigation/agricultural practices over Punjab and

Haryana states.

9.0 Future Direction

Remote sensing has made considerable progress

in assessing the water resources of country and

provide important inputs in modeling the water

balance. Remote sensing started with photo-

interpretation of images for site specific and

regional areas and developed into operational

system where satellite based hydro-

meteorological product are available regularly

and modeling is being carried out at national

scale. Present trend in remote sensing of

hydrology is to develop improved methodologies

for retrieval of various hydro-meteorological

parameters from satellite data and assimilate the

information in physically based distributed

hydrological models.

Acknowledgments

The authors are grateful to Shri Tapan Misra,

Director, Space Applications Centre for his

support and guidance to carry out hydrological

studies. The encouragements from Dr. Rajkumar,

Deputy Director EPSA and Dr. A.S. Rajawat,

Group Director, GHCAG are thankfully

acknowledged. Author is grateful to scientists of

Land Hydrology Division for their inputs and

constructive discussions.

References

Dubey, A. K., Gupta P. K., Dutta S., and Singh

R. P., (2015). Water level retrieval using

SARAL/AltiKa observations in the braided

Brahmaputra river. Marine Geodesy, 38(sup1),

549-567 DOI:10.1080/01490419.2015.1008156.

Goroshi Sheshakumar, Pradhan R., Singh, R.P.,

Singh K.K., and Parihar, J.S., (2017), Trend

14

Analysis of Evapotranspiration over India-

Observed from Long-term Satellite

Measurements, Journal of Earth System Science

(https://doi.org/10.1007/s12040-017-0891-2.

Gupta P K, Dubey A K, Goswami, N, Singh R .P

.and Chauhan P., (2015): Use of SARAL/AltiKa

Observations for Modeling River Flow. Marine

Geodesy, 38(sup1), 614-625.

DOI:10.1080/01490419.2015.1008157.

Kumar R., Singh R.D. and. Singh K.D, (2005),

Water Resources of India, Current Science 89

794-811

Mall R.K., Gupta A., Singh R., Singh R. S. and

Rathor L. S., (2006), Water resources and

climate change: An Indian perspective , Current

Science 90 1610-1626.

Navalgund RR and Singh RP (2010), The

evolution of the earth observation system in

India. J. Ind. Institute of Science, Vol. 90, No. 4,

Oct-Dec. 2010.

Oza SR, Singh RP, Dadhwal VK and Desai PS

(2006), Large area soil moisture estimation and

mapping using space-borne multi-frequency

passive microwave data. J. Ind. Soc. Remote

Sensing. 34 (4), 343-350.

Rajkumar, Sharma, R., Singh R.P., Gupta, P.,

Oza, S.R., (2017), SARAL/AltiKa Mission:

Applications Using Ka-band Altimetry, Proc.

Natl. Acad. Sci., India, Sect. A Phys. Sci., DOI

10.1007/s40010-017-0436-8.

Singh D., Gupta P.K., Pradhan R., and Singh

R.P., (2017), Discerning Shifting Irrigation

Practices from Passive Microwave Radiometry

over Punjab and Haryana. Journal of Water and

Climate (doi: 10.2166/wcc.2016.122), 303-319.

Singh R.P., and Gupta P.K., (2016),

Development in Remote Sensing Techniques for

Hydrological Studies, Proc. Indian Nat. Sci.

Acad., 82 (3), 773-786.

Singh RP, Oza SR, Chaudhari KN and Dadhwal

VK (2005), Spatial and Temporal patterns of

surface soil moisture over India estimated using

surface wetness index from microwave

radiometer (SSM/I). International Journal of

Remote Sensing, 26 (6): 1269-1276.

Singh RP, Oza, SR and Pandya MR (2006),

Observing long term changes in rice phenology

using NOAA-AVHRR and DMSP-SSM/I

satellite sensor measurements in Punjab, India.

Current Science, 91 (9), 1217-1221.

Tiwari, V.M, Wahr J., and Swensen S., (2009)

Dwindling ground water resources in Northern

India from satellite gravity observations,

Geophys. Res. Lett. 36 L18401

15

REMOTE SENSING OF SOIL MOISTURE FROM SPACE

R. P. SINGH




Ahmedabad-380015

Satellites are providing operational soil moisture information on global scale. Long term record

of more than three decades of soil moisture can be analyzed by blending multi temporal

observations of Passive Microwave Radiometer and Scatterometer. Information of Soil Moisture

is required in many applications such as irrigation scheduling, early warning of drought, flood

forecasting, Regional climate modeling etc. This article reviews satellite based microwave

techniques which are used to estimate the soil moisture. Higher moisture content in soil is

detected as high backscattering signal in Synthetic Aperture Radar (SAR) images and

Scatterometer observations and low emissivity and low brightness temperature in Passive

Microwave Radiometer based measurements. Contribution from surface roughness and

vegetation affects the retrieval of soil moisture from microwave data. SAR data provide high

spatial resolution information on soil moisture on specific region whereas Scatterometer and

Passive Microwave Radiometer provide high temporal observations of soil moisture over large

areas.

Key Words: Remote Sensing, Hydrology, Water balance, Soil Moisture, Synthetic Aperture

Radar, Passive Microwave Radiometer, Scatterometry.

1.0 Introduction

Soil moisture is a key variable determining water

and energy exchange at the land surface–

atmosphere interface. It is an important variable

for many applications in the field of agriculture,

hydrology and climate modeling. Water affects

many physical and chemical properties of soil.

Water in soil act a carrier of large amount of

nutrients required for crop growth. Optimal

management of irrigation water requires

information of soil moisture and availability of

this water to crop. Soil moisture also influences

meteorological and climatic processes as it is

important input in regional climate models. Soil

moisture was recognized as an essential climate

variable (ECV) because it plays a crucial role in

various processes occurring on the soil–

atmosphere interface.

Soil moisture in field is measured by various

methods such as (1) Gravimetry method, (2)

Tensiometer method, (3) Electrical resistance

method and (4) Neutron probe methods etc. Due

to limited and sparse ground based soil moisture

network, satellite based measurements are

preferred over large area in regional and global

hydrological studies. Satellite based sensors

placed on orbiting platform measure earth signal

in selected electromagnetic region and retrieve

soil moisture information over large region (Fig.

1). Soil moisture is detected from visible, thermal

infrared as well microwave techniques. Visible

technique depends on lowering of surface albedo

when the soil is moist in comparison to dry soil.

16

Soil moisture assessment using thermal infrared

technique depends on lowering of land surface

temperature in moist situation. Apparent thermal

inertia assessment using albedo and change in

land surface temperature during diurnal cycle

helps in delineating region with varying soil

moisture conditions. Property of High thermal

inertia of water helps in assessment of varying

soil moisture condition. Although Optical

techniques have been used in estimation of soil

moisture but suffers from low sensitivity of

detection as compared to microwave technique.

Microwave spectral region provide all weather

capability as they are less affected by

atmospheric effects. Both active and passive

microwave techniques are used to estimate soil

moisture.

Figure 1. Radar and Radiometer based Earth

imaging using Soil Moisture Active Passive

Mission of NASA (source:

http://smap.jpl.nasa.gov/mission/why-it-

matters/)

2.0 Physical Basis for Remote Sensing of Soil

Moisture

Space based active and passive sensors are used

for observations of moisture content of soil from

orbiting satellites platforms. Active systems have

their own source of illumination (Radar,

Scatterometer, Altimeter) whereas passive

systems sense natural radiations, either reflected

or emitted from the earth. In active remote

sensing, generally instruments (Radar,

Scatterometer) measure back scattered signals

from target. In the active microwave remote

sensing, information about soil moisture is

retrieved by analyzing the backscattering signal.

Water being a polar molecule has very high

sensitivity in microwave wavelengths due to

orientation polarization property. The microwave

signature of the object is governed by sensor

parameters (frequency, polarization, incidence

angle) and physical (surface roughness, feature

orientation) and electrical (dielectric constant)

property of the target. The surface water extent is

detectable on radar backscatter image due to high

contrast between smooth water and rough land

surface.

Figure 2. Observation of backscattering signal

from L band SAR data along with optical data

(FCC) over agricultural region in Central India.

Settlement, moist region and rough surface can be

seen with more backscattering in Radar image as

compared to dry and smooth surface.

The scientific rationale of the microwave

applications in estimation of water in bulk of soil

in form of soil moisture lies in the strong

dependence of radar backscatter on dielectric

constant of soil. The dielectric constant of water

at microwave frequencies is about 80 while dry

soil is about 3. The moisture content in soil is

linearly related with backscattering coefficient

17

obtained by SAR Imaging. High soil moisture

results in high backscattering of radar signal

(Fig.2)

A Passive sensor consists of only a receiver.

Emitted radiation from manmade or natural

targets is received and processed by the

radiometer to infer the target properties. Any

object above absolute zero temperature emits

electromagnetic radiation. Thus the objects we

see around, including ourselves are thermal

radiators. An ideal substance is called blackbody

which absorbs the entire radiant energy incident

on it and emits radiant energy at the maximum

possible rate per unit area at each wavelength for

any given temperature. The spectral radiance

(Mλ) being emitted by a blackbody at given and

Temperature T is given by Planck’s Radiation

Law.

Mλ = 2лhc2

λ5 [exp(hc/λkT)-1]

Where k is Boltzmann’s constant, h is Planck’s

constant, c is velocity of light, and T is the

absolute temperature in Kelvin. At microwave

frequencies, Planck’s equation gets approximated

to Raleigh-Jeans law as

Mλ = 2лckT

λ4

where k is Boltzmann’s constant, is emissivity

of the body at absolute temperature T and

wavelength λ. Emission from a body in

microwave region at particular wavelength is

proportional to the brightness temperature.

Brightness temperature observed over a region

which is product of physical temperature and

surface emissivity is used to infer many

geophysical properties. Brightness temperature is

lower when measured over moist surface as

compared to brightness temperature observed

over dry surface.

Figure 3. Brightness Temperature variations (dark is low and bright is high) in different frequencies (19,

37 and 85 GHz) observed from DMSP-F13 SSM/I data over India during 14-19 July 2002.

18

3.0 Techniques for Estimation of Soil Moisture

A statistical approach as well as a forward model

based inversion technique is mainly used in the

estimation of soil moisture. Statistical techniques

rely on regression analysis between measured

backscattering coefficient/brightness temperature

and surface soil moisture. The slope and intercept

of the regression line are dependent on land cover

variables, which can be estimated from ancillary

data. In the forward model based inversion

technique, a model is used to simulate remotely

sensed output signal (backscattering coeff.,

brightness temperature) on the basis of input land

surface characteristics (e.g. soil moisture, soil

texture, roughness, vegetation water content).

Inversion methods based on iterative

minimization between forward model simulation

and observation are used to estimate the soil

moisture. The statistical approaches are simpler

to use in comparison to the physical approaches

but require a region specific coefficient as

microwave scattering/emissions are determined

by the soil (texture, roughness) and vegetation

characteristics. Based on the sensor involved in

the soil moisture estimation, remote sensing

techniques can be categorized in broad three

categories (1) Radar Based Technique, (2)

Scatterometer based Techniques and (3) Passive

Microwave Radiometer based Techniques.

Details on each technique is as follows

3.1 Radar Based Techniques

The transmitted energy from Synthetic Aperture

Radar (SAR) is reflected and /or scattered from

the target. The signal with a propagation delay is

received and processed to deduce and understand

the target properties. Radar equation expresses

the fundamental relationship between radar

parameters, target characteristics and the received

signal. For monostatic radars, it is given by

𝑃𝑟 =𝜆2

(4𝜋)3∫

𝑃𝑡𝐺2𝜎0

𝑅4𝑑𝐴

Where Pr is the average power returned to the

radar antenna from the extended target, Pt is the

power transmitted by radar, G is the gain of the

antenna, R is the distance of the antenna from the

target, λ is the wavelength of the radar, σ0 is the

radar scattering coefficient of the target. The

integration is over the illuminated area A. The

backscattering coefficient is defined as the ratio

of the energy received by the sensor, over the

energy that the sensor would have received if the

surface scattered the energy incident upon it in

isotropic fashion. It is represented as Decibels

(dB).

Soil moisture influences the backscattered signal

due to the dielectric properties of the soil.

Backscattering coefficient is linearly dependent

upon soil moisture. Various field experiments

have shown that Backscattering coefficient

increases with an increase in soil moisture

content. The linear relationship between

Backscattering coefficient (σ0) and soil moisture

content (Mv) is empirically expressed as

σ0 = A + B. Mv

where A is the backscattering coefficient of a

completely dry soil surface and B is the

sensitivity of σ0 to change with the surface soil

moisture content. A and B are regression

coefficients dependent on soil surface roughness,

incidence angle and soil texture.

Presence of the vegetation canopy over the soil

surface affects the back scattered energy on two

ways (1) The vegetation layer attenuates the soil

backscatter contribution and (2) the vegetation

canopy contributes a backscatter component of its

own. The frequency of radar play an important

role in soil moisture assessment as attenuation

and scattering by vegetation canopy increases

19

with frequency. According to various

experiments and sensitivity analysis, lower

frequencies with steep incident angles are

preferred to higher frequencies for soil moisture

sensing. Lower frequencies are also associated

with high penetration capability of soil and

vegetation.

3.2 Scatterometer Based Techniques

Scatterometry is a form of radar remote sensing

that can estimate various geophysical properties

of surfaces. Scatterometers are originally

designed extensively to map wind speed and

wind direction over the oceans but are also used

for land applications such as estimation of surface

wetness. The returned radar pulses from small

wind-driven ripples, or capillary waves, on the

surface of the ocean are used to retrieve wind

speed and direction.

Soil moisture has been estimated (Wagner 1998)

using time series analysis of backscattering signal

of ERS Scatteromater data and auxiliary

information on soil type, soil texture, bulk density

(kg m−3), wilting level of both gravimetric and

volumetric units, field capacity (FC) in mm and

porosity/total water capacity (TWC) in mm.

Approach was based on comparison of time

series data of σ0 with standard reference incident

angle 40° of ERS Scatterometer data. From this

time series data, highest and lowest σ0 were

determined and denoted as σ0 wet(40, t) and σ0

dry (40, t), where 40° is the reference incident

angle. σ0 dry is considered to be the lowest σ0

when no liquid water is present in the soil surface

layer and σ0 wet(40, t) is the highest σ0 of the soil

surface layer when it is saturated with water. (Das

and Paul 2015)

Table 1: Microwave Scatterometers onboard different satellites (Source:

http://coaps.fsu.edu/scatterometry/about/overview.php).

Scatterometer Data Duration Spatial

Resolution

Scan Characteristics Operational

Frequency

SeaSat-A

Scatterometer

1978

50 km Two sided

Double swath

Ku band (14.6 GHz)

ERS-1

Scatterometer

1991 -1997 50 km One sided

Single swath

C band (5.3 GHz)

ERS-2

Scatterometer 1997-2011 50 km One sided

Single swath

C band (5.3 GHz)

NSCAT 1996-1997 25 km Two sided

Double swath

Ku band (13.995

GHz)

SeaWinds on

QuikSCAT

1999-2009 25 km Conical scan

One wide swath

Ku band (13.4 GHz)

SeaWinds on

ADEOS II

2002-2003 25 km Conical scan

One wide swath

Ku band (13.4 GHz)

ASCAT-A 2006-Present 50 km Two sided

Double swath

C band (5.255 GHz)

ASCAT-B 2012-Present 50 km Two sided

Double swath

C band (5.255 GHz)

OCEANSAT2 2009-2014 25 km Conical scan

One wide swath

Ku band (13.5 GHz)

SCATSAT-1 2016 25 km Conical scan

One wide swath

Ku band (13.5 GHz)

20

http://coaps.fsu.edu/scatterometry/about/overview.php

http://coaps.fsu.edu/scatterometry/about/overview.php#ers

http://coaps.fsu.edu/scatterometry/about/overview.php#ers

http://coaps.fsu.edu/scatterometry/about/overview.php#nscat

http://coaps.fsu.edu/scatterometry/about/overview.php#qscat

http://coaps.fsu.edu/scatterometry/about/overview.php#qscat

http://coaps.fsu.edu/scatterometry/about/overview.php#seawinds

http://coaps.fsu.edu/scatterometry/about/overview.php#seawinds

http://coaps.fsu.edu/scatterometry/about/overview.php#ascata

http://coaps.fsu.edu/scatterometry/about/overview.php#ascatb

http://coaps.fsu.edu/scatterometry/about/overview.php#oceansat2

http://coaps.fsu.edu/scatterometry/about/overview.php#rapidscat

The relative soil moisture content (ms) can be

calculated according to Wagner, Lemoine, et al.

(1999) as

𝑚𝑠(𝑡) = 𝜎0 (40, 𝑡) − σ𝑑𝑟𝑦

0 (40, 𝑡)

σ𝑤𝑒𝑡0 (40, 𝑡) − σ𝑑𝑟𝑦

0 (40, 𝑡)

Soil moisture of surface layer can be estimated by

ms using the information on soil characteristics.

Scatterometer based observations helps in

detection flood situation. Surface flooded with

water gives low backscattering due to specular

reflection in scatterometer data (Fig.4).

Figure 4. Detection of flooding (very low

backscattering) in parts of Bangladesh using

SCATSAT-1 data.

3.3 Passive Microwave radiometer based

Techniques

Passive microwave remote sensing involves the

measurements of natural thermal emission from

surface. Brightness temperature measured in

different frequencies is used to retrieve the

dielectric constant and soil moisture condition.

Generally wet soil which has high soil moisture is

associated with high dielectric constant and lower

emissivity. Hence High soil moisture reduces the

brightness temperature due to lower emissivity.

Reduction in emissivity at higher soil moisture

condition is more in lower frequencies and is

complex function of soil type, vegetation cover and

surface roughness. Figure 3 shows the lower

brightness temperature in moist areas in Bihar and

part of Gujarat particularly in lower frequency 19

GHz as compared to higher frequency (85 GHz).

Low brightness temperature in all frequencies in

Jammu and Kashmir region is due to lower surface

temperature.

Brightness temperature TBp which is measured by

radiometer consists of three components (1)

upwelling atmosphere emission ( uT ), (2) surface

emission (pbT ) attenuated by atmosphere and (3)

cosmic background emission (skyT ) attenuated by

atmosphere, plus down welling atmospheric

emission ( dT ), reflected at the surface and

attenuated along the upward path by atmosphere.

The schematic diagram representing these

components is shown in fig. 5.

Figure 5. Schematic diagram showing the

different components of soil-vegetation-

atmosphere interaction in observation and

modeling of microwave brightness temperature.

21

The microwave brightness temperature TBp

observed by a space borne radiometer above the

atmosphere over vegetated soil surface is modeled

as ( Njoku and Li 1999)

)}exp({)exp( askydpbauB TTrTTTpp

where a is atmospheric opacity, pr is surface

reflectivity. The brightness temperature at the top

of vegetated soil layer Tbp for homogenous medium

with temperature Te can be written as a function of

soil reflectivity psr , vegetation opacity c and

single scattering albedo p .

)]}exp(1[)]exp(1)[1(

)exp()1{(

cscp

cseb

p

pp

r

rTT

The vegetation opacity c depend upon vegetation

columnar water content wc, which is given by

following relationship

cos/cc bw

Here b is vegetation parameter, which depend upon

frequency as well as vegetation type. The

reflectivity rp of two layer soil/vegetation surface is

a function of soil reflectivity psr which is given as

)2exp( csp prr

The surface of typical agriculture fields on which

microwave observations are made for estimating

soil moisture content are generally not smooth, so it

is necessary to include roughness characteristics in

this model. A semi empirical formulation, which

uses two parameters to characterize the surface

roughness, surface height parameter h and

polarization mixing parameter Q, is used. In this

formulation the rough surface reflectivity psr is

related to that of a smooth soil por by

)exp(1 hQrrQrhvv oos

)exp(1 hQrrQrvhh oos

The parameter h is related to the surface height

standard deviation. The parameters Q and h

both are frequency dependent. For a

homogeneous soil with a smooth surface, the

reflectivities at V and H polarizations vor and

hor are given by Fresnal expressions,

2

2

2

sincos

sincos

rr

rr

ovr

2

2

2

sincos

sincos

r

r

ohr

Where r is the complex dielectric constant of

soil and θ is the incident angle (relative to

normal). The complex dielectric constant is

modeled as a function of soil moisture (mv) in

which S and C represent proportion of Sand and

Clay in soil.

2

210

210210

)(

)()(

v

vr

mCcScc

mCbSbbCaSaa

Where a ,b and c are empirical coefficients.

Studies has been carried out to estimate soil

moisture using change detection algorithm in

which a time series processing of surface wetness

index (SWI), has been used for surface soil

moisture estimation. Singh et al. 2005 have

estimated The volumetric soil moisture (Mv) as a

function of SWI

𝑀𝑣 = 𝑀𝑎𝑑 + [(𝑀𝑓𝑐 − 𝑀𝑎𝑑)/(𝑆𝑊𝐼𝑚𝑎𝑥 − 𝑆𝑊𝐼𝑚𝑖𝑛)]

(𝑆𝑊𝐼 − 𝑆𝑊𝐼𝑚𝑖𝑛)

where Mad is the air dry moisture level of soil

(m3m-3); Mfc is the field capacity of soil (m3m-3);

22

SWImax and SWImin represent the maximum and

minimum wetness indices, respectively. SWI

represents the percentage of the radiating surface

that is liquid water, relative to a dry surface. The

surface emissivity at SSM/I frequencies is

lowered by water available in the radiating

surface. The lowering of emissivity is greatest at

low frequencies, and the emissivity generally

increases linearly at higher frequencies. SWI is

proportional to the slope of emissivity (𝜀) as a

function of frequency and is defined as (Basist et

al. 1998), SWI= Δ𝜀T, where

Δ𝜀 = 𝛽0[𝜖(𝑓2) − 𝜖(𝑓1)] + 𝛽1[𝜖(𝑓3) − 𝜖(𝑓2)]

where, T is the surface temperature, f1, f2 and f3

represent vertical channels operating at 19, 37

and 85 GHz, respectively and β0, β1 are

proportionality constants. Surface wetness index

has been used to assess drought and flood

situations in India (Fig. 6).

Figure 6. Surface Wetness Index variations over

India during drought year (2002) and normal year

(2003). Year 2002 was associated with severe

drought condition in India and satellite (SSM/I)

observations showed low surface wetness index

as compared to 2003 during July Month.

Currently the Advanced Microwave Scanning

Radiometer—Earth observing system (AMSR-2)

Soil moisture and ocean salinity satellite

(SMOS), and Soil Moisture Active Passive

(SMAP) are presently operational satellite system

which are providing satellite global data.

Recently more than 30 years of soil moisture

records have been generated at global scale by

merging various satellite based observations

including observations from various

scatterometers as well as microwave radiometers

such as Special Sensor Microwave/Imager

(SSM/I), Scanning Multi-channel Microwave

Radiometer (SMMR), TRMM Microwave

Imager (TMI) and series of Advance Microwave

Scanning Radiometer (AMSR-E/AMSR-2).

4.0 Some Applications of Surface Wetness

Satellite based detection of surface wetness is

helping in improving weather forecast,

monitoring drought, predicting flood, and

assessing food production and providing valuable

input in carbon and water cycle modeling.

Remote sensing observations on rice phenology

and associated soil wetness patterns over a

decade in Punjab was found useful in

understanding how human induced changes

affected the regional water balance. Punjab

region has witnessed significant changes in

cropping pattern during the last few decades.

Initially farmers started early sowing of rice

much before the onset of monsoon through

ground water as Rice–wheat rotation followed in

Punjab left little time for land preparation for

wheat after rice. Due to this practice, Punjab

region which experienced an intensive farming

through groundwater irrigation caused a serious

depletion in the water table of the area.

Unregulated exploitation of water resource

particularly for rice cultivation during summer

season resulted into falling water tables in

majority of areas.

23

Figure 7. Variability in soil moisture observed from SMAP Observations over India during June 2015 and

June 2016. Punjab region shows changes in soil moisture due to crop irrigation. (Source: VEDAS/SAC

Webportal).

Recognizing over exploitation of ground water as

a serious concern, the Government of Punjab

enacted the Punjab Preservation of Subsoil Water

Act in 2009 to slow groundwater depletion. The

Punjab Preservation of Subsoil Water Act-2009

is aimed to conserve groundwater resource by

mandatory shifting of the transplanting date

(beyond the due date) of paddy to avoid loss of

water (Evapotranspiration) from flooded field in

hot weather before Monsoon. A recent study

based on Multi-year passive microwave (AMSR-

E and SMAP) soil wetness analysis showed an

overall delay in irrigation practices in the Punjab

and Haryana after the policy in 2009 in

comparison to pre policy period (Fig. 7 & Fig. 8).

Archival and dissemination of remotely sensed

data and scientific products are important

dimensions of earth observation system. To

ensure the utilization of Indian earth observation

data and hydrological products, web based data

archival and visualization systems are available

on major Geo Platforms of Indian Space

Research Organization:

Figure 8. Passive Microwave Radiometer based

observations showed reduced soil moisture in

first fortnight of June after Punjab Preservation of

Subsoil Water Act in 2009.

Observations of soil moisture from Passive Microwave Radiometer (SMAP) showing sudden increase in soil

moisture in Punjab in second fortnight of June. (Source: VEDAS/SAC webportal)

Observations of soil moisture from Passive Microwave Radiometer

showing reduced soil moisture in first fortnight of June after Punjab

Preservation of Subsoil Water Act in 2009 .

24

(1) Visualization of Earth Observation Data

and Archival System

(http://vedas.sac.gov.in)

(2) Meteorological and Oceanographic

Satellite Data Archival Centre

(http://mosdac.gov.in)

(3) Bhuvan (http://bhuvan.nrsc.gov.in)

These web portals provide information related to

various hydrological products including rainfall,

surface soil moisture, surface runoff,

evapotranspiration, wetlands, River basin, snow

and glaciers, inland water height of selected

rivers / reservoirs and weather forecast

Acknowledgment

Author is thankful to Dr. V. K. Dadhwal, Dr.

A.K. Varma, Dr. S.R. Oza, Dr. Hari Shankar

Srivastava, Dr. D.R. Rajak and other scientists

of Space Applications Centre, ISRO for their

constructive suggestions and fruitful discussions

on various aspect of soil moisture retrieval. Data

from various satellites (SMAP, AMSR-E, SSM/I,

SCATSAT-1) and sources (JPL, NASA; FSU,

USA; VEDAS, SAC/ISRO etc.) are thankfully

acknowledged.

References

Basist, A, Grody, N, Peterson, T And Williams,

C (1998) Using the Special Sensor Microwave

Imager to monitor land surface temperature,

wetness, and snow cover. Journal of Applied

Meteorology, 37, pp. 888–911.

Das K and Paul P K (2015) Present status of soil

moisture estimation by microwave remote

sensing Cogent Geoscience 1-21.

Njoku E.G. and Li L (1999) Retrieval of land

surface parameters using Passive Microwave

Measurements at 6-18 GHz. IEEE transactions on

Geoscience and Remote Sensing, 37, 79-93.

Singh R P, Oza S R, Chaudhari K N and Dadhwal

V K (2005) Spatial and Temporal Patterns of

surface soil Moisture over India estimated using

surface wetness index from SSM/I microwave

radiometer, International Journal of Remote

Sensing 26, 1269-1276.

Wagner, W (1998) Soil moisture retrieval from

ERS Scatterometer data (Dissertation). Vienna

University of Technology, Vienna. Publ. EUR

18670 EN, Office of Official Publications,

Europian Community.

Wagner, W, Lemoine, G, & Rott, H (1999) A

method for estimating soil moisture from ERS

scatterometer and soil data. Remote Sensing of

Environment, 70, 191–207. doi:10.1016/S0034-

4257(99)00036-X.

25

http://bhuvan.nrsc.gov.in/

HYDROLOGICAL MODELLING AND REMOTE SENSING

P. K. GUPTA




Ahmedabad-380015

Water in our planet is available in the atmosphere, the oceans, on land and within the soil and

fractured rock of the earth’s crust Water molecules from one location to another are driven by the

solar energy. Moisture circulates from the earth into the atmosphere through evaporation and then

back into the earth as precipitation. In going through this process, called the Hydrologic Cycle,

water is conserved – that is, it is neither created nor destroyed. All hydrological models are

simplified representations of the real world. Models can be either physical (e.g. laboratory scale

models), electrical analogue or mathematical. The physical and analogue models have been very

important in the past. However, the mathematical group of models is by far the most easily and

universally applicable, the most widespread and the one with the most rapid development with

regard to scientific basis and application. A hydrological model is composed of two main parts, a

hydrological core and a technological shell. The hydrological core is based on a certain

hydrological scientific basis providing the definitions of variables, the process descriptions and

other aspects. The technological shell is the programming, user interface, pre- and post-processing

facilities etc. Recent advances in the remote sensing technologies such as altimetery,

scatterometery, LANDASAT8, passive radiometers, multiple sensors in a single platform, SMAP,

SMOS etc. have made it possible to estimate various hydrological parameters such as rainfall,

river water levels, ET, soil moisture, groundwater etc. Current research is focused on

parameterization of various hydrologic-hydraulic models using remote sensing derived

hydrological variables.

Key Words: Satellite Remote Sensing, Hydrological models, flood modeling, water balance,

river flow models, curve number.

1.0 Key issues in water Resources

Effects of exploitation of water resources

Periodic or permanent lowering of

groundwater table

Increased concentration of pollutants in

the aquifer

Increased risk of salt water intrusion and

land subsidence

Irrigation

Continuing low efficiency of irrigation

projects

Environmental concerns due to

excessive irrigation

Land Degradation and Soil Erosion

Desertification due to increased human

and livestock population

Negligence of upland catchment

management

Surface and Groundwater Pollution

26

Contamination of water due to waste

disposal

Nitrogen and Pesticides pollutions due

to agricultural activities

Floods and Droughts

Changes in the land use

Changes in the hydrological regime

2.0 Hydrological processes

Following hydrological processes need to be

considered for the development of the

hydrological models;

• Canopy rain Interception

• Infiltration

• Depression storage

• Soil moisture storage

• Shallow sub-surface runoff / baseflow

• Preferential /macropore flow

• Runoff generation processes

• Infiltration excess (Horton flow)

• Saturation excess (Dunne flow)

• Flow routing

• Soil evaporation

• Vegetation transpiration

• Water surface evaporation

• Canopy water evaporation

• Groundwater flow

• Lakes

• Wetlands

• Structures that control waters

• Snowmelt

Precipitation

Precipitation occurs when atmospheric moisture

becomes too great to remain suspended in clouds.

It denotes all forms of water that reach the earth

from the atmosphere, the usual forms being

rainfall, snowfall, hail, frost and dew. Once it

reaches the earth’s surface, precipitation can

become surface water runoff, surface water

storage, glacial ice, water for plants,

groundwater, or may evaporate and return

immediately to the atmosphere. Rainfall

measurements can be done using rain gauge and

Fig. processes involve for the development of the

hydrological system model.

satellite remote sensing. Sources from which

remote sensing derived rainfall is available is

Climate Prediction Centre (NOAA), tropical

rainfall measurement mission (TRMM),

meteorological satellite (METEOSAT) etc.

Interception:

Interception is defined as the process whereby

precipitation is retained on the leaves, branches,

and stems of vegetation. This intercepted water

evaporates directly without adding to the

moisture storage in the soil. The interception

process is modelled as an interception storage,

which must be filled before stem flow to the

ground surface takes place. The size of the

interception storage capacity, depends on the

vegetation type and its stage of development,

which is characterised by the leaf area index.

Runoff:

Runoff is the water that flows across the land

surface after a storm event. As rain falls over

land, part of that gets infiltrated the surface and

RAINFALL

Through fall

Canopy storage

INFILTRATION

Soil water store

Groundwater discharge

Direct

rain onto stream

From Open waterFrom

Canopy storage

From soil

EVAPORATION

Geological lenses

From crops

TRANSPIRATION

STREAM FLOW

Interception

Figure: Processes for modelling hydrological cycle in the forest system

27

remaining water flows as overland flow. As the

flow bears down, it notches out rills and gullies

which combine to form channels. The

geographical area which contributes to the flow

of a river/channel is called a catchment of that

river/channel.

Storage:

Portion of the precipitation falling on land surface

which does not flow out as runoff gets stored as

either surface water bodies like Lakes, Reservoirs

and Wetlands or as sub-surface water body like

soil moisture and Ground water.

The following definitions may be useful:

Lakes: Large, naturally occurring inland body of

water

Reservoirs: Artificial or natural inland body of

water used to store water to meet various

demands.

Wet Lands: Natural or artificial areas of shallow

water or saturated soils that contain or could

support water–loving plants.

Unsaturated Zone (Vadose Zone):

Zone between ground surface to groundwater

table is known as unsaturated zone. The

unsaturated zone is usually heterogeneous and

characterized by cyclic fluctuations in the soil

moisture as water is replenished by rainfall and

removed by evapotranspiration and recharge to

the groundwater table. Unsaturated flow is

primarily vertical since gravity plays the major

role during infiltration.

Soil water constants

For a particular soil, certain soil water

proportions are defined which dictate whether the

water is available or not for plant growth. These

are called the soil water constants, which are

described below.

• Saturation capacity: this is the total water

content of the soil when all the pores of the soil

are filled with water. It is also termed as the

maximum water holding capacity of the soil. At

saturation capacity, the soil moisture tension is

almost equal to zero.

• Field capacity: this is the water retained by an

initially saturated soil against the force of gravity.

Hence, as the gravitational water gets drained off

from the soil, it is said to reach the field capacity.

At field capacity, the macro-pores of the soil

are drained off, but water is retained in the

micropores. Though the soil moisture tension at

field capacity varies from soil to soil, it is

normally between 1/10 (for clayey soils) to 1/3

(for sandy soils) atmospheres.

• Permanent wilting point: plant roots are able

to extract water from a soil matrix, which is

saturated up to field capacity. However, as the

water extraction proceeds, the moisture content

diminishes and the negative (gauge) pressure

increases. At one point, the plant cannot extract

any further water and thus wilts.

The Saturated Zone (SZ):

Saturated subsurface flow or ground water table

is known as saturated zone. Ground water storage

is the water infiltrating through the soil cover of

a land surface and traveling further to reach the

huge body of water underground. As mentioned

earlier, the amount of ground water storage is

much greater than that of lakes and rivers.

However, it is not possible to extract the entire

groundwater by practicable means. It is

interesting to note that the groundwater also is in

a state of continuous movement – flowing from

28

regions of higher potential to lower. The rate of

movement, however, is exceptionally small

compared to the surface water movement.

Evapotranspiration

Evapotranspiration is actually the combination of

two terms – evaporation and transpiration. The

first of these, that is, evaporation is the process of

liquid converting into vapour, through wind

action and solar radiation and returning to the

atmosphere. Evaporation is the cause of loss of

water from open bodies of water, such as lakes,

rivers, the oceans and the land surface. It is

interesting to note that ocean evaporation

provides approximately 90 percent of the earth’s

precipitation. However, living near an ocean does

not necessarily imply more rainfall as can be

noted from the great difference in the amount of

rain received between the east and west coasts of

India.

Transpiration is the process by which water

molecules leaves the body of a living plant and

escapes to the atmosphere. The water is drawn up

by the plant root system and part of that is lost

through the tissues of plant leaf (through the

stomata). In areas of abundant rainfall,

transpiration is fairly constant with variations

occurring primarily in the length of each plants

growing season. However, transpiration in dry

areas varies greatly with the root depth.

Evapotranspiration, therefore, includes all

evaporation from water and land surfaces, as well

as transpiration from plants.

Potential evapotranspiration (PET)

Pan evaporation The evaporation rate from pans

filled with water is easily obtained. In the absence

of rain, the amount of water evaporated during a

period (mm/day) corresponds with the decrease

in water depth in that period. Pans provide a

measurement of the integrated effect of radiation,

wind, temperature and humidity on the

evaporation from an open water surface.

Although the pan responds in a similar fashion to

the same climatic factors affecting crop

transpiration, several factors produce significant

differences in loss of water from a water surface

and from a cropped surface. Reflection of solar

radiation from water in the shallow pan might be

different from the assumed 23% for the grass

reference surface. Storage of heat within the pan

can be appreciable and may cause significant

evaporation during the night while most crops

transpire only during the daytime.

Overland flow:

The amount of rainfall in excess of the infiltrated

quantity flows over the ground surface following

the land slope. This is the overland flow. The

portion that infiltrates moves through an

unsaturated portion of the soil in a vertical

direction for some depth till it meets the water

table, which is the free surface of a fully saturated

region with water (the ground water reserve).

3.0 Remote Sensing and GIS

For water resources engineer, locating aerial

extent of water bodies like lakes, rivers, ponds,

etc. from remotely sensed data is an important

task. The spectral response from a water body is

complex, as water in any quantity is a medium

that is semi-transparent to electromagnetic

radiation. Electromagnetic radiation incident on

water may be absorbed, scattered and transmitted.

The spectral response also varies according to the

wavelength, the nature of the water surface (calm

or wavy), the angle of illumination and

observation of reflected radiation from the

surface and bottom of shallow water bodies. Pure

clear water has a relatively high reflectance in the

visible wavelength bands between 0.4 and 0.6μm

with virtually no reflectance in the near-infrared

(0.7μm) and higher wavelengths. Thus clear

29

water appears dark on an infrared image.

Therefore, location and delineation of water

bodies from remotely sensed data in the higher

wave bands can be done very accurately.

The satellite Remote Sensing provides

information in spatial and temporal domains,

with high resolution about the processes of the

land phase of the hydrological cycle, which is

very crucial for successful model analysis,

prediction and validation (Jagadeesha, 1999). RS

techniques can be extremely useful in estimating

a number of key variables of DHMs, particularly

for large basins with sparse data. RS technologies

are often considered as innovative ways of

obtaining data at a reduced cost (Koblinsky et al.,

1992) and replace the conventional techniques. In

addition to that RS can provide time series of data

relatively easily and enabling periodically

updating of variables. Benefit/cost ratio ranging

from 75:1 to 100:1 can be realised in using

remotely sensed data in hydrology and water

resources management (Kite and Pietroniro,

1996).

Table: RS based hydrological variables

The use of RS technology involves large amount

of spatial data management. The GIS technology

provides suitable alternatives for efficient

management of large and complex databases. The

possibility of rapidly combining data of different

types in a GIS has led to significant increase in its

use in hydrological applications. The use of RS

data, in combination with DHM, provides new

possibilities for deriving spatially distributed

time series of input variables, as well as new

means for calibration and validation of the

hydrological model (Bastiaanssen et al., 2000;

Fortin et al., 2001).

Table: Hydrological processes and address

through remote sensing derived variables.

3.0 Hydrological models classifications

Models are classified based on the process

description.

Fig. classification of hydrological models based

on the process description.

Hydrologic parameters Sensor Technology Resolution Repeat ivity

Rainfall TRMM, INSAT, NOAA CPC,

JAXA

Precip. Radar (JAXA)TMI,

VIRS VHRR

0.01 to 0.25 Deg Daily

3 hourly

Soil moisture SSMI, AMSR Radiometers 12-56 km 5-day

Groundwater GRACE gravity 100,000 km2 30 days

Lake/reservoir levels Jason-2, ALTIKA, Sentinel-3 Altimetric radar 350 m 10 day

Evapotranspiration MODIS, INSAT Visible/NIR 1 km to 8 km 1-2 days

Stream discharge Jason-2, ALTIKA Altimetric radar 350m, 175m 10 -35 day

Leaf area index INSAT, MODIS Visible/NIR 1 km 8 day comp.

Topography CARTOSAT-1, SRTM,

GTOPO, ASTER

Optical, microwave 10 m to 1 km -

Insolation INSAT, MODIS VHRR 1 km to 8 km daily

Land Surface Temp. INSAT, MODIS Thermal Infrared 8 km hourly

Land use/cover RESOURCESAT-2, MERIS,

MODIS, SPOT

Optical 56 m to 1 km yearly

Lakes/Wetland extents RESOURCESAT-2, MODIS Optical 23 m to 250 m yearly

Snow covered area RESOURCESAT-2, MODIS Optical 56 m 8 day

Albedo INSAT, MODIS Optical 1 km 16 day

NDVI INSAT, MODIS, SPOT Optical 1 km 16 day

Wind speed , humidity INSAT-

3D/MEGHSTROPICS

Optical/sounder Daily

GPP INSAT, MODIS Optical 1 km 8 day

NPP INSAT, MODIS Optical 1 km yearly

S.N. Processes RS parameters Other Data

1 Canopy rain Interception LAI

2 Infiltration LULC, Rainfall Soil

3 Depression storage DEM

4 Soil moisture LULC, Soil Moisture, DEM Soil

5 Base flow DEM Geological formation

6 Preferential flow LULC, DEM Macroporosity

7 Runoff (Saturation excess-Infiltration excess)

DEM, LULC, Rainfall Soil

8 Soil Evaporation LST, Humidity Soil

9 Water Evaporation LST, Water Bodies, Humidity

10 Canopy water Evaporation LAI

11 Transpiration LAI, Humidity, Water level (Altika) Root depth and density

12 Flow routing DEM, LULC

13 Ground Water Flow Ground Water Anomaly (GRACE) Lethology

14 Lake/Wetlands Land Water Mask

15 Structural control water Reservoir Extent Dam Locations

16 Snow Melt Albedo, Insulation, Snow cover, Short and long wave radiation, LST

Weather Parameters

Hydrological Simulation Model

Deterministic

Empirical

Stochastic

Lumped

Conceptual

Distributed

Physically based Joint Stochastic-

Deterministic

30

Some important definitions in hydrological

modeling:

Model: a conceptual or physically based

procedure for numerically solving the

hydrological processes.

A mathematical model: is a set of mathematical

expressions and logical statements combined in

order to simulate the natural system.

Static model: empirical and regression model in

which time is not independent variable

Dynamic model: require different equation with

time as independent variable and this shows the

time variability of output.

Why simulation model: are used to understand

how system work and interact one another.

Deterministic model: is a model where two

equal sets of input always yields the same output

if run through the model under identical

conditions. A deterministic model has no inner

operations with a stochastic behaviour.

Empirical model: is a model developed without

any consideration of the physical processes that

we otherwise associate with the catchment. The

model is merely based on the analysis of the

concurrent input and output time series. Also,

known as black box model.

Lumped model: is a model where the catchment

is regarded as one unit. The variables and

parameters are thus representing average values

for the entire catchment. Thus the virtual world is

just reduced to just one object.

Conceptual model: physically sound knowledge

and empirically derived equations. Physical

significance is not clear that is why not possible

to assess the parameters from direct

measurement.

Stochastic model: has at least one component of

random character which is not explicit in the

model input, but only implicit or hidden.

Therefore, identical inputs will generally results

in different outputs if run through the model

under, externally seen, identical conditions.

Physics based: description of natural system

using the basic mathematical representation of

the flows of mass, momentum and various forms

of energy. Known as white box model. These

model consists of linked Partial Differential

Equations (PDE’s) with parameters, which in

principle have direct physical significance and

can be evaluated by independent measurements.

Physical processes like conservation of mass and

momentum acting upon input variables are taken

into account.

Distributed: able to take spatial variation of

variables and parameters into account which are

spatially interactive on cell by cell basis.

Simulations: is time varying description of the

natural system computed by the hydrological

model. A simulation may be seen as the models

imitation of the behaviour of the natural system

Parameter: a parameter is a constant in the

mathematical expressions or logical statements of

the mathematical model. It remains constant in

the virtual time.

Variable: is a quantity which varies in space and

time. It can be a series of inputs to and outputs

from the model, but also a description of

conditions in some component of the model.

Modelling system: is defined as a generalized

software package, which, without program

changes, can be used to establish a model with the

31

same basic types of equation (but allowing

different parameter values) for different

catchments.

Model qualification: an estimation of the

adequacy of the conceptual model to provide an

acceptable level of agreement for the domain of

intended application.

Model Verification: substantiation that a

computerized model is in some sense a true

representation of a conceptual model within

certain specified limits or ranges of application

and corresponding accuracy.

Model calibration: involves manipulation of a

specific model parameters to reproduce the

response of the catchment under study within the

range of accuracy specified in the performance

criteria. It is important to assess the uncertainty in

the estimation of model parameters, for example

from sensitivity analysis.

