evaluating the effect of check dams on flood peaks to

Evaluating the effect of check dams on flood peaks to optimise the flood control measures

(Kan case study in Iran)

Reza. Roshani January: 2003

EVALUATING THE EFFECET OF CHECK DAMS ON FLOOD PEAK…

By

REZA. ROSHANI Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Watershed and Environ-mental Management Degree Assessment Board Prof. A.M.J. Meijerink (Chairman – Supervisor) WRS Department, ITC Dr. Ir. O.E. Seyhan (External Examiner) Free University, Amsterdam Dr. B. Saghafian (member-Iranian supervisor) SCWMRC – Tehran M.Sc.Ir. G.N. Parodi (member) WRS Department, ITC Ir. A.M. Van Lieshout (member) WRS Department, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Evaluating the effect of check dams on flood peaks to optimise the flood control measures

(Kan case study in Iran)


Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Dedication


In the name of God

To my kind mother

My dear wife and my dear Amir Hossien

Table of content


Acknowledgement………………………………………………………………………………...I

Abstract…………………………………………………………………………………………. II

List of figures ………………………………………………………………..………………… ΙΙΙ

List of tables. ………………………………………………………………………………….…V

1. Introduction …………………………………………………………………………...VI

1.1 Motivation ………………………………………………………….……………..…..1

1.2 Conceptual framework…………………………………………………….……1

1. 3 Main research question……………………………………………………………..….3

1. 4 Check dams ……………………………………………………………………………3

2. Study area. …………………………………………………………………………...….2

2.1 General………………………………………………………………………………… 5

2.2 Physiography…………………………………………………………………………... 6

2.3 Time of concentration…………………………………………………………….……10

2.4 Vegetation cover………………………………………………………………….…....11

2.4.1 Field observation………………………………………………………………… 11

2.4.2 NDVI ………………………………………………………………………….….11

2.5 Climatology ………………………………………………………………………………12

2.5.1 Rainfall………………………………………………………….…….….…………. 12

2.5.1.1 Rain gage stations……………………………………………………………... 12

2.5.1.2 Annual rainfall…………………………………………………………..……. 13

2.5.1.3 Daily rainfall……………………………………………………..….…14

2.5.1.4 Depth –area method……………………………………… ………….………15

2.5.1.5 Depth –area reduction…………………………………………………………18

2.5.1.6 Time distributed daily precipitation………………………………….……….18

2.5.2 Runoff and precipitation………………………………………………….….... 18

2.5.3 Design storm and flood…………………………………………………….….……. .20

3. Flow routing………………………………………………………….……………….…..… 22

3.1 Introduction to flow routing …………………………………………………………... 22

3.2 HEC-HMS model ……………………………………………………………………… 22

3.3 Convex routing method……………………………………………………………… …24

3.3.1 Input data preparation……………………………………………………………. ..25

3.3.2 Results of convex method…………………………………………………….……. 25

3.4 Applying Manning equations…………………………………………………………. .30


3.5 Comparing the results of convex method with

observed hydrographs………………………………………………………….…..…… 31

4. Check dam construction. …………………………………………………………………. ..33

4.1 Check dam functionality………………………………………………………………… .33

4.2 Number of check dams.………………………………………………………………..… 33

4.3 Required material………………………………………………………………….…….. 35 5. Decision making………………………………….…………………………………………. 36

5.1 General objectives…………………………………………………………….……….. .. 36

5.2 DEFINITE 2…………………………………………………….……………………….. 36

5.2.1 Introduction to DEFINITE………………………………….………………….….. .36

5.2.2 DEFINITE input data……………………………………….……………………… 36

5.2.3 DEFINITE output results…………………………………………………..………..37

5.2.4 Sensitivity analyses………………………………………….…... …………………38

6. Conclusion and recommendations………………………………………………….……… 40

References…………………………………………………………….……………...….……42

List of figures


Figure 1.1 Schematic view of supposed catchment…………………………………………...….2

Figure 1.2 Schematic objective of research …………………………………………………...…3

Figure 1.3 Schematic free body view of check dam……………………………………………...4

Figure 2.1 General position of Kan catchment within Iran……………………………….………5

Figure 2.2 Location of Kan catchment on topo map………………………………………….…..6

Figure 2.3 Digital elevation model of Study area………………………………………..…….….7

Figure 2.4 Slope map of study area using DEM…………………………………………….….…7

Figure 2.5 Sub catchments and their main river positions…………………………….……….….8

Figure 2.6 Longitudinal profiles of rivers in Kan catchment……………….………………….….8

Figure 2.7 Vegetation cover map of study area (Using NDVI map) …………………………….11

Figure 2.8 Positions of selected rain gages on DEM of Kan …………………………………….12

Figure 2.9 Annual recorded rainfalls of selected rain gage stations……………………….……..13

Figure 2.10 Relation between altitude and rainfall amount in Kan……………..…………..……13

Figure 2.11 Correlation coefficient of pairs of stations for annual rainfall

in Kan catchment versus distance …………………………………….………….….14

Figure 2.12 IDF curve of Kan station (2.9 km away from study area)…………..……………..…14

Figure 2.13 Correlation coefficient of maximum daily rainfalls of pair’s

stations versus distance of gages………………………………….………...15

Figure 2.14 Interpolated (moving avg.) daily rainfall (mm) of 6-selected

high rainfall in Kan catchment ………………………………………………….…..17

Figure 2.15 Precipitation (as% of total catchment precipitation) as a

function of area for six-selected maximum rainfall ……………………...…….…..16

Figure 2.16 Regression line of 6 selected events in graph 2.15 ……………………………….….16

Figure 2.17 Depth-area reduction curve based on 6 selected high storms………………………...18

Figure 2.18 Daily rainfall hydrograph of Solghan station (1990-1998)…….………………….….18

Figure 2.19 Position of hydrometric station in Kan ………………………………………………19

Figure 2.20 Correspond rainfall- runoff hydrographs (1990-99) …………………………………19

Figure 2.21 Daily rainfall and runoff hydrographs in Solghan station

in Solghan station …………………………………………………………………….20

Figure 2.22 The best-fit curves for maximum discharge and rainfall…………………………..21

I

Figure 3.1 Schematic basin model of Kan catchment in HMS model ……………………………23

Figure 3.2 Convex method Inflow hydrographs of Kan catchment ………………………………26

Figure 3.3 Convex routed outflow hydrographs of Kan catchment ………………………………26


Figure 3.4 Effect of TC prolongation by 0.2hr on peak flood in Kan …………………………….27

Figure 3.5 Effect of TC prolonging on peak flood in Kan catchment …………………………….27

Figure 3.6 Effect of TC prolonging in Rendan River on peak flood ………….…………………..28

Figure 3.7 Effect of TC prolonging in Kiga River on peak flood …………………………………28

Figure 3.8 Simple schema of Rendan channel by constructing

check dams …………………………………………………………………………….30

Figure 3.9 Inflow and outflow hydrographs in Rendan River by

Manning equation………………………………………………………………….…...31

Figure 3.10 Comparing the convex routed and observed hydrographs…………………….…….…32

Figure 4.1 Functionality of check dams for stream channel slope

reduction……………………………………………………………………………...33

Figure 5.1 Result of multicriteria analysis for Kan flood control……………………….…..…….38

Figure 5.2 Sensitivity of ranking for cost and flood peak

reduction criterions……….…………………………………………………..……….39

II

List of tables


Table 2.1 General physiographic characteristics of Kan catchment.. ………………………….. 7

Table 2.2 Main rivers slopes of sub catchments of study area…………………………………. .10

Table 2.3 TC calculation results by different methods…………………………………………...10

Table 2.4 Probabilities and return periods relation ……………………………………………..21

Table 3.1 Output of HMS for various slopes using Muskingum Cunge…………………………24

Table 3.2 Input data in convex model………………………………………………………..…..25

Table 3.3 Flood peak variation based on slope reduction …………………………………... ….29

Table 4.1 Number of needed check dams to gain supposed slopes…………………………..….34

Table 4.2 Characteristics of designed check dam………………………………….……….... …35

Table 5.1 DEFINITE effect table of Kan flood control project…………………………..…. ….37

III

• Acknowledgements

This research would never have carried out without the contribution of many individuals and or-

ganizations, to which I have the pleasure of expressing appreciations and gratitude.


