analysis of agricultural water productivity in the indo-ganges basin
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Presented at the Pre-Forum Basin Focal Project meeting, 7-8 November, 2008, Addis Ababa, EthiopiaTRANSCRIPT
Bharat Sharma
Basin focal Project on
Indo-Gangetic Basin
Analysis of Agricultural Water Productivity ( WP-3)
2
Water Productivity – The Concept
Water productivity (WP) is “the physical mass of production or the economic value of production measured against gross inflow, net inflow, depleted water, process depleted water, or available water” (Molden, 1997, SWIM 1). It measures how the systems convert water into goods and services. The generic equation is:
)/m(m inputWater
)$/m or (kg/muse waterfrom derived utputO)$/m or (kg/moductivityPrWater
23
2233
=
3
Why map water productivity ?
The overarching goal of Water Productivity assessment is to identify opportunities to improve the net gain from water by either:
• increasing the productivity (physical/ economic) for the same quantum of water; or
• reduce the water input without or with little productivity decrease.
4
Basin WP Mapping – What to Care ?
• Magnitude of agricultural and water productivity;
• Spatial variation of WP;
• Scope for improvement: How much and where;
• Irrigated vs. rainfed;
• Crop vs. livestock and fisheries.
5
Basin WP: Multi-indicators
• Land productivity– Individual crop yield (kg/m2)– Standardized gross value production (SGVP) ($/ha)
• Livestock and fisheries– Production ($)
• Water use (IWMI water accounting framework)– Available water (m3)– Irrigation diversion (m3)– Potential ET (mm)– Actual ET (mm)
• Water productivity– Combination of above productivity (numerator)
and water use (denominator)
6
Basin WP: The Methodology
• Basin WP initial assessment
• Sub-catchment modeling and verification
• Scaling up-down
7
District level WP Estimates based on Crop Productivity Census data and Consumptive Use Estimates
Source: Upali & Sharma, 2008
8
KHARIF RABI ALL
Trends in Water Productivity in Rice, Bangladesh Districts (1968-2004)
9
Irrigation canal commands in Punjab (Pakistan) and spatial variation in annual actual evapotranspiration (ETa) in Punjab for year 2004-05
(using Surface Energy Balance Algorithm for Land, SEBAL)
10
Sampling variation in productivity
Average Farm Size in Rechna Doab
12
10.4
9.3
10.7
0
2
4
6
8
10
12
14
Upper Middle Low er Overall Rechna
Fa
rm S
ize
(H
a)
Average Farm Area (ha)
Land Distribution Pattern in Rechna Doab
0
5
10
15
2025
30
35
40
45
50
Landless Less than
1 ha
1.01 to
2.0 ha
2.01 to3
ha
3.01 to5
ha
5.01 to10
ha
10.01
to20 ha
Greater
than 20
haFarm Categories
Pe
rce
nta
ge
Sh
are
Percent Households Percent Share of Land
11
Cropping intensity across Rchna-Doab
0
50
100
150
200
250
Cro
pp
ing
In
ten
sit
y
12
Annual Water Use Patterns from Major Sources across Sub-divisions of Rechna Doab
13
Basin WP Initial AssessmentAgricultural productivity calculation flow chart
Censusproduction data
Crop productivity
map (district wise)
Time series MODIS data
Biomass estimate(pixel wise)
Crop productivity map(kg/m2, pixel wise)
GT data
Census data
Literature info.
MODIS NPP
Yield
Biomass
Harvest index
Crop group/LULC map
Disaggregation*
Local and international prices
Crop standardized gross value productivity map
($/m2, pixel wise)
LivestockProduction*price
FisheryProduction*price
productivity map($/m2, district average)
Agriculturalproductivity map($/m2, pixel wise)
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Disaggregation*
The disaggregation procedure takes district wise average yield from census data. Assuming harvest index (HI) does not vary for same crop, the yield of pixel i is calculated as:
Average yield of district *Biomass of pixel i
Average biomassYieldpixel i =
Basin WP Initial Assessment
Agricultural productivity calculation flow chart
from district wise average yield value to pixel wise average yield value
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Basin WP Initial Assessment
Water depletion estimate flow chart
Points weather data
Points reference ET
Points potential ET
MODIS Land Surface Temperature data
Evaporative fraction map (SSEB*)
Actual ET map(mm) ET
act– the actual Evapotranspiration, mm.
ETfrac
– the evaporative fraction, 0-1, unitless.
ET0
– Potential ET, mm.
Tx
– the Land Surface Temperature (LST) of pixel x from thermal data.
TH/TC
– the LST of hottest/coldest pixels.
CH
xHfrac
TT
TTET
−−−−
−−−−====
fracact ETETET ∗=0
*SSEB: Simplified Surface Energy Balance Model
SSEB assumes linear relationship between latent heat flux (ET) and land surface temperature (Gabriel et al., 2007). Hot pixels and cold pixels are identified to represent no ET andmaximum ET.
Water depletionmap (mm)
Seasonal time series
Kc
16
Sub-catchment Modeling and Links to Basin WP Assessment
Agro-hydrologicalModel (OASIS)
Time series Landsat data
Biomass estimate(pixel wise)
Data input Weather data
Biomassmodeling
SSEBSEBALValidation
Validation
Validation
Model unitAverage WP
LandsatWP map
Water accountingcomponents
yieldYield estimate
(kg/m2)Actual ET maps
Basin MODISWP map
Verifications
Water productivity values, variations, factors and potential assessment
scenarios
17
Dataset
• 58 weather stations
• Data period: 1995-2007 (more to come)
• Item: daily mean, max, min temperature; mean sea level pressure; mean humidity; precipitation; mean & max wind speed.
Weather data Agricultural data
• District wise crop area and production
• State wise livestock and fishery production
• Local and international prices
18
DatasetC..LULC Map
10km, GIAM, 1999 500m, Thenkabail et al, 20051km, USGS, 1992-1993
19
DatasetHHMODIS 250m 16 day NDVI mega-dataset (2006)
20
DatasetHH.MODIS 1km 8 day Land surface temperature mega-dataset (2006)
Note: Curve breakdown is due to existence of clouds
21
DatasetH..
Groundtruthing (8th -17th Oct, 2008)
• Across Indus-Gangetic river basin
• >2700km covered
• 175 samples
– LULC
– Cropping pattern
– Agricultural productivity
– Water use (surface/GW)
– Social-economic survey
Rice (cultivated)Dual irri. Canal system
CottonRice mixed with tree plantation
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