Model validation: is the processes of

demonstrating that a given site specific model is

capable of making sufficiently accurately

predictions. This implies the application of the

calibrated model without changing the

parameters values that were set during the

calibration when simulating the response for

another period than the calibration period. The

model is said to be validated if its accuracy and

predictive capability in the validation period have

been proven to lie within acceptable limits

Distributed Hydrological Models (DHMs) can

serve as a tool to simulate the hydrological water

balance in the command/watershed, which is

essential to reassess the crop water demand both

in space and time. But these DHMs face the

problem of inadequate field data to describe the

processes of hydrological cycle accurately. The

amount of information available using the

conventional method is often very less than the

ideal to run a spatially distributed model

(Vachaud and Chen, 2002). Secondly,

development of more complex, physically

realistic, distributed hydrological models has

dramatically increased the demand for spatial

data (Pietroniro and Leconte, 2000).

Contrary to the lumped conceptual models, a

distributed physically based model does not

consider the water flows in an area to take place

between a few storage units. Instead, the flows of

water and energy are directly calculated from the

governing continuum (partial differential)

equations, such as for instance the Saint Venant

equations for overland and channel flow,

Richards’ equation for unsaturated zone flow and

Boussinesq’s equation for groundwater flow.

Distributed physically-based models have been

used for a couple of decades on a routine basis for

the simulation of hydrological processes.

Today, several general-purpose catchment model

codes of this type exist such as SHE (Abbott et

al., 1986), MIKE SHE (Refsgaard and Storm,

1995), IHDM (Beven et al. 1987). Distributed

physically based models give a detailed and

potentially more correct description of the

hydrological processes in the catchment than do

the other model types. Moreover, they are able to

exploit the quasi-totality of all information and all

knowledge that is available concerning the

catchment that is being modelled. The distributed

physically based models can in principle be

applied to almost any kind of hydrological

problem. However, in practice, they will be used

complementary to the other model types for cases

where the other models are not suitable. Some

examples of typical applications are:

Prediction of the effects of catchment changes

due to human interference in the hydrological

cycle, such as changes in land use (including

urbanization), groundwater development and

irrigation.

32

Prediction of runoff from ungauged catchments

and from catchments with relatively short

records.

As opposed to the lumped conceptual models,

which require long historical time series of

rainfall, runoff and evaporation data for the

assessment of parameters, the parameters of the

distributed physically-based models may be

assessed from intensive, short-term field

investigations.

Water quality and soil erosion modelling for

which a more detailed and physically correct

simulation of water flows is important.

4.0 Calibration and validation of the models

In the present study, resistance number and

seepage loss are considered as the model

calibration parameters. Resistance number used

in this model is defined as the reciprocal of the

Manning's roughness coefficient, which is

otherwise known as Strickler's coefficient. For

calibrating the model, an initial run is made with

default value of global resistance number and

seepage loss. Selection of locations for

calibration is done based on the availability of

observed flow data. Based on the comparison

between the observed and simulated flows, global

resistance number and seepage loss are adjusted.

This process is continued until the observed and

simulated values are in close agreement. For

further refinement of results, local resistance

numbers are used in the system and simulations

are done till the better match between observed

and simulated flows.

Calibrated model is validated for the period other

than considered for the calibration of the model.

Two goodness-of-fit criteria recommended by the

ASCE Task Committee (ASCE, 1993a,b), i.e.,

percent deviation of flow volume DV and Nash-

Sutcliffe coefficient R2, are considered to draw a

better conclusion from the comparison of

observed and simulated flow values. Percent

deviation of flow volume is calculated by using

the following formula.

100

V

VVD

O

SO

V

where, Vs = simulated flow volume (m3); and Vo

= observed flow volume (m3). The value of Dv

should be zero for a perfect model.

The Nash-Sutcliffe Coefficient is calculated as

follows:

2

avO

2

SO2

QQ

QQ1R

where, Qo = observed discharge (m3/s); Qs =

simulated discharge (m3/s); and Qav = mean of the

observed discharge (m3/s). The value of R2 is 1

for the perfect model.

In addition, Student's t-value is also computed to

test the significance of the difference of means of

observed and simulated flows. The Student's t-

value is estimated by using the following

formula.

1

0

s

n

S

ddt

where, ts = computed t-value, d = mean of the

residuals; d0 = hypothesized mean which is

considered as zero; S = standard deviation of

residuals and n1 = number of data.

33

Status of Application of hydrological

modelling systems to various problems

5.0 Conclusions and Future Directions

Remote sensing has a strong technological basis

for the development of advanced sensors and

processing systems from various satellite

platforms whereas hydrology is more of science

oriented describing the various processes

involved in the water cycle. Therefore, current

research focus on integrating remote sensing

derived hydrological variables into various

hydrologic and hydraulic models to bridge the

gap between the point measurements and

mathematical model simulations.

Parameterization of a hydrological system model

demands for various datasets. Some of the crucial

hydrological parameters which are estimated

from remote sensing may be utilize for the

initialization, boundary and for the calibration

and validation of models. In near future, with the

evolving satellite technology (Surafce Water

Ocean Topography (SWOT, NASA-ISRO multi

frequency SAR (NISAR), some of the

hydrological processes such as surface runoff,

river flow etc. shall be addressed from satellite

platforms.

6.0 References

ASCE Task Committee on Definition of Criteria

for Evaluation of Watershed Models (1993a).

Criteria for evaluation of watershed models. J.

Irrig. and Drain. Engrg., ASCE, 119 ( 3): 429-

442.

ASCE Task Committee on Irrigation Canal

System Hydraulic Modeling. (1993b). Unsteady

flow modeling of irrigation canals. J. Irrig. and

Drain. Engrg., ASCE, 119 (4): 615-630.

Bastiaanssen, W. G. M., Molden, D. J. and Makin

I. W. (2000). Remote sensing for irrigated

agriculture: examples from research and possible

applications. Agric. Water Mgmt., 46: 130-155.

Bos, M. G. (1997). Performance indicators for

irrigation and drainage. Irrig. and Drain. Sys., 11:

119-137.

DHI. (1988). MIKE 11 Scientific documentation

and user guide, DHI, Copenhagen, Denmark.

Fortin, P. J., Turcotte, R., Massicotte, S., Moussa,

R., Fitzback, J. and Villeneuve, P. J. (2001).

Distributed watershed model compatible with

remote sensing and GIS data II: Application to

Chaudie’re watershed. J. Hydrol. Engrg., ASCE,

6(2): 100-108.

Jagadeesha, C. J. (1999). Water resources

development and management.

GISdevelopment, 3(6): 20-22.

Kite, G. W and Piteroniro, A. (1996). Remote

sensing applications in hydrological modeling.

Hydrol. Sci. J., 41(4): 561-591.

Koblinsky, C.J., Gaspar, P., Lagerloef, G. (eds).

1992. The Future of Spaceborne Altimetry:

Oceans and Climate Change. Joint

Oceanographic Institutions Incorporated:

Washington, DC; 75 pp.

Field Status of Application

Adequacy1

of

Scientific

Basis

Scientifically1

well Tested

?

Validation2

on Pilot

Schemes

?

Practical3

Applications

Major4

Constraints

for Practical

Applications

Water Resources Assessment

*Groundwater Good Good Adequate Standard/Part Administrative

*Surface Water Very Good Very Good Adequate Standard/Part Administrative

Irrigation Good Good Partially Very Limited Techno/Admin

Soil Erosion Fair Fair Very

Limited

Nil Science

Surface Water

Pollution

Good Good Adequate Some Cases Administrative

Groundwater Pollution

*Point Source

(Landfills)

Good Good Partially Standard/Part Techno/Admin

*Non-point

(Agriculture)

Fair Fair Very

Limited

Very Limited Techno/Admin

Effect of Land Use Changes

* Flows Good Fair Fair Very Limited Science

*Water Quality Fair Fair Fair Nil Science

34

Ministry of Finance, (2002). Union budget and

economic survey, 2002-2003. Government of

India, New Delhi, India.

Swaminathan, M.S. (2000) Natural resources

management- for an evergreen revolution. In The

Hindu - Survey of Indian Agriculture 2000. pp.

9-16.

Ongley, E. D. (1996). Control of water pollution

from agriculture. Irrig. and Drain. Paper No. 55,

Food and Agric. Organization, Rome.

Palanisami, K., 1984. Irrigation water

management : The determinants of canal water

distribution in India - A micro analysis. Agricole

Publishing Company, New Delhi, 120 p.

Pietroniro, A. and Leconte, R. (2000). A review

of Canadian remote sensing applications in

hydrology, 1995-1999. Hydrol. Process., 14:

1641-1666.

Refsgaard, J. C. and Storm, B. (1995). MIKE

SHE. Computers Models in Watershed

Hydrology, V. P. Singh (ed.), Water Resources

Publications, Colorado, USA, 806-846.

Sanmugnathan, K. and Bolton, P. (1988). Water

management in third world irrigation schemes.

ODA Bulletin, No. 11, Hydraulic Research,

London, UK.

Vachaud, G. and Chen, T. (2002). Sensitivity of

a large-scale hydrologic model to quality of input

data obtained at different scales; Distributed

versus stochastic non-distributed modeling. J.

Hydrol., 264: 101–112.

35

SACHYDRO: SNOW-MELT RUNOFF MODELING

AMIT KUMAR DUBEY




Ahmedabad-380015

Introduction

Snowmelt runoff is a primary source of water

in the mountainous regions. Rivers

originating from the snow covered

mountainous regions depend on the

snowmelt runoff during the summer season

of lean flow period (Kumar et al., 1991). It is

significant to estimate spatio-temporal

variations of snow cover and snowmelt

runoff for many aspects of water resources

management such as irrigation, flood control

and soil erosion (Immerzeel et al., 2009).

Since understanding and prediction of

snowmelt variability plays a key role in snow

hydrology, it becomes essential to model the

snowmelt runoff accurately. Energy

exchanges at the snow-air interface primarily

cause the Snowmelt, which leads to the

snowmelt runoff (David et al., 1995).

Snowmelt runoff in a catchment requires

understanding of various processes

associated with snow accumulation, snow

cover properties, snow cover surface energy

exchange, water retention and movements as

well as physical characteristics of the

catchment (Singh and Singh, 1999). There

are two basic approaches under which most

of the models have been developed to

simulate the snow hydrology. First is

temperature index or degree day approach,

which assumes that snowmelt runoff is

primarily driven by the temperature due to

higher correlation between temperature

change and snowmelt.

The other approach hypothesizes that

temperature alone cannot adequately explain

the processes of snowmelt. The energy

budget approach attempts to make the

process as physically based as possible. The

goal is to simulate all energy fluxes occurring

within the snowpack to give an accurate

account of total snowmelt in response to each

of these energy fluxes over time and space. In

the mountainous regions precipitation

usually occurs in the liquid (rainfall) and

solid forms (snowfall). The power of any

hydrological model used to simulate runoff

from snowy watersheds depends on how well

the model accounts for the processes of these

precipitation types in the basin. There are

many snowmelt runoff models exist, which

are developed around the world for the

different hydrological and climatological

conditions. Broadly snowmelt runoff models

have been separated in two categories (i)

Conceptual Models, based on temperature

index or degree day approach and (ii) Energy

Budget approach, the models in this category

try to capture all the major physical processes

involved in snowmelt runoff.

Conceptual Models

Conceptual understanding of physical

processes in a simplified way is the basis of

36

conceptual hydrological models. They

generally fall in-between of empirical and

physically-based models. In snow hydrology

snowmelt runoff models use the temperature

index method as a conceptual form to

simulate the snowmelt runoff. The basis of

the temperature index approach is that there

is a high correlation between snowmelt and

air temperature due to the high correlation of

air temperature with the energy balance

components which make up the energy

budget equation. There are several

temperature index based snowmelt models

like the SSARR Model, the HEC-1 and HEC-

1F Models, the NWSRFS Model, the PRMS

Model, the SRM, the GAWSER Model. The

Snow Melt Runoff (SRM) has been used for

snowmelt modeling in Himalayan basin. The

SRM uses snow-covered area as input instead

of snowfall data, but it does not simulate the

base flow component of runoff (Jain et al.,

2012). In other words, SRM does not

consider the contribution to the groundwater

reservoir from snowmelt or rainfall. It was

originally developed by Martinec (1975) and

has been applied in over 100 basins ranging

in surface area from 0.8 km2 to 917,444 km2

in 29 different countries (Martinec et al.,

2007). SRM is a conceptual, deterministic,

degree day hydrologic model used to

simulate daily runoff resulting from

snowmelt and rainfall in mountainous

regions. SRM requires daily temperature,

precipitation and daily snow covered area

values as Input parameters.

Energy Balance Models

Snowmelt computation using the energy

budget approach follows from accounting for

the energy balance at the ground surface. It

considers all the incoming, outgoing and

stored energies, and finally determines the

net incoming energy. If the net incoming

energy is positive, this adds heat to the

system and eventually leads to melting of the

snowpack. There are several energy balance

model such as ISNOBAL used in seasonal

snow melt runoff in California, U.S. Army

Cold Regions Research and Engineering

Laboratory Model (SNTHERM), which uses

the mixture theory to simulate multiphase

water and energy transfer processes in snow

layers a simplified three-layer model, Snow–

Atmosphere–Soil Transfer (SAST), which

includes only the ice and liquid-water phases

and the snow sub model of the Biosphere–

Atmosphere Transfer Scheme (BATS),

which calculates snowmelt from the energy

budget and snow temperature by the force–

restore method and Utah Energy Balance

Snow Accumulation and Melt Model (UEB),

which incorporates and integrates the overall

roles of the principal hydro-meteorological

processes.

Fig. 1 Physical processes involved in the

snowmelt runoff (Source: Assaf, H.,

2007).

The energy balance of the snowpack is

dictated by several heat exchange processes.

The snowpack absorbs solar shortwave

radiation that is partially blocked by cloud

cover and reflected by snow surface. A

longwave heat exchange takes place between

the snowpack and its surrounding

environment that includes overlaying air

37

mass, tree cover and clouds. Convective

(sensible) heat exchange between the

snowpack and the overlaying air mass is

governed by the temperature gradient and

wind speed. Moisture exchange between the

snowpack and the overlaying air mass is

accompanied with latent heat transfer that is

influenced by vapor pressure gradient and air

wind. Rain on snow could induce significant

heat input to the snowpack. A generally

insignificant conductive heat exchange takes

place between the snowpack and the

underlying ground. Theoretically, a

snowpack has to reach a 0 oC isothermal

condition before it starts to melt. At

subfreezing temperatures, a snowpack has a

negative heat storage or internal energy, Ui,

defined as the amount of energy required to

raise the snowpack temperature to 0 oC. With

increasing heat input, Ui will increase to a

maximum value of zero at temperature 0 oC.

Any additional heat input will then go

towards melting the snowpack. Taking into

account the most significant heat exchange

processes, the rate of change of Ui can be

expressed as follows (Gray and Prowse

(1992)):

𝜕𝑈𝑖

𝜕𝑡= 𝑄𝑠 + 𝑄𝑙 + 𝑄ℎ + 𝑄𝑒 + 𝑄𝑎 + 𝑄𝑔 −

𝑄𝑚 2

where,

Qs = is the net flux (energy per unit surface

area per unit time) of incoming shortwave

insolation;

Ql = is the net flux of longwave heat

exchange between snowpack and

surrounding objects;

Qh = is the flux of convective (sensible) heat

exchange between snowpack and

surrounding objects;

Qe = is the flux of latent heat exchange via

vapor exchange through condensation and

sublimation;

Qa = is the flux of advective energy induced

by rain of snow;

Qg = is the flux of conductive energy

exchange between snowpack and underlying

ground; and

Qm = is the energy available for snowmelt,

which becomes nonzero if the internal

energy U reaches a value of zero (at

temperature 0 oC)

Although the shortwave and longwave

radiation components tend to dominate in

most cases, the relative importance of each

heat transfer mechanism can vary

considerably from one area to another and

over different times of the day and year. The

heat balance is dictated by several factors

including the snowpack and site

characteristics related to climate, topography,

orientation, latitude, altitude, tree cover, as

well as time of the day and year. A brief

description of each heat transfer mechanism

is presented below.

The mass balance of a snowpack is

governed by the following basic equation:

𝜕𝑆𝑤

𝜕𝑡= 𝑃𝑠 + 𝑅𝑓 − 𝐸𝑆 − 𝑀𝑠

1

Sw is the snow water equivalent (SWE) of

the snowpack;

Ps is the snowfall, which adds volume to the

snowpack during;

Rf is the rainfall on the snowpack that has

frozen;

ES is evaporation and sublimation; and

Ms is the snowmelt.

During the melt period the net energy will be

used to melt the snowpack until it depletes

38

completely. Hence for the melting period

potential snowmelt will occur according to

the atmospheric conditions. The snowmelt

per using area per unit time can be calculated

as

𝑆𝑛𝑜𝑤𝑚𝑒𝑙𝑡 = 𝑄𝑚

𝑓𝑤𝜌𝑤𝐵

Where,

Snowmelt is the volume of potential snowmelt

in mm/s (1 mm is equivalent to a volume of 1

mm over an area of m2, equivalent to 1 liter);

Qm is the energy available for snowmelt as

defined in equation 1 in J/m2s ;

Hfw is the heat fusion of ice equal to 334.9 J/g

(Joule/gram);

pw is water density equal to 1000 kg/m2 ; and

B is the thermal quality of the snow defined

as the ratio of heat required to melt a unit

mass of snow to that of ice at 0 oC, which

ranges in value from 0.95 to 0.97 for a

melting snowpack (USACE (1998));

Shortwave radiation

Although the intensity of solar radiation

normal to the Earth’s atmospheric perimeter

is constant at approximately 1.35 kJ=m2 per

seconds (solar constant), the actual amount

that arrives at any given point on Earth, is

drastically reduced. Over 50% of radiation is

reflected by cloud cover, scattered by air

molecules and air-borne particles, and

absorbed by air compounds of ozone, water

vapor, carbon dioxide and nitrogen (Gray and

Prowse (1992)). Incident solar radiation on

the snowpack surface is further attenuated

with respect to that on a horizontal surface as

function of local factors including land slope,

aspect, exposure, latitude, time of the year,

and the ratio of diffuse to direct-beam

radiation. Forest cover further blocks direct

sun radiation. A significant portion of

shortwave radiation could be reflected by the

snowpack surface. The reflectivity of

snowpack is generally assessed by its albedo

(A), defined as the percentage of reflected

shortwave radiation. Albedo of fresh snow

can reach as high as 90%, but can deteriorate

to as low as 30% during melting season.

Snow albedo can be significantly reduced by

forest dirt, debris, sand, and other material.

Longwave radiation

A portion of shortwave energy absorbed by

the snowpack is emitted back to overlying air

and surrounding as a longwave radiation.

This outgoing longwave energy is balanced

by a longwave radiation from cloud cover

and canopy that originated as shortwave

energy absorbed by these objects. The net

longwave energy flux is significant in

forested areas and during cloudy periods as

cloud cover and canopy reflects back most of

the energy absorbed earlier as shortwave. In

open areas in contrast significant outgoing

longwave radiation during nighttime results

in the cooling of the snowpack and delaying

the release of snowmelt. The snopwack

behaves as a near ideal radiator (black body)

and accordingly, its longwave energy

emission can be described by Stefan-

Boltzmann equation:

𝑄𝑙𝑠 = 𝜖𝜎(𝑇𝑠 + 273)4

3

Where,

Qls is the longwave energy flux emitted by

the snowpack in J/m2s (joules per square

meter per second);

ε is the emissivity of the snowpack, which

ranges from 0.97 (dirty snow) to 0.99 (fresh

snow) (Gray and Prowse (1992)) (ε = 1 for a

black body);

σ is Stefan-Boltzmann constant (5.735 x 10-

8 J/m2sK4); and

39

Ts is the snowpack surface temperature in

Celsius degrees (oC).

For cloudless conditions in open and

unforested areas, air-borne particles and air

constituents (within the first 100-meter layer)

emit longwave energy downwards into the

snowpack. The air mass can be treated

empirically as a gray body with a portion of

the longwave emission attributed to water

vapor. Accordingly, many of the open-sky

longwave radiation equations reported in the

literature incorporate parameters

representing air humidity (Quick (1995)). For

example, Anderson (1954) presents the

following equation for open-sky longwave

radiation as function of vapor pressure and

temperature:

𝑄𝑙𝑎 = 𝜖𝜎(𝑇𝑎 + 273)4(0.49 + 0.0049 𝑒𝑎)

4

Where,

Qla is the longwave energy flux emitted by

the open sky air downwards in J/m2s;

σ is Stefan-Boltzmann constant (5.735 x 10-

8 J/m2sK4 );

ea is the vapor pressure (millibars); and

Ta is the atmosphere temperature in Celsius

degrees (oC).

Clouds act as black bodies and their

longwave energy emissions can therefore be

represented by Stefan- Boltzmann equation

as follows:

𝑄𝑙𝑐 = 𝜖𝜎(𝑇𝑐 + 273)4

5

Where,

Qlc is the longwave energy flux emitted by

cloud cover in J/m2s; and

Tc is the cloud temperature in Celsius

degrees (oC).

Convective and latent heat (turbulent)

A temperature gradient above the snowpack

results in a convective heat exchange that

could be greatly accelerated under high wind

velocities. Moisture transfer either to the

snowpack via condensation or out of the

snowpack by sublimation results in a latent

heat gain or loss, respectively. Latent heat

transfer is also affected by wind conditions.

The relative significance of convective and

latent heat melt varies dramatically. The melt

contribution of these processes is relatively

low under clear warm weather due to the

stability of the overlying warm air layer. In

contrast, their contribution can be very

significant under windy conditions or during

winter rain on snow events. Turbulent heat

exchange processes are simplified for neutral

conditions of the atmosphere. Under neutral

conditions, buoyant forces are not present in

the layer that is the layer is characterized by

an adiabatic temperature lapse rate. Neutral

conditions occur when winds are strong and

there is modest heating or cooling of the

surface by radiat ion. For neutral stability

conditions, in boundary layer, the diffusion

coefficients are equal.

𝑒𝑠 = 611𝑒𝑥𝑝 (17.27𝑇

273.3+𝑇)

6

Rh =e

es

7

𝑄ℎ = 𝜌𝑐𝑝𝑢𝑧(𝑇𝑧−𝑇𝑜)

[ln (𝑧

𝑧𝑜)]

2

8

𝑄𝑒 = 0.622 𝜌𝐿𝑘2𝑢𝑧(𝑒𝑧−𝑒𝑜)

[ln (𝑧

𝑧𝑜)]

2

9

40

Where,

es is saturated vapor pressure in paskals (Pa

= N/m2)

Rh is relative humidity

Qh is the latent heat flux in J/m2s

Qe is the convective or sensible heat flux in

J/m2s

Rain melt

For rain on a melting snowpack, the

advective heat released by the rain can be

estimated as follows (USACE (1998)):

𝑄𝑎 = 𝐶𝑝𝑃𝑟(𝑇𝑟 − 𝑇𝑠)/1000

10

Where,

Qa is the advective heat flux released by rain

in J/m2s;

Cp is specific heat of rain (4.29 J/goC);

Pr is the rainfall in mm/s;

Tr is the temperature of rain (oC); and

Ts is the snow temperature (oC).

If rain falls on a subfreezing snowpack, a

portion of it is expected to freeze and release

its heat of fusion, which is generally difficult

to determine due to lack of measurements and

consequently not accounted for in the overall

snowpack heat exchange (Gray and Prowse

(1992)).

Conductive exchange with ground

Due to its low thermal conductivity, snow

reduces significantly heat exchange between

the underlying ground and atmosphere. In

most situations, the ground heat flux is quite

small and can therefore be dropped out of the

snowpack heat exchange equation (Gray and

Prowse (1992)).

Table 1 Comparisons of two different model approaches

Model

Approach

Input Parameters Output Remark

Conceptual

Models

Temperature, Precipitation, Snow

covered area, Runoff coefficient

rain, Runoff coefficient snow,

Recession coefficient, Critical

Temperature, Temperature lapse

rate, Time lag

Daily stream

flow,

Seasonal

volume of

runoff

Does not consider the

spatial variability of

physical processes for

the model input and

calibration. It could be

applied with limited

amount of available

observed data set

Energy

Balance

Models

Maximum and minimum air

temperature, solar radiation,

precipitation, wind velocity, dew

point temperature above the snow

surface and on the snow surface,

average snow surface albedo, area

of watershed and its variation with

elevation – snowline elevation,

forest cover area, stream flow

Stream flow,

snow water

equivalent,

soil moisture,

evaporation

It requires large amount

of input data to initialize

and calibrate the model

simulations

(Source: Singh and Singh, 1999)

41

Summary

The Energy balance approach is extremely

data intensive, requiring vast amounts of

input data either to force an initial run of a

model, or to calibrate it based on historical

data before running a forecast (Table 1). With

the limitation of intensive data, which is

necessary for the energy budget approach,

alternative method could be used such as the

temperature index or degree day approach

allows for snowmelt calculation with much

less input data. Conceptually based snowmelt

modeling approach is less applicable when

separation of snow surface energy fluxes and

prediction of the snow surface temperature

are important, which is often the case when

hydrological models are coupled with

atmospheric models (Marshall et al. 1999). In

addition, many (Koivusalo and Kakkonen,

2002) argue that physically based snowmelt

modeling approaches are also important to

examine the impacts of land use changes on

hydrological processes. For instance,

commonly used temperature index snowmelt

models are incapable to simulate snowmelt

processes in forested watersheds. However,

employing physically-based models with

elaborate snowmelt dynamics, one can

simulate the effects of land use changes

(forest to other land use types, or vice-versa)

on subsequent hydrological processes.

References

Akhtar, M., Ahmad, N., Booij, M.J., (2008).

"The impact of climate change on the water

resources of Hindukush–Karakorum–

Himalaya region under different glacier

coverage scenarios". J. Hydrol. 355, 148–

163.

Archer, D. (2003). "Contrasting hydrological

regimes in the upper Indus Basin". Journal of

Hydrology, 274, 198−210.

Assaf, H. (2007). "Development of an

Energy-budget snowmelt updating model for

incorporating feedbacks from snow course

survey measurements". Journal of

Engineering, Computing and Architecture.

ISSN 1934-7197, Vol. 1, Iss. 1.

Immerzeel, W.W., Droogers, P., Jong, S.M.,

Bierkens, M.F.P. (2009). "Large-scale

monitoring of snow cover and runoff

simulation in Himalayan river basins using

remote sensing". Remote Sens. Environ. 113,

40–49.

Jain, S. K. (2012). "Snowmelt runoff

modeling in a basin located in Bhutan

Himalays". india Water Week 2012- Water,

Energy and Food Security: Call for Solutions

10-14 April 2012, New Delhi.

Koivusalo, H., Kakkonen, T. (2002). Snow

processes in a forest clearing and in a

coniferous forest. J Hydrol, 262:145–164

Kumar, S., V., Haefner, H., Seidel, K. (1991).

"Satellite snow cover mapping and snowmelt

runoff modelling in Beas basin". Snow

Hydrology and Forests in High Alpme Areas

(Proceedings of the Vienna Symposium,

August 1991), IAHS Publ. no. 205, 1991.

Marshall S., Oglesby R.J., Maasch K. A.,

Bates, G. T. (1999). "Improving climatic

model representations on snow hydrology".

Environ Model Softwa 14:327–334.

Martinec, J. (1975)." Snowmelt–runoff

model for stream flow forecasts". Nordic

Hydrology, 6, 145−154.

42

Martinec, J., Rango, A., & Roberts, R.

(2007). "Snowmelt runoff model: User

manual". Las

Cruces: New Mexico State University.

Singh, P., Kumar, N. (1997). "Impact

assessment of climate change on the

hydrological response of a snow and glacier

melt runoff dominated Himalayan river". J.

Hydrol. 193 (1–4), 316–350.

Singh, P., Singh, P. V. (1999). Snow and

Glacier hydrology. Water Science and

Technology, ISBN 0-7923-6767-7, Vol. 37,

pp. 315.

Tarboton, D., G., Chowdhury, T. G., Jackson,

T., H. (1995). A spatially distributed energy

balance snowmelt model. Biogeochemistry of

Seasonally Snow-Covered Catchments

(Proceedings of a Boulder Symposium, July

1995). IAHS Publ.no. 228, 1995.

43

RAMSAR WETLANDS: SCIENCE AND EXPERIENCE OF

NALSAROVAR

TVR MURTHY

Agriculture and Land-ecosystem Division

Biological and Planetary Sciences and Applications Group

Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area


Ahmedabad 380015

Remote sensing applications relevant to wetlands have come in vogue supplementing/complimenting

the information needs for their wise use and management. Many times unique information is deduced

from remotely sensed data in conjunction with conventional approaches are synergistically used

employing Geographical Information System (GIS). Wetlands provide many valuable services at

population level, at ecosystem level and at global level. These signify the importance of wetlands for

their conservation. The value of the wetlands in terms of the human economic systems perceived by the

human being and the need to consider the value of a wetland as a part of an integrated landscape differ

with each other and most of the times conflicting. The interactions of physical, chemical and biological

components of wetlands enable them perform their vital functions. Ramsar Convention (1971) on

Wetlands of International Importance especially as Waterfowl Habitat is the oldest and first

inter-governmental conservation convention. The convention provides the framework for national

action and international cooperation for the conservation and wise use of wetlands and their resources

including biodiversity. The management of a wetland needs to be based on scientific data/information

collection and analysis at a wetland level and also at the basin level.

Key words: Ramsar, wetlands, GIS, Remote sensing, Conservation, management

1.0 Introduction

Wetlands played a major role in human history.

It is only wetlands, whether perennial rivers or

large water-bodies have always been the sites of

sources of water and consequently the

development of civilisations. Wetlands are

among the most productive ecosystems of the

world although they account only about

4 per cent of the earth’s ice-free land surface

(Prigent, 2001). In recent years, there has been

an ever strengthened recognition at global scale

for concerted effort to conserve and in some

cases to preserve the natural resources.

With the developments in science and

technology, strong evidence has been

accumulated to the extent to show that today the

conservation of our biosphere is an extremely

critical necessity. It would not be misplaced if

one says that today we are at the watershed for

wetland conservation.

At this juncture, the Ramsar Convention has

created a room for optimism for the

conservation and restoration of wetlands.

Ramsar Convention and other Conventions

have certain links that serve the purpose of

conservation of wetland in various aspects. The

following account describes it briefly.

1.1 Ramsar Convention

Ramsar Convention (1971) on Wetlands of

International Importance especially as

Waterfowl Habitat is the oldest and first inter-

governmental conservation convention. It owes

its name to its place of adoption in Iran. It came

into being due to serious decline in population

of waterfowl and conservation of habitats of

migratory waterfowl. The convention provides

the framework for national action and

international cooperation for the conservation

and wise use of wetlands and their resources

including biodiversity. Ramsar convention

entered into force in 1975 and contracting

parties from all over the world.

44

Under the text of the convention (Article 1.1)

wetlands are defined as:

“areas of marsh, fen, peat-land or water,

whether natural or artificial, permanent or

temporary with water that is static or flowing,

fresh, brackish or salt, including areas of marine

water the depth of which at low tide does not

exceed 6 m”.

In addition, the Convention (Article 2.1)

provides that wetlands:

“may incorporate riparian and coastal zones

adjacent to wetlands, and islands or bodies of

marine water deeper than six meters at low tide

lying within the wetlands”. Resultantly, a wide

variety of habitats including rivers, shallow

coastal waters and even coral reefs are included

under wetlands. However, deep seas are not

treated as wetlands.

1.2 Importance of Wetlands

Wetlands provide many valuable services at

population level, at ecosystem level and at

global level. These signify the importance of

wetlands for their conservation. The value of

the wetlands in terms of the human economic

systems perceived by the human being and the

need to consider the value of a wetland as a part

of an integrated landscape differ with each

other and most of the times conflicting. The

wetland ecosystem services are depicted

pictorially in figure 1.

Figure 1: Ecosystem services provided by lakes and reservoirs (adopted from ILEC. 2007)

1.3 Ramsar Sites in India

India became a contracting party to the Ramsar

Convention in 1981. The Chilika lagoon in

Orissa and the Keoladeo National Park in

Rajasthan are the first two wetlands designated

as Ramsar sites in 1981. Since then total 26

wetlands in the country have been designated as

Ramsar sites by 2012. Maximum number of

sites was designated during 2002. The latest one

in the series is the Nal Sarovar bird Sanctuary

in Gujarat designated during 2012 (Figure 2).

Though India has numerous wetlands of various

types, there are certain criteria of selection of

sites for Ramsar designation.

As per the Article 2.2 of the Ramsar

Convention, broadly the wetlands are

categorised under two Groups under nine

criteria. Group A sites are selected under the

Criterion 1 as “Sites containing representative,

rare or unique wetland types”. Group B sites are

sites of international importance for conserving

biological diversity. There are total eight

criteria based on species and ecological

communities under Group B. Also, there are

certain commitments by the country to ensure

preservation of the ecosystem. A detail of the

sites in terms of location, area and criteria is

given in Table-1.

45

Table 1: List of Wetlands designated as Ramsar sites in India arranged alphabetically and their criteria

(Ministry of Environment, Forests & Climate Change, Government of India)

2.0 Study Area

Nalsarovar is lies between 22040’00” –

22052’00” N latitudes and

71055’00” – 72006’00” E longitudes located

amidst the semi-arid lands of Ahmedabad and

Surendranagar districts of Gujarat. It falls in the

4B Gujarat-Rajwara biotic province of the

semi-arid bio-geographical zone. Nalsarovar

has been notified as a sanctuary under Wildlife

(Protection) Act, 1972. It is the most recent one

identified as Ramsar site on 24/09/2012 owing

to the compliance of criteria 2, 5 & 6. The

genesis of the Nalsarovar is explained by a

model suggested by Prasad et al (1997). It is a

3-stage model to explain the evolution of the

Nalsarovar during late-quaternary period,

seems appropriate. Accordingly, during stage-1

of evolution, spanning the period of

127-73 ka, a shallow sea linked the Gulf of

Kuchch with the Gulf of Khambhat (Kambay).

The sea connection broke up around the

beginning of marine isotope stage due to

regression of the sea. Subsequently only a land

link remained. In stage-2 (73-7 ka), fluvial

sediments from east were episodically

deposited in the Nal region in response to

westward migration of depositional front of

eastern rivers. In stage-3, due to advancement

of sediment front, tectonic and post-glacial sea

level rise, the elevation of Nalsarovar came to

within few meters of its present elevation at

about 7 ka when it became a closed basin.

Sr.

No. Wetland name State

Ramsar

Criteria

Date of

Declaration

Area

(ha)

1 Ashtamudi Wetland Kerala 1,2,3,8 19/08/2002 61400

2 Bhitarkanika Mangroves Orissa 2,4,6,8,9 19/08/2002 65000

3 Bhoj Wetland Madhya Pradesh 2,4,5,6 19/08/2002 3201

4 Chandertal Wetland Himachal Pradesh 2,3 08/11/2005 49

5 Chilika Lake Orissa 2,4,5,6,8,9 01/10/1981 116500

6 Deepor Beel Assam 2,5 19/08/2002 4000

7 East Calcutta Wetlands West Bengal 1 19/08/2002 12500

8 Harike Lake Punjab 2,5,6 23/03/1990 4100

9 Hokera Wetland Jammu & Kashmir 2,5,6 08/11/2005 1375

10 Kanjli Punjab 3 22/01/2002 183

11 Keoladeo National Park Rajasthan 2,5,6 01/10/1981 2873

12 Kolleru Lake Andhra Pradesh 2,4,5,6 19/08/2002 90100

13 Loktak Lake Manipur 2,5,6 23/03/1990 26600

14 Nalsarovar Bird Sanctuary Gujarat 2,5,6 24/09/2012 12000

15 Point Calimere Wildlife and

Bird Sanctuary Tamil Nadu 2,4,5 19/08/2002 38500

16 Pong Dam Lake Himachal Pradesh 2,5,6 19/08/2002 15662

17 Renuka Wetland Himachal Pradesh 3,4 08/11/2005 20

18 Ropar Punjab 5,6 22/01/2002 1365

19 Rudrasagar Lake Tripura 2,3,8 08/11/2005 240

20 Sambhar Lake Rajasthan 2,5,6 23/03/1990 24000

21 Sasthamkotta Lake Kerala 1,2,7,8 19/08/2002 373

22 Surinsar-Mansar Lakes Jammu & Kashmir 2,3,4 08/11/2005 350

23 Tsomoriri Jammu & Kashmir 2,6 19/08/2002 12000

24 Upper Ganga River (Brijghat

to Narora Stretch) Uttar Pradesh 2,5 08/11/2005 26590

25 Vembanad-Kol Wetland Kerala 4,5,6 19/08/2002 151250

26 Wular Lake Jammu & Kashmir 2,5,6 23/03/1990 18900

46

3.0 Salient results of application of RS

and GIS to Nalsarovar

Various aspects of Nalsarovar have been

studied using conventional approached, RS and

GIS. Accordingly, the results are dealt under

various sub-headings in the following

description.

3.1 Dynamics of Structural Components

of Nalsarovar

Nalsarovar, being a natural lake is fed through

surface run-off from the catchment. Thus it is

imperative that the quantum of water within the

lake is solely dependent upon the rainfall.

Further, Nalsarovar being a wetland-based bird

sanctuary, the various habitats as mentioned

earlier assumes importance in view of the

changes in the extents of open water and

vegetation areas.

The rainfall has an impact on the preferred

habitat availability for various species of avi-

fauna. To put it straight, amount of availability

of water is decisive in the availability of optimal

habitats. To arrive at the optimal water-level for

sustaining various habitats is worked out. A

drought year (2002-03) and a normal monsoon

year (2003-04 & 2004-05) are compared for

structural components open-water and

vegetation which has a bearing on the

management of the wetland. Resourcesat-1

LISS-III data of the nearest cloud-free date for

post-monsoon of 2002, 2003 and 2004 along

with 8-day composite of MODIS data from

October 31 to March 30 of 2002-03, 2003-04

and 2004-05 has been used for this purpose.

The Nalsarovar being a monsoon dependent

wetland has vividly shown the influence of

hydrology on the wetland (Figure 2).

Figure 2: Comparative extents of wetland, open water and emergent vegetation of Nalsarovar as inferred

from the multi-temporal satellite data

3.2 Water Balance of Nalsarovar

The computation of the volume of a given

wetland (water body), the corresponding

elevation to the water spread is essential

(volume = elevation * mean area). From the

satellite imagery it is possible to derive a

polygon corresponding to the water-spread. As

a first step, one needs a Digital Elevation Model

(DEM) to compute volume through mean

elevation. The DEMs are available in the form

of SRTM-DEM or Carto-DEM, which needs to

be calibrated keeping the local conditions. This

can be achieved through modelled observations

from DEM as well as observed

points either measured on ground or through

spot-heights, benchmarks or triangulated

heights from Survey of India (SOI)

topographical maps as shown in the figure 3.

This is followed by construction of bottom

topography through a calibrated elevation

model combined with the actually measured

water-level (Figure 4). Using the wetland

boundary observed in a satellite image as a

contour line based on the assumption that the

surface of water reaches the same height

everywhere within the lake was considered a

reasonable assumption in the case of

Nalsarovar.

CHANGE IN EXTENT OF WETLAND, OPEN WATER & EMERGENT VEGETATION

0

20

40

60

80

100

120

10

.10

.02

31

.10

.02

08

.11

.02

16

.11

.02

22

.11

.02

02

.12

.02

10

.12

.02

18

.12

.02

26

.12

.02

01

.01

.03

09

.01

.03

17

.01

.03

25

.01

.03

02

.02

.03

10

.02

.03

18

.02

.03

26

.02

.03

06

.03

.03

14

.03

.03

22

.03

.03

30

.03

.03

20

.10

.03

31

.10

.03

08

.11

.03

16

.11

.03

24

.11

.03

03

.12

.03

10

.12

.03

18

.12

.03

26

.12

.03

01

.01

.04

09

.01

.04

17

.01

.04

25

.01

.04

02

.02

.04

10

.02

.04

18

.02

.04

26

.02

.04

06

.04

.04

14

.04

.04

22

.04

.04

30

.04

.04

14

.10

.04

31

.10

.04

08

.11

.04

16

.11

.04

24

.11

.04

02

.12

.04

10

.12

.04

18

.12

.04

26

.12

.04

01

.01

.05

09

.01

.05

17

.01

.05

25

.01

.05

02

.02

.05

10

.02

.05

18

.02

.05

26

.02

.05

06

.03

.05

14

.03

.05

22

.03

.05

30

.03

.05

Based on LISS-III (10/10/02, 20/10/03 & 14/10/04)

and 8-days composite of MODIS (31/10 to 30/03 of 02-03, 03-04 & 04-05)

Ex

ten

t (k

m2)

Wetland Area Open WaterEmergent Vegetation

2002-03 (Drought) 2003-04 (Normal) 2004-05 (Normal)

47

Figure 3: Schematic of calibration of Carto-

DEM and Survey of India (SOI) map

through regression modelling

Figure 4: Surface interpolation of point

measurements to derive bottom

topography for Nalsarovar

Elevation values are essential to compute the

volume of the wetland. However, wetland

extent can be derived from the multi-temporal

satellite data. Under this situation if wetland

extent and corresponding elevation can be

modelled, it will be of great use to compute

volume, and changes in volume for a given

year/season as is the case of Nalsarovar. The

calibrated CARTO-DEM with interpolated

bottom topography for Nalsarovar is an obvious

choice for this exercise. Through modelling,

water-spread corresponding to elevation of 0.50

m is derived till maximum elevation of 10.50 m

was derived. Using the available software tools,

water-fill area corresponding to 0.50 m interval

elevation is derived. The logarithmic fit

(Figure 5) is found have highest R2 (0.9489).

The model equation is:

Y = 1.3873 Ln(x) -13.544, R2= 0.9489

Where,

Y = Elevation (m),

x = Water spread (Million m2)

Figure 5: Modelling the relationship between

elevation and water-spread in

Nalsarovar

Nearest available cloud-free satellite data

(LISS-III) to the last rainfall event was used as

reference storage (Sr) and changes further are

treated as change in storage for the computation

of water balance. The spatial layer consisting of

the wetland information (open water and

vegetation) provide the area while through

model equation, elevation is computed for the

corresponding area.

Change in storage can be calculated as:

∆S1 = Sr – (ET + Other loses) for Date1

∆S2 = ∆S1 – (ET1 + Other loses1)

for Date2………. Daten

Where,

∆S1 = Change in storage for date1

Sr = Reference Storage

GROUND DATA (SOI) Vs SRTM DEM (m)

y = 0.9558x - 0.2752

R2 = 0.9013

4

6

8

10

12

14

16

18

20

6 8 10 12 14 16 18 20

GROUND DATA (m)

SR

TM D

EM

(m)

Series1Linear (Series1)

WATER SPREAD Vs ELEVATION

y = 1.3873Ln(x) - 13.544

R2 = 0.9489

0

2

4

6

8

10

12

14

0 30 60 90 120 150 180

WATER SPREAD (Mm 2)

EL

EV

AT

ION

(m

)

Series1Log. (Series1)

48

∆S2 = Change in storage for date2

ET1 = Evapotranspiration for date1

The volume of the water is computed for each

data set using the elevation information

provided by the DEM. Water balance (S) is

computed by subtracting the volume of S1 from

the reference volume (Sr) and also accounting

loses due evapotranspiration (AET) and other

loses. As such other loses could not be

physically measured during this study and

hence the difference barring the AET is

considered as other loses, which include

drawing of water for agriculture, domestic

usage, surface and subsurface flows. Thus

monitoring aerial extent of open-water of a

wetland leads to depict changes in storage

(Figure 6) and also other loses (Figure 7).

Figure 6: Change in extent of wetland, open-water and storage in Nalsarovar

Figure 7: Change in extent of storage and other loses in Nalsarovar

Nalsarovar being a wetland-based bird

sanctuary, the various habitats assume

importance in view of the changes in the extents

of open water and vegetation areas. The rainfall

has an impact on the preferred habitat

availability for various species of avi-fauna. To

put it straight, amount of availability of water is

decisive in the availability of optimal habitats.