First of all I would like to extend my great appreciation to JIK director DR. Aminipouri who seri-

ously managed and supported this joint program between Jihad of Agriculture, ITC, and Khajeh

Nassir University of technology.

I extent my gratitude to our sponsor, watershed management deputy of forests and rangelands or-

ganization.

Especial thanks to my scholarly supervisors, Prof. Dr. A. Meijerink.I am deeply indebted to him for

the stimulation that he provided during countless discussions, and for his remedy ideas on integrating

a number of independent technologies and methodologies.

Thank to my Iranian supervisor Dr.B. Saghafian, for his guidances during the data gathering and pro-

posal writing in Iran.

Thanks to all JIK staffs and members especially Dr.A.Abkar and Mrs.Darvishzadeh who did their

best to hold this program.

I would like to extend my best thanks to all the JIK students. It was a pleasure time to be with them

during the course.

I am very grateful to my dear friends, D.Afshar and M.Fatemiqumi for their moral support and

friendship.

I would particularly like to thank all the ITC staffs, manageres, employees, for their pleasure behav-

iors and kindness.

IV

• ABSTRACT

The recent flood events in Iran positively confirm that most regions are subject to cyclic destruc-

tive floods and the extent of damages is increasing.


The depth and spatial extent of floods in economical terms demands further research as a priority.

Although many studies have concentrated on causes of floods, and these factors have been identified,

but the suggested flood control measures just have been concerned within the sub catchments and the

effect of those measures at the main outlet of catchment have been neglected.

Stream gradient is one of the most important factors in flood acceleration, particularly in mountain-

ous regions.

Slope plays an important role in time of concentration (TC). Check dams, as most commonly used

measures in watershed management extremely will affect the stream gradient and TC consequently.

The effect of slope variation on peak flow was explored. The most effective slope is determined and

feasible method for achieving this slope identified. In each case the peak flow reduction index is

identified. This index is the most important factor in any decision-making process.

GIS environment was used to delineate the boundaries of catchment and sub catchments. Using 100-

meter contours a digital elevation model is created. Physiographic factors such as surface slope; lon-

gitudinal profiles of stream channels are calculated.

HEC-HMS model was examined to route the rivers, which didn’t simulate feasible results.

Convex routing method is used to simulate the inflow and outflow hydrographs. This method of

routing is most capable in such cases.

Manning equation was used to calculate the peak flow after constructing check dams. The result of

convex method and manning calculated peak flow was compared.

A decision support system (Definite 2 soft ware) selects the most cost effective planning of check

dams, which should be constructed within the streams.

V


1

1. INTRODUCTION

1.1. Motivation There are several natural disasters occurring throughout the world within the years.

Floods in particularly cause huge losses to the human lives, property, infrastructure and natural re-

sources as well.

Iran is among several countries in the world, which faces severe problems of flood each year particu-

larly in urban catchments.

Tehran (capital city of Iran) is subject to serious flood dangers. For instance in 1987 a huge flood oc-

curred in northern Tehran and caused a lot of damages and losses. Subsequent to this event, it was de-

cided to implement a flood control project in north of Tehran, in the affected areas. Later on many

check dams were constructed through the waterways to control the flood. It was a costly project of

about 1 billion of tomans (about 1.2 million $.)

At that time the planners didn’t care about the cost of the project and they suggested constructing

check dams in all of the tributaries to reduce the slope of those tributaries and consequently reduce the

peak flood at the sub catchment’s outlet.

Later on it was debated whether check dams are the proper measures, and if so, to what extent? How-

ever, no study was done, and because of that reason, the present research will deal with the explained

problem in one of the northern Tehran catchment named “KAN”. This catchment has the second prior-

ity of northern Tehran flood study. (By Jihad Technical Services Co. 1996). In this thesis I will try to

find out how much flood peak reduction may occur to work out a feasible method and solution to op-

timise the cost and efficiency of the measures.

1.2. Conceptual framework A catchment is a system, which should be considered as a unit. Any changes in parts of catchment will

affect the outflow of the total catchment.

There are two major issues in dealing with floods in a stream network of a catchment:

-Peak flow of each sub catchments with and without checks dams.

-Delaying time of the hydrographs of those sub catchments

The first issue is the lowering of the peak flow in a sub catchment by construction of check dams. But

the second issue is not as clear as the first one. It means that the flood peak at the main outlet is not

just the function of sub catchments peak hydrographs. Other factor, which mostly affects the peak

flood of the whole catchment, is the delay time in arrival of the peak flows.

If the peak of two different hydrographs arrive at the main outlet at the same time the peak flow will


2

be more than in the case when these two peaks reach to the main outlet at different times, as illustrated

in figure 1.1 and 1.2. Figure

1.1 shows a typical catchment with two sub catchments. The rivers of these two sub catchments join

each other and generate the main river of catchment. Figure 1.2

shows their corresponding hydrographs. Hyd 1b and Hyd 2 represents the corresponding hydrograph

of river 1 and river 2 at sub catchments outlet. Hyd 3b is the total hydrograph of whole catchment.

If by constructing check dams in sub catchment no.1, delay is achieved, (Hyd no.1a); the total hydro-

graph will be as Hyd 3a. As can be seen a reduction of peak flow in sub catchment is achieved due to

construction of check dams. But there is an increase of peak flow at the main outlet. This is the point

of interest for designing the flood control measures. If the planners had taken the point in account, this

situation (flood peak increasing at main outlet) would not have occurred.

The most important parameter is the delaying time of hydrographs due to construction of check dams.

This aspect will be investigated in this thesis.

Furthermore, the economic feasibility as an effective parameter in any engineering projects will be

analysed.

Figure 1.1 schematic view of supposed catchment


3

Time

Dis

char

ge

Hyd 1b Hyd 2 Hyd3b Hyd 1a Hyd 3a

Figure 1.2 Schematic objective of research

1.3. Main research questions

The present research is being faced with two different aspects:

a) Hydrologic aspect, which is extremely dealing with the concept of flood routing in the rivers

and interaction of geometry of stream channel on the peak flood. In this phase the main ques-

tion is “what is the desirable slope to have the lowest peak flood at main outlet?” The result of

this phase will produce different alternatives regardless of any other considerations.

b) Economical aspect, which is the most important parameter in project feasibility and efficiency.

In this phase a cost- benefit analysis will be employed to select the most cost effective plans.

This phase will answer the question “Which alternative has the lowest cost-benefit ratio?”

1.4. Check dams

Over the years, different measures have been developed to control the floods .By applying these meas-

ures the flood peak will be retarded. Check dams are the mostly commonly used measures in flood

control projects. Especially in some regions where, there are no proper sites for large dam construc-

tion. Check dams are low structures built across the stream perpendicular to the flow. In hydraulic en-

gineering the most common use for check dams is to decrease the slope and reduce the flow velocity.

The number of needed check dams, to provide the desirable slope, will be determined based on the

project targets and its cost. Height of designed check dam will influence the number of check dams

and the total cost of project. The higher the check dams, the less number of check dams is required.

However, there is a limitation for the height of check dams. The height of check dams will be limited


4

by the effective upstream area, the depth of channel valley and type of check dams. For the gabion

type this height can be about 5 meters.

Type of check dams is widely influenced by the available materials in the project area.

In stream gradient improvement, big rocks and boulders are needed. They can be provided from

streambed. These boulders can be wrapped in a gabion mesh boxes to be used as check dam body.

The main force, which will resist against the flood kinematic force and other active forces, is the check

dam gravity force. That is why the gabion is used to make check dams. A gabion consists of units with

typical dimensions of 1×1×1 m which are tied to each other. The other advantage of gabion is its flexi-

bility, which helps the check dam to remain stable even though the foundation has settled down. The

height and geometry of check dams will be designed regarding the acting forces on the check dam.

These forces are hydrostatic forces, soil active pressures and uplift forces as active forces and weight

of check dam, depth of headwater, and the soil passive force as resistance forces.

Figure 1-2 shows the schematic view of the check dams and forces, which contributes in statically sta-

bility analysis..

The space, which is created behind the check dam, will be filled with the sediments during the first

floods events. It makes a level streambed in the reach upstream of check dam.

The sequence of this procedure ultimately will generate some drops through the stream, where the

check dams are constructed.

Uplift force

Figure 1.3- schematic free body view of check dam

Head of water

Soil active force

Hydrostatic force

Soil passive force


5

2. STUDY AREA 2.1. General Study area (Kan catchment) is situated in north of Tehran (capital city of Iran) and covering an area of

approximately 207 km2 (figure 2.1)

Figure2.1- general position of Kan catchment within Iran It is located between latitudes 35°45″49′ to 35°57″11′ N and longitudes 51°09″53′ to 51°22″29′ E

(UTM zone 39, boundary coordinates is between X=3968522 to 3971937 and Y=524027 to 520526).

It borders with Hesarak catchment in west, Jajrood basin in north and northeast, Karaj dam basin in

north and northwest, Vardij catchment in east and city of Tehran in south (Figure 2.2).

As it is shown the catchment drains to Tehran city, and therefore priority was given to Kan catchment

to implement a flood control project.

Based on a developing program of coastal zoon of Caspian Sea, in north of Iran, it has been decided to

construct a highway from Tehran to north of Iran, which will pass through the Kan catchment and en-

hances the priority for this catchment. Utilizing the Kan catchment as a recreational site for the people

and also locating a shrine within the catchment re-emphasises the priority of Kan.


6

Figure 2.2- location of Kan catchment on topo map

2.2. Physiography

Kan catchment is located in a mountainous area; mountains and very steep slopes cover more than 75

percent of whole area. Elevation difference between the lowest and highest point is about 2000 meters,

With the highest point about 3400 meter and the lowest point, at the main outlet, about 1400 meters.

High range of variation in elevation, steep slopes, and stream slopes cause very rapid flows.

Figure 3.3 shows a digital elevation model of study area, which was derived from 100-meter interval

contour lines. The surface slope map, which is derived from DEM, is shown in figure 3.4 as well. As is

shown, steep slopes cover the most parts of catchment area. The average slope of study area is about

40 percent.

The impact of slope and slope variation in flood characteristics will be discussed.


7

Figure 2.3- digital elevation model of Figure 2.4- slope map of study area using Study area Using 100-meter interval DEM (slopes are in %) Contour line (heights in M. a.s.l)

Sub catchments delineation is an important part of physiographic procedure in any hydrologic study.

According to the purpose of study and the objective of the research the study area is divided to five

sub catchments. However it is possible to make other sub divisions for those sub catchments. Figure

no: 2.5 shows the position of these sub catchments and their main streams.

The general physiographic characteristics of the Kan catchment and its sub catchments are calculated

in GIS environment. Table no: 2.1 shows the information.

Unit Area (km^2) Main river length (km)

Avg.surface

slope (%)

Max alt. (M)

Min alt. (M)

Avg alt. (M)

Rendan 67.67 11 42.75 3741 1797 2560

Sanghan 47.16 13.25 37.48 3290 1700 2333

Keshar 34.94 12.73 36.8 3254 1586 2203

Kiga 24.06 9.71 43.32 3777 1800 2686

Solghan 33.73 13.65 37.22 2700 1360 1863

Table 2.1- general physiographic characteristics of Kan catchment and its sub catchments


8

Figure 2.5- sub catchments and their main river positions As it is shown in above table, Kiga is the steepest and Keshar is the most gentle sub catchment out

of five sub catchments. The main river, which is called Kan River, begins in Rendan and lays its outlet

in Solghan. Its length is about 24.65 KM.

One of the most important factors in flood analysis is the stream gradient. This factor can be presented

by the longitudinal profile of the river. By crossing the DEM and the point map of river (which is con-

verted from segment map) longitudinal profile of river will be drawn. To have a more precise graphs

the point interval has been taken at 20 meters

Alt (M)

(a)

Distance (M)

(a)


9

Alt. (M)

Distance (M)

(b)

Alt. (M)

Distance (M)

(c)

Alt. (M)

Dis. (M)

Distance (M)

(d) Alt (M)

(e)

Figure 2.6 (a-e) longitudinal profiles of rivers in Kan catchment

Information drowns from these profiles shows the variation of altitude along the river path.


10

Stream gradient of those rivers, which will be used to determining the time of concentration are given

in below table.

Table 2.2- Main rivers slopes of sub catchments of study area

2.3. Time of concentration: There are different descriptions for time of concentration (TC) in hydrologic literature .Two mostly

common descriptions are as follows:

-The time for a drop of water to travel from the furthest hydrologic point in a catchment to the outlet.

-The time between the center of excess rainfall and the inflection point on the recession limb of the

hydrograph.

For the same volume of runoff, a longer time of concentration will reduce the peak flow magnitude

and the time to peak of hydrograph.

Different authors have developed different methods and formulas for TC calculation; Kerby (1959),

Kirpich (1940), Bransby & Williams (1961), etc.

Table 2.3- gives the results of TC calculation by different methods.

UNIT

Kerby

(Min)

Kirpich

(Min)

Kinematic

(Min)

Bransby

(Min)

Federal

(Min) Rendan 108 60.7 155.2 165.8 158.9

Sanghan 125 76.9 186.6 217.4 189

Keshar 119 70.6 174.6 205.7 177.6

Kiga 93 47.3 127.7 149.7 130.6

Solghan 145 98.2 225.8 259.9 231.9

Table 2.3- TC calculation results by different methods

As it is shown different methods give various amount of TC. Based on the observations and the gen-

eral conditions of rivers the Kerby method is the most proper method for the study area, and will be

used here.