To arrive at the optimal water level for

sustaining various habitats is worked out in the

present study. Accordingly, the extent of

submergence area is computed from the Digital

Elevation Model and presented in the figure 5.

It appears that around 10 m elevation, the water

overflows into the river Bhogava through

surface drainage present in the southern part of

Nalsarovar. From this study it is evident that

water extent at about 9 m elevation at the end

CHANGE IN EXTENT OF WETLAND, OPEN WATER & STORAGE

0

20

40

60

80

100

120

10

.10

.02

08

.11

.02

22

.11

.02

10

.12

.02

26

.12

.02

09

.01

.03

25

.01

.03

10

.02

.03

26

.02

.03

14

.03

.03

30

.03

.03

31

.10

.03

16

.11

.03

03

.12

.03

18

.12

.03

01

.01

.04

17

.01

.04

02

.02

.04

18

.02

.04

06

.04

.04

22

.04

.04

14

.10

.04

08

.11

.04

24

.11

.04

10

.12

.04

26

.12

.04

09

.01

.05

25

.01

.05

10

.02

.05

26

.02

.05

14

.03

.05

30

.03

.05

Based on LISS-III (10/10/02, 20/10/03 & 14/10/04)


Ex

ten

t (k

m2)

0

4

8

12

16

20

24

Ch

an

ge

in

Sto

rag

e (

MC

M)

Wetland AreaOpen WaterChange in Storage (MCM)


Changes in storage, other loses and AET

0

4

8

12

16

20

24

Based on LISS-III (10/10/02, 20/10/03 & 14/10/04)


Sto

rag

e &

Oth

er

los

es

(M

CM

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

AE

T-

8-d

ay

s t

ota

l

(MC

M)

Other losesStorageAET


49

of the rainy season is found to be optimal for

maintaining various habitats that in turn

support the avi-fauna for the rest of the lean

period.

3.3 Modelling of Physical Water Quality

Parameters

Statistical analysis was done where in

correlation matrices and stepwise multiple

regressions were used to explore the

relationship between water quality parameters

with LISS-III (radiance at sensor). The selected

independent variables are chosen in the order of

their relative reduction of the sum of squares

(the variable that most reduced the sum of

squares was entered first, followed by each of

the other in descending order of relative

contributions). This enabled the choice of

variables; only those with the most correlates

with dependent variable and elimination of

insignificant variables. The selection of models

was based on high values of correlation

coefficient (R2), high value of F-ratio and low

values of standard error. The regression models,

developed between the water quality

parameters and radiance values of sampling

locations were extended to the entire area of

interest (open water). The following models

were selected to represent the best statistical

relationship between water quality

measurements obtained from reference surface

and the mean values of the corresponding

optical data.

Transparency (Figure 8)

Ytrans = a + bX1 + cX2, R2 = 0.7320

Where,

Ytrans = Transparency (cm), a = 190.2583,

b = -19.4712, c = -0.3042,

x1 = band-2, x2 = band-3

Turbidity (Figure 8)

Yturb = a + bx1 + cx2, R2 = 0.7672

Where,

Yturb = Turbidity (NTU), a = -0.2879,

b = 1.2483, c= 0.0247, x1 = band-2,

x2 = band-3

Total Suspended Solids (TSS)

YTSS = a + bx1 + cx2, R2 = 0.7346

Where,

YTSS = Total Suspended Sediments (mg/l),

a = -199.6099, b = 7.4716, c = 91.3788,


Total Dissolved Solids (TDS)

YTDS = a + bx1 + cx2, R2 = 0.7830

Where,

YTDS = Total Dissolved Solids (mg/l),

a = -171.3418, b = 11.1387, c = 131.59,


Figure 8: Modelled Transparency and

turbidity in Nalsarovar

14/10/04

14/10/04

50

Figure 9: Modelled Total suspended

solids and total dissolved

solids in Nalsarovar

3.4 Modelling biomass of emergent

macrophytes

The regression analysis results have shown that

band-1, band-2 and NDVI has best statistical

relationship between wet-biomass

measurement and corresponding optical data.

The R2 obtained was 0.7242, F-ratio was

675.8009 and standard error was 18.4495. Sixty

measurements, spanning multi-temporal data

sets to derive the model equation. Accordingly,

a polynomial fit has been applied to derive

model equation for the prediction of wet

biomass given as:

YWBM-EM = a + bx1 + cx2 + dx3, R2 = 0.7242

Where,

YEM = Biomass (g) - Emergent macrophytes

a = 189.6489, b = -0.6753, c = 37.2606,

d = 1388.7922

x1 = band-1

x2 = band-3

x3 = NDVI image

Using this model equation, biomass of

emergent macrophytes could be quantitatively

assessed (Table 2).

Table 2: Temporal variation in wet-biomass of

emergent macrophytes, Nalsarovar

Field

Data/

Image

date

Wet

Biomass

(tons)

Field

Data /

Image

Date

Wet

Biomass

(tons)

14.10.04 3818 12.11.05 4232

22.11.04 4089 30.12.05 3952

11.12.04 3986 23.01.06 3744

28.01.05 3762 16.02.06 3561

21.02.05 3582 12.03.06 3440

22.03.05 3413 29.04.06 3205

10.04.05 3130 23.05.06 3102

04.05.05 2998 16.06.06 2944

01.09.05 3955 20.09.06 3913

19.10.05 4149 14.10.06 4076

3.5 Dynamics of habitats in Nalsarovar

It is possible to be delineate preferred habitats

of avifauna in Nalsarovar, using satellite data

with the aid of spectral indices. Accordingly, it

is possible to delineate open water habitat,

submerged vegetation, dense and open

emergent vegetation, dry emergent vegetation,

dead macrophytes, mud habitat with or without

vegetation, islands/woody habitats, and dry

lakebed with or without vegetation (Figure 10).

14/10/04

14/10/04

51

Figure 10: Habitat mapping of Nalsarovar based on satellite data supported by field information

3.6 Trophic State Assessment

Eutrophication is a process both natural as well

as enhanced due to anthropogenic activities that

enriches the nutrient levels of a given aquatic

ecosystem or wetland. Trophic State Index

(TSI) provides an indicator for the

determination changes in nutrient load in a lake.

The TSI equations given by Carlson (1977) for

Secchi Disc transparency, Chlorophyll and

Total phosphorous may be simplified and used

as given below:

1. TSI (SD) = 60-14.41 ln(SD)

2. TSI (CHL) = 9.81 ln(CHL) + 30.6

3. TSI (TP) = 14.42 ln(TP) + 4.15

Out of the above three parameters, Secchi disc

transparency could be modelled through

satellite data in conjunction with field

measurements. The same parameter has been

used to calculate TSI (SD) for Nalsarovar. TSI

(TP) can also be done by interpolation of in situ

measurement into a spatial layer and classified

as per Carlson and Simpson (1996) into

Oligotrphic, Mesotrophic, Eutrophic and

Hypereutrophic conditions. Accordingly, maps

have been prepared to show the various trophic

conditions existing in Nalsarovar using

temporal satellite data and in situ

measurements. These are maps

colour-coded (Figure 11).

52

Figure 11: TSI-(SD) and TSI-(TP) derived from

satellite data and in situ

measurements for Nalsarovar

3.7 Basin level information and utility

Wetland management does not stop at the

wetland shore, but must extend into the basin,

and often beyond. It has been observed that the

largest number of wetland issues have their

genesis in upstream or downstream basins.

Often degradation of the ecosystem services

provided by the wetland results from

unsustainable human interventions in the

process of resource development and

utilisation. Thus, sustainability can be best

achieved when diverse stakeholders fully

understand and appreciate their roles in

protection, conservation and sustainable

management of lake ecosystems. Hence, the

Lake Basin Management (LBM) has come into

practice (Figure 12). ILBM is a conceptual

framework for assisting lake basin managers

and stakeholders in achieving sustainable

management of lakes and their basins. It takes

into account the biophysical features of as well

as managerial requirements for lake basin

systems, that are associated with the lentic

(standing or static) water properties of lakes as

well as the inherent dynamics between humans

and nature in the process of development, use

and conservation of lake and basin resources.

Figure 12. Conceptual framework of ILBM

Remote sensing can play a pivotal role

synergistically with GIS in furthering the

technology necessary to provide the required

information at basin-level of a wetland. Some

of the areas where RS can give unique inputs to

ILBM are:

Spatial layers of Land use/Land cover,

geomorphology, Surface drainage, Slope,

Sol etc.

Integration of the information in GIS

domain may lead to

Wetland health assessment

Wetland ecosystem services

assessment

Sediment yield estimation and

watershed prioritisation for soil

conservation

Etc…….

4. Current needs and scope of remote

sensing

The current needs of wetland management can

be summerised as given below:

Detection and assessment of chemical

pollutants.

Thermal pollution

Pollution due to radioactivity

Assessment of groundwater recharge

through wetland

Etc….

53

References

ILEC. 2007. Integrated Lake Basin

Management: An Introduction. International

Lake Environment Committee Foundation:

Kusatsu, Japan. (Eds.) Masahisa Nakamura,

Hiroya Kotani, Walter Rast, Taichiro Uda,

Thomas Ballatore, Maki Tanigawa.

Prasad, S., Pandarinath, K. and Gupta, S. K.

1997. Lake quaternary evolution of the Nal

region, Gujarat, India. In Changes in Global

Climate due to Natural and Human Activities.

A. N. Das and R. S. Thakur (eds). Proc. IGBP

Symposium. Allied Publishers Ltd.

Prigent, C., Matthews, E., Aires, F. and

Rossow, W.B. 2001. Remote sensing of global

wetland dynamics with multiple satellite

datasets, Geophysical Research Letters, 28, pp.

4631-4634.

54

EVAPOTRANSPIRATION: TOOLS AND TECHNIQUES

ROHIT PRADHAN




Ahmedabad-380015

Evapotranspiration(ET) is the combination of two processes where water is lost on one hand from

soil surface by evaporation and on the other hand from vegetation by transpiration.

Evapotranspiration forms a key component of the hydrological cycle which directly influences

the climate system. Estimation of ET is also important for management of irrigation in agricultural

systems, for addressing water balance in catchments of reservoirs and rivers etc. ET and soil

moisture greatly influence the climate and are now being used extensively for improving weather

forecasts. This note provides a brief introduction to the processes of evaporation and transpiration

and its influencing parameters followed by the various models used in estimation of potential and

actual ET over land surface. This is followed by a section on estimating ET using remotely sensed

data.

Key Words: Evapotranspiration, Potential ET, evaporation models, remote sensing.

1. Introduction

As the name suggests, ET involves two

components: (a) Evaporation is the process

whereby liquid water is converted to water

vapour (vaporization) and removed from the

evaporating surface (vapour removal). Water

evaporates from a variety of surfaces, such as

lakes, rivers, soil and wet vegetation. (b)

Transpiration consists of the vaporization of

liquid water contained in plant tissues and the

vapour removal to the atmosphere. Plants

predominately lose their water through stomata

(stomata are small openings on the plant leaf

through which gases and water vapour pass).

Nearly all water taken up by plants is lost by

transpiration and only a tiny fraction is used

within the plant.

Evaporation and transpiration occur

simultaneously and there is no easy way of

distinguishing between the two processes. The

driving force to remove water vapour from the

evaporating surface is the difference between the

water vapour pressure at the evaporating surface

and that of the surrounding atmosphere.

1.1 Factors affecting evapotranspiration:

(i) Weather parameters: Net radiation, air

temperature, humidity and wind speed.

(ii) Crop factors: Differences in resistance

to transpiration, crop height, crop

roughness, albedo and crop rooting

characteristics.

(iii) Environmental conditions: Ground

cover, plant density and the soil water

content.

The evaporation process over any vegetated

landscape is linked by two fundamental

equations:

(a) Water balance: Evapotranspiration can

be determined by measuring the various

components of the soil water balance.

This approach consists of assessing the

incoming and outgoing water flux into

the crop root zone over some time period.

55

P = Eact + Q + ∆S

Where, P is rainfall, Eact is actual

evaporation, Q is runoff and ∆S is change

in moisture storage of soil.

(b) Energy balance: Evaporation of water

requires relatively large amounts of

energy. The process of evaporation is

governed by energy exchange at the

vegetation/soil surface and is limited by

the amount of energy available. The

amount of energy arriving at a surface

must equal the energy leaving the surface

for the same time period.

R = H + λEact + G

Where, R is net radiation received at

surface, H is sensible heat flux, λEact is

outgoing energy as actual evaporation

and G is heat conduction into soil. λ is the

latent heat of vaporization.

ET rate is expressed as millimeters (mm) per unit

time. This rate expresses the amount of water lost

from a surface in units of water depth. For e.g. if

we say that ET is 1 mm/day it implies that 10

cubic meter of water is lost per hectare per day

from that place. In terms of energy, 2.45 MJ of

energy is required to vaporize 1 kg water at 20oC.

This is the value of latent heat of vaporization i.e.

the amount of energy required to vaporize 1 kg of

water at given temperature.

2. Measurement and Estimation of ET

ET can be measured at field level using

instruments like lysimeters, evaporimeters, eddy

flux towers etc. However, these are expensive

and cumbersome to maintain and provide point

measurements of ET which is not valid over

larger areas. To overcome this issue, various

models have been developed over the years for

estimation of ET. These models range from

simple empirical equations to the much advanced

radiation based models used today. Most of these

models are region-specific and the empirical

coefficients apply to those locations only limiting

their use in other regions. However, these models

have been widely used by hydrologists all around

the world for estimation of ET in their study area

and later calibrated for those regions.

Before proceeding to the various models

employed for estimating ET, it is necessary to

define some of the basic terms used frequently in

ET modelling. The terms and their short forms

given below will be used throughout this section.

Potential Evapotranspiration (PET): Dingman

(1992) defines PET as the rate at which

evapotranspiration would occur from a large area

completely and uniformly covered with growing

vegetation which has access to an unlimited

supply of soil water, and without advection or

heating effects.

Reference crop Evapotranspiration: It is the

evapotranspiration from a crop with specific

characteristics and which is not short of water.

FAO-56 adopts the specific characteristics of a

reference crop with certain height (0.12 m),

surface resistance (70 s m-1) and albedo (0.23)

and then determines the reference ET using the

Penman-Monteith Equation.

Actual Evapotranspiration (AET): AET is

defined as the quantity of water transferred as

water vapour to the atmosphere from an

evaporating surface. This surface can refer to

anything from the real world eg open lake, bare

soil, vegetated surface etc.

2.1 Models for estimating PET

A brief description of some of the most widely

used models for estimating potential and crop

reference ET is given below:

(a)Thornthwaite (1948): In the Thornthwaite

evaporation method, the only meteorological data

required to compute mean monthly potential

evapotranspiration is mean monthly air

temperature. It is expressed by:

𝐸𝑡ℎ,𝑗 = 16 (ℎ𝑟

12) (

ⅆ𝑎𝑦

30) (

10𝑇𝑗

𝐼)

𝛼𝑡ℎ

56

Where, Eth,j is the estimate of PET for month j, hr

is the mean daylight hours in month j, day is

number of days in month j, Tj is the mean

monthly air temperature (in oC) and I is annual

heat index. Once the mean monthly temperature

is known, the mean monthly PET can directly be

derived for each of the twelve months of a year.

(b)Penman (1948): Penman used an energy

equation based on net incoming radiation. This

approach does not require the surface temperature

variable.

Where, EPen is daily PET from a saturated surface,

Rn is net daily radiation to the evaporating

surface, Ea is a function of daily average wind

speed and vapour pressure, ∆ is the slope of

vapour pressure curve at air temperature, γ is the

psychrometric constant and λ is the latent heat of

vaporization.

(c)Penman-Monteith (1981): This is the most

widely used model for estimating PET from a

vegetated surface. It is expressed as:

Where, ETPM is the Penman-Monteith PET, Rn is

net daily radiation at the vegetated surface, G is

soil heat flux, ρa is mean air density at constant

pressure, ca is specific heat of the air, ra is

aerodynamic resistance, rs is surface resistance.

FAO has provided an excellent guide for

estimating crop reference ET in agricultural

regions based on Penman-Monteith equations.

This was formulated to make a universal method

of estimating PET. It is called the FAO-56 model

and is represented by the equation:

Where ETRC is the reference crop ET, and Ta is

mean daily air temperature. All other variables

have the same meaning as mentioned in previous

models.

(d)Priestley-Taylor (1972): This model computes

PET in terms of energy fluxes without an

aerodynamic component using the following

equation:

Where, EPT is the Priestley-Taylor PET, αPT is the

Priestley-Taylor constant which was taken as

1.26 in the original.

(e)FAO-24 Blaney-Criddle: The FAO-24

Reference Crop version of Blaney - Criddle is

defined as:

Where, ETBC is the Blaney-Criddle reference crop

ET, RHmin is minimum relative daily humidity,

n/N is measured sunshine hours to possible

sunshine hours, py is percentage of actual daytime

hours for the day compared to the daylight hours

for the entire year. ei (i=0 to 5) are the empirical

coefficients.

(f)Turc (1961): The Turc method is one of the

simplest empirical equations used to estimate

reference crop ET.

Where, ETTurc is Turc reference ET, Rs is the

incoming solar radiation and Ta is the average air

temperature.

(g)Hargreaves-Samani (1985): The HS equation

which estimates reference crop ET is as follows:

Where, ETHS is the reference crop ET, CHS is

empirical coefficient, Ra is extra-terrestrial

radiation, Tmax, Tmin and Ta are the maximum,

minimum and average air temperatures

respectively. This equation is used for weekly or

monthly time scales for better results.

57

(h)Morton (1985): Morton’s approach uses

iterative solution of the following two energy-

balance and vapour transfer equations for PET at

the equilibrium temperature.

Where ETMo is the Morton’s estimate of PET, fv is

vapour transfer coefficient and is a function of

atmospheric stability, ɛs is the surface emissivity,

σ is Stefan-Boltzmann constant, Te and Ta are

equilibrium and air temperatures. This is a part of

the CRAE model which computes both PET and

AET.

The above mentioned models showcase only a

few of the ones being used today for estimation

of PET. Hydrologists continuously make

improvements in these models to better represent

their study area by conducting vigorous

calibration or introducing subsequent correction

terms.

2.2 Models for estimating AET

Once the potential of evaporation is determined

at a place using weather data, AET or actual

evapotranspiration is computed by incorporating

the limiting factors like actual soil moisture, state

of vegetation on ground etc. The following

models are used regularly for AET estimation:

(a)Morton models: Bouchet (1963) stated that

PET and AET depend on one another via

feedback from land and atmosphere

simultaneously, given that the area is sufficiently

large and homogeneous with no or little advective

heat and moisture. This complementary

relationship (CR) is given by:

ETAct = 2 ETWet - ETPot

ETwet is potential or wet environment

evapotranspiration and ETpot is the point

potential ET at a place whose area is so small that

its heat and water vapour fluxes have no effect on

the overpassing air.

Morton’s CRAE (Complementary Relationship

Areal Evapotranspiration) model computes AET

for land environments. It calculates point

potential ET (ETPot) using the equation in

previous section and wet-environment areal

evapotranspiration or ETwet by modifying the

Priestley-Taylor approach as:

Where, ETwetMo is wet-environment areal ET, Rne

is net radiation for the soil/plant surface at

equilibrium temperature, p is atmospheric

pressure and ∆e is slope of saturation vapour

pressure curve at equilibrium temperature, b1 and

b2 are empirical coefficients.

(b)Chen and Dudhiya (2001): This was

developed as a part of a coupled land surface-

hydrology model in the Penn state-NCAR fifth-

generation Mesoscale Model (MM5). The PET is

computed using Penman-based energy balance

approach. AET is computed as sum of direct

evaporation from soil, wet canopy evaporation of

intercepted water and canopy transpiration. These

components are derived as a fraction of PET

based on the vegetation fraction, canopy

resistance and few other parameters.

𝐸𝑑𝑖𝑟 = (1 − 𝜎𝑓)𝛽𝐸𝑃

β =θ − θw

θref − θw

𝐸𝐶 = 𝜎𝑓𝐸𝑝 (𝑤𝐶

𝑠)

𝑛

𝐸𝑡 = 𝜎𝑓𝐸𝑝𝐵𝐶 (1 − (𝑤𝐶

𝑆)

𝑛)

Where, Edir is the direct evaporation from soil, σf

is the vegetation fraction, Ep is potential ET, β

determines the availability of water for

evaporation as a function of soil moisture θ, θref

and θw are field capacity and wilting point for that

soil type. Ec is wet canopy evaporation

(intercepted water), Et is canopy

evapotranspiration, Wc is intercepted canopy

58

water content, S is maximum canopy capacity, Bc

is a function of canopy resistance.

(c)FAO-56: FAO-56 method (Allen et al 1998)

for estimation of ET in agricultural areas involves

the computation of PET using Penman-Monteith

approach and then experimentally determined

ratios of ETc/ETo, called crop coefficients (Kc),

are used to relate AET to PET.

𝐸𝑇𝑐 = 𝐾𝑐𝐸𝑇0

Where, ETc is the crop ET, Kc is crop coefficient

and ETo is potential ET. Due to variations in the

crop characteristics throughout its growing

season, Kc for a given crop changes from sowing

till harvest. The effects of characteristics that

distinguish field crops from the reference grass

crop are integrated into the crop coefficient Kc.

Instead of using one value of Kc, dual crop

coefficients can also be used to distinguish soil

and crop ET. FAO-56 provides a handbook

detailing the entire procedure and experimental

values of Kc for different crops at various stages

of growth. This is the most widely used procedure

for estimating ET throughout the world.

Other notable models for AET include Granger-

Gray model (1989) and Szilagyi-Jozsa model

(2008). For more details on the above mentioned

models, including solved examples, please refer

to MacMahon et al 2013 (main paper and

supplementary material).

3. Remote Sensing and ET

In the previous section, many widely used models

were discussed in brief. When we wish to apply

such models over a large area, say a state or a

country, it becomes impractical to use

meteorological data from ground stations for ET

estimation as they are not well distributed over

the country. Remote sensing satellites orbiting

around Earth can provide a holistic view of an

entire region and can be used to estimate various

meteorological parameters. These satellite

observations are well distributed over space and

time and hence prove to be a viable tool for

estimating ET for large regions.

Satellites DO NOT provide any direct measure of

ET. Instead, they measure different

meteorological variables and land surface /

vegetation parameters that are then used in

different models for estimation of ET.

Space-based remote sensing satellites are

categorized based on their orbits into two types:

polar orbiting and geostationary. Polar-orbiting

satellites (e.g. ResrouceSAT-2, LANDSAT

series, Aqua/Terra-MODIS etc.) are placed

usually at ~500-800 km altitude and move around

the earth to capture images of the whole earth

surface. But the revisit time (i.e. the time interval

between successive observations of same region

on Earth’s surface) of such polar satellites is

anywhere from 2 days to 30 days depending on

the swath covered. These satellites provide

variables like leaf area index, snow cover, land

use-land cover etc. which can be used in the

models mentioned in previous section.

Geostationary satellites stay stationary relative to

a fixed point on earth surface at an approximate

altitude of 36,000 km. These type of satellites

provide continuous data recording over a fixed

coverage area at frequent time intervals.

Geostationary Indian weather satellites such as

Kalpana-1, INSAT 3D and INSAT 3DR estimate

parameters like rainfall and earth’s radiation

budget using its various spectral channels and

provide data every 30 minutes. This data can be

used to estimate parameters like land surface

temperature, down-welling shortwave radiation,

upwelling longwave radiation, cloud cover etc. to

determine the surface energy budget and then be

implemented in the PET estimation models.

A few of the notable remote sensing based ET

estimation methodologies are the MOD16

MODIS ET algorithm (Mu et al 2007,2011),

SEBEL (Bastiaanssen et al 1998, Bastiaanssen

2000), METRIC (Allen et al 2007) and

methodologies by Batra et al 2006, Cleugh et al

2007, Yao et al 2013 etc. For further study on

remote sensing based ET estimation please refer

to the above papers. Following are two case

studies of operational ET estimation methods

using satellite data.

59

3.1 Case Study: MOD16 Algorithm

Estimation of PET/AET from space based

platforms can be best understood with the help of

a case study. NTSG employs a standard

methodology to compute ET using MODIS

(Moderate-resolution Imaging Sensor) onboard

Terra/Aqua satellites (Mu et al 2007, 2011).

This methodology uses the following set of

inputs:

(a) Remote-Sensing Derived Inputs: Land

cover, Leaf Area Index (for vegetation

fraction), Albedo, FPAR (Fraction of

absorbed Photosynthetically Active

Radiation). These products are available

at 8/16 day intervals and derived from

MODIS.

(b) Meteorological Inputs: Air pressure, air

temperature, humidity, solar radiation.

These inputs are taken at daily basis from

global weather forecasting models

calibrated against ground stations.

The first step involves partitioning of incoming

solar radiation into net radiation available to

plants and net radiation to soil. This is done by

computing the vegetation fraction using the LAI

parameter. Potential soil evaporation is computed

using the Penman-Monteith approach which

utilizes meteorological parameters.

Plant transpiration is computed by first estimating

canopy conductance, water cover fraction,

aerodynamic resistance and plant intercepted

radiation. Then, plant transpiration is computed

using a Penman-Monteith based approach.

𝜆𝐸𝑡𝑟𝑎𝑛𝑠 =(𝑠 𝐴𝑐𝐹𝑐 + 𝜌 𝐶𝑝(𝑒𝑠 − 𝑒)

𝐹𝑐

𝑟𝑎) ∗ (1 − 𝐹𝑤𝑒𝑡)

𝑠 + 𝛾 (1 + 𝑟𝑠

𝑟𝑎⁄ )

Where, s is slope of saturated vapour pressure

curve, Ac is energy available to plant canopy, Fc

is vegetation fraction, Cp is specific heat of air, es

is saturated vapour pressure, rs is surface

resistance and ra is aerodynamic resistance, Fwet is

wet surface fraction and is a function of relative

humidity.

Wet canopy evaporation is computed by using

LAI and wet fraction to estimate wet canopy

aerodynamic resistance. Following the Biome-

BGC model λEwet_can is computed.

The soil surface is divided into saturated surface

and moist surface by the parameter Fwet. The

potential soil evaporation is computed as sum of

evaporation from saturated and moist soil

surfaces. The actual soil ET is computed using the

complementary hypothesis by Bouchet (1963).

𝜆𝐸𝑠𝑜𝑖𝑙 = 𝜆𝐸𝑤𝑒𝑡_𝑠𝑜𝑖𝑙 + 𝜆𝐸𝑝𝑜𝑡_𝑠𝑜𝑖𝑙(𝑅𝐻

100)𝑉𝑃𝐷/𝛽

Where, 𝜆𝐸𝑠𝑜𝑖𝑙 is total actual soil ET, 𝜆𝐸𝑤𝑒𝑡_𝑠𝑜𝑖𝑙 is

AET due to wet soil, 𝜆𝐸𝑝𝑜𝑡_𝑠𝑜𝑖𝑙is the potential ET

from moist soil surface (unsaturated), RH is

relative humidity in %age, VPD is vapour

pressure deficit and β is set as 200.

The total daily ET (λE) is computed as sum of

evaporation from wet canopy surface, the

transpiration from dry canopy surface and

evaporation from soil surface.

𝜆𝐸 = 𝜆𝐸𝑤𝑒𝑡_𝑐𝑎𝑛 + 𝜆𝐸𝑡𝑟𝑎𝑛𝑠 + 𝜆𝐸𝑠𝑜𝑖𝑙

This MODIS ET product has been extensively

validated by using Eddy covariance towers from

FLUXNET (Mu et al 2011).

The following global map of mean annual ET

during 2000-2006 was produced by the NTSG

using the above mentioned algorithm.

Fig.1. Mean annual ET during 2000-2006

(Adapted from Mu et al 2011).

60

Fig 2. Flowchart of MODIS ET algorithm (Adapted from Mu et al 2011).

3.2 Case study: Indian Perspective

Bhattacharya et al (2010) developed a simplified

single-source energy balance scheme to estimate

ET. Indian geostationary satellite Kalpana-1’s

Very-High Resolution Radiometer (VHRR) data

was used to obtain the major inputs for the ET

model, namely Land Surface Temperature (LST),

surface albedo, insolation and air temperature.

This methodology is based on an energy balance

approach. The available energy at surface is given

as sum of latent and sensible heat fluxes. A

parameter called evaporative fraction (Λ) is

introduced as the ratio of latent flux and total

available energy.

Λ = 𝜆𝐸

(𝜆𝐸 + 𝐻)

Subsequently, λE is estimated by multiplying the

net energy with this evaporative fraction term.

𝜆𝐸 = (𝑅𝑛 − 𝐺) 𝛬

Λ term is computed using the relationship

between LST and albedo and their relationship

with soil moisture. For wet soil, its albedo is 2-3

times lower compared to dry soil. Surface albedo

determines the outgoing shortwave radiation.

This method uses a third order polynomial

relationship between LST, Soil moisture and

albedo as stated in Bastiaanssen et al (1998).

Fig 3. A concept figure of LST (TS) vs surface

albedo curve. At a given albedo, Dry edge (DC)

and wet edge (EF) represent maximum (TH) and

61

minimum (TE) LST lines. (Adapted from

Bhattacharya et al 2010).

The evaporative fraction at a given time in day

(noontime) is approximated by:

𝛬 =𝑇𝐻 − 𝑇𝑆

𝑇𝐻 − 𝑇𝐸

This term is multiplied to the net radiation to

obtain actual ET over Indian region. Figure 4

shows the output of this methodology at 0.08o

resolution.

Figure 4. Estimated AET in mm/day for India for

two 8-day periods in Nov and Dec 2005. Adapted

from Bhattacharya et al 2010.

4. Future Prospects

Most models for ET estimation rely on other

proxy parameters to solve one of the two

fundamental equations. This results in significant

errors in estimated ET.

The state-of-art techniques in the field of ET, for

e.g. Jasechko et al 2013, use the stable isotope

ratios of oxygen (18O/16O) and hydrogen (2H/1H)

to separate transpiration from evaporation. It

relies on the fact that evaporation process results

in enrichment of heavy isotopes of O and H in the

leftover water. However, transpiration process

does not produce fractionation of these isotopes.

Their analysis of catchments of some major lakes

of the world has shown that terrestrial water flux

is dominated by transpiration and not

evaporation.

ET plays a major feedback role in the land-

atmosphere system, thus affecting the global

climate. As the climate warms up, it is expected

that global evaporation losses will increase.

However, in a recent study by Jung et al 2010, the

authors have shown that there is indeed a decline

in global land ET owing to reduced moisture

supply. They computed and analyzed global ET

data for 27 years using integrated flux tower

measurements and remote sensing inputs. Also,

they related the impact of major El-Nino to the

changes in spatio-temporal behavior of ET.

Upcoming ISRO mission, GISAT (Geostationary

Imaging SATellite), is an advanced earth-

observation satellite. As the name suggests,

GISAT will be a geostationary satellite providing

high resolution multi-spectral and hyper-spectral

observations over India in optical, near-infrared

and thermal wavelengths, multiple times in a day.

This satellite will be capable of providing all the

necessary parameters required for estimation of

ET over India on a daily basis. This will greatly

improve the evaporation estimates over Indian

region and will help us better understand ET and

its associated processes.

Evapotranspiration is not easy to measure and

even today it has huge potential for

improvements. Symons in 1867 best described

estimating evaporation as “...the most desperate

art of the desperate science of meteorology”

(Monteith, 1997). With the little we know and the

vast unknowns left to explore, ET is definitely

one of the most challenging and exciting

branches of Hydrology.

5. References

Allen, R. G. and Pruitt, W. O. (1986). Rational

use of the FAO Blaney-Criddle Formula. J. Irrig.

Drain. E. ASCE, 112, 139–155.

Allen, R. G., Pereira, L. S., Raes, D., and Smith,

M. (1998). Crop evapotranspiration Guidelines

for computing crop water requirements, FAO

Irrigation and Drainage Paper 56, Food and

Agriculture Organization of the United Nations.

62

Allen, R. G., Tasumi, M., and Trezza, R. (2007).

Satellite-based Energy Balance for Mapping

Evapotranspiration with Internalized Calibration

(METRIC)—Model. Journal of irrigation and

drainage engineering, 133(4), 380-394.

Bastiaanssen, W. G. M. (2000). SEBAL-based

Sensible and Latent Heat Fluxes in the Irrigated

Gediz Basin, Turkey. Journal of hydrology,

229(1), 87-100.

Bastiaanssen, W. G. M., Menenti, M., Feddes, R.

A., and Holtslag, A. A. M. (1998). A Remote

Sensing Surface Energy Balance Algorithm for

Land (SEBAL): Part 1: Formulation. Journal of

hydrology, 212, 198-212.

Batra, N., Islam, S., Venturini, V., Bisht, G., and

Jiang, L. E. (2006). Estimation and Comparison

of Evapotranspiration from MODIS and AVHRR

Sensors for Clear Sky Days over the Southern

Great Plains. Remote Sensing of Environment,

103(1), 1-15.

Bhattacharya, B.K., Mallick, K., Patel, N.K., and

Parihar, J.S. (2010). Regional clear sky

evapotranspiration over agricultural land using

remote sensing data from Indian geostationary

meteorological satellite. Journal of Hydrology,

387, 65-80.

Bouchet, R. J. (1963). Evapotranspiration reelle

et potentielle, signification climatique,

International Association of Hydrological

Sciences Publ., 62, 134–142.

Chen, F. and Dudhiya, J. (2001). Coupling an

Advanced Land Surface-Hydrology Model with

the Penn State-NCAR MM5 Modeling System.

Part I: Model implementation and sensitivity.

Monthly Weather Review, 129, 569-585.

Cleugh, H. A., Leuning, R., Mu, Q., and Running,

S. W. (2007). Regional evaporation estimates

from flux tower and MODIS satellite data.

Remote Sensing of Environment, 106(3), 285-

304.

Dingman, S. L. (1992). Physical Hydrology,

Prentice Hall, Upper Savage, New Jersey.

Granger, R. J. and Gray, D. M. (1989).

Evaporation from natural non-saturated surfaces,

Journal of Hydrology. 111, 21–29.

Hargreaves, G. H. and Samani, Z. A. (1985).

Reference crop evapotranspiration from

temperature, Appl. Eng. Agric., 1, 96–99.

Jasechko, S., Sharp, Z.D., Gibson, J.J., Birks,

S.J., Yi, Y., and Fawcett, P.J. (2013). Terrestrial

water fluxes dominated by transpiration. Nature

Letter. 496. doi:10.1038/nature11983.

Jung, M., Reichstein, M., Ciais, P., Seneviratne,

S.I., Sheffield, J., Goulden, M.L. et al (2010).

Recent decline in the global land

evapotranspiration trend due to limited moisture

supply. Nature Letter. 467, 951-954.

McMahon, T.A., Peel, M.C., Lowe, L.,

Srikanthan, R., and McVicar, T.R. (2013).

Estimating actual, potential, reference crop and

pan evaporation using standard meteorological

data: a pragmatic synthesis. Hydrol. Earth Syst.

Sci., 17, 1331-1363.

Monteith, L.J. (1997). Evaporation Models. In:

Agricultural Systems Modelling and Simulation.

Edited by Robert M. Peart and R. Bruce Curry.

University of Florida, Gainesville, Florida: 197-

234.

Monteith, L.J. (1981). Evaporation and surface

temperature, Q. J. Roy. Meteor. Soc., 107, 1–27.

Morton, F. I., Richard, F., and Fogarasi, S.

(1985). Operational estimates of areal

evapotranspiration and lake evaporation –

Program WREVAP, NHRI Paper 24, Inland

Waters Directorate, Environment Canada,

Ottawa.

Mu, Q., Heinsch, F. A., Zhao, M., and Running,

S. W. (2007). Development of a Global

Evapotranspiration Algorithm Based On MODIS

and Global Meteorology Data. Remote Sensing

of Environment. 111(4), 519-536.

Mu, Q., Zhao, M., and Running, S. W. (2011).

Improvements to a MODIS global terrestrial

63

evapotranspiration algorithm. Remote Sensing of

Environment. 115(8), 1781-1800.

Penman, H. L. (1948). Natural evaporation from

open water, bare soil and grass, Proc. R. Soc.

Lond. A. 193, 120–145.

Priestley, C.H.B. and Taylor, R.J. (1972). On the

assessment of the surfaces heat and

evapotranspiration using large-scale parameters.

Monthly Water Review. 100(2):81-92.

Szilagyi, J. and Jozsa, J. (2008). New findings

about the complementary relationship-based

evaporation estimation methods. Journal of

Hydrology. 354, 171–186.

Thornthwaite, C. W. (1948). An approach toward

a rational classification of climate. Geogr. Rev.,

38, 55–94.

Turc, L. (1961). Estimation of irrigation water

requirements, potential evapotranspiration: A

simple climatic formula evolved up to date, Ann.

Agronomy, 12, 13–49.

Yao, Y., Liang, S., Cheng, J., Liu, S., Fisher, J.B.,

Zhang, X., Jia, K. et al. (2013). MODIS-driven

Estimation of Terrestrial Latent Heat Flux in

China Based on a Modified Priestley-Taylor

Algorithm. Agricultural and Forest Meteorology.

171, 187-202.

64

SATELLITE ALTIMETRY AND ITS APPLICATIONS TO

HYDROLOGY (SURFACE WATER INFORMATION OVER INLAND

WATER BODIES)

SHARD CHANDER

Space Applications Centre, Ahmedabad, India, 380015

*S. Chander, Email: [email protected]

Abstract: Satellite altimetry was started in 1960 for helping the ocean and gravitational science needs

by making use of two way travel time of electromagnetic pulse in microwave frequency. Initially its

accuracy was limited to few meters, but soon with the advancement of technology centimetre level

accuracy was achieved over Open Ocean. At this frequency the foot-print size is order of few

kilometres. But over inland water bodies its accuracy is limited to few tens of centimetre, due to a

number of reasons, i.e. due to smaller in size, land contamination, and inaccurate range corrections.

Over inland waters dedicated processing is required starting from the raw altimeter measurements, i.e.

“Waveform”. Waveform means a graph between time and returned power. We need to analyse this

waveform in detail, i.e. shape, amplitude, leading edge, and trailing edge. Range information can be

estimated by retraking this waveform. This range information is then needs to be corrected for

atmospheric and air-water interface effects. We have developed a complete chain of algorithms for

retrieving the accurate water level using raw satellite measurements. We have tested this methodology

over Ukai reservoir over Tapi River. The results were validated with the in-situ gauge measured water

level by flood cell of the DAM authority. We have also carried out the GPS survey in-synchronous with

the satellite pass. With dedicated inland water bodies refined methodology we have achieved water

level within RMSE less than 15 cm. Altimetry derived reservoir/ river levels can subsequently be used

to deal with key inland water resources problems such as flood, rating curve generation for remote

locations, reservoir operations, and calibration of river/lake models.

Keywords: Waveform retracking, Altimeter inland range corrections, GPS Kinematic mode.

Introduction

Terrestrial waters only represent 1% of the total

amount of water available on earth, but still

they have crucial impact on terrestrial life and

human needs. They play a major role in climate

variability by continuously exchanged with

atmosphere and oceans through evaporation

and transpiration. The terrestrial branch of the

global water cycle is now recognized as one of

the major importance factor for climate

research, with other important factors like

inventory and management of water resources.

Due to unavailability of the routine in-situ

observations, the global distribution of spatial-

temporal variations of the continental waters

are still poorly known. So far these

measurements over inland water bodies rely on

hydrological models coupled with

atmosphere/ocean global circulation models

forced by observations. In-situ gauging

networks have been installed for several

decades for providing time series of water level

and discharge rates. This information is used for

water resources allocation, navigation, land use,

hydroelectric energy and flood hazards. But

sometimes installing and maintaining a gauge is

not feasible due to geographical, political and

economic limitations. Moreover the decrease in

65

mailto:[email protected]

number of in-situ networks is reported by

several authors.

Recently remote sensing techniques emerged as

an alternative to the traditional water level

monitoring through gauge. Satellite

measurements can provide time scale ranging

from months to decades. Among these, satellite

altimetry is one of the promising technique for

systematic monitoring of water levels of

reservoirs, lakes and rivers. Altimetry has the

advantage of taking being able to take global,

homogeneous, repeated measurements, thus

enabling systematic monitoring to be carried

out over several years. The measured surface

heights are referenced to the same frame. Other

remote sensing techniques like SAR

interferometry and passive and active

microwave observations can detect the

changing areal extent of the inland water body.

Altimeter information coupled with SAR

interferometry, radiometer measurements in

complement to the in-situ observations, can

significantly improve our understanding of

hydrological processes and climate variability.

Unprecedented information can be expected by

combining models, surface observations and

observations from space, which offer global

geographical coverage, good spatial-temporal

sampling and continuous monitoring with time.

Satellite Altimeter

An altimeter is an instrument used to accurately

determine surface height. It sends out a pulse of

energy in the microwave region of the

electromagnetic spectrum and records the

return signal. The time taken for radar pulse to

travel from the instrument to the Earth’s surface

and back again can be used to calculate the

distance between the two. To do this, the

satellite’s exact position must be known.

Corrections then have to be made as the radar

pulse is affected by traveling through the

atmosphere. The radar altimeter signal is

refracted and attenuated by the atmosphere. We

need external data to evaluate these

atmospheric effects. For example, the pressure

field at the sea surface enables us to estimate the

signal path delay due to the vertical structure of

the dry atmosphere. Atmospheric water vapour

content, which also contributes to signal

refraction and attenuation, can be estimated

from measurements made by radiometers on-

board the satellite. However, radiometers are

not stable over time, so an external estimation

based on meteorological models is useful to

correct instrument drift. Sea level pressure is

also used to isolate the signal due to the

deformation of the ocean surface caused by

atmospheric pressure.

A space-borne radar altimeter is basically

pulsed radar transmitting a narrow

(nanosecond) rectangular pulse towards the

nadir. It makes three basic measurements on the

backscatter echo from the ocean surface; the

time delay τ between the transmission and

reception of the echo, the integrated power P of

the returned pulse echo and certain

characterization of the shape of the returned

pulse, in particular, the slope of the rising or the

leading edge of the returned pulse. All

oceanographic information derived from

altimeter will contain these three

measurements. The time delay measurement

contains information about the ocean surface

topography, albeit relative to satellite orbit.

Combined with precision orbit determination,

the information can be used to construct the

topography of the ocean surface along the sub-

satellite path. Shape (geometrical form) and

amplitude of the returned pulse, on the other

hand, contain useful information about the large

and small scale roughness of the ocean surface

respectively. The slope of the leading edge of

the returned pulse can be used to derive the

significant wave height, whereas the returned

power or the backscatter coefficient σ˚ can be

used to estimate the wind speed near the ocean

surface.

66

Fig. 1: Basic of Satellite Altimetry

Radar altimetry measures the range between the

satellite and earth surface by transmitting a

short pulse of electromagnetic wave. Difference

between the satellite altitudes above a reference

surface i.e. a conventional ellipsoid (WGS84) is

determined through precise orbit computation.

This range estimation is then corrected for

atmospheric and geophysical signals for precise

retrieval of the water level above the reference.

The altimeter satellites are placed onto a repeat

orbit at regular time intervals (called the orbital

cycle), during which a complete coverage of the

Earth is performed. With continuous operation

across all surfaces, the instrument behaves like

a string of pseudo-gauges, sampling the

elevation at discrete intervals along a narrow

satellite ground track. The instruments can thus

observe monthly, seasonal, and inter-annual

variations over the lifetime of the mission, and

unlike many gauge networks that operate using

a local reference frame, all altimetry height

measurements are given with respect to one

reference datum to form a globally consistent,

uniform data set. There are a number of

altimeter are on-board presently, i.e. Jason-3,

Jason-2, SARAL, Sentinel-3 etc.

SARAL/AltiKa

SARAL (Satellite with ARgos and ALtika),

launched on 25th February 2013 by

ISRO/CNES, is the 1st altimeter using such a

high frequency (Ka band). SARAL/AltiKa

mission is one among the global altimetry

system, which is designed to provide precise

and accurate observations of ocean circulation

and sea surface elevation. It carries a radar

altimeter and a microwave radiometer. It is

placed at an altitude of 800 km and revisits the

same place on earth after a period of 35 days.