River Rendan Sanghan Keshar Kiga Slogan

Gradi-

ent(%)

9.6 8 8.7 15.2 4.5


11

2.4. Vegetation cover 2.4.1. Field observation:

The slopes of study area study area have a very poor vegetation cover. The riverbanks are orchards,

which are safer from flood damages locally.

The poor vegetation cover, because of poor rainfall, shallow soil, and overgrazing, conclusive to rapid

overland flow generating flood occurrence.

2.4.2. NDVI:

NDVI (Normalized Difference Vegetation Index) is a useful method for recognizing the vegetation

cover by applying satellite images.

This function requires 2 satellite bands (one with visible or red values and the other near-infra red val-

ues). For study area by using the “LANDSAT TM” images, which are taken in 2000 the NDVI map, is

produced .The employed band are band 3 and ban 4.The result value of NDVI map will be between –1

to 1.The negative values represents the water bodies and clouds, the values closed to zero represents

the bare soil and rocks, and the positive values represents the vegetation. For finding the orchards and

any other dense vegetation the NDVI map was classified in two-categories.

As it is shown in below figure there are just some small patches of vegetation in hillsides, and some

Figure 2.7- vegetation cover map of study area

(Using NDVI map)

irrigated lands, which are shown by continuous, and narrow pattern and covers about 2% of total

catchment area.


12

2.5. Climatology 2.5.1. Rainfall

The first step to flood studying in any supposed area is, having the proper data and information about

the rainfall. Rainfall is the most effective parameter in flood generation.

If there is good information about rainfall, the result of flood simulating and flood analysing will be

more realistic. Thus it is needed to go through the rainfall in detail.

2.5.1.1. Rain gage stations

There are five rain gage stations, which cover the study area. Except for one, the other four stations

have been equipped in 1996. The available recorded data for these stations are just for five years. The

old one has had discontinuous data since 1968 to 1989, which are not reliable. But since 1990 the re-

corded data of this station is reliable with no gaps. Although there are some other stations around the

Kan catchment because of distances and different climatologic conditions, their data are not useful for

study area. Only the Kan station, located in outside the catchment in plain of Tehran has selected, be-

cause of its low proximity and long data series.

During the fieldwork and rain gage station inspecting, we found another rain gage. The operator ex-

perience and his responsibility were such that we did not consider this station.

Figure 2.8 positions of selected rain gages on DEM of Kan

2.5.1.2. Annual rainfall:

Rainfall analysing needs a common periods of records for involved stations. A period of 5 years (1996-2000) is selected for annual rainfall analysing in study area.


13

0

100

200

300

400

500

600

700

1996-97 1997-98 1998-99 1999-2000 2000-001

YEAR

RA

INFA

LL(M

M)

kan keshar sanghan kiga rendan solghan

Figure 2.9- annual recorded rainfall of selected rain gage stations in Kan (1996-2000)

It shows a more or less regular pattern of changes in rainfall amount in different rain gages.

One of the most important and applicable information about rainfall is rainfall gradient. It shows rela-

tion between the rainfall amount and the elevation of different stations. A physical view of rainfall will

be extract by this relation.

0

100

200

300

400

500

1000 1200 1400 1600 1800 2000 2200

altitude (m)

rain

fall

(mm

)

Figure 2.10- relation between altitude and rainfall amount in Kan catchment

As it is shown there is a positive gradient for rainfall up to about 2100 in study area.

The recent researches have proved a negative gradient for rainfall in northern Tehran catchment. Al-

though there is not a very sharp threshold for this phenomenon but many researchers believe to around

2600 meters (Northern Tehran watershed management plan, meteorology report, by Jihad Technical

services Co. 1992). Spatial variation of rainfall was expressed as the correlation coefficient of annual

rainfall amount and corresponding distance between different rain gage stations. They have been plot-

ted in a graph. As it can be seen Keshar has the lowest correlation coefficient.


14

It can be the effect of elevation, where the keshar has the lowest mean elevation among the new estab-

lished stations.

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16 18

Distance (km)

Cor

rela

tion

coef

ficie

nt

Rendan Kan Keshar Solghan Sanghan

Figure 2.11 correlation coefficient of pairs of stations for annual rainfall in Kan catchment

Versus distance

2.5.1.3. Daily rainfall

Annual rainfall gives general information about the rainfall in study area. But annual rainfall data is

not a proper data to study the flood.

For flood studies one has to work with daily rainfall over long Periods. The major parameters, which

will be used in flood studying, are; intensity, duration, and frequency of the rainfall. Unfortunately

regarding the duration of the data in the study area this information is not available. Therefore Kan

station, which has long duration data, will be used.

An IDF curve of Kan station, which is derived from 30 years duration data (Watershed management

plan of Kan, by Jihad research’s Co. 2000), is as below:

05

101520253035404550

15 30 45 60 75 90 105 120

RAINFALL DURATION(min)

INTE

NSI

TY(M

M/H

R)

2 year 3 year 5 year 10 year 25 year 50 year 100 year

Figure 2.12- IDF curve of Kan station (2.9 km away from study area)

The spatial pattern of maximum daily precipitation of the five stations within the Kan catchment is

shown by the correlation coefficient between pairs of stations. See figure 2.13.


15

As can be noted at a short distance of about 4 km, the daily maximum precipitation is poorly corre-

lated (C.C=0.4, 0.25); therefore high intensity rainfalls do not cover large part of the catchment uni-

formly.

It means that, considering the available data, the spatial pattern of rainfall is not known well.

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12distance(km)

corr

elat

ion

co.

Figure 2.13- Correlation coefficient of maximum daily rainfalls of pair’s stations Versus distance of gages (compared with Rendan station)

2.5.1.4. Depth-area methods

Studying the depth and distribution of rainfall events over a catchment area is reliable method to know

the rainfall properties. Within a GIS it is possible to produce the isohytal maps. A point interpolation

procedure has to be followed as rainfall amount at discrete points. Six maximum rainfalls are selected

to produce daily isohytal maps. The moving average method for point interpolation is used. Using the

aggregation function in ILWIS the total depth of rainfall with high rainfall for the whole catchment is

calculated. Effect of altitude is ignored.

Figure no: 2.14 shows a classified result for six selected events in the Kan catchment. The spatial pat-

terns are shown in figure 2.14.The figures shows clearly the uneven spatial distribution of the different

rainfall events. As the figures show the autumn and winter storms are more generalized than the spring

and summer storms.

In general high intensity rains are localized in a small part of catchment but contribute substantially to

the total catchment rainfall, as is shown in figure 2.15.

The figure no2.15 gives more information about the contribution of sub areas in total rainfall of whole

catchment.


16

0102030405060708090

100

0 25 50 75 100 125 150 175 200 225CONTRIBUTING AREA(KM^2)

PREC

IPIT

ATI

ON

AS

% O

F TO

TAL

PREC

IPIT

ATI

ON

13/8/1998 24/12/1998 16/4/1998 23/1/1998

14/5/1998 12/11/2000

Figure 2.15- precipitation (as% of total catchment precipitation) as a function of area for six-

selected maximum rainfall, selecting with the largest precipitation class.

It can clearly be seen that most of the rainfall storms, which occur in late spring and also in summer, are

more localized than the rainfall events, which occur in other seasons. It can also can be seen that 50%

percent of total catchment area (approx. 100 km^2) yields about 40% of total rainfall. However about

40% 0f total rainfall is yielded by less than 20% of total catchment area. These conditions are the re-

flection of rainfall intensity. It means that storms with high intensities are more localized than the

storms with low intensities. With perfectly events spatial distribution, the percent of precipitation would

be a linear function (1:1) of area. See figure 2.16

y = 0.4958x - 4.6135R2 = 0.9821

0.00

20.00

40.00

60.00

80.00

100.00

0 50 100 150 200 250CONTRIBUTED AREA(KM^2)

PREC

IPIT

ATI

ON

AS

% O

F TO

TAL

PREC

IPIT

ATI

ON

Figure 2.16- Regression line of 6 selected events in graph 2.15


17

Figure 2.14- interpolated (moving avg.) daily rainfall (mm) of 6-selected high rainfall in Kan

13/8/99 24/12/9

23/1/99 14/5/99

16/4/99 12/11/2000

EVALUATING THE EFFECET OF CHECK DAMS ON FLOOD PEAK …

18

2.5.1.5 Depth-area reductions

There is another method to get more information about real rainfall distribution.

This method usually determines the percent of reduction of maximum point rainfall with increasing

areas. As it can be seen for instance for 100 km2 the rainfall reduction will be about 75% and for total

catchment this amount will be about 45%. See figure 2.17.

00.10.20.30.40.50.60.70.80.9

1

0 50 100 150 200 250Area km^2

Rai

nfal

l red

uctio

n(%

)

Figure 2.17- depth-area reduction curve based on 6 selected high storms in Kan catchment

2.5.1.6 Time distributed daily precipitation

Plotting the daily rainfall data in one period will give useful information about temporal distribution of

precipitation. The distribution in time of daily rainfall for Solghan station in (1990-1999) is shown in

figure 2.18.

2.5.2 Runoff and precipitation

There are 5 hydrometric stations within the Kan catchment. See figure 2.19.

The most reliable data belongs to Solghan station at the main outlet of Kan catchment. The four other

stations have no long-term data. Hence Solghan hydrographs will be used to study the flood character-

istics in Kan. For common period of recorded daily rainfall and daily discharge (1990-1999) they are

plotted in a same graph. See figure 2.20

01020304050607080

1990

1991

1992

1993

1994

1995

1996

1997

1998

Year

Rai

nfal

l(mm

)

Figure 2.18- Daily rainfall hydrograph of Solghan station in Kan catchment (1990-1998)


19

Figure 2.19- Position of hydrometric station in Kan

Figure 2.20-correspond rainfall (Solghan)-runoff (total catchment) hydrographs (1990-99)

This graph shows the poor relation between rainfall and discharge, which is due to the poor knowledge

of the catchment rainfall; for instance; the large amount of rainfall which, has occurred in 1994

(78mm) produced a low discharge (approx. 10 m3/s). Meanwhile a less amount of rain in 1995 (55mm)

has produced a greater discharge (approx.130 m3/s). The other interesting point, which can be ex-

tracted by studying the hydrograph, is the effect of snowmelt. Arrival of warm air can cause apprecia-

ble runoff

Daily rainfall and runoff data of Solghan station for 2 years (Oct.96 to Sep. 98) are plotted in figure

no: 2.21.

During 301st to 361st days there is no rainfall and no runoff. Since 360th to 440th days there is rainfall

but still no runoff. This is due to accumulation of snow, and partly dries catchment condition.

During 480th to 540th days there is a considerable rainfall amount but the amount of discharge is not

considerable. It can be the effect of rainfall intensity.

0

20

40

60

80

1990

1991

1992

1993

1994

1995

1996

1997

1998

YEARS

RA

INFA

LL(M

M)

020406080100120140

DIS

CH

AR

G(C

MS)

RAINFALL DISCHARG


20

0.005.00

10.0015.0020.0025.0030.0035.0040.0045.0050.00

1 61 121 181 241 301 361 421 481 541 601 661 721

DAYS

RA

INFA

LLl(m

m)

0

10

20

30

40

50

60

RU

NO

FF(c

ms)

RAINFALL RUNOFF

Figure 2.21- daily rainfall and runoff hydrographs in Solghan station (Oct. 1996-Sep. 1998)

It means the Intensity of rainfall is not so high to produce high discharges. During the 540th to 570th

days despite of low rainfall, high runoff occurred. It is because of soil saturation by snowmelt and rain

during previous rainy days and the catchment has low capacity to absorb water. The peak flow should

have been caused mainly by rain, which only partly measured by gages. In period of 570th to 620th

days there is no rainfall but still runoff is continued. It is effect of springtime snow melting and ground

water discharge. In this research we will study only the direct runoff.

2.5.3 Design storm and flood

Conclusion drawn from rainfall and runoff analysing should lead to design storm and design peak

flood for study area. As it was explained in previous sections there is no sufficient correlation for

maximum rainfall events within the catchment. In order to simulate a realistic amount of rainfall and

Peak flow we should have a judgement based on observed events and existing data.

Frequency analysis has been done for peak discharge (30 years record period) and maximum rainfall

(12 years record period) for Solghan station. The best distribution for both of them is identified. See

figure 2.22 (a) and 2.22(b).

Based on frequency analysis the discharge of about 345 m3/s (which is equal to the maximum observed

flood data) will be generated by 100 years return period of daily rainfall of about 135 mm. This

amount of rainfall occurs just in Solghan sub catchment.

Considering the results of depth-area reduction methods if we suppose a point rainfall, by generalizing

this point to the total catchment, about 50% reduction will be occurred. Therefore this amount will be

reduced by 67.5 mm. If we assume a 25mm/hr rainfall with 2.5 hours duration (aprox. Equal to the TC

of Solghan) occurs, this amount of rainfall will be provided.


21

(a)

Actual Data

Distribution

Log Pearson Type III

Weibull Probability

Rainfall (mm)

10

20

30

40

50

60

0.0 0.2 0.4 0.6 0.8 1.0

(b) Figure2.22 (a), (b) the best fit curves for maximum discharge and rainfall in Solghan station Considering that: T= (1/1-P) (3.1)

Where T= return period (year)

P= weibull probability

The calculated return periods for various probabilities could be as table 2.4

Table 2.4 probabilities and return periods relation

Probability 0.995 0.99 0.98 0.96 0.90 0.80 0.667 0.50 Return period (Year)

200 100 50 25 10 5 3 2

Actual Data

Distribution

Log Pearson Type III

Weibull Probability

Discharge (m3/s)

0

100

200

300

400

0.0 0.2 0.4 0.6 0.8 1.0


22

3. FLOW ROUTING

3.1. Introduction to flow routing

Flow routing is a mathematical procedure for predicting the changing magnitude, speed, and shape of a

flood wave as a function of time at various points along the river as a flood wave travels through the

river.