AltiKa altimeter (Ka band; the main mission

instrument) and a dual frequency microwave

radiometer (23.8 GHz / 37 GHz; to correct the

altimeter measurement for atmospheric range

delays induced by water vapour), both shares

the same antenna. The radio-positioning

DORIS system and Laser Reflector Array

(LRA) is the instruments on board for precise

67

orbit determination. Technical specifications of

SARAL makes it a superior candidate

especially for terrestrial surface studies

compared to the previous/ existing altimeters.

Ka band frequency (35.75 GHz), higher

bandwidth (500 MHz), smaller pulse width

(2ns), smaller foot print (1km), and higher pulse

repetition frequency (4 kHz) with continuous

ice tracking mode retracker can provide very

useful information about terrestrial surfaces.

SARAL primarily due to the almost negligible

ionospheric correction, smaller de-correlation

times, smaller footprint (whereas antenna width

is almost reduced to half in case of Ka band) as

compared to Ku band and better along track

resolution can be considered as a mile-stone

instrument for terrestrial surface studies.

Data processing & Methodology

Radar echoes over terrestrial surfaces are

hampered by interfering reflections due to

water, land and rough topography. As a

consequence complex and multi-peaked

waveforms are acquired. Proper classification is

required to understand the behavior of the

waveform, that can tells about the information

beneath the surface. Here linear discriminant

analysis (LDA) and naive Bayes classifiers

techniques has been used for classifying the

waveforms within predefined shapes.

Knowledge about the waveform shape prior to

retracking is useful for optimizing the choice of

the retracking method. Brown, Beta-5, Ice-2

and Offset Centre of Gravity (OCOG)

waveform retracking algorithms were

implemented for finer processing using

waveform retracking. New sub-waveform

modified retracker was also developed

especially for multi-peak waveforms based on

the sub-waveform selection within the full

waveform and then retracked taking into

consideration the volume and surface scattering

effect.

Fig. 2: Flow chart for retrieving the Water Level information using Satellite Altimetry

68

Geophysical range correction algorithms were

modified for inland water bodies. Dry

tropospheric correction (DTC) that considers

sea level pressure was modified based on the

altitude of the measurement. For this purpose

the height above the EGM2008 geoid was

computed by subtracting the EGM2008 geoid

height above a reference ellipsoid from the

water level height above the same ellipsoid.

Reanalysis pressure fields were also tested for

observing the uncertainty in the pressure

measurement itself, by using following

pressure fields: NCEP, ECMWF and ERA.

Total column water vapour and temperature

was used to estimate the wet tropospheric

correction. GIM maps were used for extracting

the TEC information that was converted to

ionospheric delay. For smaller inland water

bodies earth tide is applied, but elastic-ocean

and ocean-loading tides are only applicable for

larger water bodies (1000 km2). The inverse

barometric (IB) correction is not applied

because the lakes/reservoirs are closed systems.

The sea state bias (SSB) correction is also not

applied because wind effects tend to be

averaged out along-track.

Results and Discussion

This methodology was validated over Ukai

Reservoir, Gujarat, India. Time-series water

level over the Ukai reservoir, was collected

from the flood-cell, Ukai division for the period

1972-2015. Altimetry derived water level over

Ukai reservoir was compared with the in-situ

gauge dataset, and found to be matched within

8±2 cm. GPS field trip was also conducted in

synchronous with the altimeter pass and found

to be matched with good accuracy. Water level

retrieved using SARAL altimeter over the Ukai

reservoir for the period March 2013 to April

2015 is shown in Figure 3. Using similar

methodology water level was disseminated

through MOSDAC website for 10 major Indian

Inland water bodies including rivers, lakes and

reservoirs.

Fig. 3: Water Level Retrieval using Satellite Altimetry over Ukai reservoir, India

(Altimeter pass and location of Virtual station is shown in the Landsat image)

Ukai Reservoir

69

Conclusions and future directions

Over inland water bodies due to land

contamination within the altimeter foot-print,

generally altimeter gets very complex multi-

peaked waveforms. Due to complex

waveforms, the accuracy of the retrieved water

level is limited to few tens of centimetre, but

with the proposed methodology we have

achieved retrieval accuracy better than 15 cm.

Knowledge about the waveform shape prior to

retracking is useful for optimizing the choice of

the retracking method. Waveform retracker

should be robust so that it can take care for a

number of waveform shapes that generally

found in nature. The range corrections

dedicated to inland water bodies are important

to remove the seasonal trend from the water

level retrieval. Overall, the altimeter retrieved

water level was found to be matched well both

with the gauge measurement. Altimeter has a

limitation taht it only gives information along

the nadir track, but generally over inland water

bodies we require across track information as

well. Surface water and Ocean topography

(SWOT) is a proposed altimeter that will utilize

Ka band radar interferrometery technique to

estimate the slope and extent information of the

water surface in across track direction.

References for further reading

Bao, L., Y. Lu, Y. Wang. 2009. Improved

retracking algorithm for oceanic altimeter

waveforms. Science Direct. 19: 195-203.

Birkett, C.M. 1998. Contribution of the TOPEX

NASA radar altimeter to the global monitoring

of large rivers and wetlands. Water Resources

Research. 34: 1223-1239.

Cretaux, J.F., W. Jelinsky, S. Calmant, A.

Kouraev, V. Vuglinski, M.B. Nguyen, M.C.

Gennero, F. Nino, R.B.D. Rio, A. Cazenave and

P. Maisongrande. 2011. SOLS: A lake database

to monitor in the Near Real Time water level

and storage variations from remote sensing

data. Advances in Space Research. 47: 1497-

1507.

Davis, C.H. 1997. A robust threshold retracking

algorithm for measuring ice-sheet surface

elevation change from satellite radar altimeter.

IEEE Trans Geoscience Remote Sensing.

35(4): 974-979.

Deng, X. and W.E. Featherstone. 2006. A

coastal retracking system for satellite radar

altimeter: application to ERS-2 around

Australia. Journal of Geophysical Research.

111:C06012. doi:10.1029/2005JC003039.

Fu, L.L. and A. Cazenave. 2001. Satellite

altimetry and earth sciences: a handbook of

techniques and applications. Academic, San

Diego, CA, 624 pp.

Hwang, C., J.Y. Guo, X.L. Deng, H.Y. Hsu and

Y.T. Liu. 2006. Coastal gravity anomalies from

retracked Geosat/GM altimetry: improvement,

limitation and the role of airborne gravity data.

J Geod. 80: 204-216.

Legresy, B., F. Papa, F. Remy, G. Vinay,

M.V.D. Bosch and Q.Z. Zanife. 2005. Envisat

radar altimeter measurements over continental

surfaces and ice caps using the ICE-2 retracking

algorithm. Remote Sensing of Environment.

95: 150-163.

Martin, T.V., H.J. Zwally and A.C. Brenner.

1983. Analysis and retracking of continental ice

sheet radar altimeter waveforms. Journal of

Geophysical Research. 88: 1608-1616.

Wingham, D.J., C.G. Rapley and H. Griffiths.

1986. New techniques in satellite tracking

system. Proceedings of IGARSS symposium,

Zurich: 1339-1344.

70

WATER QUALITY MONITORING FROM SPACE

ASHWIN GUJRATI




Ahmedabad-380015

Introduction

Water provides basic resources for various

human uses and diverse habitat ecosystem

services, supporting high levels of biodiversity.

Thus, it is paramount that water quality is

assessed every so often to determine suitability

and safety for varying purposes. Monitoring

and understanding the water quality i.e.

physical, chemical and biological status of

global water is immensely important to

scientists and policy makers.

Water quality of any system can be measured

by the following physical, chemical and

biological parameters. The physical data

includes, pH, temperature, dissolved oxygen,

turbidity, Secchi depth, specific conductivity.

Chemical analysis includes concentrations of

Silicate, Nitrate, Nitrite, Ammonium, total

Nitrogen and total Phosphorus. Biological

parameter includes chlorophyll, dissolved

organic matter and suspended particulate

matter.

The in-situ measurements of water quality are

often very scare because of large areas to

monitor. Furthermore, these measurements do

not represent the actual water quality at a large

scale since measurements are restricted to

region specific. Consequently, one may

consider measurement techniques so as to get

relevant information especially at large scales

and to be able to characterise water quality over

a whole region. In this context, remote sensing

from space is a perfect tool to get the required

information. Satellite data may be able to

provide a greater amount of spatial information

at an improved cost compared to spot sample

grabs.

Remote sensing of water

Conventional monitoring approaches tend to be

limited in terms of spatial coverage and

temporal frequency. Remote sensing has the

potential to provide an invaluable

complementary source of data at local to global

scales. But remote sensing of water can

measure only those water quality parameters

that have optically active constituents.

Constitutes that interact with light and changing

the energy spectra of reflected solar radiation

emitted from surface waters (Ritchie et al.,

2003). These include phytoplankton pigments

(chlorophylls, carotenoids, phycocyanin, etc.),

colored dissolved organic matter (CDOM), and

inorganic and non-living suspended matter,

which coincide well with the previously

mentioned parameters determining the majority

of water quality issues in inland waters.

The measured radiance originates from sunlight

that passes through the atmosphere, is reflected,

absorbed, and scattered by constituents in the

water bodies, and is transmitted back through

the atmosphere to the satellite-based sensor

(Fig. 1) (http://www2.dmu.dk/resc-

oman/project/Backgrounds/challenges.htm).

The processes of scattering and absorption by

optically active constituents in the water affect

the spectrum and radiance distribution (light

field) of the light emerging from the water – the

so called water-leaving radiance. The scattering

and absorption characteristics of water and its

constituents are described as the inherent

71

http://www2.dmu.dk/resc-oman/project/Backgrounds/challenges.htm

http://www2.dmu.dk/resc-oman/project/Backgrounds/challenges.htm

optical properties (IOPs). The spectral quality

and quantity of the water-leaving radiance is

largely determined by the inherent optical

properties. The modification/alteration of the

radiance has been used to determine water

constituents, typically the desired parameter has

been the chlorophyll-a concentration, colored

dissolved organic matter (CDOM), and

inorganic and non-living suspended matter. In

essence, water colour is determined by inherent

optical properties.

Fig.1: Schematic diagram of Remote sensing of water

Inherent optical properties (IOP's) are those

properties that depend only upon the medium,

and therefore are independent of the ambient

light field within the medium. The two

fundamental IOP's are the absorption

coefficient and the volume scattering function.

Other IOP's include the index of refraction, the

beam attenuation coefficient and the single-

scattering albedo. Apparent optical properties

(AOP's) are those properties that depend both

on the medium (the IOP's) and on the geometric

(directional) structure of the ambient light field,

and that display enough regular features and

stability to be useful descriptors of the water

body. Commonly used AOP's are the irradiance

reflectance, the average cosines, and the various

diffuse attenuation coefficients.

Remote sensing of water is broadly divided into

retrieval over Case 1 waters and Case 2 waters.

Case 1 waters are waters in which the

concentration of phytoplankton is high

72

compared to non-biogenic particles. Absorption

by chlorophyll and related pigments therefore

plays a major role in determining the total

absorption coefficient in such waters, although

detritus and dissolved organic matter derived

from the phytoplankton also contribute to

absorption in case 1 waters. Case 1 water can

range from very clear (oligotrophic) water to

very turbid (eutrophic) water, depending on the

phytoplankton concentration. Case 2 waters are

"everything else," namely waters where

inorganic particles or dissolved organic matter

from land drainage dominate, so that absorption

by pigments is relatively less important in

determining the total absorption.

Literature review

Two types of methods are commonly used for

interpreting water quality from remotely sensed

data: empirical and analytical approach (Bhatti

et al. 2010; Cannizzaro and Carder 2006;

Giardino et al. 2007; Kallio 2000; Knaeps et al.

2010; Ritchie et al. 2003). The empirical based

approaches are most commonly used method

which are determined through statistical

relationships between measured spectral

properties (i.e. radiance or reflectance) versus

the measured water quality parameter of

interest (e.g. Lee et al. 1996; Garver & Siegel,

1997; Hoge & Lyon, 1996, 2005; Le et al.,

2009a; Ritchie et al. 2003; Wang et al., 2005;

Bhatti et al. 2010). Usually algorithm

development searches for a combination of

radiance signals at several wavelengths to find

ratio, or other combination, that relates radiance

at particular band empirically to the desired

water quality parameter. The coefficients

contained in these algorithms are generally

derived by pooling data collected at various

spatial and temporal scales. Empirical

approaches are region dependent, that works

better for one site but may fail on other site. On

the other hand, analytical algorithms are based

on radiative transfer equations works equally

well for different water bodies and usually

perform better than the empirical algorithm (L.

Li el al.2013).

Recently many analytical and semi-analytical

algorithms for inland waters are developed for

retrieving inherent optical properties from

remote sensing reflectance. Gege (2012)

developed an analytic model for the direct and

diffuse components of the downwelling

irradiance in the water column. Giardino et al.

(2012) developed a software package

incorporating their Bio-Optical Model Based

tool for Estimating water quality and bottom

properties from Remote sensing images

(BOMBER). Brando et al. (2012) present an

adaptive implementation of the linear matrix

inversion (LMI) method which accounts for

variability in both IOPs and mass-specific IOPs

(SIOPs) over space and time in wide-ranging

optically-complex waters. More sophisticated

neural network and physics-based inversion

methods have also been used to estimate in-

water inherent optical properties (IOPs)

(Odermatt et al., 2012). Salama and Verhoef

(2014) present a new, forward model analytical

inversion solution (“2SeaColor”) for the

retrieval of the depth profile of the downwelling

diffuse attenuation coefficient.

Remote sensing of inland water bodies poses a

challenge due to its highly complex optical

nature as compared to clear marine waters.

Simulated remote sensing reflectance spectra of

water with different water quality parameters

plotted with their true colour is shown in figure

2. The optical complexity of inland waters

stems from the fact that these waters are

typically characterised by high concentrations

of phytoplankton biomass (typically on the

order of between 1 and 100 mg m−3

chlorophyll-a (chl-a), and up to 350 mg m−3

(Gitelson et al., 1993), mineral particles,

detritus and CDOM that typically do not co-

vary over space and time. Moreover, their

optical properties are highly variable between

and even within water bodies.

73

Fig.2: Remote sensing reflectance spectra of different water colour

Table 1: Variables that affect the water quality parameters

Variables that can affect

remote sensing of physical

water-quality

characteristics

Variable Explanation

Time of year

The Earth receives 7 per cent more energy from the sun on 1 January

than on 1 July because of an oval orbit.

Sun-elevation angle

More solar energy is specularly reflected from water surfaces at low

sun-elevation angles than at high angles. Also,- the path length of

solar energy through the atmosphere is longer at low sun-elevation

angles, and more solar energy is absorbed and scattered.

Aerosol and molecular

content of atmosphere

These constituents determine the amount of solar energy absorbed

and scattered by the atmosphere. Some energy, received by a

satellite, is backscattered before reaching the water surface.

Water-vapour content of

the atmosphere

Water vapour affects energy absorption at near infrared and thermal

infrared wavelengths.

Specular reflection of

skylight from water surface

Specularly reflected skylight is received by a satellite. The intensity

and wavelength distribution of this energy depends on atmospheric

scattering, which produces skylight.

Roughness of water surface

A rough surface may produce more or less specular reflection than a

smooth surface. At high sun-elevation angles, the area of sun glint

may be within the satellite fields of view.

Film, foam, debris, or

floating plants on water

surface

These features may not be resolved on a satellite image, but they

contribute to the spectral characteristics of the measured signal.

Water colour

Dissolved, coloured materials increase absorption of solar energy in

water.

Water turbidity

The concentration, size, shape, and refractive index of suspended

particles determine turbidity and increase the amount of energy

backscattered in water bodies.

Reflectance and

absorptance characteristics

of suspended particles

Particles may be inorganic sediments, phytoplankton, zooplankton,

or a combination. When present in high concentrations, particles

affect the spectral distribution of backscattered energy.

74

Multiple reflections and

scattering of solar energy in

water

The spectral results of these processes are difficult to predict, but

may not be important.

Depth of water and

reflectance of bottom

sediments

Water clarity determines the importance of bottom reflectance. Solar

energy may not reach bottom in a turbid water.

Submerged or emergent

vegetation

Vegetation may change bottom reflectance, obscure water surface, or

contribute to the spectral characteristics of the measured signal.

Radiative Transfer Model

In optically shallow waters, the upwelling

irradiance just below the surface, Eu(0), results

from adding the flux backscattered by the water

column and the flux reflected by the bottom

substrate and then transmitted through the

column as shown in fig 3 below,

𝐸𝑢(0) = [𝐸𝑢(0)]𝐶 + [𝐸𝑢(0)]𝐵

The subscripts C and B stand for water column

and bottom. The first component corresponds to

the photons that have never interacted with the

bottom, whereas those that have interacted with

the bottom at least once form the second

component.

Fig.3: Schematic diagram of radiative transfer model

To estimate the first component on RHS, we

consider an infinitely thin layer of uniform

thickness dZ at depth Z. At this level, the

downwelling irradiance is Ed(Z). The

backscattering coefficient (or reflectance

function) for the downwelling light stream is

denoted bbd; the fraction of upwelling irradiance

created by this layer is

𝑑𝐸𝑢(𝑍) = 𝑏𝑏𝑑𝐸𝑑(𝑍)𝑑𝑍

Ed(Z) can be expressed as

𝐸𝑑(𝑍) = 𝐸𝑑(0)exp(−𝐾𝑑𝑍)

Ed(0) is the downwelling irradiance at null

depth, and Kd is the diffuse attenuation for

downwelling irradiance. Before it reaches the

surface, dEu(Z) suffers an attenuation along the

path from Z up to 0, expressed by exp(-kZ),

where k is the vertical diffuse attenuation

coefficient for upward flux. This coefficient

refers to an exponential attenuation (with

distance travelled upward) of the upward flux

while travelling upward originating from any

thin layer. The contribution of the considered

layer to the upwelling irradiance just below the

surface is denoted dEu(Z0); this term is

expressed as

𝑑𝐸𝑢(𝑍 → 0) = 𝑏𝑏𝑑𝐸𝑑(0) exp[−(𝐾𝑑+ 𝑘)𝑍] 𝑑𝑍

The contributions of each layer between Z and

0 in forming Eu(0, Z) can be summed, so that

75

𝐸𝑢(0, 𝑍) = 𝑏𝑏𝑑𝐸𝑑(0)

× ∫ exp[−(𝐾𝑑 + 𝑘)𝑍] 𝑑𝑍𝑍

0

𝐸𝑢(0, 𝑍) = (𝐾𝑑 + 𝑘)−1𝑏𝑏𝑑𝐸𝑑(0) × [1

− exp{−(𝐾𝑑 + 𝑘)𝑍}]

For an infinite water depth (Z=∞), above

equation reduces to

𝐸𝑢(0,∞) = (𝐾𝑑 + 𝑘)−1𝑏𝑏𝑑𝐸𝑑(0)

𝐸𝑢(0, 𝑍) = 𝑅(0,∞)𝐸𝑑(0)

𝑅(0,∞) =𝑏𝑏𝑑

(𝐾𝑑 + 𝑘)

R(0,∞)represents the reflectance at null depth

of the deep ocean, hereafter denoted R∞. For a

column limited by the presence of a perfectly

absorbing bottom at a depth H,

𝐸𝑢(0,𝐻) = 𝑅∞𝐸𝑑(0)

× [1 − exp{−(𝐾𝑑 + 𝑘)𝑍}]

= [𝐸𝑢(0)]𝐶

and thus provides the first term in first equation.

If the bottom is a Lambertian reflector with an

albedo A, the reflected flux at level H (i.e.

immediately above the bottom) is

[𝐸𝑢(𝐻)]𝐵 = 𝐴 ×𝐸𝑑(𝐻)

= 𝐴 × 𝐸𝑑(0)exp(−𝐾𝑑𝐻)

This contribution of the bottom to the upwelling

irradiance will be attenuated from H up to the

surface. If we suppose that this upward flux is

attenuated with the same K as above, the

contribution of the bottom to the upward

irradiance reaching the surface becomes

[𝐸𝑢(𝐻)]𝐵 = 𝐴 × 𝐸𝑑(0)exp[(−𝐾𝑑 + 𝑘)𝐻]

By adding, we obtain

𝐸𝑢(0) = 𝐸𝑑(0)(𝑅∞× [1 − exp{−(𝐾𝑑 + 𝑘)𝑍}]

+ 𝐴 × exp[(−𝐾𝑑 + 𝑘)𝐻])

Dividing by Ed(0) and rearranging, the

reflectance, R(0, H), below the surface of a

homogeneous ocean bounded below by a

reflecting bottom at depth H, is

𝑅(0,𝐻) = 𝑅∞ + (𝐴 − 𝑅∞)exp[(−𝐾𝑑 + 𝑘)𝐻]

To the extent that the two kinds of upward

fluxes, either scattered by the series of thin

layers or reflected by the bottom, do not have

the same geometrical structure, they are not

attenuated in the same way. If KB and KC denote

the attenuation coefficients for the upward

streams originating from the bottom and from

the water column respectively, Equation must

be written as

𝑅(0,𝐻) = 𝑅∞ + exp(−𝐾𝑑H) ×[Aexp(−𝐾𝐵𝐻)

− 𝑅∞exp(−𝐾𝐶𝐻)]

Authors have simplified the above equation for

implementation with various approximations

based on their study area and practice

difficulties of measurement. The coefficients

were derived using either Monte Carlo or

hydro-light simulations.

References

Bhatti, A.M., Schalles, J., Rundquist, D.,

Ramirez, L & Nasu, S. (2010). Accuracy 2010

Symposium, July20-23, Leicester, UK

Brando, V.E., Dekker, A.G., Park, Y.J., &

Schroeder, T. (2012). Adaptive semianalytical

inversion of ocean color radiometry in optically

complex waters. Applied Optics, 51(15), 2808–

2833.

Cannizzaro, J. P., and K. L. Carder. 2006.

Estimating chlorophyll a concentration from

remote-sensing reflectance in optically shallow

waters. Remote Sensing of Environment 101

(1): 13-24.

Garver, S. A., & Siegel, D. A. (1997). Inherent

optical property inversion of ocean color

spectra and its biogeochemical interpretation .1.

Time series from the Sargasso Sea. Journal of

Geophysical Research-Oceans, 102, 18607–

18625.

Gege, P. (2012). Analytic model for the direct

and diffuse components of downwelling

spectral irradiance in water. Applied Optics,

51(9), 1407–14019.

76

Giardino, C., V. E. Brando, A. G. Dekker, N.

Strombeck, and G. Candiani. 2007. Assessment

of water quality in Lake Garda (Italy) using

Hyperion. Remote Sensing of Environment 109

(2): 183-195.

Gitelson, A., Garbuzov, G., Szilgyi, F.,

Mittenzwey, K. -H., Karnieli, A., & Kaiser, A.

(1993). Quantitative remote sensing methods

for real-time monitoring of inland waters

quality. International Journal of Remote

Sensing, 14, 1269–1295.

Hoge, F. E., & Lyon, P. E. (1996). Satellite

retrieval of inherent optical properties by linear

matrix inversion of oceanic radiance models:

An analysis of model and radiance

measurement errors. Journal of Geophysical

Research-Oceans, 101, 16631–16648.

Kallio, K. 2000. Remote sensing as a tool for

monitoring lake water quality. In Hydrological

and limnological aspects of lake monitoring,

ed. P. Heinonen, G. Ziglio, and A. van der

Beken, 237-245. Chichester, England: John

Wiley & Sons, Ltd.

Knaeps, E., D. Raymaekers, S. Sterckx, and D.

Odermatt. 2010. An intercomparison of

analytical inversion approaches to retrieve

water quality for two distinct inland waters. In

Proceedings of the Hyperspectral Workshop.

Frascati, Italy.

Le, C. F., Li, Y. M., Zha, Y., Sun, D. Y., & Yin,

B. (2009a). Validation of a quasi-analytical

algorithm for highly turbid eutrophic water of

Meiliang Bay in Taihu Lake, China. IEEE

Transactions on Geoscience and Remote

Sensing, 47, 2492–2500.

Lee, Z. P., Carder, K. L., Peacock, T. G., Davis,

C. O., & Mueller, J. L. (1996). Method to derive

ocean absorption coefficients from remote-

sensing reflectance. Applied Optics, 35, 453–

462.

Odermatt, D., Gitelson, A., Brando, V.E., &

Schaepman, M. (2012). Review of constituent

retrieval in optically deep and complex waters

from satellite imagery. Remote Sensing of

Environment, 118, 116–126.

Ritchie, J. C., P. V. Zimba, and J. H. Everitt.

2003. Remote sensing techniques to assess

water quality. Photogrammetric Engineering &

Remote Sensing 69 (6): 695-704.

Salama, M. S., & Verhoef, W. (2015). Two-

stream remote sensing model for water quality

mapping: 2SeaColor. Remote Sensing of

Environment, 157, 111-122.

Wang, P., Boss, E. S., & Roesler, C. (2005).

Uncertainties of inherent optical properties

obtained from semianalytical inversions of

ocean color. Applied Optics, 44, 4074–4085.

77

ADAPTATION STRATEGIES FOR GROUND WATER

SUSTAINABILITY IN THE FACE OF CLIMATE CHANGE IN

INDIA

R.C.JAIN

Adviser ,Gujarat Water Resource Development Corporation,Govt. of Gujarat,Gandhinagar.

Ground Water Management Expert,Maharashtra Water Resources Regulatory Authority,Govt. of

Maharshtra,Mumbai.

Formerly Chairman, Central Ground Water Board & Central Ground Water Authority

Ministry of Water Resources,River Development and Ganga Rejuvenation,Govt. of India

Email:[email protected]

Abstract: As per key findings of climate change projections for India, the increase in the frequency of

extreme precipitation, will also mean that much of the monsoon rain would be lost as direct run-off resulting

in reduced groundwater recharge and increased ground water withdrawal ,which might further exasperate

the present scenario of imbalanced development. The adaptation strategies proposed for mitigating the

increasing stress on ground water resources due to climate change for enhancing recharge of groundwater

aquifers, mandating water harvesting and artificial recharge in urban areas, ground water governance,

incentivising to promote recharging of ground water, intelligent power rationing for irrigation ,optimizing

water use efficiency, conjunctive management etc. have been examined at great length in terms of the

technical feasibility as well as social relevance of implementation in the light of extensive experience gained

in the country.

Sustainable development of ground water resources and various mitigation programs required in the event

of possible climate change in the country can be accomplished only with the help and active cooperation of

all stakeholders such as the Ministries of Government of India for Water Resources, Environment &

Forests, Power, Rural Development, Agriculture, Science & Technology and the institutions working under

them; State Governments & their organizations; Associations of Industry, Non-Government Organizations,

District Administrations and Panchayati Raj Institutions and the individuals users. To be successful in this

mission we also have to create conditions for complete synergy in the activities of all the stakeholders. The

role and space for various stakeholders namely Farmers, NGOs, local communities, Canal system managers

and Groundwater Recharge SPV, in groundwater recharge strategy as a major response to climate change

is outlined.

Keywords: managed aquifer recharge, energy-irrigation nexus , intelligent power rationing,

synergy, conjunctive management

1. Introduction India’s National Action on Climate Change

(NAPCC), unveiled by the Prime Minister’s

Office in 2008, highlights the increasing stress on

water resources due to climate change, and points

to the need to increase efficiency of water use,

explore options to augment water supply in

critical areas, and ensure more efficient

management of water resources”(MOWR11). It

calls for measures to enhance recharge of the

sources and recharge zones of deeper

groundwater aquifers, mandating water

78

harvesting and artificial recharge in relevant

urban areas, incentives to promote recharging of

ground water, optimize water use by increasing

water use efficiency by 20%, regulation of power

tariffs for irrigation and to augment storage

capacities of surface water storage structures,

including through the renovation of existing

tanks.

Intergovernmental Panel on Climate Change

(IPCC 8) in its recent released report has

reconfirmed that the global atmospheric

concentration of carbon dioxide (CO2) and

greenhouse gases (GHGs) have increased

markedly as a result of human activities since

1750. The global increase in CO2 concentration

is primarily due to fossil fuel use and land use

change. These increases in GHGs have resulted

in warming of the climate system by 0.74ºC

between 1906 and 2005. The rate of warming has

been much higher in recent decades. This has, in

turn, resulted in increased average temperature of

the global ocean, sea level rise, decline in glaciers

and snow cover. There is also a global trend for

increased frequency of droughts, as well as heavy

precipitation events over most land areas and

extreme events.

2. Projected Climate Change

Annual mean surface temperature rise by the end

of century, ranging from 3 to 5ºC under A2

scenario and 2.5 to 4ºC under B2 scenario of

IPCC, with warming more pronounced in the

northern parts of India, from simulations by

Indian Institute of Tropical Meteorology (IITM),

Pune. Indian summer monsoon (ISM) is a

manifestation of complex interactions between

land, ocean and atmosphere. The simulation of

ISM’s mean pattern as well as variability on inter-

annual and intra-seasonal scales has been a

challenging ongoing problem. Some simulations

by IITM, Pune, have indicated that summer

monsoon intensity may increase beginning from

2040 and by 10% by 2100 under A2 scenario of

IPCC.Climate projections for the Fifth

Assessment Report of the Intergovernmental

Panel on Climate Change

(IPCC) made using the newly developed

representative concentration pathways (RCPs)

under the Coupled Model Inter-comparison

Project 5 (CMIP5) by Rajiv Kumar Chaturvedi et

al 4 of Indian Institute of Science, Bangalore

,provides multi-model and multi-scenario

temperature and precipitation projections for

India for the period 1860–2099 based on the new

climate data. They found that that CMIP5

ensemble mean climate is closer to observed

climate than any individual model.

The key findings of this study are: (i) under the

business-as usual (between RCP6.0 and RCP8.5)

scenario, mean warming in India is likely to be in

the range 1.7–2ºC by 2030s and 3.3–4.8ºC by

2080’s relative to pre-industrial times; (ii) all-

India precipitation under the business-as-usual

scenario is projected to increase from 4% to 5%

by 2030s and from 6% to 14% towards the end of

the century (2080’s) compared to the 1961–1990

baseline; (iii) while precipitation projections are

generally less reliable than temperature

projections, model agreement in precipitation

projections increases from RCP 2.6 to RCP 8.5,

and from short- to long-term projections,

indicating that long term precipitation projections

are generally more robust than their short-term

counterparts and (iv) there is a consistent positive

trend in frequency of extreme precipitation days

(e.g. > 40 mm/day) for decades 2060s and

beyond.

CMIP5 model-based time series of temperature

and precipitation anomalies (historical and

projections) from 1861 to 2099 relative to the

1961–1990 baseline for the RCP scenarios are

given in Fig.1. Shaded area represents the range

of changes projected by the 18 models for each

year. The model ensemble averages for each RCP

79

are shown with thick lines. The observed

temperature and precipitation trend from CRU is

shown by the green line and the solid black line

‘historical’ refers to model ensemble values for

historical simulations. Projected change in the

frequency of extreme rainfall days for future

decades relative to the 1861–1870 baseline is

given in Fig 2.

Figure 1 . CMIP5 model-based time series of temperature and precipitation anomalies (historical and

projections) from 1861 to 2099 relative to the 1961–1990 baseline for the RCP scenarios. (After, Rajiv Kumar Chaturvedi et al ,2012)

3. Climate Change and Water Resources

The importance of climate change impacts on

water resources has been well brought in the

Third Assessment Report of the

Intergovernmental Panel on climate Change

(IPCC) 8 , which says “ Climate Change will lead

to an intensification of the global hydrological

cycle and can have major impacts on regional

water resources, affecting both ground and

surface water supply. Rising global temperatures

are expected to raise sea level and change

precipitation and other local climate conditions.

Changing regional climate could affect forests,

crop yields, and water supplies. It could also

threaten human health, and harm living beings

and the ecosystem.

Changing climate is expected to influence both

evaporation and precipitation in most of the areas.

In those areas where evaporation increases more

than precipitation, soil will become drier, water

level in lakes will drop and rivers will carry less

water. Lower river flows and lower lake levels

could impair navigation, hydroelectric power

generation, water quality and reduce the supplies

of water availability for agricultural, domestic

and industrial uses. Melting snow provides much

of the summer water supply; warms temperatures

could cause the snow to melt earlier and this

reduce summer supplied even if rainfall increased

during the spring.

Various studies carried out in India in different

basins to assess the impact of Green House Gases

(GHG) and global warming including

80

development of simulation models (SWAT)

revealed that under GHG scenario the conditions

may deteriorate in terms of severity of droughts

in some basins and enhanced intensity of flood in

other basins of the country. However, there is a

general overall reduction in the available runoff,

which has direct bearing on ground water

recharge. This may have considerable

implications on Indian agriculture and hence on

our food security and farmers livelihood. The

strategies may range from change in land use,

cropping pattern, to water conservation flood

warning system.

Figure 2. Projected change in the frequency of extreme rainfall days for future decades relative to the

1861–1870 baseline (After, Rajiv Kumar Chaturvedi et al ,2012) .

4. Ground Water Scenario

Ground water is one of the most precious natural

resource and has played a significant role in

maintenance of India’s economy, environment

and standard of living. Besides being the primary

source of water supply for domestic and many

industrial uses, it is the single largest and most

productive source of irrigation water. India is a

vast country having diversified geological,

climatological and topographic set-up, giving rise

to divergent groundwater situation in different

parts of the country. The prevalent rock

formations, ranging in age from Achaean to

Recent, which control occurrence and mocement

of groundwater, are widely varied in composition

and structure. Broadly two groups of water

bearing rock formations have been identified i.e.

threshold

81

(i) Porous rock formations which can be further

classified into unconsolidated and semi

consolidated formations having primary porosity;

and (ii) Fissured rock formations which mostly

have secondary or derived porosity. Similarly,

there are significant variations of landforms from

the rugged mountainous terrains of the

Himalayas, Eastern and Western Ghats to the flat

alluvial plains of the river valleys and coastal

tracts, and the aeolian deserts of Rajasthan. The

rainfall patterns too show similar region-wise

variations. The topography and rainfall virtually

control run-off and groundwater recharge. The

entire country has been broadly divided into five

distinct ground water regions considering the

characteristic physiographic features as well as

occurrence and distribution of ground water.

Mountainous Terrain and Hilly areas: This

region is occupied by varied rock types including

granite, slate, sandstone and limestone. The yield

potential of aquifers ranges from 1 to 40 litres per

second (lps). However, because of high

conductivity and hydraulic gradient, it offers very

little scope; for ground water storage. The valley

fills in mountains function as underflow conduits

and act as the major source of recharge. Springs

are main source of water supply. Indo-Gangetic-

Brahmaputra Alluvial Plains: Indo-Gangetic-

Brahmaputra alluvial plains, occupy the States of

Punjab, Haryana, Uttar Pradesh, Bihar, Assam

and West Bengal. The plain is underlain by thick

pile of sediments. Thickness of the sediments

increases from north to south. At places, the

thickness of the alluvium exceeds 1000m. The

thick alluvial fill constitutes the most productive

ground water reservoir in the country. In the

present scenario the ground water development in

this region is at low level except the western parts

in the States of Haryana and Punjab. The deeper

aquifers in these areas offer good scope for

further exploitation of ground water. In Indo-

Gangetic-Brahmaputra plain, the deep wells yield

in the range of 25-50 lps.

Peninsular shield Area: The area is located

south of Indo-Gangetic plain and consists mostly

of consolidated sedimentary rocks, Deccan

basalts and crystalline rocks in the states of

Karnataka, Maharashtra, and Tamil Nadu.

Occurrence and movement of ground water in

these rock formations are restricted to weathered

material and interconnected fractures at deeper

levels. These have limited ground water

potential. Ground water occurs within depth 50m,

occasionally down to 150m, and rarely beyond

200m depth. Ground water development is

largely through dug well and bore wells. The

yield of borewells tapping deep fractured zones

in hard rocks varies from 2-15 lps.

Coastal Area: Coastal areas have a thick cover

of alluvial deposits and form potential multi-

aquifer systems covering states of Gujarat, kerala,

Tamil Nadu, Andhra Pradesh and Orissa.

However, inherent quality problems and the risk

of seawater ingress impose severe constrains in

the development of these aquifers. In addition,

the ground water development in these areas is

highly vulnerable to up-coning of saline water.

The yield of tubewells varies from 20-25 lps.

Central Alluvial Areas: This region has been

grouped separately owing to its peculiarity in

terms of presence of three discrete fault basins,

the Narmada, the Purna and Tapti, all of which

contain extensive valley-fill deposits. The

alluvial deposit ranges in thickness from about 50

to 150m. Ground water occurrence in the area is

restricted to deep aquifer systems tapping fossil

water. For example, in parts of Purna valley the

ground water is extensively saline and unfit for

various purposes. The yield potential of

tubewells varies from 1-10 lps.

The Hydrogeological map of India is depicted in

Plate – I showing ground water potential of the

broad groups of water bearing formations.

82

Ground Water Availability :Rainfall is the

major source of ground water recharge, which is

supplemented by other sources such as recharge

from canals, irrigated fields and surface water

bodies. The rainfall is unevenly distributed. The

average rainfall in country is around 117cm. It is

below 75 cm in the northwestern part covering

parts of the states of Rajasthan, Gujarat, Haryana

and the southern part, covering the states of

Karnataka and Tamil Nadu. The amount of

ground water withdrawal and situation of low

rainfall are the factors which decides the overall

stress on ground water and accordingly the

assessment units are being categorized as over-

exploited & critical blocks.

Large development of ground water resources in

the country takes place from the unconfined /

shallow aquifers, which hold replenishable

ground water resource. Central Ground Water

Board has assessed the replenishable ground

water resource in the country in association with

the concerned State Government authorities

(CGWB2).The annual replenishable ground water

resources have been assessed as 447bcm.

83

Keeping an allocation for natural discharge, the

net annual ground water availability is 411 bcm.

The annual ground water draft (as on 31st March,

2013) is 253 bcm. The Stage of ground water

development works out to be about 62%. The

development of ground water in different areas of

the country has not been uniform. Highly

intensive development of ground water in certain

areas in the country has resulted in over –

exploitation leading to decline in ground water

levels and sea water intrusion in coastal areas.

Out of 6584 assessment units (Blocks/Mandals /

Talukas) in the country, 1034 units in various

States have been categorized as ‘Over-exploited’

i.e. the annual ground water extraction exceeds

the net annual ground water availability and

significant decline in long term ground water

level trend has been observed either in pre-

monsoon or post- monsoon or both. In addition

253 units are ‘Critical’ i.e. the stage of ground

water development is above 90 % and within

100% of net annual ground water availability and

significant decline is observed in the long term

water level trend in both pre-monsoon and post-

monsoon periods. There are 681 semi-critical

units, where the stage of ground water

development is between 70% and 100% and

significant decline in long term water level trend

has been recorded in either Pre-monsoon or Post-

monsoon. 4520 assessment units are Safe where

there is no decline in long term ground water level

trend. Apart from this, there are 96 blocks

completely underlain by saline ground water.

In addition to annually replenishable ground

water, extensive ground water resources occur in

the confined aquifers in the Ganga-Brahmaputra

alluvial plains and coastal – deltaic areas. These

aquifers have their recharge zones in the upper

reaches of the basins. The resources in these

deep-seated aquifers are termed as ‘In-storage

ground water resources’. In alluvial areas, these

resources are normally renewable over long

periods of time, except in case of sedimentary

rock aquifers in Rajasthan, which comprise

essentially non-renewable fossil water.

5. Proposed adaptation strategies

In view of the clear evidences of change in global

surface temperature, rainfall pattern,

evapotranspiration and extreme events and its

possible impact on the hydrological cycle, it is

pertinent to reassess the availability of water

resources. It is critical for formulating relevant

national and regional long-term development

strategies in a holistic way. The various

mitigation measures within the constraints

imposed by the possible climate change and

hydrologic regimes and future research needs are

discussed in following paragraphs.

5.1 Rainwater harvesting and Artificial

Recharge

Extreme climate events such as aridity, drought,

flood, cyclone and stormy rainfall are expected to

leave an impact on human society. They are also

expected to generate widespread response to

adapt and mitigate the sufferings associated with

these extremes. Societal and cultural responses to

prolonged drought include population

dislocation, cultural separation, habitation

abandonment, and societal collapse. A typical

response to local aridity is the human migration

to safer and productive areas. However, climate

and culture can interact in numerous ways.

Historical societal adaptations to climate

fluctuations may provide insights on potential

responses of modern societies to future climate

change that has a bearing on water resources,

food production and management of natural

systems. Decentralized rainwater harvesting from

roof catchments in cities has the potential to

supplement centralised water supply strategies to

create an overall more resilient urban water

84

supply. This result highlights the importance of

implementing a diverse range of water sources

and conservation for urban water management.

The efficiency of translation of rainfall runoff

into recharge is highly dependent on strategy and

location.

The immediate priority for augmentation of

ground water are the areas already overexploited

resulting into severe decline in ground water

level, coastal areas affected by sea water ingress

due to haphazard and unscientific development of

ground water and the areas infested with

pollution due to various reasons. While

prioritizing the areas, the possible impact of

climate change needs to be dovetailed. As a long

term measure an attempt has been made to

provide a conceptual framework for utilization of

surplus monsoon runoff for artificial recharge of

ground water and consequently a “National

perspective plan for recharge to ground water by

utilizing surplus monsoon runoff” has been

prepared by CGWB1. The report provides

availability of non-committed surplus monsoon

runoff in 20 river basins of the country vis-a vis

the subsurface available space under different

hydrogeological situations for saturating the

vadose zone to 3 m below ground level. It was

estimated that it is possible to store 21.4 Mham in

the ground water reservoir through out the

country out of which 16.05 Mham can be utilized.

As per the the Master Plan (2013)3 for artificial

recharge to ground water, out of the geographical

area of 32,87,263 sq. km of the country, an area

of about 9,41,541 sq.km. has been identified in

various parts of country which need artificial

recharge to ground water. This includes the hilly

terrain of Himalayas also where the structures are

basically proposed to increase the fresh water

recharge and improve the sustainability of

springs. It is estimated that annually about 85,565

MCM of surplus surface run-off can be harnessed

to augment the ground water.

In rural areas, suitable civil structures like

percolation tanks, check dams, nala bunds, gully

plugs, gabion structures etc. and sub-surface

techniques of recharge shaft, well recharge etc.

have been recommended. Provision to conserve

ground water flow through ground water dams

has also been made. It is envisaged to construct

of about 1.11 crore artificial recharge structures(

11 Million) in urban and rural areas at an

estimated cost of about Rs. 79,178 crores (US $

15835 Million) . This comprises of mainly around

88 lakh structures utilizing rain water directly

from rooftop and more than 23 lakh artificial

recharge structures utilizing surplus run-off and

recharging ground water in various aquifers

across the country. The break-up includes around

2.90 lakh check dams, 1.55 lakh gabion

structures, 6.26 lakh gully plugs, 4.09 lakh nala

bunds/cement plugs. 84925 percolation tanks,

8281 sub-surface dykes, 5.91 lakh recharge shaft,

1.08 lakh contour bunds, 16235 injection wells

and 23172 other structures which includes point

recharge structures, recharge tube wells, stop

dams, recharge trenches, anicuts, flooding

structures, induced recharge structures, weir

structures etc. In hilly terrain of Himalayas

emphasis has been given for spring development

and 2950 springs are proposed for augmentation

and development

Assuming the importance of artificial recharge

and rain water harvesting, the Model Bill on

Ground water prepared and circulated by

Ministry of Water Resources has been amended

in 2006 to accommodate the this important aspect

and all the state Governments have been asked to

formulate their own rule and law for better

governance of ground water adopting suitable

augmentation measures where ever required or

else impose regulatory measures to ensure

sustainability of this vital resource.

An increase in precipitation in the basins of

Mahanadi, Brahmani, Ganga, Godavari and

85

Cauvery is projected under climate change

scenario. Unless remedial measures are

implemented to control the runoff, frequency of

floods in these areas are unavoidable. During and

after the floods, ground water plays a significant

role as alternative source of drinking water. The

construction of “ Sanctuary wells “ in such areas

at suitable locations or near the shelters houses

may provide a solution for solving the drinking

water crisis during the flood times. Preferably the

Sanctuary wells may be constructed tapping the

deeper aquifers which are less vulnerable to

contamination because of inundation.

5.2 Conjunctive Management

Conjunctive management will play crucial role as

a mitigatory measure since climate change will

lead to extreme situation of water level rise in

some areas and water level decline in other areas.

In such event Conjunctive management need to

be adopted so as

To evolve a suitable plan for controlling the

problem of rising water levels by adopting

the technique of conjunctive use of surface

and ground water, and proper drainage.

To prepare sector/ block-wise plans for

development of ground water resource in

conjunction with surface water based on

mathematical model results.

To test the sustainability of the present

irrigation pattern with respect to conjunctive

use of water resources and suggest

improvement for future.

To evaluate the economic aspect of

groundwater development plan with respect

to Cost benefits ratio, internal rate of return

and pay back period etc.

In areas of India with massive evaporation losses

from reservoirs and canals but high rates of

infiltration and percolation, the big hope for

surface irrigation systems—small and large—

may be to reinvent them to enhance and stabilize

groundwater aquifers that offer water supply

close to points of use, permitting frequent and

flexible just-in-time irrigation of diverse crops.

Already, many canal irrigation systems create

value not through flow irrigation but by

supporting well irrigation. In the Mahi Right

Bank system in Gujarat, with a command area of

about 250,000 ha, it is the more than 30,000

private tube wells—each complete with heavy-

duty motors and buried pipe networks to service

30 to 50 ha—that really irrigate crops; the canals

merely recharge the aquifers. An elaborate study

by Central Groundwater Board (1995) lauded the

Mahi irrigation system as a “model conjunctive

use project” in which 65 percent of water was

delivered by canals and 35 percent was

contributed by groundwater wells.

However, what conjunctive use was occurring

was more by default than by design as the

enterprising farming community of the area have

taken the initiative and realized fully the

advantages of adopting the conjunctive use

techniques for reaping optimal benefits.

Further, there is an urgent need to adopt

participatory Irrigation management ensuring

participation of stakeholders since inception of

the conjunctive use projects.

Due to variations in rainfall and runoff in

different basins of the country, it is expected that

imbalances in availability of surface and ground

water may aggravate the conditions of water

logging at one end and scarcity at other end.

The major irrigation command areas are more

vulnerable to such extreme events and hence

there is an urgent need to implement conjunctive

use practices in field conditions so as to control

rising water level scenario, water logging and

even water shortages in tail end areas.

86

5.3 Intelligent management of energy-

irrigation nexus

As of now, managing the energy-irrigation nexus

with sensitivity and intelligence is the region’s

principal tool for groundwater demand

management. The current challenge is twofold.

First, diesel-based groundwater economies of the

Indo-Gangetic basin are in the throes of an energy

squeeze; some recent studies (Shah 2007, 2009)13,

show that, further rise in diesel prices, will

undermine the potential benefits of conjunctive

use of ground and surface waters in water

abundant areas of Ganga basin. Electrification of

the groundwater economy of these regions

combined with a sensible scheme of farm power

rationing may be the most feasible way of

stemming distress outmigration of the agrarian

poor. In the electricity-dependent groundwater

economy of western and peninsular India, the

challenge is to transform the current degenerate

electricity-groundwater nexus into a rational one.

Tariff reform has proved a political challenge in

many of these states; but other ‘hybrid’ solutions

need to be invented. Gujarat’s experience under

the Jyotigram scheme illustrates a ‘hybrid’

approach based on intelligent rationing of power

supply (Shah and Verma14; Shah et al 15). But

other states in the region too are moving in the

direction of demand management by rationing

power. Punjab has effectively used stringent

power rationing in summer to encourage farmers

to delay rice transplantation by a month and in the

process significantly reduced groundwater

depletion. Andhra Pradesh gives farmers free

power but has now imposed a seven-hour ration.

It is surmised that power rationing can be a simple

and effective instrument for groundwater demand

management.

5.4 Institutional and Regulatory Measures

One of the most important mitigation measure is

strengthening of institutional as well as

regulatory framework of the country in relation to

ground water. In spite of the fact that water is a

state subject and it need to be regulated at the

state level, there are several states in which there

is no independent department or set up to look

after the ground water governance. Hence, there

is an urgent need of institutional strengthening at

appropriate level and adoption of regulatory

measures in strict sense.

In this connection the implementing agencies for

regulatory measure may be decided by the

Central and State government. The

implementation may be through the State Ground

Water Department/PHED or local development

board or authority. The implementation should be

entrusted to one single department in the state and

not to a number of departments with a view to

better implementation, monitoring of the progress

etc.

If the programme has to be implemented in more

than one department in the state due to

unavoidable and certain special consideration,

one of the departments should be designated as

Nodal Department for coordinating the all the

activities related mitigatory measure related

climate change and ground water and sending

consolidated progress to the Central Government.

The Panchayati Raj Institution should also be

involved in the implementation of the schemes,

particularly in selecting the location of stand post

spot sources, operation and maintenance.

Planning Commission12 in its report of the Expert

Group on “ Ground Water Management and

Ownership “ has discussed the requirement of

certain institutional changes and suggested that

the mandate of Central Ground Water Board to be

shifted to a facilitator rather than a regulator to

assist better implementation of management

options. For effective implementation of ground

water management plan a three tier institutional

arrangements involving Central, State and

Districts level agencies needs to be formulated.

87

5.5 Increasing Ground Water Use Efficiency

Water use efficiency programs, which include

both water conservation and water recycling,

reduce demands on existing water supplies and

delay or eliminate the need for new water

supplies for an expanding population. These

effects are cumulative and increasing. Water

conservation savings have increased each year

due to expansion of and greater participation in

these water conservation programs. Water

recycling, or the use of treated wastewater for

non-potable applications, is used in a variety of

ways, including for irrigation and industrial

processes. This in turn will provide

environmental benefits as well as significant

aesthetic and human health benefits. A reduction

in water-related energy demand due to water

conservation and water recycling reduces the air

pollutants and allows to respond to the water

supply challenges posed by global climate

change. Water conservation and water recycling

programs clearly save energy and reduce air

pollutant emissions.

Broadening the limits of the quality of water used

in agriculture can help manage the available

water better in areas where scarcity of water is

due to salinity of the available ground water

resources. Cultivation of salt tolerant crops in

arid/semi-arid lands, dual water supply system in

urban settlements - fresh treated water for

drinking water supply and brackish ground water

for other domestic uses are some such examples.

Recycling of water after proper treatment for

secondary and tertiary uses is another alternative

that could be popularized to meet requirements of

water in face of the scarcity of resource in the

cities. It has been estimated that parts of Haryana,

Punjab, Delhi Rajasthan, Gujarat, Uttar Pradesh,

Karnataka and Tamil Nadu have inland saline

ground water of the order of about 1164 BCM.

Yields of many crops, vegetables and fruit plants

e.g. barley, dates and pomegranate, when

irrigated with saline or brackish water are not

significantly affected. Saline/ brackish water can

be successfully used to irrigate such plants and

fresh or good quality water can be saved for use

by other sensitive crops or for other uses.

Brackish water can also be utilized for

pisciculture / aquaculture. Therefore, additional

resource of 1164 BCM of saline/brackish ground

water resource would be available for use.

Studies are required to be undertaken on use and

disposal of brackish / saline ground water

Studies have shown that that substantial quantity

of water could be saved by the introduction of

micro irrigation techniques in agriculture. Micro

irrigation sprinklers and drip systems can be

adopted for meeting the water requirement of

crops on any irrigable soils except in very windy

and hot climates. These water conservation

techniques would provide uniform wetting and

efficient water use.

Changes in cropping pattern aimed at higher

return of investment may lead to increased

exploitation of ground water, as the experiences

in Punjab and Haryana have shown. Suitable

scientific innovations may be necessary to solve

this issue. Less water intensive crops having

higher market value, scientific on-farm

management, sharing of water and rotational

operation of tube wells to minimize well

interference and similar alternatives can provide

viable solutions for balancing agro-economics

with environmental equilibrium.

In order to increase the ground water use

efficiency suitable incentives for community

management of new wells, for construction of

recharge structures, for energy saving devices

like installation of capacitors and frictionless foot

valves and for adoption of micro irrigation can be

offered to the users in water stressed areas ( Over-

88

exploited and Critical blocks) instead of putting

ban for further exploitation of ground water.

5.6 Adopting the Concept of Virtual Water

Virtual water is defined as water embedded in

commodities. It is said that the largest exported

commodity in the world is ‘Water’, which is in

terms of virtual water contained in the food

grains. As a thumb of rule, a grain crop transpires

about 1 cubic meter of water in order to produce

1 kilogram of grain. Thus exporting or importing

1 kilogram of grain is approximately equivalent

to exporting 1 cubic meter of water. The best

example of virtual water in Indian context can be

thought in terms of producing fodder in the water

surplus areas of Indo Gangetic plains and

transported to the water stressed areas of Gujarat,

Rajasthan, Punjab etc. this way water used for

fodder production in these states can be reduced

and water saved can be fruitfully utilized for other

priority sectors. Thus the concept of Virtual

Water can help in combating the impact of

climate change on ground water by planning the

suitable cropping pattern depending upon the

availability of ground water.

5.7 Coastal Aquifer Management

Recent studies on the likely impact of sea level

rise to the tune of one meter along Indian coast

provide an idea about the land which could be

inundated and the population that would be

affected provided no protective measures are

taken. The ingress of salinity in the coastal

aquifers with respect to sea level rise and ground

water abstraction is most likely in the event of

climate change. Most vulnerable areas along the

Indian coastline are the Kutch region of Gujarat,

Mumbai and South Kerala. Deltas of rivers

Ganges (West Bengal), Cauvery (Tamil Nadu),

Krishna and Godawari (Andhra Pradesh) and

Mahanadi (Orissa). The future studies should be

focused on developing efficient monitoring

mechanism, Filling the data gap through ground

water exploration, hydrochemical and modeling

studies.

5.8 Capacity Building and Training Needs

As per an estimate within the country about

10,000 professionals, 20,000 sub-professionals

and nearly 1, 00,000 skilled personnel are

employed in the work of ground water

investigation, development and management.

Many of the sub professionals like drillers etc.

have no formal training in ground water which is

very essential for getting optimum benefits for its

sustainable development. In view of the

increasing importance of ground water and

anticipated climate change there is a need to

create exclusive infrastructure to cater the need of

training requirements of ground professional in

the country. The country had so far not been able

to create the requisite training facilities. The

professionals being assigned to the work usually

possessed a Master’s Degree in Geology /

Geophysics / Chemistry or Bachelors Degree in

Engineering. Though the Universities and

Technical institutes are well equipped to carry out

academic teaching programmes in the mother

disciplines like geology, Geophysics they have

limited facilities for training the field professional

on specialized aspects of ground water

assessment, management application of advanced

tools like modeling , GIS etc.

In the context of water resources, training in the

form of capacity building is indispensable for (a)

strengthening the enabling institutional

environment which takes the organizations in the

right directions; (b) optimizing the available

water resources which is becoming more and

more critical with the passage of time; (c)

establishing responsibility and accountability at

all appropriate levels of hierarchy to usher in the

needed efficiency; (d) understanding and

appreciating value of water as a social and

economic good; (e) developing and encouraging

89

reliable information on policies, programmes,

and projects, and systems of sharing this

information to bring in transparency; and (f) keep

finding innovative solutions to problems,

technical or otherwise, facing the sector to

manage the resource sustainably.

6. Synergy amongst stakeholders

Sustainable development of ground water

resources and various mitigation programs

required in the event of possible climate change

in the country can be accomplished only with the

help and active cooperation of all stakeholders

such as the Ministries of Government of India for

Water Resources, Environment & Forests, Rural

Development, Agriculture, Science &

Technology and the institutions working under

them; State Governments & their organizations;

Associations of Industry, Non-Government

Organizations, District Administrations and

Panchayati Raj Institutions and the individuals

users. To be successful in this mission we also

have to create conditions for complete synergy in

the activities of all the stakeholders.In this regard

Ministry of Water Resources has taken a step

forward by constituting the “Advisory Council on

Artificial Recharge to Ground Water " involving

members from all walks of life. However, the

stakeholders in grass root levels need to be

sensitized to the social relevance of technical

decision on mitigation.

Although the groundwater agencies at central and

state level are the custodians of our groundwater

resource, in reality, multiple agencies in public

and private sectors are involved as major players

in India’s groundwater economy. As climate

change transforms groundwater into a more

critical and yet threatened resource, there is dire

need for coordinating mechanisms to bring these

agencies under an umbrella framework to

synergize their roles and actions. Even as

governments evolve groundwater regulations and

their enforcement mechanisms, more practical

strategies for groundwater governance need to be

evolved.

In hard-rock regions of the country, together with

intelligent management of the energy-irrigation

nexus, mass-based decentralized groundwater

recharge offers a major short-run supply-side

opportunity. Public agencies are likely to attract

maximum farmer participation in any programs

that augment on-demand water availability

around farming areas. Experience also shows that

engaging in groundwater recharge is often the

first step for communities to evolve norms for

local, community-based demand management.

In alluvial aquifer areas, conjunctive

management of rain, surface water, and

groundwater is the big hitherto under-exploited

opportunity for supply-side management.

Massive investments being planned for

rehabilitating, modernizing, and extending

gravity-flow irrigation from large and small

reservoirs need a major rethink in India. In view

of the threat of Climate Change, indeed, we need

to rethink our storage technology itself. Over the

past 40 years, India’s landmass has been turned

into a huge underground reservoir, more

productive, efficient, and valuable to farmers than

surface reservoirs. For millennia, it could capture

and store little rainwater because in its

predevelopment phase it had little unused

storage. The pump irrigation revolution has

created 250 km3 of new, more efficient storage in

the subcontinent. Like surface reservoirs, aquifer

storage is good in some places and not so good in

others. To the farmer, this reservoir is more

valuable than surface reservoirs because he has

direct access to it and can obtain water on

demand. Therefore, he is far more likely to

collaborate in managing this reservoir if it

responds to his recharge pull. Indeed, he would

engage in participatory management of a canal if

it served his recharge pull. This is best illustrated

by the emergence of strong canal water user

90

associations of grape growers in the Vaghad

system in Nasik district of Maharashtra.

Vineyards under drip irrigation in this region

need to be watered some 80 to 100 times a year,

but canals are useless: they release water for a

maximum of just 7 times. Yet grape growers have

formed some of the finest water user associations

in the region for proactive canal management

here mostly because they value canals as the

prime source of recharging the groundwater that

sustains their high-value orchards (Shah17).

References

CGWB (1996). National perspective plan for

recharge to ground water by utilizing surplus

monsoon runoff. CGWB, Govt. of India, New

Delhi.

CGWB (2017). Dynamic Ground water

Resources of India (As on 31st

March,2013).CGWB, Govt. of India, Faridabad

CGWB (2013). Master Plan for Artificial

Recharge to Ground Water in India. CGWB,

Govt. of India, New Delhi.

Chaturvedi , Rajiv Kumar et al (2012).

CURRENT SCIENCE, VOL. 103, NO. 7, 10

OCT. 2012

Gosain, A.K. and Rao, S., Climate Change and

India, (2003). Vulnerability Assessment and

Adaptation (Eds. Shukla, P.R. et al.), Universities

Press (India) Pvt. Ltd, Hyderabad, 2003, p. 462.

Goswami, B.N. et al (2006) Increasing Trend of

Extreme Rain Events over India in a Warming

Environment. Science, 314, 1442 (2006).

India’s Initial National Communication, 2004

(NATCOM-I) to UN Framework Convention on

Climate Change (UNFCCC).

IPCC, (2007). The physical science basis.

Summary for policy makers. Intergovernmental

Panel on Climate Change.

Jain,R.C.(2011). Impact of Climate Change on

Ground Water Sustainability and the Role of

MNREGA in Rain Water Harvesting and

Artificial Recharge to Ground Water.

Proceedings of the Third National Ground Water

Congress published by Central Ground Water

Board, New Delhi , March 22-23,2011,p 96-114.

Lal, M. and Aggarwal, D., (2000): Vulnerability

of Indian coastline to sea level rise, Climate

change and its impacts in India, Asia-Pacific Jr.

Environment & Development.

Ministry of Water Resources, (2008). National

Action Plan on Climate Change.

http://mowr.gov.in

Planning Commission, (2007). Report of the

Expert Group on “Ground Water Management

and Ownership”. Planning Commission, Govt. of

India. Sept.’2007.

Shah, T., (2008). Taming the Anarchy?

Groundwater Governance in South Asia,

Washington D.C.: RFF Press. 2008

Shah, T. and Verma, S., (2008). Co-management

of Electricity and Groundwater: An Assessment

of Gujarat’s Jyotirgram Scheme. Economic and

Political Weekly, Vol.43(7):59-66.2008.

Shah, T. et al (2009). Is irrigation Water Free? A

Reality Check in the Indo-Gangetic Basin. World

Development Report,Vol. 37(2).2009.

91

http://mowr.gov.in/

GROUNDWATER QUALITY IN INDIA : IMPLICATIONS AND

MANAGEMENT

R.C.JAIN

Adviser ,Gujarat Water Resource Development Corporation,Govt. of Gujarat,Gandhinagar.

Ground Water Management Expert,Maharashtra Water Resources Regulatory Authority,Govt. of

Maharshtra,Mumbai.

Formerly Chairman, Central Ground Water Board & Central Ground Water Authority

Ministry of Water Resources,River Development and Ganga Rejuvenation,Govt. of India

1. Introduction

Groundwater is a major source of

providing water for domestic, industrial and

agricultural purposes in many places of the

world .In India, groundwater quality is generally

good but is getting contaminated due to

geogenic and anthropogenic sources. There are

increasingly widespread indicators of

degradation in the quality and quantity of

groundwater, caused by over abstraction and

inadequate pollution control. To assess the

geogenic and anthropogenic contaminations in

groundwater and its impacts to the inhabitants

and the environment a review of the

contaminations due to fluoride, arsenic, iron,

nitrate, uranium, etc., in the groundwater system

of India is presented. This paper aims to raise

the awareness among stakeholders and

policymakers, to highlight the groundwater

quality issues, to provide an outline for the

systematic consideration of the groundwater

management, and to formulate approaches for

more sustainable management of groundwater

resources.

A major attraction of using

groundwater for drinking purposes is that it

usually requires little or no treatment, but this is

no longer the case. The quality of groundwater

is deteriorating rapidly due to geogenic and

anthropogenic activities which are excessive

exploitation, disposal of waste and spillage of

chemicals, removal of vegetation, etc. Active

reserach is carried out across the globe in

determining the groundwater quality of various

regions since several decades where high

concentration of various ions such as fluoride,

arsenic, iron, nitrate, etc., in groundwater has

been reported (Brindha and Elango 2011;

Custodio 2016). The problem of the high

fluoride in groundwater has been reported by

several researchers in India, China, Japan, Sri

Lanka, Iran, Pakistan, Turkey, Southern

Algeria, Mexico, Korea, Italy, Brazil, Malawi,

North Jordan, Ethiopia, Canada, Norway,

Ghana, Kenya, South Carolina, Wisconsin and

Ohio (Brindha and Elango,2011). The well

known areas subjected to excess arsenic

concentrations in groundwater are Bangladesh,

India (West Bengal), Argentina (Pampas),

Australia, USA, Brazil, Russia and China

(Margat and van der Gun, 2012). Very high

concentrations of nitrate (>100mg/L) in

groundwater was reported in India (Zhao,

2015), and concentrations of nitrate above 50

mg/L to 100 mg/L were reported in several

others countries like Australia, Argentina,

South Africa, China, Mexico, Iran, USA(Zhao,

2015). Iron contamination in groundwater has

become one of the most discussed issues

nowadays. The high concentration of Iron is

observed in several coutries by many

researchers in India, Bangladesh, Australia,

New Zealand, China, Russia, Iran, Kazaksthan,

South Africa, Brazil, USA, Canada and Sweden

(Rajmohan and Elango, 2005; Lepokurova et

al., 2014). Nitrate is one of the major threat to

the groundwater system in India. High

concentrations of nitrate in groundwater found

in several regions has been reported by many

researchers (Brindha et al., 2012; Verma et al.,

2014). Mitigation methods has become

indispensable to avoid further contamination of

groundwater and also to improve the

groundwater quality which requires knowledge

about the causes and effects of deterioration of

groundwater quality.

92

2. Various Groundwater Quality

Issues in India

2.1 Geogenic sources

The high concentration of fluoride, arsenic and

iron in the groundwater is due to the impact of

geology and geochemical processes in an area.

Fluoride in groundwater above 1.5 mg/L is

considered as not suitable for drinking purposes

as recommended by (BIS & WHO). Fluoride

concentration above permissible limits has been

reported in 20 Indian states including, Andhra

Pradesh, Assam, Bihar, Chattisgarh,Delhi,

Gujarat, Haryana, J&K, Jharkhand, Karnataka,

Kerala, Madhya Pradesh, Maharashtra, Orissa,

Punjab, Rajasthan, Tamil Nadu, Telangana,

Uttar Pradesh and West Bengal.

The high concentrations of arsenic in drinking-

water has emerged as a major issue. With

newer-affected sites discovered during the last

decade, a significant change has been observed

in the arsenic contamination, especially in West

Bengal and Bihar. The WHO and BIS limit for

arsenic content in drinking water is 10 ppb.

High levels of arsenic in groundwater above

permissible limit was found in the parts of 154

districts in 21 states/Uts namely Assam,

Andhra Pradesh ,Telengana ,

Bihar,Chhattisgarh,Delhi, Gujarat

,Haryana,Himachal Pradesh,Jammu &

Kashmir,Jharkhand,Karnataka , Madhya

Pradesh,Manipur, Odisha,Punjab, Rajasthan,

Tamil Nadu, Uttar Pradesh , West Bengal and

Diu

namely alluvial plains of Ganges covering six

districts of West Bengal. Some other Indian

states which are affected by arsenic in

groundwater are namely Bihar, Chattishgarh,

Jharkhand and Uttar Pradesh,.

High concentration of iron in ground water has

been observed in 23 states/Uts in India. The

permissible limit for iron in groundwater is 0.3

mg/L as per WHO/BIS guidelines. The highest

value (52 mg/L) has been found in Lakmipur,

Assam. Deterioration of groundwater quality

by iron has been reported in Assam,

Chhattisgarh, Karnataka, Orissa and West

Bengal. Localized pockets are observed in

statesof

A.P.,Bihar,Chattisgarh,Goa,Gujarat,Haryana,J

&K,Karnatka, Uttar Pradesh, Punjab,

Rajasthan, Maharashtra, Madhya

Pradesh,Manipur,Meghalaya,Oisha, Jharkhand,

Tamil Nadu and Kerala.

The chronic exposure of uranium radionuclides

in groundwater is a potential health risk factor

in India. The permissible limit of uranium in

groundwater is 0.03 mg/L as per WHO

guidelines. The high concentration of uranium

in groundwater occurs in Andhra Pradesh,

Jharkhand, Orissa, Mehgalaya, Rajasthan,

Telengana and Punjab.

2.2 Anthropogenic sources

Industrial discharges, urban activities,

agriculture fertilizers and disposal of sewage

wastes are affecting the groundwater quality

adversly. Leakage of spills from oil tanks,

release of chemical effluents from dying

industries, fertilizers and pesticides applying to

agricultural land, leakage in septic tanks and

from waste disposal sites tend to accumulate

and migrate to the groundwater table leading to

contamination of the groundwater system.

Such contaminations can render groundwater

unsuitable for potable use.

2.2.1 Agriculture

Intensive use of chemical fertilizers and

pesticides in the agricultural land results in the

leaching of the residual nitrate causing high

concentration of nitrate in groundwater. Nitrate

can cause the health problems in infants and

animals, as well as the eutrophication of water

bodies. Groundwater with nitrate concentration

of above 13 mg/L is considered to be

contaminated by anthropogenic activities.

However, the maximum acceptable limit of

nitrate concentration in potable water is 45

mg/L (BIS) and 50 mg/L (WHO). In India,

nitrate concentration above permissible level

of 45 mg/L has been reported in 21 states,

Andhra Pradesh,

Bihar,Chattisgarh,Delhi,Goa,Gujarat, Haryana,

Himachal Pradesh,J&K,Karnataka, Kerala,

Madhya Pradesh, Maharashtra, Orissa,Punjab,

93

Rajasthan, Tamil Nadu, Telangana, West

Bengal ,Uttarakhand and Uttar Pradesh.

2.2.2 Industries

Due to rapid industrilization growth,major

cities of India generates large quantity of

industrial effluents which is dumped in to the

nearby water bodies. The shallow aquifer of

Ludhiana city has been polluted by the stream

effluents from 1300 Industries around the city.

The chemical effluents from tanneries in

Vellore district of Tamil Nadu were released

into Palar river that contaminates the

groundwater of the region. The disposal of

untreated effluents from various industrial units

in Baddi-Barotiwala Industrial belt of District

Solan, Himachal Pradesh results in the

contaminatation of groundwater by heavy

metals. The extensive industrial activites in the

region of Pydibhimavaram Industrial Area,

Andhra Pradesh, India resulted in the

contamiantion of aquifer due to chloride,

sulfate, nitrite and trace elements of Fe, Ni, Cd

and Pb from the industrial effluents. The

industralized area where petrochemical storage

tanks are located in Chennai, Tamil Nadu, was

affected by PAHs pollution from early 90’s. In

addition, a recent survey undertaken by Centre

for Science and Environment, eight places in

Gujarat, Andhra Pradesh and Haryana has been

reported with traces of heavy metals such as

lead, cadmium, zinc and mercury.

3. Aquifer Contamination Relation

Dynamics

Geochemical analysis is the major approach

for defining ionic concentrations in

groundwater. The nature of groundwater is

very complex and it becomes challenging for

intrepretation. Consequently, the origin and

chemical composition of groundwater can vary

considerably. Hence, it is necessary to

thoroughly examine the spatial and temporal

groundwater chemistry in an aquifer to

interpret the sources and processes of

groundwater contamiantion.

3.1 Spatial variation in Groundwater

quality

Spatial variation of hydro-chemical constituents

of groundwater acts as tool to interpret the

hydrogeological condition of the aquifer. The

predictable changes in quantity and quality of

dissolved constituents in groundwater during

the transit from areas of ground water recharge

to ground water discharge is useful to analyze

the physical and hydrogeological properties of

groundwater system. Variations in different

geologic formation can cause the changes in

groundwater chemistry. Mineral composition of

rocks, weathering pattern, soil type, flow

pattern, flow pattern, vegetation cover, and

climate change are the major phenomena to

determine the groundwater chemistry.

Significant difference in geochemistry of

groundwater is observed at varying depths also

accounts for spatial variation.

3.2 Temporal variations in Groundwater

quality

Temporal variations of aqueous chemical

constituents within hydro-geologic

environments provide a valuable insight into

the natural physio-chemical processes, which

govern groundwater chemistry. Temporal

variation is an approach to examine the

variation of ionic concentration with elapsed

time at each well. The temporal variations in

chemical concentration of groundwater is

attributed due to: rapid groundwater movement

after recharge in shallow aquifer system,

natural or artificial fluctuations of the zone of

saturation into and out of weathering profiles

and mixing of groundwater of differing

chemistries in highly pumped aquifers.

4. Impact on Public Health

The most widespread groundwater

contamination in India is Fluoride. The next

important contamination is Arsenic. Both Inland

brackish water and coastal saline water under

marine and estuarine contribute to Salinity

hazards. Iron and Manganese in ground water

are also widely distributed in India. Uranium,

Radon and Strontium contamination from

geogenic source, reported from limited areal

extent in parts of the country. Chromium

pollution in ground water is due to

94

anthropogenic activities, specially in leather

indurtial areas. Selenium contamination is

localised in Punjab and Himachal Pradesh has

also been reported. Nitrate contamination,

resulting from biologic nitrification process

mainly due to agricultural activities has caused

point-source pollution in various parts of the

country. Uptake of excess level of the above

contaminants in the human health through

drinking water and through food chain can be

described as under.

4.1 Arsenic

Significant research on health effects of chronic

arsenic toxicity in human has been carried out in

India during the last thirty years. The symptoms

of such toxicity are dependent on the magnitude

of the dose and duration of its exposure.

Pigmentation and Keratosis are the specific skin

lesions characteristic of chronic arsenic toxicity.

‘Raindrop’ pattern of pigmentation normally

marked in the non-exposed part of the body,

such as trunk, buttocks and thighs. Arsenical

hyperkeratosis appears predominantly on palms

and the plantar aspect of the feet. It has been

observed that the men had two to three times the

prevalence of Keratosis and pigmentation

compared to those for women apparently

ingesting the same dose of arsenic from drinking

water.

The evidence of carcinogenicity in humans from

exposure to arsenic has also been detected.

Other diseases from arsenic toxicity may be

respiratory distress due to irritation of mucous

membranes, resulting into laryngitis, bronchitis,

mycocardial depolarization and cardiac

arrhythmias that may lead to heart failure,

gastrointestinal effects like burning lips, painful

swallowing, thirst, nausea and abdominal colic.

Anaemia and leucopenia are also common.

Arsenic exposure during pregnancy can

adversely affect several reproductitive end

points including spontaneous abortion, preterm

birth, still births, neonatal and prenatal mortality

have also been documented. Chronic exposure to

arsenic may also cause skin cancer, urinary

bladder cancer and lung cancer in addition to

Gangrene, Pedal Oedema (non-pitting) etc. Guha

Mazumder et al (2015) has indicated from

studies of impact of dietary arsenic intake in

human body can provide a potential pathway of

arsenic exposure even where arsenic intake

through water was reduced to 50µg/l in arsenic

endemic region in West Bengal.

4.2 Fluoride

Excessive intake of fluoride (more than 1.5

mg/l) may result in slow, progressive crippling

scourage known as Fluorosis. However, low

level fluoride is required by human system in

preventing Dental Carries. (Sinha Ray, 2015).

Normal level fluoride in water, urine and blood

is upto 1 mg/l, 0.1 mg/l and 0.02 mg/l

respectively.

Fluorosis occurs in three forms: dental, skeletal

and non-skeletal Fluorosis (Figure:2). Dental

Fluorosis becomes visible from discolouration of

permanent teeth aligned horizontally and/or

discolouration in spots away from gums, mostly

in children. The dental Fluorosis is the loss of

luster and shine or dental enamel, discolouration

starting from white, yellow, brown to black.

Skeletal Fluorosis is due to excessive quantity of

fluoride deposited in the skeleton, being more in

cancellous bones compared to cortical bones. In

skeletal Fluorosis, generalized bone and joint

pain occur in mild cases which are followed by

stiffness of joints with restricted movement of

spine and joints. Finally flexion deformity

develops in spine and joints. Crippling

deformity includes Kyphosis, Scoliosis flexion

deformity of knee joints, Paraplegia and

Quadriplegia. Skeletal flurosis affects both

children and adults. Non-skeletal Fluorosis

includes ill effects of skeletal muscle,

Erythrocytes, Gastro-Intestinal System,

ligaments or combination of all. Compared to

female patients male patients are highly affected

by non-skeletal Fluorosis due to calcium

deficiency in Red Blood Cell as also due to

strenuous work.

4.3 Iron

High iron (more than 0.3 mg/ltr) makes the taste

of the water astringent. It may appear brownish

due to precipitation of ferric hydroxide and may

stain utensils, laundry and equipment. As per

95

EPA, although iron in drinking water is safe to

ingest, the iron sediments may contain trace

impurities or harbor bacteria that can be

harmful. Excess iron stored in Spleen, Liver,

Bone marrow can cause Haemochromatosis.

Chronically consuming excess amounts of iron

causes iron overload. Iron overloading may lead

to haemochromatosis, a severe disease that can

damage the body’s organs. If it is not treated, it

can lead to heart disease, liver problems and

diabetes.

4.4 Manganese, Uranium, Radon,

Strontium, Chromium and Selenium

Manganese is easily concentrated in the brain,

especially in the basal ganglia which can cause

irreversible neurological syndrome similar to

Parkinson’s disease. Water containing low

amounts of Uranium is usually safe to drink.

Intake of a large amount of Uranium might

damage the kidneys. Long term chronic intakes

of uranium isotopes in food, water or air can be

carcinogenic. Prescribed limit by WHO is 15

µg/l. Prescribed radioactivity exposure limit is 1

mSv/year. A study conducted in U.S.A.

estimates 12% lung cancer deaths (Grans, 1985)

are linked to radon (radon-222 and its shortlived

decay products). Strontium is non-toxic and a

daily intake of 0.8-5 mg is harmless, if it

contains non-radioactive strontium. The risk

involves from intake of radioactive strontium is

mainly carcinogenic and mutagenic mechanism,

possibly increasing infant mortality. The toxicity

and carcinogenic properties of Chromium (III)

in the cell can lead to DNA damage. The acute

toxicity of Chromium (VI), due to its strong

oxidation properties can reach the blood system

and damage kidneys, liver and blood cells

through oxidation reactions. Selenium exposure

in humans takes place either through food or

water with symptoms like loss of finger, toe

nails and hair and progressive deterioration of

health. It can also cause nausea, headache, tooth

decay, staining of teeth and nails with brittleness

and longitudinal streaks.

4.5 Nitrate and Salinity

Ingestion of Nitrate can cause met-

hemoglobinemia in infants under six month of

age. Bacterial reduction from nitrate to nitrite in

the intestinal tract is responsible for this disease.

Severe met-hemoglobinemia may result brain

damage and death. Intake of high level of nitrate

for a longer period is linked to gastric problems

due to the formation of nitrosamines in adult

human. In India, it has been estimated that

about 2 million hectres of land are now affected

by brackish to saline water. Generally such

brackish water occurs within 100 m depth.

Salinity does not cause serious health effects as

compared to geogenic contaminants.

5. Mitigation Measures

Potential mitigation techniques are vital for

improving the quality of contaminated water.

The mitigation methods vary depending on the

origin, type and nature of contamination.

Avoiding the potential zones of contamination

is the best way , but water scarcity and limited

options of alternate source prevents us , and

therefore better technological and economical

mitigation strategies should be adopted.

Critical concerns

The primary task of providing contaminant free

safe water needs to address the following

critical concerns:

Water quality monitoring and Health

Risk Assessment.

Identification of contaminated as well as

safe sources.

Provision of alternate sources of safe

drinking water.

Establishing a transparent system of

information sharing by all stakeholders.

Long-term change in Agriculture and

Irrigation practice, restricting the use of

ground water in critical areas.

Technology options

Based on the experience in India and

neighbouring countries, the following are major

technological options for providing safe water

in groundwater contaminatd areas:

Tapping ground water from alternate

pollution (Arsenic, Fluoride, Chromium,

96

Nitrate etc.) free aquifers at a deeper

levels and scaling-off the polluted

aquifer on the top.

Large scale surface water based piped

water supply for the communities by

drawing water from the rivers and

traeting them for pathogenic microbes.

Conservation and quality upgradation of

traditional surface water sources like

ponds, dug wells etc.

Removal of pollutants from ground

water by In-situ and Ex-situ Treatment

techniques.

These tchnologies can be used both

large, medium and short scale water

supply projects. Domestic filters for

households uses can also be developed

based on such appropriate technologies.

6. Policies and Strategies for Ground

Water Quality Management

Effective policies for ground water protection

must consider the institutional and cultural

environment in the country, the interrelationship

of quantity and quality of ground water,

financial viability of any proposed measures for

protection and acceptability of the measures to

society. It is essential that effective policy

development includes the public, government

agencies and other stakeholders potentially

affected.

6.1 International practices

The European Union (EU) water policy for

ground water protection has been based on six

basic principles: a high level of protection,

application of the precautionary principle, the

prevention of pollution, the rectification of

pollution at source, adoption of the polluter pays

principle and the integration of environmental

protection in to other policies such as

agriculture, transport and energy. The policy

also included water pricing and ensuring that the

citizen is more involved in decision making. It

also emphasized that “The Polluter Pays”

principle will be incorporated through the use of

appropriate economic instruments.

6.2 Indian perspective

Safe drinking water is a constitutionally

guaranteed right in India. According to the

Constitution of India water supply is a State

subject and the Union Government is only

responsible for setting standards. State

Governments have established departments for

supply of domestic water in urban and rural

areas who are also to look after the quality of the

water supplied. The National Water Policy

(2002) of India also states “Both surface and

ground water should be regularly monitored for

quality. A phased programme should be

undertaken for improvements in the water

quality.” It is estimated that around 37.7 million

Indians are affected by water-borne diseases

annually, resulted economic burden being $600

million a year.

The over-dependency on ground water has

caused 66 million people in 22 States at risk due

to excessive fluoride and around 10 million

people at risk due to arsenic in 6 States. There

are also problem due to excessive salinity,

specially in coastal areas, iron, manganese,

nitrates and other contaminants. The major

ground water contaminants that affect human

health are fluoride, arsenic, nitrate and faecal

coliforms. Most of the ground water coliforms

and related pathogens accounts for a number of

waterborne disease like diarrhea, gastro-

enteritis, jaundice, hepatitis, cholera, typhoid,

polio etc.

Chakraborti et al (2011) examining India’s

ground water quality management suggested a

variety of policy options like artificial ground

water recharge, increasing ground water

efficiency, improving crop productivity,

agricultural diversification and reducing

uncontrolled ground water withdrawal promoted

by highly subsidized agricultural electricity,

educating and mobilizing communities by

creating awareness of challenges and

empowering the communities for ground water

quality protection.

6.3 Groundwater quality data assimilation

The primary step towards ensuring ground water

protection is to generate reliable and accurate

information about the water quality. While

CPCB and State PCB laboratories has set

97

standards for surface water effluent quality, the

Bureau of Indian Standards (BIS) has been

responsible for developing drinking water

quality standards for India. The Central Ground

Water Board (CGWB) along with the State

Ground Water agencies are primarily

responsible for monitoring ground water quality.

Other institutions like National Environmental

Engineering and Research Institute (NEERI), the

National Institute of Hydrology, the All India

Institute of Hygiene and Public Health, various

Universities etc undertake regular water quality

research. All State Public Health Engineering

Departments have established water quality

testing laboratories in State, district and zonal

levels. The huge Data on ground water quality

being collected by different agencies, need to be

collated and data Banks need to be established,

both Centrally and State wise.

6.4 Institutional issues for policy

development

A groundwater quality protection policy framing

requires primarily creation of a Policy Task

Force involving Key Institutions of

Environment, Health, Agriculture, Industry,

Water Resources and Local Government with

the setting up of a Lead Agency to co-ordinate

the Policy and strategy for groundwater

protection and management. Effective

institutional frameworks and clarity and

accountability of responsibilities facilitate

achieving objectives of ground water protection.

6.5 Capacity – building

To support an intersectoral approach, inter-

disciplinary training is required to ensure the

necessary competence and skills to resolve

ground water issues. It is necessary to treat the

catchment as an integrated unit for both planning

and management purposes. Community based

water quality monitoring approach may yield

better results. Education of local people in both

aspects of hygiene and availability of resources

quality-wise will help in improving public

health.

6.6 Legislative framework

The Easement Act, 1882 provides every land

owner with the right to collect and dispose,

within his own limits, all water under the land

and on the surface (Suhag, 2016). This gives

landowners significant power over ground

water, and excludes landless ground water users

from its purview.

As water falls under the State List of the

Constitution, State Legislative Assemblies can

make laws on the subject. Although under the

Article -21 Supreme Court and various High

Courts gave judgements on water related

concerns like access to safe drinking water and

considered safe drinking water as a fundamental

right, State Legislative Assemblies only can

make laws on this subject as the constitution

provides water under the State List. The Central

Government through publication of Model Bills

(2013) provides guidelines based on which the

State Governments may enacts their laws.

Abstraction rates of water may have a

fundamental influence on water quality. Its

control requires a sound legal basis and good

enforcement. There is a realization worldwide

that individual rights need to be sub-ordinate to

protection of quantity and quality of

groundwater and ‘rights’ may be substituted by

‘permissions’.

6.7 Groundwater quality monitoring

Four major objectives of monitoring ground

water quality such as determination of

background ground water quality, determination

of permit compliance, detection of groundwater

contamination and characterization of the

effectiveness of corrective action. They also

found that ground water quality Variables may

sometimes exhibit seasonality and predictability

cyclic behavior and are frequently non-normally

distributed. It was also found that significant

variability occurs in vertical concentration

gradients, horizontal concentration gradients,

time or volume of pumping, sample collection

procedures and data management procedures

and suggested skewness test for evaluating the

normally assumption in ground water quality

data.

98

6.8 Tools for groundwater quality

protection

Some specific tools and incentives may be

employed to maximize the impact on

groundwater protection policies and regulation

which include establishing integrated pollution

control measures, use of prohibition and the use

of codes of practice. The use of incentives is

often as effective as the use of prohibition or

controls. Legislation developed for general

environmental protection and pollution control

can be employed to deal with the activities

which affect the quality of ground water.

Important tools can be used to protect ground

water are Wellhead protection plans,

Vulnerability assessment, Codes of practice,

economic instrument, education, community

awareness and involvement and land use

planning.

6.9 Aquifer mapping and aquifer modeling

Scientists are increasingly taking the help of

mathematical simulation techniques in different

types of ground water development schemes

based on evaluation of hydrogeological mapping

and information collected thereon. Applying a

model is an exercise in thinking about the way a

system works without any deteterious effect.

Aquifer mapping should be suitably designated

to collect accurate and precise informations

about hydrological and hydrogeological

components pertaining to concerned aquifer

system to be modeled. Nevertheless, aquifer

modeling is going to be the emerging tool in

ground water protection in near future.

6.10 Land use planning and management

Land use planning and pollution of ground water

are interrelated. Land uses and economic

activities, partially in drinking water catchments,

need to be under government regulatory control.

Most planning controls to protect ground water

quality are implemented by local Governments

but should be incorporated into national

planning policies and regional planning

regulations. Controls on land zoning and sub-

division imposed by State Governments can be

very effective tools for protecting groundwater.

Practices that cause groundwater pollution may

be changed by providing incentives. Tax

incentives to use less susceptible to leaching or

degradation of soil by a specific type of fertilizer

or pesticide can be an effective tool. Price

mechanism can also be used to restrict the

amount of contamination that reaches

groundwater Stringent application of “Polluter

Pays” principle may reduce the contamination

effect.

6.11 Water resource management

Water Resource Management that can have an

impact on ground water quality is artificial

recharge to ground water system using potable

water. Artificial recharge helps in diluting some

pollutants in the aquifer. Maintaining an

effective water balance by controlling discharge-

recharge relationship can also enhance the

sustainability of ground water development

schemes. Rain Water Harvesting practices can

be an important means to sustain large scale

ground water withdrawal resulting quality

hazards, specially in small watersheds and mini-

water supply schemes. Since the practice is

proving to be quite effective, it is necessary to

have such wise water management and a

national approach should be developed to ensure

that artificial recharge and rainwater harvesting

schemes form the integral part of the overall

ground water quality protection strategies.

6.12 Consultation and participation

Understanding the needs of the community is a

pre-requisite and part of the process of

establishing a dialogue from which a mutual

trust can be developed for achieving common

goals and plans. Continuous information

gathering and educating the stakeholders on

knowledge, attitudes and practices are important

for ensuring accuracy of assessments,

monitoring of progress and change and

establishing trust among Stakeholders.

Transparency of information dissemination is

extremely important and requires development

plans. This process can ensure ownership of the

process by all involved. Community

participation is an ongoing process of

information gathering dialogue and negotiation.

99

6.13 Mass awareness and social

empowerment

IEC material should be suitably tailored to create

sufficient awareness among the rural and urban

communities about early precautionary signals

related to the diseases like arsenocosis,

fluorosisetc based on the sound water quality

monitoring.

6.14 Issue of social convergence

Different agencies including the Government of

India are propagating decentralized water quality

testing along with other agencies. There is a

need to address the issues of monitoring and

intervention jointly, involving NGOs,

Government agencies and other Stakeholders.