Flood forecasting, reservoir design, catchment simulation and water resource planning generally utilize

some form of routing technique. While a river is carrying a flow some part of the flow will be stored

along the different reaches. This is simply because the slope of the surface is not uniform during

floods. This procedure along a river causes some differences between inflow and outflow hydrographs.

Since we are going to investigate the effect of slope and its variation on the peak flood, the flow rout-

ing models will be required.

As flow routing has been an important type of hydrologic analysis, which is complex and requires

computations, many routing models have been developed.

Routing models are concerned with two different methods, hydrologic routing and hydraulic routing.

Hydrologic routing uses the equation of continuity with an analytical or assumed relation between

storage and discharge within a system. Hydraulic routing uses both the continuity and momentum

equations.

In river routing applications, the hydrologic as well as the hydraulic routing type offer advantage of

simplicity wherever it is applicable. Selection of a flow routing model for a purposed application is

affected by so many factors such as capability of model to response the user’s questions, required ac-

curacy, type of available data and so on.

Many trials were done to select a suitable model for river routing in Kan catchment.

3.2 HEC-HMS model

The HEC- HMS model was examined. HMS contains different type of routing models such as Musk-

ingum, Maskingum- Cunge, and kinematic wave. To select a routing model, one must consider the

routing method’s assumptions and conditions.

The most important assumption and limitations, which can be faced in our application, are:

- Backwater effect: practically none of the routing models that are included in HEC-HMS will

simulate the channel flow well if the down stream conditions have a significant impact on up-

stream flow.

- Interaction of channel slope and hydrograph characteristics: As the channel slopes lessen, assump-

tion made to many of the models included in HEC-HMS will be violated.


23

- Availability of data for calibration: in general if the observed data are not available, the physically

–based models will be easier to set up and apply with some confidence. Thus these empirical

models will not give accurate results in the case that there is not enough observed data.

These three points make it doubtful whether the HMS model can be utilized for our application.

Various routing options in HMS were tried but none of them were sensitive to changes of slope. Con-

sidering the effect of check dams in slope breaking it seems these methods will not applicable for this

application.

Beside these points, because of the narrow width of rivers there is no considerable capacity to be used

as storage in flood routing. Thus these models should not be used for this kind of application.

Figure 3.1 schematic basin model of Kan catchment in HMS model

As it can be seen there is no considerable changes in discharge due to slope reduction in streams.

The first run has considered the original slope of those rivers and the amount of peak discharge in riv-

ers has been simulated. Second run has considered 50 % reduction in gradient of rivers.

For instance in Kiga which is the steepest one by reducing the gradient to half, peak discharge just

about 0.3 m3/s has been reduced. Despite of the small reduction in river discharges the total discharge

at the main outlet has been increased a little. While the natural peak discharge was about

167.68 m3/s by reducing the slopes to half the total discharge is simulated about 168.68 m3/s.


24

Table 3.1 output of HMS for various slopes

Using Muskingum Cunge routing method

3.3. Convex routing method Many numerical approaches have been developed to route stream flow. Most of these methods have

been simplified by assumptions. However, even these simplifications needs detailed data.

There are more, simple routing methods, which have been developed by taking two basic hydraulic

equations in consideration; Continuity and momentum equations. Some of the models just take the

continuity alone. One of these models is convex method (Weinman and Laurreson 1979).

Application of convex method requires 1) determination of the flood wave travel time through the

stream which is named (T*), and (2) selected time step for flood routing which is named (t).

The inflow hydrograph is assumed as a triangular hydrograph.

There is a condition to select the time step for routing in convex method. The selected time step should

be less than T* and 20% of the time to peak of the inflow hydrograph. This condition causes diffusiv-

ity in outflow hydrograph. The diffusivity factor indicates the tendency of hydrograph to be flattening.

This tendency is due to the pressure force and increases with depth and decrease with slope and chan-

nel roughness as the flow moves downstream.

As the method is a physically based method, it mostly deals with Chezy and Manning equations and

their required data. The time of rising and falling limbs of hydrograph also are required. Since most of

the required data are available, it was decided to use it in this study.


25

3.3.1 Input data preparation

To run the convex routing model it is required to determine the peak discharge of a catchment. As it

was explored in rainfall analysing, to get a more accurate and realistic value for flood discharges, it

was decided to take 25mm/hr, as the intensity of design storm in study area.

By applying this intensity of rainfall, the calculated peak flow of entire catchment will be in a reason-

able range.

Rational formula will be used to calculate the peak discharge in various sub catchments. Comparing

the recorded rainfall and runoff data determines the runoff coefficient of study area. Based on existing

data this coefficient was calculated of about 20%.

As it was explained before, the convex routing method will simulate the outflow hydrograph assuming

a triangular inflow hydrograph. The main input data to run this model is: length of stream channel,

width of streambed, bed slope, Manning coefficient, time to peak (TP) and the peak discharge of cor-

responding channel.

The geometric data of channels are determined based on field measurements and some distance

measurements in ILWIS. The input data to run the convex model are given table 3.2.

Catchment QP (m3/s) River length

(Km)

Channel width

(M)

Manning

(n)

Bed Slope (%)

Tp (hr)

Rendan 91.35 11.0 4.5 0.06 9.6 1.1

Sanghan 63.67 13.25 4.1 0.065 8.7 1.3

Keshar 47.1 12.73 4.3 0.065 8 1.2

Kiga 32.84 9.71 4.5 0.06 15.3 0.8

Solghan 45.54 13.65 7 0.06 4.5 1.6

Table 3.2- input data in convex model 3.3.2 Results of convex model

The convex model has been run for all sub catchments by considering 0.1 hr time step. The model cal-

culates a simple triangular inflow hydrograph regarding the time steps and other assumed data. The

input hydrographs are shown in figure 3.1.

The starting time is a function of streams distances to the main outlet and its TC. It is obvious that the

closer the distance to the outlet, the earlier time of starting. Based on these two parameters, the hydro-

graphs are arranged.

Delaying time of each sub hydrograph is an essential factor to generate the total hydrograph of entire

catchment. It is taken in account to produce the total hydrograph of Kan catchment. As it clearly

shown the total flood peak is about 210 m3/s, which will pass trough the main outlet in Solghan.

Regarding the model requirements to rout the flow and assuming a diffusivity factor equal to 0.5 the

out flow hydrograph for those sub catchment and consequently for whole catchment have been simu-

lated. The outflow hydrographs are shown in figure 3.2.


26

0

50

100

150

200

250

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50TIME(HR)

DIS

CH

AR

G(C

MS)

SOLGHAN KESHAR SANGHANRENDAN KIGA TOTAL

Figure 3.2- Convex method Inflow hydrographs of Kan catchment

0

50

100

150

200

250

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0TIME(HR)

DIS

CH

AR

GE(

CM

S)

SOLGHAN KESAR SANGHAN RENDAN KIGA TOTAL

Figure 3.3- convex routed outflow hydrographs of Kan catchment

These two figures clearly show the effect of delaying time of sub hydrographs on total peak flow. Fig-

ure 3.2 indicates that the outflow sub hydrographs have been flattening due to the diffusivity factor.

For instance in Rendan the peak inflow is about 91 m3/s, meanwhile peak outflow is about 85 m3/s and

such a reduction has occurred in other rivers. Beside the peak flow, time to peak in those rivers has

been lengthened. However no sufficient reduction in total peak flow is simulated. The sub hydrographs

peaks are reduced but still the total peak is about 210 m3/s.

Considering different TC for various rivers will give completely different values for flood peak.

Some of these results, which have been obtained by assuming different TC for some of streams, are

given in below figures.


27

0

25

50

75

100

125

150

175

200

225

250

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5TIME(HR)

DIS

CH

AR

G(C

MS)

kig+0.2hr san+0.2hr kas&sol+0.2hr sol+0.2hr ren&kig+0.2hr normal

Figure 3.4- effect of TC prolongation by 0.2hr on peak flood in Kan catchment As is shown in figure 3.3 prolonging the TC of streams by 0.2 hr does not have much impact on flood

peak and shape of hydrograph. Even in some cases it increases the peak at outlet.

0255075

100125150175200225

0.0 1.0 2.0 3.0 4.0 5.0 6.0TIME(HR)

DIS

CH

AR

G(C

MS)

normal hy. ki&ren+0.2hr ren+0.2hrsan&kes+0.2hr ren+0.4hr ren+0.6hr

Figure 3.5- effect of TC prolonging on peak flood in Kan catchment Different combinations of assigning times of concentrations to sub catchments tested. The cases,

which resulted in lowering peak flow, are retrieved. Figure 3.5 and 3.6 shows the impact of TC pro-

longing in Rendan and Kiga on peak flood.


28

0

50

100

150

200

250

0 1 2 3 4 5 6TIME(HR)

DIS

CH

AR

G(C

MS)

normal ren+0.2hr ren+0.4hr ren+0.6hr ren+0.8hr ren+1hr

Figure 3.6- effect of TC prolonging in Rendan River on peak flood reduction in Kan catchment

0

50

100

150

200

250

0 1 2 3 4 5 6TIME(HR)

DIS

CH

AR

G(C

MS)

normal kiga+0.4hr kiga+0.6hr kiga+0.8hr kiga+1hr

Figure 3.7- effect of TC prolonging in Kiga River on peak flood reduction in Kan catchment Different alternatives, which affect the peak flood, are shown in below table.

In some cases slope reduction on numbers of streams has no more lowering than the slope reduction

within individual stream. For instance prolonging the TC of both Kiga and Rendan by 0.3 hr has less

impact than prolonging TC of just Rendan by the same time. This is shown in below table.

As it clearly shows different alternatives gives various flood reduction indexes. The maximum index

indicates that by reducing the slope of Rendan by 80% (prolonging the TC by1 hour) the peak flood

will be reduced by 31 %.

Catchment Slope reduction (%) Flood peak reduction (%)

at main outlet


29

Rendan 0.21 0.10 Rendan 0.42 0.15 Rendan 0.60 0.22 Rendan 0.69 0.25 Rendan 0.76 0.28 Rendan 0.80 0.31 Kiga 0.25 -0.02 Kiga 0.46 0.00 Kiga 0.61 0.03 Kiga 0.72 0.05 Kiga 0.80 0.07 Kiga 0.85 0.08 Kiga 0.87 0.09 Kiga 0.88 0.10 Kiga 0.90 0.12 Sanghan 0.63 0.07 Sanghan 0.75 0.11 Sanghan 0.78 0.13 Sanghan 0.81 0.15 Sanghan 0.85 0.16 Sanghan 0.88 0.17 Sanghan 0.90 0.19 Sanghan 0.94 0.22 Solghan 0.33 -0.03 Solghan 0.44 -0.04 Solghan 0.56 -0.06 Solghan 0.67 -0.05 Solghan 0.78 -0.04 Solghan 0.82 -0.03 Keshar 0.23 -0.01 Keshar 0.49 -0.06 Keshar 0.61 -0.08 Keshar 0.72 -0.10 Keshar 0.84 -0.08 Keshar 0.89 -0.07 Keshar 0.91 -0.06 Keshar 0.93 -0.03 Keshar 0.94 -0.02

Table 3.3-flood peak variation based on slope reduction of sub catchments

3.4 Applying Manning equations


30

Since the Manning formula is the most commonly used formula in river and hydraulic engineering ap-

plications and it is based on hydraulic radius and slope, it was selected to compare the results.

Two different cross sections have been supposed; Natural condition of Rendan river (initial slope,

manning coefficient, and cross section) and secondary condition after constructing check dams.

V1 V2

C1 C2 C3

C3 C4

Figure 3.8- Simple schema of Rendan channel by constructing check dams

Manning formula indicates;

Q1= A1 * n1 –1 * R1 2/3 * S1 0.5 (3.1)

Q3= A3 * n3 –1 * R3 2/3 * S3 0.5 (3.2)

By applying continuity; A1 * n1 –1 * R1 2/3 * S1 0.5 = A3 * n3 –1 * R3 2/3 * S3 0.5

Whereas C1 is the initial cross section and C3 is the check dam site cross section. By substituting the Y1=4.29 m Y3=5.33 m B1= 4.5 m B3=5.2 m S1= 0.096 S3=0.001 (very gentle slope) N1=0.065 N3=0.045 Z1=z3= 1:1 channel bank side slope In equations 3.1 and 3.2 and assuming that the calculated perimeter in natural channels is two times of smooth channels we will have: A1=19.85 m2 A3=41.92 m2 P1=23.6 m P3=25.677 m R1=0.84 m R3=1.633 m V1=4.6 m/s V3=2.18 m/s The slope and corresponding velocity in C2 (the ultimate cross section) will be calculated.

V2=(V1+V3)/2=3.39 m/s

S2=(S1+S3)/2= 5%

If the desirable slope is assumed about 2% then the corresponding velocity will be as:

V2=(4*V3+V1)/5=2.66 m/s

If we assume that the offloading time of flow is proportional to flow velocity then:

V1/V2=T2\T1 where T1 and T2 are the offloading time of flow. Assuming the convex input flow as

the input hydrograph and considering no lateral inflow the offloading time after constructing check

dams will be:

T2=4.6*3*/2.66 = 5.18 hrs

The area under the inflow hydrograph is the total amount of flow, which will be:


31

Total volume= 91.35*3*3600/2= 493290 m3

Considering the equality of the areas under the curve and similarity (figure 3.8) we will have:

Q2=493290*2/(5.18*3600)= 52.9 m3/s

0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00

time(hr)

disc

harg

e(cm

s) inflow

outflow

Figure 3.9- inflow and outflow hydrographs before and after constructing check Rendan River By Manning equation

Manning equation can’t be considered as a real routing method because it doesn’t deal with the length

of river as a main component in routing procedure. It can be used just for determining the peak flow at

any certain cross section. Due to the absence of stream length it will exaggerate the differences in short

reaches.

3.5 Comparing the results of convex model with observed hydrographs To have a general view of the utilized methods and approaches it will be more useful, if the output re-

sult of the routing model and the impact of check dams compare with the real events and physically

based formulas.

To compare the result of convex routing model with the real time events, two observed outflow hydro-

graphs in Solghan station are plotted with the convex model out flow hydrographs in a dimensionless

graph. It will be a good scale to evaluate the applied models and approaches.

What ever the shapes of simulated and observed hydrographs look closed to each other, the simulating

method is more confirmed.