Community awareness needs to be built up in

the context of such social convergence by

upgrading the existing laboratories managed by

both Government agencies and NGOs so that

capacity in terms of monitoring, analysis of

microbial parameters data management, at the

local level can be enhanced. There is a need for

addressing bacteriological and chemical

contamination in totality.

6.15 Nutrition Management

The nutrients and the sources often

recommended for Nutritional Interventions for

fluoride and arsenic are calcium (milk, dahi,

green leafy vegetables, sesame (Til) seeds,

cheese/paneer, drumstick, arbi, etc) iron (beet

root, apple, raw or green banana), Vitamin C

(amla, guava, lemon, oranges, tomato, spouted

cereals and pulses), Vitamin E (vegetable oil,

nuts, whole gram cereals, dried beans, etc),

Antioxidants (papaya, carrot, pumpkin, mango,

spinach(palak) and other leafy vegetable, garlic,

onion, chilli, pepper, cabbage, cauliflower,

radish, leechi, watermelon, soyabean,

mushrooms, ginger, sweet corn etc). For arsenic

patients, selenium rich food like meat etc have

also been found useful.

7. Conclusion

Toxicity in ground water has already posed

considerable problems in India. Protecting

ground water from toxic contaminants, geogenic

and anthropogenic, needs effective management.

Ground water system, very complex in nature,

the chemistry of toxic chemical, constituents in

ground water, source of contaminated chemical

constituents and the efficacy of different

component of ground water quality protection

have not yet been completely understood.

Because of uncertainty in planning for ground

water protection, it is imperative that ground

water quality plans be continuously reviewed

and reassessed. Effective steps need to be taken

commensurate with availability of the

technology options.

References

Brindha, K., Rajesh, R., Murugan, R., and

Elango, L., 2012. Nitrate pollution in

groundwater in Some rural areas of Nalgonda

district, Andhra Pradesh, India. Journal of

Science and Engineering, 54(1), pp. 64-70.

CGWB (1999) High Incidence of Arsenic in

Ground Water in West Bengal, Central Ground

Water Board, Eastern Region Kolkata.

CGWB (2010) Ground water quality in shallow

aquifers of India, Central Ground Water Board,

Ministry of Water Resources,Govt.of

india,Faridabad.

Chakraborti, Dipankar., Das. Bhaskar, Mathew

T. Murril, Examining India’s Groundwater

Quality Management- Environ. Sci. techno.

2011. 45, 27-33.

Guha Mazumder,D.N., 2015. Human health

hazards due to arsenic and fluoride

contamination in drinking water and food chain,

International seminar on Challenges to ground

water management: Vision 2050. pp. 385-392.

Guidence Manual Integrated Fluorosis

Mitigation by NEERI, UNICEF, ICMR and

PHE Dept, Govt. of M.P. (2007)

Lepokurova, O.E. and Ivanova, I.S., 2014.

Geochemistry of iron in organogenic water of

Western Siberia, Russia. Procedia Earth and

Planetary Science, 10, pp. 297-302.

Rajmohan, N., and Elango, L.,

2005. Distribution of Iron, Manganese, Zinc and

100

Atrazine in groundwater in parts of Palar and

Cheyyar river basins, South India. Journal of

Environmental Monitoring and

Assessment, 107, pp. 115–131.

Roopal Suhag, February 2016, PRS publication.

Overview of Ground water in India

Sinha Ray.S.P., Geogenic Fluoride

Contamination in Groundwater- Integrated

fluorosis management- Bhujal Manthan,

CGWB, Kurukshetra , 2015

Statement of Objectives and Reasons, Draft

Model Bill for the Conservation, Protection and

Regulation of Groundwater, 2011,

http://www.planningcommission.nic.in/aboutus

/committee/ wrkgrp12/wr/wg_model_bill.pdf.

WHO and UNICEF (2001) Global Water Supply

and Sanitation Assessment 2000 Report. World

Health Organisation / UNICEF, Geneva/ New

York.

Zhao, Z. 2015. A Global Assessment of Nitrate

Contamination in Groundwater. Report, pp. 1-

27.

101

http://www.planningcommission.nic.in/aboutus%20/committee/

http://www.planningcommission.nic.in/aboutus%20/committee/

FLOOD ASSESSMENT THROUGH 1D/2D COUPLED HYDRODYNAMIC

MODELING

DHRUVESH PATEL

Civil Engineering Department

School of Technology, Pandit Deendayal Petroleum University (PDPU)

Raisan, Gandhinagar-382007, Gujarat, India

Flood is a major disaster responsible for huge demolition, loss of properties and life due to heavy

amount of water released in a short span of time. Therefore, its assessment is important to identify

the low lying area and reduce the vulnerability & risk in future. In this article, an application of

1D/2D couple hydrodynamic model and high resolution satellite image is described for flood

assessment. Surat city situated in south Gujarat is considered for a case study. It was experienced

in 2006, high amount of water released from the Ukai dam and near about 75-80 % Surat city was

under inundation. Resulting, 300 people was died and INR 21000 crores property loss. To prevent

such disastrous event in future and evacuate the people from low lying area in time, a flood

assessment has been conducted through HEC-RAS based 1D/2D coupled hydrodynamic model.

299 cross sections carried out after 2006 flood of Tapi river is considered for 1D modeling, where

as 0.5 m contour of 5 m grid for Surat city and 30 m grid of SRTM for Tapi basin is considered

for 2D modeling. The entire modeling approach is coupled in HEC-RAS and simulated under the

unsteady flow condition. Number of hydrodynamic parameters is adjusted through trial and error

method and the simulated results are calibrated and validated for the year of 1998 and 2006. It has

been observed that the major section situated in and around Surat City is not capable to hold the

water 25768 m3s-1 released from the Ukai dam in 2006, under the levees condition. In future, if

water will be released above the existing carrying capacity, similar catastrophic floods will

happen again.

The method describe in this article shows the application of HEC-RAS hydrodynamic

modeling along with satellite technology. It can be applicable to any similar case study for flood

assessment. The obtain results in 1D and 2D will be applicable for flood mitigation analysis,

flood warning, Emergency Action Plan (EAP), etc.

Key Words: Flood Mapping, Flood Assessment, HAC-RAS, Hydrodynamic Modeling

1.0 Introduction

Flood is defined as extremely high flows or

levels of rivers, lakes, ponds, reservoirs & any

other water bodies, whereby water inundates

outsides the water bodies area. Flood is

unpredictable and unexpected event occurring

from time to time in river basins and natural

drainage systems, which not only damages the

natural resources, lives and environment, but

also causes loss of health & economy. India is

one of the world’s most flood prone countries

with 113 million people exposed floods.

According to UN report India’s average annual

economic loss due to disasters are estimated to

be $9.8 billion, out of which more than $7

billion loss is due to floods. About 40 million

hectare of land in India is prone to be flooded as

per National Flood Commission. Flooding in

urban areas is a particularly challenging

102

problem. Unplanned urban growth increases risk

to natural hazards like floods.

Causes of Flood

1. Heavy intensity of rainfall in catchment area

2. Sedimentation of rivers and reservoirs

3. Obstruction in river flow

4. Contraction of river section

5. Inadequate cross drainage works

6. Heavy melting of snow and ice

7. Sudden failure of dam

8. Topography of the catchment area

9. Strong winds in coastal areas

10. Suddenly opening of the gate of dam

(Suddenly heavy discharge from dam)

Types of Floods

Flood can be classify based on speed, geography

or causes of flooding. Various types

of flooding will be described here:-

1. Flash flood

2. River flood / Fluvial flood

3. Coastal flood / Surge flood

4. Urban Flood

5. Pluvial flooding / Ponding`

It is not possible to prevent floods but it is

possible to reduce the damages due to floods by

controlling the floods. Thus flood control or

flood management is defined as the prevention

or reduction of the flood damages.

Floods are not fully preventable but the

associated hazards could be minimized if flood

prone areas are known in advance (Sahoo and

Sreeja 2015). Therefore, to reduce the loss of

life and property in floodplains it is necessary to

predict the water levels of rivers in urban

locations, including the inundation extent for the

development of risk maps for insurance

assessments and effective management plans for

future flood risk reduction(Patel and Srivastava

2013; Timbadiya et al. 2014). Flood inundation

mapping (FIM) and identifying the flood risk

zones are primary steps for formulating any

flood management strategy (Sahoo and Sreeja

2015). Understanding the effects of flood

inundation in terms of area, depth and time are

mandatory for efficient flood risk

management(Sahoo and Sreeja 2015).

2. Hydrodynamic modeling

Currently, many hydrodynamic models are

available for 1D, 2D and 1D/2D coupled

hydrodynamic modeling, which allows the

simulation of different flood scenarios (Quiroga

et al. 2016). Hence, numerical models are

important tools for understanding flood events,

flood hazard assessment and flood management

planning.

Accurate delineation of flood extents and depths

within the flood plain is necessary for flood

management, mitigation and to make accurate

decisions regarding construction and urban

development (Noman et al. 2003; Reza

Ghanbarpour et al. 2011; Salimi et al.

2008).Several researchers worked out to

simulate accurate prediction of river flow and

hydraulic behavior of river channels by a

number of hydraulic models such as MIKE 11,

1D HEC-RAS, INFOWORK, FLOW-R2D,

HSPF, UNET, WMS,LISFLOOD-FP,

TELEMAC-2D and RiverCAD (Ali et al. 2012;

Bates et al. 2000; Bellos and Tsakiris 2014;

Castellarin et al. 2009; Horritt and Bates 2002;

Parsa et al. 2013; Reza Ghanbarpour et al. 2011;

Timbadiya et al. 2011a; Tsakiris and Bellos

2014). Out of all these models, Hydrological

Engineering Centres River Analysis System

(HEC-RAS) is widely used worldwide and is

public domain software. HEC-RAS is developed

by the U.S. Army Corps of Engineers which

allows to perform one-dimensional steady and

unsteady river flow hydraulic calculations,

sediment transport-mobile bed modelling and

water temperature analysis (Brunner 2008;

Brunner 2010; Hicks and Peacock 2005; Horritt

and Bates 2002). HEC-RAS model represent the

terrain as a sequence of cross-sections and

103

simulate flow to estimate the average velocity

and water depth at each cross-section (Parsa et

al. 2013). (Johnson et al. 1999) used the HEC-

RAS model to predict and define desirable lands

within 10 km of the river Greybull, Wyoming,

USA in the Bighorn Basin.

3. HEC-RAS Hydrodynamic modeling

The HEC-RAS software was developed at the

hydrologic Engineering Center (HEC), which is

a division of the Institute for Water Resources

(IWR), U.S Army Corps of Engineer. The

software was designed by MR. Gary W.

Brunner, leader of the HEC-RAS development

team. This software allows you to perform one-

dimensional steady flow, unsteady flow and

sediment transport calculations.

The HEC-RAS system will ultimately contain

three 1D, 2D and 1D/2D coupled analysis

components for:-

1. Steady flow water surface profile

computations

2. Unsteady flow simulations

3. Movable boundary sediment transport

computations

A key element is that all three components will

use a common geometric data representation and

common geometric and hydraulic computation

routines. The first version of HEC-RAS (1.0)

was released in July of 1995. Now version 5.0 in

Jan of 2016.

HEC-RAS is designed to perform 1-D, 2D and

1D/2D coupled hydraulic calculations for a full

network of natural and constructed channels.

The steady flow water surface profile

component of the modeling system is intended

for calculating water surface profiles for steady

gradually varied flow. The system can handle a

single river reach, a dendritic system, or a full

network of channels. The steady flow

component is capable of modeling subcritical,

supercritical, and mixed flow regime water

surface profiles. The basic computational

procedure is based on the solution of the One-

Dimensional energy equation. Energy losses are

evaluated by friction (Manning’s equation) and

contraction/expansion (coefficient multiplied by

the change in velocity head). The momentum

equation is utilized in situations where the water

surface profile is rapidly varied. The steady flow

system is designed for application in flood plain

management and flood insurance studies to

evaluate floodway encroachments.

HEC-RAS software window

Fig.1 HEC-RAS 5.0 software window

104

4) Case study

Hydrological aspect of river Tapi

Tapi river has a total length of 724 km, out of

which the 214 km is in Gujarat state and it meets

the Arabian Sea in the Gulf of Cambay

approximately at 19.2 km west of Surat

city(CWC 2000-2001).The Tapi covers an area

of approximately 3837 km2 in Gujarat state. The

length of the river from the Ukai dam to the

Arabian Sea is considered the Lower Tapi River

(LTR), which is estimated as 122 km. The

average bed slope of the river between Ukai dam

to Hope Bridge is 0.00045 and upto the sea is

0.00001. The Lower Tapi river consists of 2

inline structures named Kakrapar weir and

Singanpur weir as well as 5 major Bridges

across river Tapi at Surat city (Patel and

Srivastava 2013). Four major river gauge-

discharge stations named Kakrapar weir, Ghala,

Singanpurweir and Hope Bridge are situated on

LTR (Fig. 2). The Ghala station is monitored by

Central Water commission (CWC) and the

others are monitored by SIC. Hope Bridge is

located at Surat-Olpad-Sahol road, 103.3 km

downstream of Ukai dam, which is designed for

a high flood level (HFL) (GTS-RL +11.5 m).

Based on the gauged data at Hope Bridge, the

safe and danger level for Surat city is decided.

Before the 2006 flood event, the pre-fix warning

level at Hope Bridge was 8.0 m for the

corresponding discharges of 11,328 m3/s while

the maximum 12.5 m water level was observed

with the corresponding discharges of 25,770

m3/s in 2006 flood

(https://www.suratmunicipal.gov.in/Bridgecell/).

2.2 Surat City:

Surat city is located in Gujarat; it is known for

its textile trade, diamond cutting and polishing

industries, situated 100 km downstream of Ukai

dam and 19.4 km upstream of the mouth of river

Tapi. Surat is divided into 7 zones i.e. West

zone, Central zone, North zone, East zone, South

zone, South East zone and South west zone. Its

zone boundary covers126.52 km2 as per the

SMC zone map of 2006. The Surat city is

bounded by latitude 21o 06” to 21o 15” N and

longitude 72 o 45” to 72 o 54” E and falls in

Survey of India (SOI) map number 46C/15, 16.

Surat had a population of 4.5 million in the 2011

census, making it the second largest city in the

state of Gujarat, after Ahmedabad

(https://en.wikipedia.org/wiki/Surat). Surat city

forms an arc of a circle, the bends enclosed by

its walls stretching for about a mile and a quarter

along the bank of Tapi

(https://www.suratmunicipal.gov.in/). From the

right bank of the river, the ground rises slightly

towards the north, but the height above mean sea

level is 13 m. The topography is controlled by

the river and is flat in general and the general

slope is from north-east to south-west.

Furthermore, the city can be divided into two

geomorphic units namely, coastal zone and

alluvial area. The coastal area represents marshy

shoreline with an extensive tidal flat stretch

intercepted by estuaries. Alluvial deposits from

the River Tapi cover the alluvial area. The area

is covered by recent alluvium of the Quaternary

Age. The alluvial plain is characterized by the

flood p lain of the river Tapi and river Mindhola

where there is a thick alluvial cover. The alluvial

plain merges into a dry, barren, sandy coastal

zone. The coastal area around the river is

covered by mud. The marine deposits underlie

the alluvium. The alluvium consists of sand and

clay layers. The climate of Surat city can be

broadly divided into four seasons: Summer,

Rainy, Autumn and Winter. Summer for three

months from March to May, Rainy from June to

September, Autumn from October and

November and the Winter season is from

December to February. The summers are quite

hot with temperatures ranging from 37.8 oC to

44.4 oC. The climate is pleasant during the

105

https://en.wikipedia.org/wiki/Gujarat

https://en.wikipedia.org/wiki/Ahmedabad

https://en.wikipedia.org/wiki/Surat

monsoon while autumn is temperate. The

winters are not very cold but the temperatures in

January range from 10 oC to 15.5oC. The

average annual rainfall of the city has been 1143

mm. The city has experienced the catastrophic

floods in the years of 1933, 1959, 1968, 1970,

1994, 1998 and 2006. It has been estimated that

the single flood event, which occurred during 7–

14 August 2006, in Surat and Hazira twin-city,

resulted in the deaths of 300 humans and

property damage worth INR 21000 crores (Patel

and Srivastava 2013). After the 2006 flood the

Surat Irrigation department and SMC has carried

out the embankment (levees) improvement work

in and around Surat city. The right and left bank

embankments RLs have been improved

significantly upto 16.55 to 21.21 m and 16.00 to

18.40 m respectively against the maximum

12.5m gauge level measured in 2006 flood.

Since 2006, no major flood has been observed.

However, there is a growing concern that

climate change, illegal settlement along the bank

of river Tapi, emergency dam releases, and

uncompleted embankment work could lead to

increasing flooding risk of Surat city.

5) Modeling and Analysis

1.1 1D HEC-RAS model

HEC-RAS is a one-dimensional, water surface

profiling application developed by the U.S.

Army Corps of Engineers (USACE) Hydraulic

Engineering Centre. HEC-RAS is a hydraulic

model that is composed of three 1D hydraulic

examination modules designed for 1) steady

flow water surface profiles 2) Unsteady flow

simulation and 3) Sediment transport, movable

boundary computations (Lee et al. 2006). In this

study, the unsteady, gradually varied flow

simulation function of HEC-RAS is used, which

depends on finite difference solutions of the

Saint-Venant equations [Equations (1)-(2)]

(Timbadiya et al. 2011b).

𝜕𝐴

𝜕𝑡+

𝜕𝑄

𝜕𝑥= 0 (1)

𝜕𝑄

𝜕𝑡+

𝜕(𝑄2 𝐴⁄ )

𝜕𝑥+ 𝑔𝐴

𝜕𝐻

𝜕𝑥+ 𝑔𝐴(𝑆𝑜 − 𝑆𝑓) = 0 (2)

Here A = cross-sectional area normal to the

flow; Q = discharge; g = acceleration due to

gravity; H = elevation of the water surface above

a specified datum, also called stage; So = bed

slope; Sf = energy slope; t = temporal coordinate

and x = longitudinal coordinate (Timbadiya et

al. 2011b).

The method is based on the energy

relationship that starts the calculations from one

end of the range (supercritical flow at upstream

to subcritical flow at downstream) and then

continues the calculation from this section to the

next one (Parsa et al. 2013).

The work is further extended in the1D/2D

environment. The HEC-RAS 5.0.3 is fully

solved in using the 2D Saint Venant equation

(Brunner 2016; Manual 2016; Patel et al. 2017;

Quiroga et al. 2016):

∂ζ

∂t+

∂p

∂x+

∂q

∂x= 0 (3)

∂p

∂t+

∂

∂x(

p2

h) +

∂

∂y(

pq

h) = −

n2pg √p2+q2

h2 −

gh∂ξ

∂x+ pf +

∂

ρ ∂x(hτxx) +

∂

ρ ∂y(hτxy) (4)

∂q

∂t+

∂

∂y(

q2

h) +

∂

∂y(

pq

h) = −

n2qg √p2+q2

h2 −

gh∂ξ

∂y+ qf +

∂

ρ ∂y(hτyy) +

∂

ρ ∂y(hτxy) (5)

where h is the water depth (m), p and q are the

specific flow in the x an y direction (m2/s), ξ is

the surface elevation (m), g is the acceleration

due to gravity (m/s2), n is the Manning

resistance, ρ is the water density (kg/m3), τxx,

τyy and τxy are the components of the effective

shear stress and f is the Coriolis (1/s1) (Patel et

al. 2017; Quiroga et al. 2016)

Initially, a 2D computation mesh is generated

for Lower Tapi basin. The 30 m x 30 m cell

106

spacing is selected for 2D flow area generation

for LTB for DEM (SRTM 30*30) which

generated the total 4484708 grid cells. At the

second stage SA/2D Area connect option is used

to locate the levees and retaining wall inside the

of 2D flow areas 11643.58 m and 10123.94 m

long levees are created on right and left bank of

Tapi surrounding Surat city. 1391.11 m and

6,606.2 m long retaining wall are created on

right and left bank of Tapi. For 1D simulation,

the release from the Ukai dam in 2006 (Flood

Hydrograph) and Tidal level in the sea are

considered for the upstream-downstream

boundary conditions along with T.S. gate

opening for Singanpur weir under the unsteady

flow condition. Whereas flow hydrograph (Ukai

dam release) and stage hydrograph (Tidal level)

is considered for upstream and downstream

boundary conditions for 2D simulation. The

roughness resistance was estimated based on

supervised classification scheme in ERDAS

IMAGINE 10. In order to ensure the stability of

the model, the time steps were estimated

according to the Courant-Friedrichs -Lewy

condition (Brunner 2016; Manual 2016; Patel et

al. 2017; Patel et al. 2018):

C =V∆T

∆x ≤ 1.0 (With maximum C = 3.0) (6)

Or

∆T ≤ ∆x

V (With C = 1.0) (7)

where, C is the Courant Number, V is the flood

wave velocity (m/s), ΔT computational time

step(s) and Δx is the average cell size (m)

(Brunner 2016). The model is simulated under

the unsteady flow condition and the flood

inundation (depth), flood velocity, water surface

elevation (WSE), arrival time, duration for each

hour are obtained.

Analysis:

1D Hydrodynamic Analysis:

The flow hydrograph and Tidal level have been

considered for the upstream and downstream

boundary conditions, including 2 inline

structures and 5 major bridges. The detail profile

of the simulated 2006 flood is shown in Fig. 2.

Fig.2 Simulated maximum discharge carrying

capacity of sections from Ukai Dam to Arabian Sea

The results clearly indicate that most of the

sections, having discharge capacity less than

12740 m3/s, are located near Surat city (Fig. 2).

It is found that the river carrying capacity near

Nehru (Hope) Bridge is reduced to 5096 m3/s

which ultimately shows that any volume of

water more than 5096 m3/s is enough to flood

the area. In the 2006 floods, the South West

zone and West zone were flooded badly and

inundated upto 3-4 m in water .It is observed

that the Tapi sections between RD-LD 22 to

RD-LD 30 and RD-LD 44 to RD-LD 47at Surat

can carry the maximum discharge of 5096 m3/s

to11326 m3/s without causing significant

damages to urban dwellings and infrastructures.

1D/2D Hydrodynamic Analysis:

Flood Inundation Map Superimpose on Google

Earth Image

107

HEC-RAS 5.0.3 is being used to run the extreme

flooding event. Water release 25768 m3/s in

2006 flood is considered for preparation of

Water Surface Elevation, Water Depth, Velocity

and Arrival time under the Levees condition

(Fig. 3-7).

Fig. 3 Maximum Water Depth map for Ukai Dam, Lower Tapi Basin (Source Patel et.al. 2018)

Fig. 4 Maximum Water Surface Elevation map for

Surat city

Fig. 5 Maximum Water Depth map for Surat City

Fig. 3 shows the maximum water depth from

Ukai dam to Surat city. For further exploring the

possibility of inundation and identify the

probable area, road, street and known milestone

the entire simulation is super imposed on Google

Earth Image. It has been seen that the west zone

is the low lying area of Surat city and areas of

Rander, Usmani park, Choksiwwadi, Yoginagar

108

and Adajan near Morarji Road is under

inundation of 4-5 m (Fig. 4-5) , corresponding

released of 25768 m3/s. Similar the centre zone

is under inundation of 2-3 m (Fig. 5) . Velocity

of water is marked 0.51 m/s in west zone from

Singanpurweir to in downstream at Sardar

bridge, whereas at upstream maximum velocity

was 1m/s. In south, south east, south west, east

and north zones maximum velocity observed

was 0.51 m/s (Fig. 6). Looking to lower velocity

in major part of flood prone area, water was

retained and will be affected the people and their

valuables significantly.

It has been seen from the analysis and observed

map, the west zone, south west zone and central

zone are low lying areas. The 84 % area of west

zone is under inundation at the discharge of

10101 m3/s released from the Ukai. The

simulated results show that the west zone has the

maximum chance to get flooded in such a future

flood event whereas the North zone is safe.

Fig. 6 Maximum Velocity map for Surat City Fig. 7 Maximum Arrival Time map for Surat City

To prepare the detail EAP and evacuate the

people from low lying area, it is important to

identify the flood arrival time to the Surat city.

Fig. 7 shows that the arrival time of flood in

different zones. It is clearly indicated that the

west zone will be affected first and 90-95 % will

be under inundation within 30-33 hrs so it is

important to start the rescue operation from west

zone. Furthermore, the maps are overlaid on

Google Earth image so it is easy to identify the

roads and rail network during the emergency

exit, and reduce the death toll at Surat city (Patel

et al. 2018).

6) Application of HEC-RAS

The results gained from this research will be

very helpful to policy makers and hydrologist

for developing integrated hydrodynamic (1D,

2D, 1D/2D coupled) model, flood mitigation

measures and the advance flood forecasting

system to protect Surat city against flooding.

This study provides strong supportive evidence

of the potentiality of new HEC-RAS 5.0.1 for

flood inundation modeling. The assessment of

the HEC-RAS with respect to this peculiar

aspect is an important step for successful and

improved development of the hydrodynamic

model and thus can provide important assistance

in building flood mitigation strategies for any

similar cased worldwide. The study will also

provide guidance to the authorities for

significant dam operation and expansion of

levees in future.

109

Acknowledgement

The author would like to thank Bhaskaracharya

Institute for Space Applications and Geo-

Informatics (BISAG), SAC-ISRO, National

Bureau of Soil Survey and Land Use Planning

(NBSS & LUP), National Resources

Information System, Survey of India (SOI),

Central Water Commission (CWC), Surat

Irrigation Circle (SIC) and Surat Municipal

Corporation (SMC) of Surat district for

providing necessary data, facilities and support

during the study period.

Reference

Ali H, Lee Teang S, Majid M, Hadi M (2012)

Incorporation of GIS Based Program into

Hydraulic Model for Water Level Modeling on

River Basin Journal of Water Resource and

Protection 4:25-31

Bates P, Stewart M, Desitter A, Anderson M,

Renaud JP, Smith J (2000) Numerical

simulation of floodplain hydrology Water

Resources Research 36:2517-2529

Bellos V, Tsakiris G (2014) Comparing Various

Methods of Building Representation for 2D

Flood Modelling In Built-Up Areas Water

Resources Management:1-19

Brunner G (2008) HEC-RAS River Analysis

System, Version 3.0 User’s manual CPD–68,

Hydrologic Engineering Center, Davis,

California

Brunner G (2010) HEC-RAS, River Analysis

System Hydraulic Reference Manual (Version

4.1). Report CPD-69 Retrieved from United

States Army Corps of Engineers, Hydraulic

Engineering Center website: http://www hec

usace army mil/software/hec-

ras/documents/HECRAS_4

1_Reference_Manual pdf

Brunner GW (2016) Combained 1D and 2D

Modeling with HEC-RAS. US Army Corps of

Engineers, Institute for Water Resources,

Hydrologic Engineering Center,

http://www.hec.usace.army.mil/misc/files/ras/Co

mbined_1D_and_2D_Modeling_with_HEC-

RAS.pdf,

Castellarin A, Di Baldassarre G, Bates P, Brath

A (2009) Optimal cross-sectional spacing in

Preissmann scheme 1D hydrodynamic models

Journal of Hydraulic Engineering 135:96-105

CWC (2000-2001) Water Year Book 2000-

2001, Tapi basin, hydrological observation

circle, Gandhinagar, Gujarat, India

Hicks F, Peacock T (2005) Suitability of HEC-

RAS for flood forecasting Canadian Water

Resources Journal 30:159-174

Horritt M, Bates P (2002) Evaluation of 1D and

2D numerical models for predicting river flood

inundation Journal of Hydrology 268:87-99

https://www.suratmunicipal.gov.in/

https://www.suratmunicipal.gov.in/Bridgecell/

Johnson GD, Strickland MD, Buyok JP, Derby

CE, Young DP (1999) Quantifying impacts to

riparian wetlands associated with reduced flows

along the Greybull River, Wyoming Wetlands

19:71-77

Lee KT, Ho Y-H, Chyan Y-J (2006) Bridge

blockage and overbank flow simulations using

HEC–RAS in the Keelung River during the

2001 Nari typhoon Journal of Hydraulic

Engineering 132:319-323

Manual H-R (2016) Hydrologic Engineering

Center US Army Corps of Engineers

Noman NS, Nelson EJ, Zundel AK (2003)

Improved process for floodplain delineation

from digital terrain models Journal of Water

Resources Planning and Management 129:427-

436

Parsa AS, Heydari M, Sadeghian MS,

Moharrampour M (2013) Flood Zoning

Simulation by HEC-RAS Model (Case Study:

Johor River-Kota Tinggi Region) Journal of

River Engineering x1:2013

Patel DP, Ramirez JA, Srivastava PK, Bray M,

Han D (2017) Assessment of flood inundation

mapping of Surat city by coupled 1D/2D

110

http://www/

http://www.hec.usace.army.mil/misc/files/ras/Combined_1D_and_2D_Modeling_with_HEC-RAS.pdf



http://www.suratmunicipal.gov.in/

http://www.suratmunicipal.gov.in/Bridgecell/

hydrodynamic modeling: a case application of

the new HEC-RAS 5 Natural Hazards:1-38

Patel DP, Srivastava PK (2013) Flood hazards

mitigation analysis using remote sensing and

GIS: correspondence with town planning

scheme Water resources management 27:2353-

2368

Patel DP, Srivastava PK, Singh SK, Prieto C,

Han D (2018) Preparation of EAP for Ukai Dam

using 1D/2D Coupled Hydrodynmaic Modeling

and Google Earth Image. In: DRIP (ed)

International Dam Safety Conference - 2018,

Thiruvananthapuram, 2018. Central Water

Commission (CWC), pp 168-169

Quiroga VM, Kure S, Udo K, Mano A (2016)

Application of 2D numerical simulation for the

analysis of the February 2014 Bolivian

Amazonia flood: Application of the new HEC-

RAS version 5 RIBAGUA-Revista

Iberoamericana del Agua 3:25-33

Reza Ghanbarpour M, Salimi S, Saravi M, Zarei

M (2011) Calibration of river hydraulic model

combined with GIS analysis using ground-based

observation data Res J Appl Sci Eng Technol

3:456-463

Sahoo SN, Sreeja P (2015) Development of

Flood Inundation Maps and Quantification of

Flood Risk in an Urban Catchment of

Brahmaputra River ASCE-ASME Journal of

Risk and Uncertainty in Engineering Systems,

Part A: Civil Engineering:A4015001

Salimi S, Ghanbarpour MR, Solaimani K,

Ahmadi MZ (2008) Floodplain mapping using

hydraulic simulation model in GIS Journal of

Applied Sciences 8:660-665

Timbadiya P, Patel P, Porey P (2011a) HEC-

RAS based hydrodynamic model in prediction

of stages of lower Tapi River ISH Journal of

Hydraulic Engineering 17:110-117

Timbadiya P, Patel P, Porey P (2014) A 1D–2D

Coupled Hydrodynamic Model for River Flood

Prediction in a Coastal Urban Floodplain Journal

of Hydrologic Engineering 20:05014017

Timbadiya PV, Patel PL, Porey PD (2011b)

Calibration of HEC-RAS model on prediction of

flood for lower Tapi River, India Journal of

Water Resource and Protection 3:805

Tsakiris G, Bellos V (2014) A numerical model

for two-dimensional flood routing in complex

terrains Water Resources Management 28:1277-

1291

111

NATIONAL WETLAND INVENTORY AND ASSESSMENT (NWIA)

J G PATEL

Space Applications Centre, ISRO, Ahmedabad

1 Introduction

Wetlands played a major role in human history. It

is only wetlands, whether perennial rivers or large

water-bodies have always been the sites of sources

of water and consequently the development of

civilisations. Wetlands are among the most

productive ecosystems of the world although they

account only about 4 per cent of the earth’s ice-free

land surface (Prigent, 2001). In recent years, there

has been an ever strengthened recognition at global

scale for concerted effort to conserve and in some

cases to preserve the natural resources. With the

developments in science and technology, strong

evidence has been accumulated to the extent to

show that today the conservation of our biosphere

is an extremely critical necessity. Ironically, a

comparable rate of destruction of wetlands has

proceeded until recently, almost unopposed and

unpublicised. The ever increasing demand for

economic growth during the last half century with

utter disregard for the long term ecological

consequences to the human society and

environment has resulted in exploitation of the

wetlands. It would not be misplaced if one says that

today we are at the watershed for wetland

conservation. At this juncture, the Ramsar

Convention has created a room for optimism for the

conservation and restoration of wetlands. Ramsar

Convention and other Conventions have certain

links that serve the purpose of conservation of

wetland in various aspects.

1.1 Ramsar Convention

Ramsar Convention (1971) on Wetlands of

International Importance especially as Waterfowl

Habitat is the oldest and first inter-governmental

conservation convention. It owes its name to its

place of adoption in Iran. It came into being due to

serious decline in population of waterfowl and

conservation of habitats of migratory waterfowl.

The convention provides the framework for

national action and international cooperation for

the conservation and wise use of wetlands and their

resources including biodiversity. Ramsar

convention entered into force in 1975 and

contracting parties from all over the world. Under

the text of the convention (Article 1.1) wetlands are

defined as:

“areas of marsh, fen, peat-land or water, whether

natural or artificial, permanent or temporary with

water that is static or flowing, fresh, brackish or

salt, including areas of marine water the depth of

which at low tide does not exceed 6 m”.

In addition, the Convention (Article 2.1) provides

that wetlands:

“may incorporate riparian and coastal zones

adjacent to wetlands, and islands or bodies of

marine water deeper than six meters at low tide

lying within the wetlands”. Resultantly, a wide

variety of habitats including rivers, shallow coastal

waters and even coral reefs are included under

wetlands. However, deep seas are not treated as

wetlands.

1.2 Convention on Biological Diversity

(CBD)

Convention on Biological Diversity was

enunciated during Rio earth Summit in 1992. India

became a party to the International Convention on

Biological Diversity (CBD) in May 1994. The

three objectives of the convention are:

Conservation of biological diversity.

Sustainable use of components of

biological diversity.

Fair and equitable sharing of benefits

arising out of the utilisation of genetic

resources.

As described by the Conference of the Parties

(Countries) the ecosystem approach is the primary

framework for action under the Convention. The

Conference of the Parties in its Fifth Meting

endorsed the ecosystem approach and also gave

operational guidance. The goal of ecosystem

management is to maintain and enhance ecosystem

health. CBD aims to encourage and enable all

countries to conserve biological diversity, to ensure

that its use in support of national development is

sustainable, and to reconcile national interests with

the maintenance of highest possible levels of global

112

biodiversity. Each country has its own unique

combination of living species, habitats and

ecosystems that together make up its biodiversity

resource. CBD also stipulates that each country

may exploit sustainably its own biodiversity in

any way, which it sees fit. Because each country

also contributes to overall global biodiversity, it

has a corresponding responsibility to play a part in

its maintenance.

1.3 Common Concerns of Ramsar, CBD and

other Conventions

With the increasing number of environmental

conventions now in existence, the watchwords

must be partnership and coordination. Links of

course already exist between Ramsar and other

international environmental conventions e.g.

famed wetland like Keoladeo, Bharatpur in India

figure on the lists of both Ramsar and the 1972

World Heritage Convention. The 1975 Convention

on International

Trade in Endangered Species of Wild Fauna and

Flora (CITES) deals with trade in a number of

wetland species and so has strong link with

Ramsar. There is obvious potential for cooperation

between Ramsar and the 1979 Bonn Convention on

Migratory species. The adoption under Bonn, of

the ‘Agreement on Convention of African-

Eurasian Migratory Water birds’ opens the door for

even broader cooperation, and there are prospects

of similar agreements in other regions; thus the

Ramsar Conference in Brisbane in March 1996,

adopted the ‘Brisbane Initiative’ on the

establishment of a network of listed sites along the

East Asian-Australian flyway.

Ramsar convention has made a considerable

contribution to the conservation and wise use of

biological diversity in wetlands. Newer

conventions such as the Montreal Protocol on

substances that deplete ozone layer, Conventions

on biological diversity, Climate change and

Combating desertification adopt holistic approach

to conservation of biological diversity. Global

Environmental Facility (GEF) has recently adopted

an operational strategy covering four focal issues

(biodiversity, climate change, ozone layer

depletion and international waters), all of which

have relevance to wetlands. UN Framework on

climate change also has major implications for

wetlands, as for example, changes in weather

patterns could mean that existing wetlands decline,

to be replaced by new ones in other sites. Sea level

rise is another phenomenon with a potential to

bring severe changes in coastal wetlands.

Similarly, international waters – wetlands such as

the courses of major rivers or coastal zones require

coordination and consultation between the states

concerned, as provided by GEF’s focal issue on

international water, and in Ramsar’s article on

shared water systems. Of particular relevance to the

Ramsar Convention is the CBD which defines

biological diversity as:

‘The variability among living organisms from all

sources, including, inter alia, terrestrial, marine

and other aquatic ecosystems and the ecological

complexes of which they are part; this includes

diversity within species, between species and of

ecosystems’.

In the context of India, a number acts and legal

provisions have been promulgated for the

protection of environment and conservation of

natural resources. Some of these acts which have

relevance for wetland conservation include Forest

Act, 1972; the Forest (Conservation) Act, 1980; the

Wildlife (Protection) Act, 1972; the Water

(Prevention and Control of Pollution) Act, 1974;

the Water (Prevention and Control of Pollution)

Cess Act, 1977 and the Umbrella provisions of the

Environment (Protection) Act, 1986. A notification

has been issued declaring the coastal stretches of

seas, bays, estuaries, creeks, rivers and backwaters

which are influenced by tidal action in the

landward side up to 500 m from the high tide line

and the land between low tide line and high tide

line as the Coastal Regulation Zone Notification,

1991 under the provisions of Environment

(Protection) Act, 1986. The Environment

(protection) Act also specifies protection of

ecologically fragile areas under which a number of

wetland ecosystems in the country are being

notified. Considerable efforts have been put in by

the Government of India to evolve institutional

mechanism for conservation of wetlands. Some of

these are National Committee on Wetlands,

National Committee on Mangrove and Coral reefs.

These committees advise the Government on

policy guidelines, identification of priority

wetlands for intensive conservation and

monitoring, implementation of management action

plans, research and preparation of an inventory of

wetlands. As a consequence of this the Ministry of

Environment, Forests & Climate Change

(MoEF&CC), Government of India had declared

27 sites as notified wetlands, of which 26 are

already been declared Ramsar wetlands of

International Importance. Some of the wetlands

have been declared as Wildlife Sanctuaries for the

113

protection of wildlife. In addition, various states

have constituted authorities on wetland and lake

development e.g. Chilika Development Authority

(CDA), Lokatak Development Authority (LDA),

Jammu and Kashmir Lakes and Waterways

Development Authority (J&K-LWDA), besides

declaring a number of wetlands as notified under

their own territory in the purview of respective

Ministries/Department of Environment and

Forests. Besides the initiative taken by the

Government, the Non-Governmental

Organisations (NGOs) have also been contributing

their might in the conservation of wetland

ecosystems along with their faunal and floral

diversity.

1.4 Definition

Wetlands are historically defined by scientists

working in specialised fields such as botany or

hydrology. A botanical definition would focus on

the plants adapted to flooding and/or saturated

conditions while a hydrologist’s definition would

emphasise the position of water table relative to

surface over a period of time. It seems a more

complete definition of wetlands involves a

multidisciplinary approach. The following are

some of the standard definitions widely used by

Government departments and institutions.

1.4.1 U.S. Fish and Wildlife Service

“Wetlands are lands transitional between

terrestrial and aquatic system where the water

table is usually at or near the surface or land is

covered by shallow water (Cowardin et al, 1979).

For purposes of this classification, wetlands must

have one or more of the following attributes:

1) at least periodically the land supports

predominantly hydrophytes

2) the substrates is predominantly

undrained hydric soil

3) the substrate is non soil and is

saturated with water or covered by

shallow water level at some time

during the growing season of each year

1.4.2 Soil Conservation Service of the U.S.

Department of Agriculture

In defining the wetlands from an ecological point

of view, SCS emphasises three key attributes:

1) hydrology – the degree of flooding or

soil saturation

2) wetland vegetation (hydrophytes)

3) hydric soils

All areas considered wetlands must have enough

water at some time during the growing season to

stress plants and animals not adapted for life in

water or saturated soils. Accordingly, wetlands are

defined as areas that have predominance of hydric

soils and are inundated or saturated by surface or

ground water at a frequency and duration sufficient

to support, and under normal circumstances do

support, a prevalence of hydrophytes typically

adapted for life in saturated soil conditions.

1.4.3 U.S. Environmental Protection Agency

Those areas that are inundated or saturated by

surface or ground water at a frequency and duration

sufficient to support, and that under normal

circumstances do support, a prevalence of

vegetation typically adapted for life in saturated

soil conditions. Wetlands generally include

swamps, marshes, bogs and similar area.

1.4.4 International Biosphere Programme (IBP)

Part of the surrounding ecological structure and

several stages in the succession from open water to

dry land or vice versa, occurring at sites situated as

a rule between the highest and lowest water levels

as long the flooding or waterlogging of the soil is

of substantial ecological significance.

1.4.5 RAMSAR/International Union for the

Conservation of Nature and Natural Resources

(IUCN)

Areas of marsh, fen, peat-land or water, whether

natural or artificial, permanent or temporary with

water that is static or flowing, fresh, brackish or

salt, including areas of marine water the depth of

which at low tide does not exceed 6 m.

A modified RAMSAR/IUCN definition has been

used in the national inventory of wetlands in India,

which is amenable to be used with remotely sensed

data (Garg et al, 1998). It defines wetlands as, “all

submerged or water-saturated lands, natural or

man-made, inland or coastal, permanent or

temporary, static or dynamic, vegetated or non-

vegetated, which necessarily have a land-water

interface”.

1.5 Importance of Wetlands

114

Wetlands provide many valuable services at

population level, at ecosystem level and at global

level. The value of the wetlands in terms of the

human economic systems perceived by the human

being and the need to consider the value of a

wetland as a part of an integrated landscape differ

with each other and most of the times conflicting.

It is needless to mention that wetlands essential for

preserving the biodiversity and ecological security.

A detailed account of this is presented with

authority by Mitsch and Gosselink (2000).

Following are the significant functions, values and

attributes of wetlands which owe their importance

to wetlands.

a) Functions Water storage

Storm protection and flood mitigation

Shoreline stabilisation

Ground water recharge and discharge

Water purification

Retention of sediments, nutrients and

pollutants

Stabilisation of local climate

particularly temperature and rainfall.

b) Values

Water supply – maintenance of

quantity and quality

Fisheries

Agriculture – through maintenance of

water table

Grazing

Timber production

Energy sources such as peat and plant

matter

Wildlife resources

Recreation and tourism opportunities

c) Attributes Biological diversity: wetlands support

concentrations of avifauna, especially

waterfowl; fish, reptiles, mammals,

and invertebrate species as well as

several plant species besides a variety

of micro-organisms like plankton of

both phyto and zoo origin.

Cultural heritage: open landscapes,

wildlife and local traditions.

The wetland ecosystem services are depicted

pictorially in figure 1.

Figure 1: Ecosystem services provided by wetlands (adopted from ILEC. 2007)

1.6 Environmental Threats to Wetlands

Wetlands have been under constant threat of

environmental degradation due to natural as well as

anthropogenic activities. Some of the major

environmental threats to the wetlands and their

biodiversity are:

1.6.1 Encroachment

People consider wetlands as low value lands or

wastelands and in order to ‘develop’ such lands

they have been encroached for agriculture, urban

expansion and other such purposes. This problem

seems to plague the wetlands inspite of

Legislation/Acts.

1.6.2 Pollution

A large number of wetlands are subjected to

inflows of domestic sewage, solid waste and

industrial effluents. Fertiliser and pesticide run-off

from agricultural lands aggravate the pollution

load. Pollution results in eutrophication, reduces

dissolved oxygen, increases the biological oxygen

demand etc which, many a times cause the large-

scale mortality of fish and other aquatic life.

115

Eutrophication creates ecological conditions that

are deleterious to most aquatic life forms.

1.6.3 Aquaculture development

The indiscriminate use of wetlands for aquaculture

is a major threat to the ecological character of

wetlands. The intensive input of feed for the fish

and prawn culture, subsequent draining of the

nutrient rich water into adjacent sea/river system

results in eutrophication and degradation of

wetlands.

1.6.4 Siltation

Siltation is a natural ecological process in the

filling up of the wetlands.

However, the anthropogenic activities in the

catchment of a wetland would accelerate the

process. This natural process coupled with

anthropogenic activities would lead to shrinkage

and loss of many wetland habitats as well as

alteration in biological composition.

1.6.5 Weed infestation

The eutrophication process creates conducive

conditions for the weeds to proliferate the wetlands

and poses a threat. Aquatic species like Eichornia

crassipes and Ipomea aquatica infestation is a

common problem in India. They alter and impair

the ecological functions of wetlands. In addition to

the above, natural succession, changes in

hydrological cycle and sea level etc. are some of

the other factors responsible for changing the

character quality of wetlands. Cumulative strain on

wetlands brought out by the above factors is

evident in the form of:

Decrease in biological diversity

Deterioration of water quality

Sedimentation and shrinkage in areas under

wetlands

Decrease in migratory bird population

Decrease in fish productivity

Prolific growth of unwanted aquatic biota.

2.0 Remote Sensing Based Inventory of

Wetlands In India

Space Applications Centre (ISRO), Ahmedabad, at

the behest of the Ministry of Environment, Forests

and Climate Change (MoEF&CC), Government of

India, carried out first scientific inventory of

wetlands for India using IRS LISS-I/II data (of

1992-93 timeframe). This inventory of wetlands

was carried out partly on 1: 250, 000 and partly on

1: 50, 000 scales with an estimated wetland extent

to about 8.26 million ha (Garg et al, 1998). These

estimates (24 categories) do not include rice

paddies, rivers, canals and irrigation channels etc.

Subsequently, a need was felt for creation of

wetland database in GIS environment for

monitoring, conservation and planning in the 16th

Meeting of SC-B. In pursuance of the decision of

the 16th SC-B (Standing Committee on

Bioresources and Environment) of NNRMS

(National Natural Resources Management System)

meeting, Space Applications Centre (ISRO),

Ahmedabad was entrusted to formulate a project

proposal for creating a digital database of wetlands

in the country and to develop a wetland information

system. Consequently, a pilot project for

development of GIS based wetland information

system (WINSYS) for West Bengal was funded by

MoEF&CC and executed by Space Applications

Centre, Ahmedabad. Like-wise, a wetland

information system for Loktak Lake (Loktak

Resources Information System) was also

developed with the financial assistance from the

MoEF&CC, Govt. of India.

In view of the increasing importance of wetlands

worldwide, Ministry of Environment, Forests and

Climate Change, Govt. of India has once again

given responsibility to SAC to formulate a proposal

for 1:50, 000 scale wetland inventory in India. A

peer review has suggested for a minor change in the

classification system adopted earlier (1992-92)

resulting 19 wetland categories/classes while

keeping identical hierarchy. Subsequently,

“National Wetland Inventory and Assessment”

(NWIA) project was carried out by SAC on 1: 50

000 scale that resulted into a digital database and

state-wise, and national wetland atlases based on

Resourcesat-1 LISS-III data of 2006-07 timeframe.

The estimated extent of wetland in the country was

about

15.26 million ha (Panigrahy et al, 2011).

2.1 Wetland Classification System

For devising a suitable wetland classification

system it is essential that it should be simple, easy

to replicate and incorporate all or most of wetland

types. In India no suitable wetland classification

existed for comprehensive inventory of wetlands in

the country prior to the execution of Nation-wide

Wetland Mapping Project based on satellite remote

sensing by the Space Applications Centre,

Ahmedabad. The classification system is based on

Ramsar Convention definition of wetlands, which

provides a broad framework for delineating

116

wetlands and is amenable to remote sensor data,

has been used for inventory of wetlands. It

considers all parts of a water mass including its

ecotonal area as wetland. In addition, Ramsar

Convention, considers fish and shrimp ponds,

saltpans, reservoirs, gravel pits etc. as wetlands. In

the present wetland inventory of India, Modified

National Wetland Classification system will be

used for wetland delineation and mapping (Table

2.1). The Wetland Classification System besides

including all wetlands incorporates deep-water

habitats and impoundments. Main criteria followed

in this system are:

Wetland hydrology, i.e. manifestation of water on

the satellite imagery.

Wetland vegetation -- mainly hydrophytes and

other aquatic vegetation in a part or whole of the

water body as observed on satellite data.

Salient features of the classification system are:

It takes into account all wetlands whether inland or

coastal, natural or man-made.

It provides information on the extent of vegetation

present in the wetlands, both in pre-monsoon and

post-monsoon seasons, wherever discernible on

satellite imagery.

Table 2.1: Wetland Classification System

117

3 Objectives

Main objectives of the project were

National level Wetland mapping and

inventory on 1: 50,000 scale by analysis

of Resourcesat-1 LISS III data of post

and pre-monsoon seasons (2006-07).

Creation and organisation of digital

database in GIS environment.

Preparation of State-wise wetland

atlases and

Design and Development of National

Wetland Information System

4 Database Design, Creation and

Organisation

In a national level project involving collection and

analysis of voluminous data it is desirable to have

uniform standards not only in analysis of remote

sensing data but also in non-spatial data collection

and GIS operations. To ensure uniformity in the

execution of the project, it is desirable to have a

procedure manual. A Technical Guideline and

Procedure Manual was prepared for NWIA project

This guideline cover technical aspects and details

of classification system, satellite data analysis,

standards for database creation and organisation,

map template and symbology of maps, accuracy

assessment etc.

A seamless framework for India as defined under

National Spatial Framework (NSF) database

standards was followed in GIS database creation

(Anon. 2005a, 2005b, Anon. 2007). The database

design and creation standards suggested by

NNRMS guidelines was followed in wetland

database creation.

5. Methodology

During the course of the time the developments

have enabled mapping of wetlands along with

certain information on their structural components

in a semi-automated to automated way. There are

various methods for extraction of water

information from remote sensing imagery,

which according to the number of bands used, are

generally divided into two categories, i.e. Single-

band and

multi-band methods. In single-band method

usually involves choosing a band from multi-

spectral image to differentiate land from water by

subjective threshold values while multi-band

methods take advantage of reflective differences of

each involved band. Certain spectral indices

compatible to LISS-III data have been used to

enhance the structural components of wetlands in

the present study. They are:

1) Normalised difference water index (NDWI)

was suggested by Mcfeeters, 1996.

The bands chosen are green and NIR. Selection

of these wave lengths was done due to: a)

maximise the typical reflectance of water

features by using green light wavelength; b)

minimise the low reflectance of NIR by water

features; and c) take advantage of the high

reflectance of NIR by terrestrial vegetation and

soil features. The open water futures will have

positive values while soil and terrestrial

vegetation features will have zero or negative

values. It is expressed as (𝐺𝑟𝑒𝑒𝑛−𝑁𝐼𝑅)

(𝐺𝑟𝑒𝑒𝑛+𝑁𝐼𝑅)

2) The reflectance pattern of built-up land in

green and NIR is similar to water i.e.

they both reflect green light more than they

reflect in NIR. The average reflectance of built-

up land in MIR is greater than that of green.

Therefore, if a MIR band is used instead of NIR

as used in the NDWI, the built-up land should

have negative values. Based on this, a remedy

is given as Modified normalised difference

water index (MNDWI), as suggested by

Hanqiu xu, 2006 using MIR instead of NIR. It

is expressed as (𝐺𝑟𝑒𝑒𝑛−𝑀𝐼𝑅)

(𝐺𝑟𝑒𝑒𝑛+𝑀𝐼𝑅)

3) Normalised difference vegetation index

(NDVI) as used by Townshend and Justice,

1986; Tucker and Sellers, 1986 takes

advantage of the condition where the presence

of features that have higher NIR reflectance

and lower red reflectance (e.g. Terrestrial

vegetation) will be enhanced, while those with

low red reflectance and very

118

low NIR reflectance (e.g. Water) will be

suppressed or even eliminated. Vegetated

surfaces tend to have positive values, bare soils

may have near zero and open-water features

have negative values. The results of this index

range from -1 to +1. It is expressed as (𝑁𝐼𝑅−𝑅𝑒𝑑)

(𝑁𝐼𝑅+𝑅𝑒𝑑)

4) Lacaux et al, 2007 observed that the classic

NDVI did not perform well for vegetation

within the wetland. The behaviour vegetation

inside and outside of wetlands cannot be

distinguished. Thus the latter can not be used

for detecting the vegetation within the wetland.

Reflectance of water is higher (narrow

difference) in green and compared vegetation

while reverse (large difference) in MIR. Thus

a new index named as Normalised difference

pond index (NDPI) is suggested to exploit the

advantage of difference at green and MIR. It is

expressed as (𝑀𝐼𝑅−𝐺𝑟𝑒𝑒𝑛)

(𝑀𝐼𝑅+𝐺𝑟𝑒𝑒𝑛)

5) Pure water has a specific radiometric response:

its reflectance is weak in green (less than 10

%), becomes very small in red and quasi-null

in NIR. The increase in turbidity and its

associated radiometric responses make the

open water features (ponds) behave like bared

soil (Guyot, 1989). Since the values of the red

radiometric responses are much larger than that

of the green ones, the relationship between the

green and red wavelengths is reversed

(Campbell, 1996; verbyla, 1995). To meet the

turbidity sensing of open-water of wetlands,

Lacaux et al, 2007 suggested Normalised

difference turbidity index (NDTI). It is

expressed as (𝑅𝑒𝑑−𝐺𝑟𝑒𝑒𝑛)

(𝑅𝑒𝑑+𝐺𝑟𝑒𝑒𝑛)

Comprehensive usage of multispectral as well as

spectral indices, it envisaged to map/inventory of

wetlands along with peak open-water, open-water

and vegetation besides qualitative turbidity. An

example of utility of the spectral indices in

delineation of structural components of a wetland

is given in Figure 4a-4d

Figure 4a: Extraction of wetland boundary using spectral indices

119

Figure 4b: Extraction of water spread using spectral indices

Figure 4c: Extraction of wetland vegetation using spectral indices

Figure 4d: Extraction of turbidity information using spectral indices

120

Wetland database of national wetland inventory

has been prepared using utility shown in figure4a-

4d. The approach followed is given in the Figure 5a

and 5b. The entire database was prepared in GIS

domain following the NNRMS guidelines. Eight

spatial layers for wetland with a unique 16 digit

identification code and nine base/reference layers

were generated as digital database:

Wetland extent: It is the wetland boundary

Water spread: There are two layers

representing post-monsoon and pre-monsoon

water spread during the year of data

acquisition.

Aquatic vegetation spread: Layer pertaining to

presence of vegetation (floating and emergent)

is generated, as manifested on pre-monsoon

and post-monsoon imagery.

Turbidity level of open water: A layer

pertaining to a qualitative turbidity of the open

water in the wetlands rated as low, medium and

high is generated for pre- and post-monsoon

seasons.

Small wetlands (<2.25ha) - mapped as point

features.

Base layers like major road network, railway,

settlements, and surface drainage were created.

The results were organised in a seamless digital

database for retrieval of information at district,

state and national level. The State and National

level results were brought out in Atlas form.

Figure 5a: Steps in the extraction of wetland components

Figure 5b - Schematic for extraction of wetland structural components and database creation

121

6.0 NWIA Findings

Entire country including the main land and

islands territories has been considered for

inventory and assessment of wetlands. Total

wetland area estimated is 15.260 Mha, which is

around 4.63 per cent of the geographic area of the

country. Total 201503 wetlands have been mapped

at 1:50,000 scale. In addition, 555557 small

wetlands (<2.25 ha) have also been identified.

Excluding rivers/streams, the total wetland area

estimated to be 10 Mha.

6.1 Category-wise wetland distribution in the

country

Inland-Natural wetlands accounted for around 43.4

per cent of the total area, while Coastal - Natural

wetlands account for 24.3 per cent. As far as

wetland units are concerned tanks are maximum in

number (122370) as mappable units. However,

555557 small wetlands (< 2.25 ha) are mapped as

point features (3.64 %).

Category-wise wetland distribution in the country

Sr. No. Wetland category Total wetland

area(ha)

% of wetland

area

1 Inland Wetlands -Natural 6623067 43.40

2 Inland Wetlands -Man-made 3941832 25.83

Total - Inland 10564899 69.22

3 Coastal Wetlands -Natural 3703971 24.27

4 Coastal Wetlands -Man-made 436145 2.86

Total - Coastal 4140116 27.13

Sub-Total 14705015 96.36

5 Wetlands (<2.25 ha) 555557 3.64

Total 15260572 100

The wetland status in terms of open water and

aquatic vegetation show significant seasonal

change. There is a significant reduction in the

extent of open water (about 32.5%) from post-

monsoon to pre-monsoon (8.60 Mha to 5.80 Mha).

It is reflected in all the Inland wetland types.

Aquatic vegetation is observed in lake/pond,

riverine wetland, ox-bow lake, tank/pond and

reservoir. The aquatic vegetation in wetlands

(floating and emergent) is more during pre-

monsoon (14 %) than during post-monsoon (9 %).

The qualitative turbidity of water in wetlands is low

in 37.3% areas, moderate in 48.5% and high in

14.2% area in post-monsoons season. During pre-

monsoon season low turbidity was observed in

32.6% area, moderate turbidity in 51.1% and high

turbidity in16.3% area.

6.2 Type-wise wetlands

The major wetland types in inland category are

river/stream, reservoir, tank/pond and lake/pond. In

coastal wetland category major types are inter-tidal

mudflat, lagoon, and creek. Among all the wetland

types river/stream is the major type, occupying

5.26 Mha area (34.46%).

Reservoirs occupy 2.48 Mha (16.26%), inter-tidal

mudflat occupy 2.41 Mha (15.82%), tanks/ponds

occupy 1.31 Mha area (8.6%) and lakes/ponds

occupy 0.71 Mha area(4.78%).

122

Type-wise wetland area of India

Wettcode Wetland category Total wetland

area (ha)

% of

wetland

area

1101 Lake/Pond 729532 4.78

1102 Ox-bow lake/ Cut-off meander 104124 0.68

1103 High altitude wetland 124253 0.81

1104 Riverine wetland 91682 0.60

1105 Waterlogged(Natural) 315091 2.06

1106 River/Stream 5258385 34.46

1201 Reservoir/Barrage 2481987 16.26

1202 Tank/Pond 1310443 8.59

1203 Waterlogged(Man-made) 135704 0.89

1204 Salt pan(Inland) 13698 0.09

2101 Lagoon 246044 1.61

2102 Creek 206698 1.35

2103 Sand/Beach 63033 0.41

2104 Intertidal mud flat 2413642 15.82

2105 Salt Marsh 161144 1.06

2106 Mangrove 471407 3.09

2107 Coral Reef 142003 0.93

2201 Salt pan(Coastal) 148913 0.98

2202 Aquaculture pond 287232 1.88

Sub-total 14705015 96.36

Wetlands (<2.25 ha) 555557 3.64

Total 15260572 100.00

Mangroves, Coral reefs, Beach and High altitude

lakes (>3000 m elevation), though contribute very

small percentage to total wetlands, are some of the

unique wetland types of India. There are 178

Lagoons and 4703 high altitude lakes in the

country.

The Himalayan region is dotted with hundreds of

lakes from low elevation to the high elevations.

Lakes lying above 3000 m elevation, known as

high altitude wetlands, are mapped and a detailed

inventory prepared for the first time. Apart from

their ecological significance, the high altitude

wetlands play crucial role in biodiversity, wild life

habitat and socio-economic aspects.

6.3 State-wise wetland distribution in India

The country has thirty five States/Union

Territories(UTs). State-wise distribution of

wetlands showed that Lakshadweep has 96.12% of

geographic area under wetlands followed by

Andaman & Nicobar Islands(18.52%), Daman &

Diu(18.46%) and Gujarat(17.56%), have the

highest extent of wetlands. Puducherry(12.88%),

West Bengal(12.48%), Assam(9.74%), Tamil

Nadu(6.92%), Goa(5.76%), Andhra

Pradesh(5.26%), and Uttar Pradesh(5.16%) are

wetland rich states. The least extents(less than 1.5

% of the state geographic area) have been observed

in Mizoram(0.66%) followed by Haryana(0.86%),

Delhi(0.93%), Sikkim(1.05%), Nagaland(1.30%),

and Meghalaya(1.34%).

123

State/Union Territory-wise wetland area in India

States

Sr.

No.

State

Wetland

area (ha)

% of state

geographic area

1 Jammu & Kashmir 391501 1.76

2 Himachal Pradesh 98496 1.77

3 Punjab 86283 1.71

4 Uttarakhand 103882 1.94

5 Haryana 42478 0.86

6 Delhi 2771 0.93

7 Rajasthan 782314 2.29

8 Uttar Pradesh 1242530 5.16

9 Bihar 403209 4.40

10 Sikkim 7477 1.05

11 Arunachal Pradesh 155728 1.78

12 Nagaland 21544 1.30

13 Manipur 63616 2.85

14 Mizoram 13988 0.66

15 Tripura 17542 1.59

16 Meghalaya 29987 1.34

17 Assam 764372 9.74

18 West Bengal 1107907 12.48

19 Jharkhand 170051 2.13

20 Orissa 690904 4.49

21 Chhattisgarh 337966 2.50

22 Madhya Pradesh 818166 2.65

23 Gujarat 3474950 17.56

24 Maharashtra 1014522 3.30

25 Andhra Pradesh 1447133 5.26

26 Karnataka 643576 3.36

27 Goa 21337 5.76

28 Kerala 160590 4.13

29 Tamil Nadu 902534 6.92

Total 15017354

Union Territory

Sr. No.

Union Territories

Wetland area (ha)

% of UT geographic area

1 Chandigarh 350 3.07

2 Daman & Diu 2068 18.46

3 Dadra & Nagar Haveli 2070 4.25

4 Lakshadweep 79586 96.12

5 Puducherry 6335 12.88

6 Andaman & Nicobar Islands 152809 18.52

Total 243218

124

6.4 Inventory and Assessment of High

Altitude Lakes

All wetlands lying above 3000 m elevation are

designated as high altitude ones in this work.

Mapping was carried out is done at 1:50,000 scale.

Small lakes (<2.25 ha area) are also mapped as

point features and assigned 1.0 ha nominal area.

The Digital Elevation Model (DEM) derived from

ASTER/SRTM data was used to generate elevation

contours and classify the lakes as per altitude

range. Spatial database of these lakes was prepared

at state and district level using coding system,

where each lake has a unique identification

number.

High altitude lakes give unique signature on the

satellite images, Depending upon the state of water

whether liquid phase or frozen, the boundary of the

lakes are prominent and can be discerned with high

accuracy. The pre and post monsoon images reflect

the status of water, vegetation and state

(solid/liquid).

The Indian Himalayas cover approximately

591,000 km2 or 18 per cent of India's land surface

and spread over six Himalayan States viz Jammu

and Kashmir, Himachal Pradesh, Uttrakhand,

Sikkim, Arunachal Pradesh and some parts of West

Bengal. Total 4703 lakes are mapped which lie

above 3000 m elevation. This includes 1996 small

lakes (<2.25 ha area). The total area of these lakes

is 126249 ha. The lakes categorised under various

sizes, show that there are only 12 lakes belonging

to the very large size category having more than

500 ha area. However, they contribute to highest

share of lake area (75.61%). Number wise, the

smallest size lakes (<2.25 ha) have the largest share

(42.44%), followed by very small ones (<10 ha)

with 42.33% share.

Size-wise distribution of high altitude lakes

Sr. No. Class Range No. of

lakes Area (ha)

1 Very Large > 500 ha 12 95462

2 Large 100-500 ha 30 4861

3 Medium 25-100 ha 179 7434

4 Small 10-25 ha 495 7559

5 Very Small < 10 ha 1991 8429

6 < 2.25 ha < 2.25 ha 1996 2505

Total 4703 126249

Altitude-wise, maximum numbers of lakes are

observed in the elevation range of 4000-5000 m.

There are 2642 lakes (56.2% of total number)

mapped in this elevation range with 100817 ha area

(79.9% area). Very large lakes are also observed in

this elevation range. Only 761 lakes are mapped in

the very high altitude range of >5000 m elevation.

Altitude-wise distribution of high altitude lakes

Sr. No. Category Altitude range (m) No. of lakes Area

(ha)

1. High Altitude 3000-4000 1300 8460

2. Higher Altitude 4000-5000 2642 100817

3. Very high Altitude >5000 761 16972

Total 4703 126249

125

Two states: Jammu and Kashmir and Arunachal

Pradesh harbour very large number of high altitude

lakes. Jammu and Kashmir has the highest share of

lakes. Number-wise, around 44.7% of lakes (2104

number) are found in this state with 87.24% share

of total area. Arunachal Pradesh with 1672 lakes

contributes 9.4% of area, indicating small size of

the lakes. Only 3 lakes are mapped in West Bengal

state with 82 ha area (contributing to 0.06% of total

lake area).

State-wise distribution of high altitude lakes

Sr. No. State No. of lakes Lake area (ha) % Lake area

1 Arunachal Pradesh 1672 11863 9.40

2 Himachal Pradesh 272 617 0.49

3 Jammu and Kashmir 2104 110131 87.23

4 Sikkim 534 3325 2.63

5 Uttarakhand 118 231 0.18

6 West Bengal 3 82 0.06

Total 4703 126249 100.00

6.5 Important Wetlands of India

The Wetland Ecosystem in India is spread over a

wide range of varied climatic conditions, which is

ranging from the wetlands in cold Jammu and

Kashmir to hot and humid conditions in Peninsular

India, thus there is a great diversity of these

Wetlands. Many of these wetlands are unique from

the point of biodiversity, scenic beauty, shelter of

migratory birds, resident avifauna etc. Under the

conservation of Wetlands in India, numbers of

wetlands have been recognized that are a part of

National Parks and Sanctuaries. Twenty-six

wetlands have been declared as Ramsar Sites.

Various types of sanctuaries and parks like bird,

wildlife, marine, and education have been notified

in the country. Wetland maps of 150 selected

important wetland sites of India were prepared.

7.0 End use / Utility of the findings of the

project

This project has generated for the first time a digital

database of wetlands in India at 1:50, 000 scale

following a standard classification system relevant

to the country. A well defined map projection for

each state has been used to ensure high level of map

accuracy. The data are organized at district, state

and Survey of India topographic map sheet level.

In addition to the wetland layers, ancillary layers in

terms of road/rail, canal, drainage network, major

settlements etc are integrated in the data base to

facilitate analysis and interpretation. For the first

time the high altitude wetlands in the Himalayas

have been mapped. Each wetland has been given

an unique code.

Thus, this data-base forms the stepping stone for

various activities related to research, management,

action plan in conservation and preservation of

wetlands in India taken up by

central/state/educational institutes/NGOs etc.

This database also provides the baseline data

towards monitoring the wetlands to assess the

effectiveness of various conservation/preservation

measures taken up. The remedial measures relevant

to the environmental problems of wetlands include

attention to control the high level of eutrophication

in many wetlands (mapped as aquatic vegetation

status) particularly in case of inland natural

lakes/ponds, increased turbidity status, mainly

arising from sediment flow from catchments,

optimization measures for aquaculture ponds,

preservation of the ox-bow lakes/cut off meanders

and riverine wetlands, which are declining.

The information on high altitude wetlands in the

Himalayas has great utility for research activities

related to climate change.

References

Anon, 2005a, NNRMS Standards, A National

Standards for EO images, thematic & cartographic

126

maps, GIS databases and spatial outputs,

ISRO:NNRMS:TR:112:2005, Committee Report.

Anon, 2005b, Natural Resources

Database(NRDB), Project Plan Document,

NRDB/SAC/PD/01/05, September 2005,

SAC/ISRO.

Anon, 2007, Natural Resources Repository – a

space based spatial data infrastructure, Programme

Document, March 2007, NNRMS Secretariat,

DOS, Bangalore.

Birol, E., Karousakis, K. and Koundouri, P. 2005.

Using a Choice experiment to account for

preference heterogeneity in wetland attributes: The

case of Cheimaditida wetland in Greece.

Environmental Economy and Policy Research

Discussion Paper Series, Department of Land

Economy and University of Cambridge.

Campbell, J.B. 1996. Introduction to remote

sensing (4th ed). New York, USA: Guilford

Publications, ISBN1-59385-319-X.

Cowardin, L.M., V. Carter, F.C. Golet, and E.T. La

Roe. 1979. Classification of wetlands and deep

water habitats of United States. FWS/OBS-79/31,

U.S. Fish and Wildlife service, Washington, DC.

103 pp.

De Groot, R.S., Stuip, M.A.M., Finlayson, C.M.

and Davidson, N.C. 2006. Valuing wetlands;

guidance for valuing the benefits derived from

wetland ecosystem services, Ramsar Technical

Report No. 3/CBD Technical Series No. 27.

Ramsar Convention Secretariat, Gland,

Switzerland & Secretariat of the Convention on

Biological Diversity, Montreal, Canada. ISBN 2-

940073-31-7.

Finlayson, C.M. and D’Çruz, R. and Davidson,

N.C. 2005. Ecosystems and human well-being:

wetlands and water Synthesis. Millennium

Ecosystem Assessment, Wold Resources Institute,

Washington D.C.

Garg, J.K., Singh, T.S. and Murthy, T.V.R. (1998).

Wetlands of India. Project Report:

RSAM/SAC/RESA/PR/01/98, June 1998, 240 p.

Space Applications Centre, Ahmedabad.

Garg J.K. and Patel J. G., 2007. National Wetland

Inventory and Assessment, Technical Guidelines

and Procedure Manual, Technical Report,

SAC/EOAM/AFEG/NWIA/TR/01/2007, June

2007, Space Applications Centre, Ahmedabad.

Guyot, G. 1989. Signatures spectrales des surfaces

naturelles. Teledetection satellitaire, 5, Col. SAT.

Ed. Paradigme, pp.178.

Hanqiu xu, 2006. Modification of normalised

difference water index (NDWI) to enhance open

water features in remotely sensed imagery.

International Journal of Remote Sensing, 27, pp.

3025-3033.

ILEC. 2007. Integrated Lake Basin Management:

An Introduction. International Lake Environment

Committee Foundation: Kusatsu, Japan. (Eds.)

Masahisa Nakamura, Hiroya Kotani, Walter Rast,

Taichiro Uda, Thomas Ballatore, Maki Tanigawa

Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione,

J.A. and Lafaye, M. 2007. Classification of ponds

from high-spatial resolution Remote sensing:

Application to Rift valley fever epidemics in

Senegal. Remote Sensing of Environment, 106, pp.

66-74.

McFeeters, S.K. 1996. The use of Normalised

Difference Water Index (NDWI) in the delineation

of open water features. International Journal of

Remote Sensing, 17, pp. 1425-1432.

Mitsch, W.J., and James, G. Gosselink, 2000.

Wetlands (Third Edition). John Wiley & Sons, Inc.

New York. 920 pp.

Panigrahy, S., T. V. R. Murthy, J. G. Patel and T.

S. Singh, 2012. Wetlands of India: inventory and

assessment at 1: 50,000 scale using geospatial

techniques. Current Science, Vol. 102, No. 6, 25.

Panigrahy, S, Murthy T V R, J G Patel and Singh

T S, 2011, National Wetland Atlas, India,

SAC/EPSA/ABHG/NWIA/ATLAS/34/2011,

Space Applications Centre (ISRO), Ahmedabad,

India, 306p. ( www.moef.nic.in).

127

http://www.moef.nic.in/

Prigent, C., Matthews, E., Aires, F. and Rossow,

W.B. 2001. Remote sensing of global wetland

dynamics with multiple satellite datasets,

Geophysical Research Letters, 28, pp. 4631-4634.

Townshend, J.R., and Justice, C.O. 1986. Analysis

of dynamics of African vegetation using the

Normalised difference Vegetation Index.

International Journal of Remote Sensing, 7, pp.

1435-1445.

Tucker, C.J. and Sellers, P.J. 1986. Satellite remote

sensing of primary productivity. International

Journal of Remote Sensing, 7, pp. 1395-1416.

Verbyla, D.L. 1995. Satellite remote sensing of

natural resources. Boca Raton, USA: Lewis

Publishres/CRC press LLC, ISBN1566701074.

128

URBAN FLOODING

GAURAV JAIN

Content Generation and Databases Division

VEDAS Research Group (EPSA)


Ahmedabad-380015

Urban floods are the floods occurring in urban areas, primarily caused by heavy rainfall exceeding

the capacity of drainage network. In recent years, urban flooding has severely affected several

Indian cities, thereby necessitating efforts to manage storm water flows efficiently. Urban storm

water flow models, such as US Environmental Protection Agency’s (EPA) Storm Water

Management Model (SWMM), attempt to simulate the rainfall-runoff processes in urban areas.

These models, however, require detailed information characterizing urban catchments and the

underlying storm-water drainage network. Remote sensing data along with GIS may be applied

to estimate various parameters influencing runoff flows over urban catchments. Satellite data

acquired from very high spatial resolution Cartosat series satellites are useful in estimating

pervious and impervious surfaces cover, proportion of impervious area, Manning’s coefficient on

pervious and impervious surfaces, and percentage area with zero depression storage. Urban land

cover is characterized by heterogeneous mix of both anthropogenic and naturally occurring

materials. Hyper-spectral imaging may be used for detailed identification of urban materials with

the aid of narrow band absorption features. Furthermore, Cartosat-1 stereo pair derived digital

elevation models (DEM) are useful in estimating surface slope and establishing the overall

surface-runoff flow pattern in the urban catchment. The very high vertical accuracy DEM, as

generated using LIDAR surveys, are however desirable for detailed simulations in urban areas.

Key Words: urban flooding, runoff coefficient, SWMM, urban catchment.

1.0 Introduction

Urban floods are the floods occurring in urban

areas, primarily caused by heavy rainfall

exceeding the capacity of drainage network.

Urban areas are paved by roads and are covered

by impervious surfaces such as buildings etc.

Therefore, infiltration into ground water is

significantly lowered resulting into increased

runoff. This runoff has to be channeled by

drainage network for reducing flooding, as it is

known that if the inflow exceeds the drainage

capacity, the excess water may flood the streets

and low-lying areas in the city.

Urban storm-water flow models attempt to

simulate the rainfall-runoff processes in urban

areas. Urban catchments respond considerably

faster to rainfall than rural catchments due to their

impervious nature. The existence of surface and

sub-surface storm-water drainage network

drastically alters the flow trajectories in urban

areas. Urban storm water flow models therefore

invariably includes hydrological models for

estimation of surface and sub-surface runoff, and

hydraulic models for routing storm water flows

through the drainage network (Zoppou 2001).

Storm Water Management Model (SWMM),

developed by the US Environmental Protection

129

Agency (EPA), is a comprehensive computer

model for simulating hydrological and hydraulic

processes operating in an urban watershed. The

model however requires detailed information

characterizing urban catchments and the

underlying storm-water drainage infrastructure.

The input to the model comprises various

physical and hydrological parameters

representing urban catchment. The physical

parameters such as the area of the sub-catchment,

and the diameter and length of the pipes, do not

require calibration and can be measured directly.

It is the hydrological parameters, which are

challenging to estimate. These parameters may be

determined either by calibration of SWMM using

reference hydrographs, or by using appropriate

inference models. The calibration of SWMM

involves controlled modification of control

parameter values until the simulated and

monitored outputs are in agreement. An

alternative method for determining the control

parameters is to select their values on the basis of

inference models using hydrological, hydraulic or

other characteristics of the catchment. The

‘inference models’ are the models used to infer

the control parameter values.

In recent years, large areas of several

metropolitan cities in India such as Mumbai,

Surat, Chennai and Delhi have been witness to

unforeseen inundations often driven by short

spells of heavy rainfall. The National Disaster

Management Authority (NDMA, 2010) of

Government of India published guidelines for

management of urban flooding. The key actions

recommended by the guidelines are:

Ministry of Urban Development will be

Nodal Ministry for Urban Flooding;

Establishment of Local Network of

Automatic Rainfall Gauges (ARGs) for Real-

time Monitoring with a density of 1 in every

4 km2 in all 2325 Class I, II and III cities;

Strategic Expansion of Doppler Weather

Radar Network in the country to cover all

Urban Areas for enhanced Local-Scale

Forecasting Capabilities with max. possible

Lead-time;

Establishing Urban Flood Early Warning

System;

All 2325 Class I, II and III cities and towns

will be mapped on the GIS platform;

Contour Mapping will be prepared at 0.2 - 0.5

m contour interval;

Inventory of existing storm-water drainage

system will be prepared on GIS platform;

Future Storm-water Drainage Systems will

be designed with a Runoff Coefficient of up

to 0.95 in using Rational Method taking into

account the Approved Land-use Pattern;

With accelerated urbanization and economic

growth, under the shadow of growing weather

uncertainties often attributed to climate change,

cities in India have to be prepared to face such

exigencies. The conventional approach of storm

water drainage infrastructure planning and

design, based on empirical relationships and

event-driven models, has to give way to more

scientific approach of extensive simulation and

modelling. The applications of such models,

however, are difficult due to the tedious and time-

consuming input data processing, limited

presentation capabilities and uncertainty in model

calibration. Remote sensing data along with GIS

may be applied to estimate various parameters

characterizing urban catchment as required by

SWMM for modelling storm water runoff.

2.0 Urban Catchment

Urban drainage basin is sub-divided into smaller

hydrological units called sub-catchment areas, on

the basis of surface topography and drainage

system elements. The runoff generated on each

sub-catchment area is discharged through its

outlet point, to either a node of the drainage

network or to any other sub-catchment area. All

land parcels in the study area are identified as

sub-catchment areas, and discharge their storm

water runoff over roads. Roads are further

130

segmented into small sub-catchment areas and

their respective discharge outlets are assigned

either on the adjoining road segments at lower

elevation, or to the nearest drainage network

node. Thus, the storm water runoff in an urban

catchment originating at land parcels, flows to

roads, subsequently enters into the drainage

network, and is finally discharged into the river.

The drainage network may be combined, i.e.

receiving both household sewage as well as storm

water flow, or it may be separate sewerage system

receiving only the storm water flow. Figure 1

shows the schematic diagram of urban water

system in combined system of sewerage.

Figure 1: Urban Water System - Combined System

(Butler D, Davies JW. 2004)

3.0 Runoff Estimation

A sub-catchment area is treated as a non-linear

reservoir as shown in Figure 2. The inflow to this

“reservoir” is from precipitation, while outflow is

commonly in form of evaporation, depression

storage, infiltration, and surface runoff. Surface

runoff per unit area (Q) occurs only when the

depth of water (d) in the "reservoir" exceeds the

maximum depression storage (dp). The outflow

due to excess precipitation is computed using

Manning's equation as given below:

21

35

)( Sddn

WQ p ------- (1)

Where, W is the characteristic width, n is

Manning’s coefficient of roughness, and S is the

slope.

The sub-catchment area is divided into pervious

and impervious sub-areas. The dominant

pervious and impervious surfaces on each sub-

catchment area are used to estimate respective

Manning’s coefficient for pervious and

impervious surfaces (Table 1). Satellite data

acquired from very high spatial resolution

Cartosat series satellites are useful in estimating

pervious and impervious surfaces cover,

proportion of impervious area, Manning’s

coefficient on pervious and impervious surfaces,

and percentage area with zero depression storage.

Figure 2: Sub-catchment area (Rossman 2005))

Urban land cover is characterized by

heterogeneous mix of both anthropogenic and

naturally occurring materials (Figure 3), which

hinders estimation of impervious surfaces using

multispectral data. The narrow-band hyper-

spectral data acquired by Airborne Visible and

Infrared Imaging Spectrometer - Next Generation

(AVIRIS-NG), developed by NASA, enables

separation of several such urban materials with its

5 nm spectral bandwidth. Figure 4 shows the

estimation of the fraction of pervious and

131

impervious surface materials in part of

Ahmedabad city using AVIRIS-NG data,

applying linear spectral un-mixing.

Figure 3: Spectral profile of urban land cover

Figure 4: Estimation of impervious surface cover

using hyperspectral data (a) AVIRIS-NG (Natural

Color Composite); (b) % Impervious Surface Cover

Impervious surfaces do not permit infiltration of

rainfall into upper soil zone, while in pervious

areas infiltration is modelled using Horton’s

equation:

t

cc effff )( 0 ------- (2)

Where, f is infiltration capacity of soil at time t, f0

is initial infiltration rate, fc is constant infiltration

rate, and β is the decay constant.

Table 1: Manning’s Coefficient on Pervious and

Impervious Surfaces

Type of

surface

Land cover /

land use

Manning’s

coefficient

Impervious

surface

Built-up area 0.012

Roads 0.011

Pervious

surface

Crop land 0.170

Recreational

(Parks etc.)

0.150

Fallow land 0.050

Vacant land 0.05

A portion of rainfall received by the sub-

catchments is stored as depression storage (dp) in

both pervious and impervious surfaces. The depth

of depression storage on pervious and impervious

surfaces is estimated on the basis of land cover

(Table 2). An impervious area can further have

two types of sub-areas, depending on whether

depression storage is permitted or otherwise. The

area covered by buildings, which is mapped as

building footprints using high resolution satellite

data, is identified as impervious area without

depression storage.

Table 2: Depression Storage on Pervious and

Impervious Surfaces

Surface Depression Storage

Impervious surfaces 1.25 - 2.50 mm

Lawns 2.50 - 5.00 mm

Pasture 5.00 mm

Forest litter 7.50 mm

Source: Rossman 2005

The average percent slope (S) of each sub-

catchment is derived from the DEM. The

characteristic width (W) of the overland flow path

for sheet flow runoff is estimated as ratio of sub-

catchment area, to the average maximum

overland flow length. The maximum overland

flow length is the length of the flow path from

farthest drainage point of the sub-catchment

before the flow becomes channelized. The

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

400 900 1400 1900 2400

Ref

lect

ance

Wavelength (nm)

China Mosaic Red Tile Roof

Metal Sheet Bitumen Road

Marble Floor Tarpaulin

Bare Soil Lawn Grass

132

obstruction to flow path due to buildings is also

considered while computing the flow length.

The depth of water over the sub-catchment (d) is

continuously updated with time (t in seconds) by

solving a water balance equation over the sub-

catchment, and the surface runoff (Q) on each

sub-catchment is thus estimated.

4.0 Flow Modeling

The storm water infrastructure comprises of the

network of sub-surface drains connected by

manholes, which eventually discharges into the

receiving water bodies such as Rivers. The storm

water flow in the drainage network is modeled as

one-dimensional gradually-varied unsteady flow

represented by Saint Venant’s equations. Saint

Venant’s equations are approximations of the

momentum and continuity equations given

below:

1. Continuity equation

qx

uy

t

y

)(

------- (3)

2. Momentum equation

y

u

g

qSS

x

y

x

u

g

u

t

u

gf

0

1

------- (4)

Where, u is flow velocity, y is water depth, x is

the distance, t is the time, q is lateral inflow per

unit length perpendicular to the channel, g is

acceleration due to gravity, S0 is the bed slope,

and Sf is the friction slope. These differential

equations of the flow are solved by SWMM using

finite difference method.

5.0 Simulation

The spatial datasets of sub-catchment areas

(polygon features), conduits (line features), nodes

(point features), and outfalls (point features), are

converted into SWMM input file using an

interchange tool developed by SAC (Figure 5).

The tool converted the Shapefiles of input

datasets into the input file format (.INP file) of

SWMM. The rainfall time series and tidal curves

are updated after opening the INP file in SWMM.

Figure 5: Shapefile to INP conversion tool

Rainfall-runoff simulation over part of Surat city

was carried out to estimate sub-catchment runoff,

node depth, node inflow, node surcharge, node

flooding, link flow, link surcharge, and outfall

loading using SWMM model. The study area,

located on the eastern bank of Tapi River, is

spread over 24.88 km2 land area with 3303 plots,

and 85 km long separate storm-water drainage

network. The study area received 118.0 mm of

precipitation during the 72-hour simulation

period, which generated 212.8 ha-m of surface

runoff after losing 28% of the inflows to

evaporation and infiltration. The simulation also

modeled the effect of tidal fluctuations in the Tapi

River, which is recipient of discharge from the

outfalls. The model further estimated that 25% of

the surface runoff entered into the storm-water

drainage network as wet weather inflow, thereby

discharging 65.6 ha-m of storm water to Tapi

River. The runoff coefficient (ratio of total runoff

to the total precipitation) of vacant land and

agricultural areas is less than 0.6 while that for

heavily built-up areas and paved roads is more

than 0.9 (Figure 6).

133

Figure 6: Runoff coefficient as derived from

dynamic simulation in SWMM

When the water level in a node rises above the top

of highest conduit, the closed conduit becomes

full and acts as conduit under pressure, leading to

node surcharging. Surcharging may increase the

capacity of storm water drain, but it is not

desirable and serves as a fore-warning for

flooding. Further increase in water level may lead

to overflow at a node, resulting into flooding and

inundation. The model provides an estimate of

duration and time of occurrence of surcharging as

well as flooding.

It was observed that 263 nodes in drainage

network out of 356 nodes, surcharged for more

than one hour during the simulation period while

19 of these nodes were even flooded for more

than an hour as shown in fig. 5 & 6. The

simulation model can further be used to assess the

response of drainage network to storms of

different recurring periods, the effect of low

impact development techniques, assessing the

drainage network accessibility, and for

management of storm-water drainage network.

Figure 7: Duration of Surcharge at Drainage Nodes

Figure 8: Duration of Flooding at Drainage Nodes

134

6.0 Conclusion

Remote sensing data, particularly the very high-

resolution data with better than 1.0 m spatial

resolution, has been widely used for the

measurement of impervious surfaces in urban

areas. Impervious land cover, which is an

important environmental indicator, is a major

contributor of storm water runoff in cities.

However, due to existence of storm water

drainage infrastructure, mere knowledge of

impervious land cover may not suffice for this

estimation of runoff. This necessitates the

application of storm water flow models such as

US EPA’s SWMM, which simulate the rainfall-

runoff processes operating in an urban area,

thereby accounting for both the overland flow as

well as the transport of runoff by the storm water

drainage infrastructure.

Urban storm water flow models may prove to be

immensely beneficial in ascertaining the effects

of various storm water management strategies,

and evaluating the storm – readiness and climate

resilience of the cities. In developing countries

such as India, where large section of urban

population lives in slums, which are usually

situated in low-lying areas with high flood risk,

modelling storm water flows may also assist in

timely evacuation and better management of

potential disasters due to urban flooding. The

availability of sub-meter resolution satellite

images such as 0.45 m GSD World-View-2 and

0.61 m GSD QuickBird images, coupled with

very high-resolution DEM as may be acquired

from the Air-borne LIDAR surveys, offers

opportunities for using space-borne technologies

for dynamic rainfall-runoff simulations in urban

areas, and mitigating the risks of urban flooding.

Acknowledgements

This article highlights the methods and salient

findings of the work related to urban storm water

modeling under SAC TDP/R&D project titled

“Development of Urban Storm Water Model and

Transportation Planning” (Jain et al., 2013). The

author would like to thank Shri. A. S. Kiran

Kumar, Shri Tapan Mishra, Shri Shashikant

Sharma, Dr. A.S. Rajawat, Shri Ritesh Agrawal,

Shri R.J. Bhanderi and Shri P.Jayaprasad. Thanks

are also due the students and faculty members of

S.V. National Institute of Technology who

participated in this study.

References

Butler D, Davies JW. (2004), Urban drainage. 2nd

ed. London: Spon Press, Taylor and Francis

Group.

Jain, G., Agrawal R., Bhanderi, R. J., Jayaprasad,

P., Patel, J. N., Samtani, B. K., and Agnihotri, P.

G. (2013), Report No. SAC/ EPSA/ MPSG/ GSD/

TDP R&D/ PR/78/2013 Urban Storm Water

Management Model, Geo-Sciences Division,

Space Applications Centre, Ahmedabad, 112 p.

___ (2016), Estimation of sub-catchment area

parameters for Storm Water Management Model

(SWMM) using geo-informatics, Geocarto

International, 31:4, 462-476, DOI:

10.1080/10106049.2015.1054443.

NDMA. (2010), Management of Urban Flooding,

National Disaster Management Authority, New

Delhi.

Rossman L A. (2010), Storm Water Management

Model user’s manual, Version 5.0. Cincinnati

(OH): Environmental Protection Agency.

Zoppou, C. (2001), Review of urban storm water

models", Environmental Modelling & Software,

Vol 16, Issue 3, 195-231.

-x-

135

REMOTE SENSING OF ISOTOPES FOR HYDROLOGICAL

APPLICATIONS

NIMISHA SINGH




Ahmedabad-380015

The isotopic distribution of water vapor has increasingly been used for understanding cloud processes,

global hydrologic processes and linkages between the atmospheric and terrestrial water resources. Studies

of the isotopic composition of atmospheric water vapor can provide unique constraints on how water is

transported, mixed, and changes phase in the atmosphere and are thus a useful tool in the study of the

Earths hydrological cycle. Satellite measurements of isotopic composition of water vapor have provided

insights into the sources of water into the upper troposphere and lower stratosphere. Sensors with very

high spectral resolution in infrared wavelengths like TES, SCIAMACHY etc have the capability to

measure the concentration of trace gases and certain isotopes by analyzing the absorption lines of these

molecules in the observed spectra. Here we present the basics of remote sensing of stable isotopes of

water and its applications in hydrology.

Key Words: Isotopes, Fractionation, TES, Hydrology.

1.0 Introduction

Isotopes are defined as variants of a particular

chemical element that contain same number of

protons but different number of neutrons in their

nuclei and hence differ in relative atomic mass,

but not in chemical properties. The

environmental isotopes are basically classified in

two categories i.e. stable isotopes and

radioactive isotopes. The term ‘environmental’

means that they are present in the natural

environment either as a result of natural

processes or introduced by anthropogenic

activities. Stable isotopes do not decay

spontaneously which means they are stable over

time. Variation in the abundances of these stable

isotopes is very small. For most studies, ratio of

abundances are required. Stable isotopes are

used as tracers. Example of stable isotopes are 18O, 2H, 13C etc. Radioactive isotopes emit alpha

and beta particles and decay over time. They are

used for dating/ age indicators. Examples of

radioactive isotopes are 3H (Tritium), 14C etc.

The most relevant isotopes for atmospheric and

hydrologic sciences are 18O for oxygen and 2H

for hydrogen (Gat 1996; Mook 2001). Water

isotopes are good tracers for the origin,

condensation and evaporation history of an air

parcel. There are nine possible isotopes of water.

H216O is the most common (99.73098%) with

H218O, H2

17O and HDO, existing in much

smaller but still measurable quantities

(0.199978%, 0.037888% and 0.031460%

respectively). Stable isotopes have been used in

hydrology since the late 1950s and early 1960s.

Isotope hydrology is a precise technique

136

available to study stable water isotopes in

atmosphere and hydrosphere. This uses stable

and radioactive isotopes of water and its

dissolved constituents to trace the pathways

involved in the hydrological cycle. Studies of

the isotopic composition of atmospheric water

vapor can provide unique constraints on how

water is transported, mixed, and changes phase

in the atmosphere and thus present a useful tool

for the study of Earth’s hydrological cycle.

Satellite measurements of isotopic composition

of water vapor have provided insights into the

sources of water into the upper troposphere and

lower stratosphere (Moyer et al., 1996; Kuang et

al., 2003) and for characterizing the distribution

of hydrological processes in the free troposphere

(Zakharov et al., 2004; Herbin et al., 2009;

Brown et al., 2008).

2.0 Spectroscopy

Spectroscopy is the study of interaction between

matter and electromagnetic radiation. Radiation

originating from a medium/ object can either be

emitted, reflected or transmitted by it.

Measurement of this radiation intensity as a

function of wavelength is referred to as

spectroscopy. Spectral measurement devices are

called spectrometers, spectrophotometers or

spectral analyzers. At the heart of a spectrometer

is a light dispersing medium. Light that reaches

the spectrometer is focused onto a dispersing

element that breaks it into its constituent

wavelengths. For e.g. a prism disperses light due

to the fact that glass has different refractive

index for different wavelengths. This dispersed

light is then focused onto an array of detectors

where each detector now measures a unique

wavelength and determines the intensity of light

in that wavelength. Combining all these

measurements as a function of wavelength

provides us the spectra of light. Spectra of atoms

and molecules consists of various spectral lines

and each line represents a resonance between

two different quantum states. The various types

of spectroscopy can be distinguished by the

nature of interaction between the energy and the

material and is classified as follows:

(i) Absorption: Absorption spectroscopy uses

the fraction of energy transmitted through

the material to determine what

wavelengths are absorbed by the

medium.

(ii) Emission: Emission spectroscopy measures

the radiative energy released by a

material as function of wavelength. TES

is an example of emission spectrometer.

Electrons coming from higher energy

state to lower energy state cause energy

to be released as photons.

Figure 1. Schematic layout of TES optics.

(iii) Reflection (elastic): The measurement of

reflected and/ or scattered light by a

surface is called reflection spectroscopy.

Hyperspectral sensors such as AVIRIS,

HySI are examples of reflection

spectrometers.

(iv) Inelastic spectroscopy: When light on

interaction with the material transfers

some of its energy, its wavelength shifts

due to inelastic scattering. This includes

Raman and Compton scattering.

(v) Coherent or resonance spectroscopy: This

technique involves the coupling of two

quantum states of the material in a

coherent interaction by the radiative

energy.

137

3.0 Isotope Hydrology

Isotopic quantities are expressed as a ratio (R) of

concentrations of the heavy (rare isotope) to the

abundant (light isotope). Stable isotope ratios are

expressed as parts per thousand (per mil- ‰)

relative to a standard and are given by delta (δ-

notation). General expression is given as:

Delta Isotope= [Rx/Rs -1] x 1000 (‰) (1)

Where, Rx = heavy isotope / light isotope in

sample, Rs = heavy isotope / light isotope in

standard

The stable isotopic composition of water is

further expressed relative to the International

Atomic Energy Agency (IAEA) Vienna

Standard Mean Ocean Water (VSMOW)

standard. Vienna Standard Mean Ocean Water is

a water standard defining the isotopic

composition of fresh water. Isotope δ-notation

for water isotope is given as:

δD = [(HDO/ H2O)sample /(HDO/ H2O)VSMOW -1] x

1000 (‰) (2)

The δD value represents the deuterium isotope

ratio to the standard in the Vienna Standard

Mean Ocean Water (VSMOW). The value of

(D/H)VSMOW is 3.11× 10-4. More negative δD

implies fewer of the heavy isotopes and is

referred to as more depleted in the heavy

isotope. Less negative δD values are referred as

less depleted /more enriched in the heavy

isotope.

3.1 Isotopic Fractionation

The isotopic composition of water in the

atmosphere is determined by fractionation that

occurs at all stages in hydrological cycle.

Evaporation and condensation are mass

dependent process, which influences how

different isotopologues of a substance change

phase. In liquid phase, water molecules with

heavy oxygen or hydrogen isotopes will have

greater binding energies and lower diffusive

velocities, which cause them to evaporate less

readily than the light isotopologue. As a result,

when evaporation takes place, the resulting

vapour has fewer, by proportion, of the heavy

isotopes (Figure 2).

Figure 2. Distribution of water isotopes in the

hydrological cycle.

Evaporation from bodies of liquid water is a

fractionating process with depleted HDO/H2O

ratio in the vapour phase. Over land, water

vapour enters the atmosphere by transport,

evaporation and plant transpiration, each being a

fractionating pathway. When condensation

occurs from a vapour reservoir, there is a

preferential transfer of the heavy isotopologue to

the condensate resulting in fewer of the heavy

isotopologue in the vapour and more of the

heavy isotopologue in the condensate.

Condensation and precipitation preferentially

remove the heavier isotopologue from the gas

phase. This means that evaporation from the

ocean surface and condensation during transport

both deplete the deuterium concentration of

water vapour relative to its source.

138

Figure 3. Isotopic fractionation in the hydrological

cycle.

4.0. Remote Sensing of Isotopes

Remotely sensed measurements of water vapor

and its isotopologue (or other atmospheric trace

gases) are inferred by how these molecules

spectrally affect light as it is transferred from a

source (e.g. sun or earth), through the

atmosphere to a detector. The earliest studies of

water vapor isotopic composition relied on

cryogenic and mass spectrometric technique,

which dominated the field until the mid-1990s.

It was then followed by space based

measurements with the Atmospheric Trace

Molecule Spectroscopy (ATMOS) instrument

which flew aboard the space shuttle in 1994

(Gunson et al., 1996). This was followed by the

advent of laser absorption spectroscopic

techniques (Scherer et al., 1997). The TES

(Tropospheric Emission Spectrometer)

instrument [Worden et al., 2006, 2007] on board

the Aura satellite measures high-resolution

thermal infrared spectra from which the lower

mid-tropospheric δD are retrieved. TES on

board the Aura satellite is an infrared, high

resolution Fourier Transform Spectrometer

covering the spectral range 650-3050 cm-1 (3.3-

15.4 µm) at a spectral resolution of 0.1 cm-1

(nadir viewing) or 0.025 cm-1 (limb viewing).

Figure 4. Tropospheric emission spectrometer

(TES).

Two observation modes (Figure 5) are essential

because many of the spectral features that TES

observes are very weak (especially the nitrogen

oxides) and limb mode markedly enhances their

measurement capability (with the deficiency that

cloud interference is more likely in nadir

viewing, where TES has a good resolution).

Figure 5. TES (Nadir and limb views).

4.1 Retrieval Technique

In remote sensing, the measurement y is a

function F of the unknown state X and other

parameters b plus the measurement error e:

y = F(x,b) + e (3)

139

where the forward model F applies the radiative

transfer theory relating the atmospheric state to

the measurements. The retrieval of parameter �̂�

is the result of operating on the measurement

with some retrieval method R:

�̂� = R(y,�̂�,𝐱𝐚,c) (4)

where ∧ represents the estimated quantity, xa is

the a priori estimate of x. In TES data processing

at Level 1A, the raw data from the spacecraft is

decommutated and the instrument outputs

(interferograms) are reconstructed. File headers

also contain important data such as time, date,

spacecraft and target locations and instrument

pointing angle. At Level 1B, the interferograms

are phase corrected, fourier transformed to

spectra, radiometrically calibrated and re-

sampled onto a common frequency grid. At level

2, vertical concentration profiles of molecular

species such as H2O, HDO, NOx, CO, CO2 etc.

are extracted from the data through a process of

retrieval.

To perform this retrieval, iterative fitting of an

atmospheric radiative transfer model with the

observed radiances are performed. This

considers the measurement noise and some

known atmospheric parameters to run a forward

model simulating the transfer of radiative energy

from the atmosphere to the detector. This

radiative energy consists of emission from the

background (land) and the target (atmosphere).

HITRAN model consists of spectra of many

molecules which are well known from lab

observations across a varying range of

wavelengths with very high spectral resolution.

In the forward model, by varying the

composition of constituent species in the

atmosphere at varying pressure levels, a

simulated radiance is generated. A cost or error

function is created which is minimized to match

the satellite observed radiances with the forward

model simulation. That simulation which

matches precisely with the observation is

selected and its concentrations are set as the

retrieved values.

Figure 6. TES data processing steps

(interferograms, retrieved spectra, global trace

gas concentrations).

Figure 7 shows a typical TES observed radiance

profile and the bands used for HDO retrieval are

highlighted in red (Beer et al 2001). Figure 8

shows measurements of the global distribution

of the Tropospheric HDO/H2O ratio using

spectral radiances taken by the Tropospheric

Emission Spectrometer (TES). It is important

that the estimate of the HDO/H2O ratio is robust

against the spectral interference of H2O on HDO

due to pressure broadening and the TES spectral

resolution.

140

Figure 7. TES observed tropical radiance of

HDO/H2O.

Generally, global isotope distributions show

increased depletion with latitude and decreased

depletions near regions of convection (figure 8).

The intertropical convergence zone (ITCZ)

appears as a band of higher δ values (about 50%

higher than the surrounding regions) near the

equator. Monsoonal regions (South Asia and

South America) show a nearly 100% increase in

δD values during the peak monsoon months.

Figure 8. Mean δD at 825 hPa for the month of

July (2005-2011).

5.0 Future Prospects

For studying evapotranspiration, stable isotope

ratios of oxygen (18O/16O) and hydrogen (2H/1H)

can be used to separate transpiration from

evaporation. Jasechko et al., 2013 showed that

terrestrial water flux is dominated by

transpiration and not evaporation when analyzed

over major lakes of the world. It relies on the

fact that evaporation process results in the

enrichment of heavy isotopes of O and H in the

leftover water. However, no fractionation occurs

during steady-state transpiration so that the

mean-isotopic composition of transpired vapor is

virtually identical to the soil water. It has been

observed that isotopic variability for weather

and climate phenomenon is dominated by

convective process. Based on sensitivity of the

isotopic composition of water vapor to

convective processes, several studies can be

done to use water vapor measurements to better

understand convective processes. It can be

applied to estimate the fraction of vapor arising

from rain re-evaporation over the tropical

oceans. Wright et al., 2017 used satellite datasets

to show that rainforest transpiration enables an

increase of shallow convection that moistens and

destabilizes the atmosphere during initial stages

of the dry-to-wet season transition.

References

Beer R., Glavich T.A. and Rider D.M. 2001.

Tropospheric emission spectrometer for the

Earth Observing Systems Aura satellite. Appl.

Opt. 40, 2356-2367.

Brown D., Worden J. and Noone D. 2008.

Comparison of atmospheric hydrology over

convective continental regions using water vapor

isotope measurements from space. J. Geophys.

Res.-Atmos., 113, D15124.

Gat J.R. 1996. Oxygen and hydrogen isotopes in

the hydrological cycle. Annu. Rev. Earth Planet.

Sci. 24, 225-262.

Gunson M. R., Abbas M. M., Abrams M. C.,

Allen M., Brown L. R., Brown T. L., Chang A.

Y., Goldman A., Irion F. W., Lowes L. L.,

Mahieu E., Manney G. L., Michelsen H. A.,

Newchurch M. J., Rinsland C. P., Salawitch R.

J., Stiller G. P., Toon G. C., Yung Y. L. and

Zander R. 1996. The Atmospheric Trace

Molecule Spectroscopy (ATMOS) Experiment:

141

Deployment on the ATLAS space shuttle

missions. Geophys. Res. Lett., 23, 2333-2336.

Herbin H., Hurtmans D., Clerbaux C., Clarisse

L. and Coheur. P.-F. 2009. H2O and HDO

mesurements with IASI/MetOp. Atmos. Chem.

Phys., 9, 9433-9477.

Jasechko S., Sharp Z.D., Gibson J.J., Birks S.J.,

Yi Y., and Fawcett P.J. 2013. Terrestrial water

fluxes dominated by transpiration. Nature Letter.

496. nature11983.

Kuang Z.M., Toon G.C., Wennberg P.O. and

Yuang Y.L. 2003. Measured HDO/H20 ratios

across the tropical tropopause. Geophys. Res.

Lett., 30, 1372.

Mook W.M.E. 2001. Environmental Isotopes in

the Hydrological Cycle. Principles and

Applications. UNESCO/IAEA Series,

http://www.naweb.iaea.org/napc/ih/volumes.asp.

Moyer E.J., Irion F.W., Yuang Y.L. and Gunson

M.R. 1996. ATMOS stratospheric deutrated

water and implications for troposphere

stratosphere transport. Geophys. Res. Lett.23,

2385-2388.

Scherer J. J., Paul J. B., OKeefe A. and Saykally

R. J..1997. Cavity ringdown laser absorption

spectroscopy: History, development, and

application to pulsed molecular beams.

Chemical Reviews, 97, 25-51.

Wright J.S.., Fu R., Worden J.R., Chakraborty

S., Clinton N.E., Risi C., Sun Y. and Yin L.

2017. Rainforest-initiated wet season onset over

the southern Amazon. P.Natl. Acad. Sci. USA,

114, 8481-8486.

Worden J., Bowman K., Noone D., Beer R.,

Clough S., Eldering A., Fisher B., Goldman A.,

Gunson M., Herman R., Kulawik S. S., Lampel

M., Luo M., Osterman G., Rinsland C., Rodgers

C., Sander S., Shephard M. and Worden H.

2006, Tropospheric Emission Spectrometer

observations of the tropospheric HDO/H2O

ratio: Estimation approach and characterization.

Journal of Geophysical Research: Atmospheres,

111 (D16), D16, 309.

Worden J., Noone D., Bowman K., and Beer R.

2007. Importance of rain evaporation and

continental convection in the tropical water

cycle. Nature, 445, 528-532.

Zakaharov V.J., Imasu R., Gribanov K.G.,

Hoffmann G. and Jouzel J. 2004. Latitudinal

distribution of the deuterium to hydrogen ratio in

the atmospheric water vapour retrieved from

IMG/ADEOS data. Geophys. Res. Lett., 31

L12104.

142

http://www.naweb.iaea.org/napc/ih/volumes.asp

INTRODUCTION TO GEOGRAPHICAL INFORMATION SYSTEM (GIS)

R J BHANDERI

Space Applications Centre,

ISRO, Ahmedabad – 380 015

1.0 INTRODUCTION

GIS, basically refers to the science and technology

dealing with the character and structure of spatial

information, its methods of capture, organisation,

classification, qualification, analysis,

management, display and dissemination as well as

the infrastructure necessary for the optimal use of

the information. The integration of spatial

information for the natural resources development

has been done using manual methods in the past.

But with the increase in the volume and

dimensionality of data sets, it has become essential

to use automated GIS to meet the demands in

natural resources development. GIS is defined as

"database management system to Capture,

storage, manipulate, analyse, retrieve and

display of spatial and non-spatial data in

integrated manner".

The environment in which a GIS operates is

defined by hardware (the machinery including a

host computer), a digitizer or scanner for

converting the input data, a plotter for presentation

of processed outputs and video display unit for

commanding the system by a user, the software

(programs that tell the computer what to do) and

the data. In this context GIS can be seen as a

system of hardware, software and procedures

designed to support the capture, management,

manipulation, analysis, modeling and display of

spatially-referenced data for solving complex

planning and management problems. Although

many other computer programs can use spatial data

(e.g. AutoCAD and statistics packages), GIS

include the additional ability to perform spatial

operations. Thus, the major components of GIS are

I) the end use or management, ii) data acquisition,

iii) data input, iv) data storage and retrieval, v)

analysis and vi) information presentation.

Even though most of the real world can be

observed with the naked eye, it is often difficult to

interpret and systematize what is observed. It

becomes even more difficult when the image of

that reality is stored in digital form as spatial data

and attribute tables. To convert these data into

useful information for easy retrieval and

utilization, there are various spatial analysis

techniques available in GIS. GIS provides number

of functionalities and tools to analyse the data. GIS

users can manipulate, retrieve, integrate and

analyse spatial and non-spatial data in integrated

manner to meet the objectives of the project.

2.0 DATA CAPTURE IN GIS

The entire geographic variation on the earth can be

captured into GIS using points, lines and polygons

(areas). Symbols and labels are used to describe

these features. Points define discrete location of

geographic features which are too small to be

depicted as lines or areas i.e. telephone poles,

wells, electric poles etc. Lines represent the shape

of geographic objects too narrow to depict as areas,

such as roads, canals, rivers, contours etc. which

have length but no area. Areas are closed polygons

that represent the shape and location of

homogeneous, real world features such as

143

administrative boundaries (State, District, Taluka

etc.), land use types, soil classes, vegetation

categories etc. However, the map scale dictates the

type of map feature used to represent a real-world

geographic feature. For example, a polygon feature

on large scale may appear as a point on small scale.

Because small scale maps depict large ground areas

with low spatial resolution and represent little

detail. On the other hand, large scale maps depict

small ground areas with high spatial resolution and

represent more details. The entire geographic data

is captured in GIS environment either by

digitization process or scanning the map. Data can

also be captured by converting an ASCII formatted

file (GPS co-ordinates, ground survey co-ordinates

etc.) and by converting digital data from different

sources (Figure-1).

Figure-1 : Data Capture in GIS environment

3.0 DATA STORAGE

As it is discussed earlier, geographic variation on

the earth's surface is primarily represented on a

map as points (wells, telephone poles etc..), lines

(roads, canals, drainage etc.) and polygons (areas

under Agriculture, Forest etc.). Current GIS differ

according to the way in which they organize reality

through the data model. Each model tends to fit

certain types of data and applications better than

others. The data model chosen for a particular

project or application is also influenced by the

software available, the training of the key

individuals, historical precedent.

3.1 The data model

The procedure used to convert the geographic

variation into discrete objects in GIS environment

is called a data model. There are two data models

available to represent this variation in GIS. They

are i) Raster data model and ii) Vector data model.

Figure-2 shows Vector and Raster data model.

144

Figure-2 : Vector and Raster Data Models

3.1.1 Raster data model

In raster data model, the map is divided into a

regular grid of square or rectangular cells which are

individually coded. The conventional sequence is

row by row from the top left corner, each cell

contains a single value. Simple raster are limited by

the area it can represent within the limitations of

storage. The fineness of the data is limited by the

cell size. The raster GIS is space-filling since every

location in the study area corresponds to a cell in

the raster. One set of cells and associated values is

a layer. There may be many layers in a database,

e.g. soil type, elevation, land use, land cover etc.

Conceptually, the raster models are the simplest of

the available data models. However, they occupy

more storage space. This problem is now handled

by resorting to coding such as run-length coding,

chain coding, block coding etc. A more elegant

structure in raster data model is the quad tree

structure. In quad tree structure, the area of interest

is recursively decomposed into smaller grids and

the decomposition continues till each of the

smallest grid represent a homogeneous area.

Therefore, quad tree describes a class of

hierarchical data structure. The resolution of

decomposition depends upon the number of times

the decomposition process is applied. The storage

requirements of a quad tree are lower than that of

the simple raster. Hence, these days, much

attention is given to quad tree structures. Recent

advancements in this area suggests a variety of

quad tree data structures. Among them, Region

quad tree and Polygon Map (PM) quad tree

structures are famous.

3.1.2 The vector or object GIS

The vector model uses discrete line segments or

points to identify locations. Discrete objects

(boundaries, streams, cities) are formed by

connecting line segments. Vector objects do not

necessarily fill space, not all locations in space

need to be referenced in the model. Thus, the

Vector data model is based on vectors (as opposed

145

to space-occupancy raster structures). The

fundamental primitive is a point and is represented

using an x, y (Cartesian) coordinate system. Each

point is recorded as a single location. Lines are

recorded as a series of x, y coordinates. Areas are

defined by sets of lines. Areas are recorded as a

series of x, y coordinates defining area that enclose

the area. It is important to note that the points i.e.

x, y pairs along the arc are called vertices and the

end points of the arc are called nodes in GIS

terminology. The term polygon is synonymous

with area in vector databases because of the use

of straight-line connections between points. It is

because of this, vector model tends to dominate in

transportation, utility, marketing applications.

However, raster and vector data models are used

in resource management applications.

3.2 Raster data versus Vector data models

A raster model tells what occurs everywhere, at

each place in the area. A vector model tells where

everything occurs, gives a location to every object.

Vector data is precise and has no approximate

errors for measured quantities like area, length,

perimeter etc. Raster data suffers to present precise

details of measured quantities due to the

discretization. Generally, raster data has higher

storage requirements. However, overlay and spatial

analysis operations are computationally faster than

vector data. Raster data is not easily amenable to

association of attribute data with spatial features

such as points, lines and polygons. This is primarily

because of the fact that the basic entity in raster

data is the grid cell and the entities such as points,

lines or polygons are not recognised as objects in

their own merit.

Most of the GIS's in the market choose one of these

two data models as the primary method of

representing spatial data and provide conversion

utilities from one form to the other.

3.3 Defining spatial relationships

Spatial relationships in GIS are defined by

topology. Topology is a mathematical procedure

for explicitly defining properties and spatial rela-

tionships of geographic features which include,

connectivity of lines, direction of a line, length of a

line, adjacency (contiguity) of areas and definition

of areas. There are two data models exist to define

these relationships. They are a) topologic model

and b) Non-topologic model. A topologic data

model stores data efficiently and provides the

framework for advanced geographic analysis. The

model builds areas from the list of individual lines

that define area borders. The system stores linear

co-ordinates only once because two areas that are

adjacent may share the common line between them.

In contrast, the non-topologic data model stores

each closed area as a single entity. The line shared

by adjacent areas must be entered and stored twice,

either by double digitizing or copying the line. This

duplicate data makes geographic analysis difficult

because of the system's inability to observe

topologic relationships between areas that share a

common border. The non topologic model is a

common data model supported by many computer

aided drafting (CAD), mapping and graphic

systems.

The topology helps to perform various types of

overlaying analysis and modelling. The

relationships can not be established without

building the topology.

3.4 Descriptive data in GIS

The GIS stores aspatial data associated with

geographic features similarly to the way it stores

coordinates. Attributes are stored as sets of

numbers (integer, real) and characters (logical or

Boolean). The storage is no different from the data

handled by conventional data base systems. The

attribute data is stored using either hierarchical,

network or relational models. Most of the data base

management packages available today are

146

relational (Oracle, Ingres, Integra SQL, Sybase

etc.).

3.5 Connecting features and attributes

The importance of GIS lies in its link between the

graphic (spatial) and the tabular (aspatial) data.

Basically there are three characteristics of this

connection. They are i) There is one-to-one

relationship between features on the digital map

and the records in the feature attribute table, ii) The

link between the feature and its record is

maintained through a unique numerical identifier

assigned to each feature (label points) and iii) The

unique identifier is physically stored in two places

i.e. in the file that contain the x, y coordinates and

with the corresponding record in the feature

attribute table. So, once this connection is

established one can query the digital map to display

attribute information or create a map based on the

attributes stores in the feature attribute table.

4.0 BASIC SPATIAL ANALYSIS

TECHNIQUES

4.1 GIS Data manipulation

Spatial data manipulation: Spatial data can be

manipulated by various techniques like;

Reclassify: Reclassifies polygon based

on attributes

Dissolve: merges equal category

polygons

Generalize: Generalizes data according

to the level of details as per the scale

Append: joins two layer side by side like

mosaicking.

Rubber sheeting: To adjust the layer

with the reference layer, here shape and

size will be distorted.

Figure 3 Spatial data manipulation

There are many more functions and tools available

in GIS to manipulate the spatial databases.

Non-spatial data manipulation: Non-spatial data

can be manipulated by different techniques like

Reselect , Reclassify, New indices, Relate and Join

two tables etc.

4.2 Retrieval of GIS database

GIS database can be retrieve by simple browsing

(display) and querying on the database.

Spatial query:

Reclassification

Dissolve Append

Generalise

147

Spatial query is the process of selecting a subset of

a study area based on spatial characteristics. This

query usually implemented by selecting a specific

feature or by drawing a graphic shape around a set

of features. For example, user can click and select

the features spatially or by drawing a box or

irregular shape. User also can ask the attributes

about the feature by point-and-ask query (what lies

here?) through a single click on the feature. It is

also possible to get the information about layers

other than displayed but having same frame of

reference and study area.

Attribute query:

Attribute querying is the process of identifying a

subset of features based on the categories of the

attributes. Attribute queries are usually

implemented based on logical condition. User can

ask the location of the features based on its attribute

value. For examples where lies the city Ahmedabad

?. Apply logical condition and display the areas,

which are satisfying specific criteria.

Statistics:

Summation of area for each category within theme,

min, max, standard deviation and mean etc. can be

calculated

4.3 Integrated analysis

In vector based GIS, these operations are

performed on two layers (maps) at a time to form a

new composite map through the geometric

intersection of the features. The layer on which

manipulation is performed is called the input layer

and the layer that controls the area of operation is

called the analysis layer (ARC/INFO User's

manual).

Overlay analysis:

Vector overlay: Integrate more than one layer

(Union, Identity, Intersect)

UNION process is used only on the maps having

polygon features. It performs the geometric

combination of the features of the two themes.

Union operation keeps all the areas from both the

coverages. Splitting the arcs of the input layers at

the intersections creates new polygons in the output

layer. Therefore the number of polygons in the

output layer is more compared to the number of

polygons in any of the input layers (Figure-4). This

particular operation is performed, when the

application demands that the combined region and

combined attributes of the two input thematic

layers are required for querying or for further

analysis.

IDENTITY operation is performed to create an

output layer by combining the features of the

overlapping areas of input and analysis layers. It is

mostly used to preserve the boundaries of thematic

map precisely. For example, showing the district

boundaries in the state map without there being any

mismatch between the district boundaries and the

state boundary. This operation is performed to

overlay points, lines or polygons on polygons and

keep all input coverage features.

INTERSECT operation is performed to overlay

points, lines or polygons on polygons but keep only

those portions of the input coverage features falling

within the overlay coverage features. The input

layer can have points, lines or polygons but the

analysis layer must have polygon topology. This

particular operation is normally performed to find

out the number of tube wells, dug wells in a

watershed, roads crossing the human settlements,

land use distribution in a defined administrative

boundary etc.

UNION

148

Figure-4 Vector overlay (Union, Identity and Intersect)

Raster overlay operation: (Add, Subtract,

Multiply, Division, Difference)

ADD performs arithmetic function of addition of

cell values of two layers. Information from the

input layer is added in the corresponding cell value

of the another layer (Figure –5). This information

is later recoded, if required for further analysis.

SUBTRACT is performed by subtracting the

values one layer from the other layer. It is carried

out normally to find out the changes through time.

MULTIPLICATION operation is carried out by

multiplying the cell values of input layers and the

result is written in the output layer. This type of

operation is needed to extract a small area from the

larger data set layer. For example, extraction of

land use information of a district from the state data

set. In this operation, the cell values outside the

district data set are assigned is '0'.

DIVISION operation is performed by dividing the

cell values of one set by the corresponding values

of another data set. This operation is used when one

needs to calculate densities i.e. calculation of

population density from population and area layers.

DIFFERENCE: is performed by calculating

absolute difference values between two layers.

IDENTITY

INTERSECT

149

Subset and masking (Clip, Erase):

In this operation, two input layers are overlaid

similar to feature combination analysis. However,

the extent of analysis layer defines the area of

interest to be retained or removed. In CLIP

operation, the features that fall within the boundary

of the analysis layer are retained in the resultant

layer with the attributes of the input layer only

(Figure 6). In this operation, the input layer can be

points, lines or polygons but the analysis layer

should contain polygons. This function is

performed to extract a smaller data set from a larger

data set. ERASE is a reversal process of CLIP in

which the features of the input layer within the

boundary of the analysis layer are erased and the

features that fall outside the boundary of analysis

layer are retained. This is used to create a

compliment of CLIP operation.

150

Figure - 6 Clip and Erase operation

Feature extraction and updation (Split,

Update):

SPLIT operation is the enhancement of CLIP

operation. This operation is normally carried out to

produce an output layer of various CLIP operations

based on certain criteria. This operation is meant

for advance users to perform a series of CLIP

operations through a single command.

UPDATE operation is used to perform cut and

paste analysis. Similar to earlier operations, the

analysis layer defines the area of control of the

input layer that needs to be updated. Thus, the

output layer will have features from the input layer

in the non-overlapping area and the features from

the analysis layer will be in the overlapping area. It

is used to generate a time series map showing the

changes in thematic information through time. For

example, showing the change in land use pattern

through time, Urban sprawl through time etc.

Figure 7 shows the split and update operation.

Figure – 7 Split and Update operation

151

Proximity analysis

GIS is also useful to construct the proximity

boundaries for polygons at a distance specified by

the user. These resultant polygons are known as

proximity polygons or buffer zones. The distance

input used for the operation is called as " search

radius" or "buffer distance". Buffers can be

generated on points, lines and polygon (both inside

and outside of polygon) features. This operation is

basically carried out to find out the closeness or

proximity between the features. Ownership details

around a mining area, water bodies, flood hazard

zones etc. Nearness function is useful to find out

the distance of a road from a settlement. Figure-8

shows buffer and Near operation.

Figure–8. Buffer and Near operation

In all the integration operations, a large number of

small polygons are formed at the edges of the

boundary of the output layer due to the mismatch

of the boundaries of input layers. These small

polygons in GIS terminology are termed as

"SLIVERS" (Figure-9). These polygons have to be

removed before the output layer of integrated

operation is taken for further use. The slivers are

normally removed on the basis of the minimum

mapping unit (mmu) area of the thematic map

scale.

Figure –9. Eliminate operation

NEAR BUFFER

152

The spatial analysis techniques mentioned above

provide a variety of choices to user to carry out

spatial modeling exercises. However, it all depends

upon the technical understanding and experience of

the user to identify the need of a particular function.

5.0 ADVANCE SPATIAL ANALYSIS

TECHNIQUES

5.1 3D analysis

Another important application of GIS is to handle

and manipulate the third dimension data in a spatial

context because the surfaces are continuous

phenomena rather than discrete objects. Most of the

GIS packages handle and manipulate spatial data in

a 2-D cartesian co-ordinate system. This restricts

the representation of third dimension i.e. vertical

dimensions of the earth, ocean and atmosphere.

This calls for representing the z-axis as an attribute

of x,y co-ordinates of a spatial location. This being

an isometric model, visualisation is obtained by

projecting the x,y and z-attribute on to x,y,z

reference system. The limitation here is that a

single z-axis defines the surface and multiple z-

values can be handled by stacking the surfaces.

Therefore, the surface is not folded. Thus, this

representation is known as 2.5-D model and is

adopted by most of the commercial GIS packages

for representing the third dimension of surface

features. Hence, these surfaces are called

functional surfaces. They can be used to represent

earth's surface, statistical surfaces such as climatic,

demographic, bathemetric, water table etc.

However, In real 3-D model, the objects are

structured in three-dimension space by x,y,z axes.

It requires the definition of each location with x,y,z

values. The handling of this representation is quite

complex and is yet to be standardised.

Generation of TIN and Lattice (DEM) using

elevation data:

Using elevation data (spot height and contour), a

TIN (Triangular Irregular Network) can be

generated. TIN is a terrain model that uses a sheet

of continuous, connected triangular facets based on

Delaunay Triangulation of irregularly spaced

points (nodes). By definition, 3 points form a

Delaunay triangle if and only if the circle passes

through them contains no other point (NCGIA).

The boundaries created in this process form a set of

polygons called Theissen polygons or Vornoi or

Dirichlet. Therefore, TIN represents a surface as a

set of non-overlapping contiguous triangular

facets.

Using this TIN a lattice can be generated. A lattice

is a set of 'Z' values at equally spaced points and

separated by a common distance in the x,y

directions. Therefore, a lattice is the surface

interpretation of a grid. Each mesh point contains

the 'Z' value of a particular location. Surface z-

values of locations between lattice mesh points are

approximated by interpolation between adjacent

mesh points. In a lattice, each mesh point

represents a value on the surface only at the center

of the grid cell and it does not imply an area of

constant value. User can generate the surface grid

using elevation data directly by TOPOGRID

facility available in Arc/Info GIS.

Iterpolation techniques used to generate TIN and

Lattice data:

Spatial interpolation is the procedure of estimating

the value of properties at unsampled sites within

the area covered by existing observations. The

interpolation techniques are used to provide

contours for displaying data graphically, to

calculate some property of the surface at a given

point, to change unit of comparison when using

different data structures in different layers etc.

There are a number of quantitative interpolation

methods used in different types of GIS's for

carrying out different types of applications. Some

of these interpolation techniques are described for

understanding this phenomena.

153

Two interpolators are used on TIN surface models

i.e. Linear interpolation and Quintic interpolation.

In linear interpolation, the surface value to be

interpolated is calculated based solely on the z-

values for the nodes of the triangle within which

the point lines on the basis of the assumption that

the surface is continuous. However the quintic

interpolator considers the surface as both

continuous and smooth. This smooth characteristic

is accomplished by considering the geometry of the

neighboring triangles when interpolating the z--

value of a point in a TIN triangle. The advantages

of linear interpolation method include quicker

calculations and more predictable results.

However, its disadvantage is that it produces

discontinuous slopes at triangle edges. This

increases the possibility of abrupt changes in slope

at triangle edges. Therefore, linear interpolation

should be used when modeling surfaces have

systematically sampled to include as many

significant surface measurements as feasible.

In Lattice data models, two types of interpolators

are used to model the surfaces. They are Bilinear

interpolation and Cubic convolution interpolation.

Bilinear interpolation computes the output grid cell

value from the values of the four nearest input cells

based on the weighted distance to these cells. Cubic

convolution interpolation calculates the output

mesh point value in the same manner as the bilinear

interpolation except that the weighted values of the

sixteen nearest input mesh points are used.

154

Applications of DEM

The DEM's are primarily used to generate

perspective views, calculations of slope and

aspect (Figure–10) , generation to iso-lines,

identifying z-values for different points, obtaining

surface length, calculating volume of the surface,

relief shading, getting cross section or profiles of

the surface, viewsheds for defining the visibility

etc. These derivatives of DEM analysis are useful

to carry out cut and fill estimations in civil

engineering applications, for draping of thematic

information, visibility analysis, military

applications etc.

5.2 Network analysis

Network analysis is another important area of

application in GIS. Network consists of

interconnected conduits. The connectivity of

conduits permits movement of resources through

the network. Therefore, the analysis is based on a

set of connected linear features. It is visualised as

road network through which vehicles move,

drainage network through which water flows,

sewage through sewer lines, electricity through

circuits etc. In Network GIS, the line coverages are

made to simulate real-world networks modeling the

movement of resources. Hence, GIS Network is a

collection of commands that allows to simulate all

the elements, characteristics and functions of

networks as they appear in the real world.

5.2.1 Elements of a Network

The network consists of different elements and

each is associated with an attribute defining the

characteristics of the element. The elements in the

network are links, turns, stops, barriers and

centers.

Links : It is the basic element of the network and

serves as a conduit for the movement of the

resources. The links are connected to each other at

nodes. Links have two types of attributes viz.

Resistance and Resource demand. Resistance is the

amount of impedance offered by the links for the

flow of resources. Therefore the resistance is the

measure for traversing from one end of the link to

the another end of the link. This may be uni-

directional (rate of flow) or bi-directional (time

taken to travel both ways of the road). Resource

demand is the number or amount of resource

associated with each link feature, such as students,

customers, water etc.

Turns : It represent the direction of flow of

resources from one link to another connected at the

node. Therefore, the turn is the direction specific

and the flow of the resources could be restricted by

the turns. The attributes for the turns are impedance

such as turning time (time taken in negotiating a

turn at the junction of links) and restrictions such

as no left turn. As turns are direction specific, the

attributes could be different for the turns in

different directions.

Barriers : The barriers prevent movement between

links such as node locations through which

resources can not flow. Therefore, these are

basically obstacles defined in a network to simulate

and visualise a specific condition of resource

movement. There is no impedance associated with

this element.

Stops : These are locations where resources are to

be picked up or dropped off along paths through the

network. For example, a bus stop could be defined

as a stop where passengers can be picked up and or

dropped off. Resource demand are attributes of

stops and are a measure of the amount of resource

to be picked up or dropped off.

Centers : These are the locations which have a

supply of resources to distribute to links in the

network. For example, a reservoir which has a

specific volume or water to distribute to pipes in a

network. Therefore, resource capacity is the

important attribute of the center and is a measure

155

of the total resource that can be supplied to or by

the center. Another important attribute is the

influence zone of the center and is the measure of

the limit upto which the resources can be supplied

from or received by a service center.

In all, Network GIS provides a set of tools to build

network data sets consisting of the features and

their attributes necessary to model the flow of

resources through the network. The Network GIS

have three primary functions viz. routing,

allocation and geo-coding.

Figure – 11 Elements of network

5.2.2 Route optimization

Optimal paths for the movement of resources

through a network can be generated by specifying

the source point through which the route will

pass, stops that must be made and the destination.

It evaluates every possible path from the starting

point to the destination in a network to determine

which path is optimal with the lowest total

impedance. The optimal path is calculated by

finding the arc at every intersection of roads that

has the lowest total for both the directional

impedance and the turn impedance, then adding

arc to the path.

Optimum route using distance as well as time

criteria can be generated. Finally it presents the

directions to be followed on the route, distance to

be covered on each arc (road segment) and the time

(both at peak and off-peak) to be taken on each

arc/road segment. It also presents the total distance

and time of the optimum route.

5.3 Allocation Analysis

Allocation function facilitates the modeling of

resource distribution or collection through a spatial

network. It allocates resources based on demand

and capacity. As the links are are assigned to

service center, a portion of the resource of the

centre is allocated to meet the demand of the link.

Links are allocated based on the least resistance

rule i.e. a cumulative resistance from link and

turns, similar to optimum path generation. The

allocation of the links to the service centre occurs

simultaneously for each service centre until the

following predetermined conditions are satisfied.

Stop

Link

Node

Center

Turns

Barrier

156

The influence zone of each centre point

allocate resources to links till the pre-defined

limit is reached.

The centre's capacity has been totally

allocated and no more resource allocation is

possible and thus assignment of links is also

not possible.

In this case, the limit upto which allocation can be

accessed and the capacity of the service centre are

satisfied. Therefore, the Allocate procedure lets

one to model the distribution of resources from one

or more centres and also to model the flow of

resources toward a centre or away from a centre.

Another important allocation problem is to

determine the optimal location for one or more

facilities and optimal allocation of demand to the

facilities. Hence, location-allocation procedures

can be used for any type of location decision that

involves locating a set of facilities that will service

the surrounding population. These procedures are

most suited when customers travel to their closest

facility or when all facilities are equally attractive.

There are mainly three applications for location-

allocation analysis.

Where the goal is to maximise profit. For

example, people locating manufacturing

facilities want to minimise their shipping

costs and people locating retail stores seek to

maximise their accessibility to customers.

Where the goal is to maximize public

welfare. For example, public libraries or

public schools should maximize the equity

of service.

Emergency Services. Where the goal is to

maximize the population that can be reached

during an emergency.

The location-allocation procedures require

information about demand locations and candidate

centre locations. These procedures then determine

the locations for centres and the allocation of

demand to centres according to a specified

objective mentioned above.

Therefore, the location and allocation is the process

of determining the best or optimal location for one

or more facilities so that the service or good is

accessible to the population in most efficient

manner.

5.4 Addresses Geocoding

Address geocoding is a procedure which uses

addresses to identify locations on a map. It also

involves address matching i.e. matching is a way to

locate an incident for which there is an address

such as a crime location, and plot the incident on a

map. The applications of geocoding are police

report files, emergency dispatch, school districting,

route planning and customer mailing lists

References

P A Burrough, 1986. Principles of Geographical

Information Systems for Land Resources

Assessment, Clarendon, Oxford.

Tor Bernhardsen, (2002), Geographic Information

System – An Introduction. Jhon Wiley & Sons, Inc.

David L Verbyla, (2002), Practical GIS Analysis.

Roger Tomlinson, (2003), Thinking About GIS.

ESRI press

Yogini Vanikar, (1993), Spatial Analysis

Techniques – I, Training course on GIS for

resource management and development planning –

Volume 1, organized by Space Applications

Centre.

157

SVC Sastry and R J Bhanderi, (1993), Spatial

Analysis Techniques – II, Training course on GIS

for resource management and development

planning – Volume 1, organized by Space

Applications Centre.

S K Pathan, (1999), Geographical Information

System - Spatial Analysis Techniques, CARG-99,

Organised by Indian Society of Remote Sensing

and Space Applications Centre.

KLN Sastry, (2001), GIS Spatial Analysis

Techniques, CARG-2001. Organized by ISRS,

ISG, SAC, and MMRDA (Mumbai Metropolitan

Region Development Authority, Mumbai) at

Mumbai.

ESRI, 1990. Understanding GIS.

ESRI Arc/Info GIS software package help, ArcDoc

Version 8.1.2 (2001).

NCGIA Core Curriculum, Volumes - 1,2 and 3,

Michael F Goodchild and Karen K Kemp, d (1990),

A NCGIA, University of California, Santa Barbara,

USA publication.

S K Pathan, "The role of RS and GIS techniques in

Urban Planning"

Tutorial notes on Geographic Information System,

GIS-The Basics, Indian Society of Geomatics,

1996, Space Applications Centre (ISRO).

158

Space Applications Centre, ISRO Ahmedabad- 380015, INDIA

satellite based hydrology and modeling

Documents