However the accuracy of measurements should be considered as a main issue in this comparison.

Figure 3.9 shows these hydrographs.


32

TIME

DIS

CH

AR

G

Convex routed Observed 1/12/2000 Observed 18/10/2000

Figure 3.10 comparing the convex routed and observed dimensionless hydrographs in Solghan station

As it can be seen there are some breaking points in falling limb of observed hydrographs. It can be the

effect of delayed discharges within the catchment. Convex routed hydrograph doesn’t show this point

clearly.

Generally and with respect to the lack of detail data the results of convex method is not so far from the

real events and recorded data.


33

4. CHECK DAM CONSTRUCTION

4.1. Check dam functionality The overall slope (after construction of check dams) is a linear function of the number of check dams.

If the crest of downstream check dam and toe of the upstream one be at the same level the channel bed

will be almost level. See figure 4.1.

Some other functionality of check dams can be supposed such as water storage within the first duration

of operating by infiltration.

This study deals just with slope reduction functionality. The other ones can be studied later on in more

detail in the future.

Secondary slope Initial slope

Height of check dam

Figure 4.1- functionality of check dams in stream channel slope reduction

4.2. Number of check dams Numbers of check dams, which should be constructed through a stream to reduce the slope,

Is a function of original slope, overall slope, length of stream, and height of check dams.

N.O.C.D = (S1-S2)*L / H (4.1)

Where as; S1: original slope %

S2: secondary slope %

N.O.C.D: number of check dams

H: height of check dam (m)

L: length of channel (m)

By applying equation 4.1, number of check dams with various heights for the supposed slopes in table

3.2, can be calculated. See Table 4.1 The table also shows the secondary time of concentration of

streams based upon corresponding slope.

Catchment Secondary slope Secondary TC NOCD NOCD


34

S2 (%) TC2 (min) H=1.5m H=3m

Rendan 7.6 116 148 74

Rendan 5.6 125 296 148

Rendan 3.8 136 422 211

Rendan 3 144 486 243

Rendan 2.3 153 535 268

Rendan 1.9 160 563 282

Kiga 11.5 99 248 124

Kiga 8.3 107 456 228

Kiga 6 116 604 302

Kiga 4.3 125 713 357

Kiga 3.1 135 792 396

Kiga 2.3 145 842 421

Kiga 2 150 862 431

Kiga 1.8 153 872 436

Kiga 1.5 160 891 446

Sanghan 3 157 445 223

Sanghan 2 173 530 265

Sanghan 1.8 177 551 276

Sanghan 1.5 185 572 286

Sanghan 1.2 195 601 301

Sanghan 1 203 622 311

Sanghan 0.8 214 636 318

Sanghan 0.5 239 664 332

Table 4.1 Number of needed check dams to gain supposed slopes

If the height of check dams becomes double the number of check dams will be half. But regarding the

stability of the structures the required material will increase. Height and needed material increment are

not linearly proportional to each other, because the two important forces which acts on check dams are

proportional to the second power of height.

4.3. -Required material


35

Considering the forces, which are acting on the check dams, two types of gabion check dams have

been considered. The main characteristics of these two types are given in below table.

It can be clearly seen that volume of needed material per meter of unit width of rivers for 1.5-meter

height check dam is about one-third of 3-meter height check dam.

Table 4.2- characteristics of designed check dams

These factors will be transferred to “DEFINITE” software to generate a decision support system

(DSS).

This software will be used to select the most feasible alternative to be carried out in Kan catchment as

an optimised flood control project.

5. DECISION MAKING

Check dam

Type

Height

(m)

Crest width

(m)

Bottom width

(m)

Needed Material

(M3)/m

Type 1 1.5 2 4 6.75

Type 2 3 3 6.5 21.375


36

5.1. General objectives In view of the stress on district level planning in the country, and regarding the limitation of funds de-

cision makers need to have a clarified perspective of the offered alternatives.

There are two main types of decision criteria evaluation; objective and subjective evaluation. Ob-

jective evaluation deals with effective potentials and their impacts considering the market value of in-

volved criteria. Subjective evaluation will deal with various approaches that share to common purpose

of suggested alternative options. It will help make a rational and feasible decision.

The method, which is employed by the subjective evaluation, is called multiple criteria decision- mak-

ing (MSDM). These methods can provide a simple and clear performance of those alternatives to help

the decision makers to have a judgment and choose the best option. There is some applicable software

to be involved with these methods. A useful package for this application is “Definite ” which will ap-

ply a complete decision support system.

5.2. DEFINITE

5.2.1 Introduction to DEFINITE

DEFINITE (decisions on a finite set of alternatives) is a decision support software package that has

been developed to improve the quality of decision-making. DEFINITE is, in fact, a whole toolkit of

methods that can be used for a wide variety of problems. If the alternatives can be identified, then

DEFINITE will help us to solve the problem of interest.

The program contains a number of methods for supporting problem definition as well as graphical

methods to support representation. To be able to deal with all types of information DEFINITE includes

multicriteria methods, cost-benefit analysis and graphical evaluation methods. Related procedures such

as weight assessment, standardization, discounting and a large variety of methods for sensitivity analy-

sis are also available. A unique feature of DEFINITE is a procedure that systematically leads an expert

through a number of rounds of an interactive assessment session and uses an optimisation approach to

integrate all information provided by the experts to a full set of value functions.

DEFINITE supports the whole decision process, from problem definition to report generation. Its

structured approach ensures that the decisions arrived at are systematic and consistent.

5.2.2 DEFINITE input data

Due to the hydrologic analysis 20 alternative options based on reduction of Qp by constructions of

check dams identified. By defining the problem in DEFINITE software these 20 options will be as

considered alternatives.

For those alternatives 4 common effects have been identified and quantified. The quantitative values

are transferred to DEFINITE to do the multicriteria analysis.

1.number of check dams


37

2.height of check dams

3.cost of each option

4.flood reduction index

These are the most important factors in engineering measures of watershed management projects and

will affect the general characteristics of project.

These criteria have been standardized using the maximum method of standardization. The maximum

method is the best choice whenever we want to exaggerate the differences on purpose. The other ad-

vantage of this method is that the standardization values are proportional to the original values.

Especially in analysing the cost effects it is most important: if an alternative is twice expensive it

means it is twice worse and its chance is twice less.

Among the different criteria the priority orders have been justified regarding the actual execution ex-

pected difficulties and project maintenance.

There are various options to be selected, as a weighting method. The direct method, which is based on

the experiments and manager judgment, is the most reasonable method to be chosen. These

are the major inputs for DEFINITE software to be run and select the most rational alternatives, which

can be recommended to decision- makers.

By applying” inefficient alternatives option” in DEFINITE the number of alternatives were reduced

by 12 alternatives.

Table 5.1 DEFINITE effect table of Kan flood control project

5.2.3 DEFINITE output results:

As was mentioned 12 most efficient alternatives were selected. By maximum standardization, direct

assessment method of weighting, and ratio scale measurement, the program was run and the following

results were taken. I assumed that the weight of cost and flood peak reduction is equal. See figure 5.1


38

Figure 5.1 result of multicriteria analysis for Kan flood control As it can be seen alternative number 5 which indicate that, constructing 565 check dam with 1.5 meter

height in Rendan river, by spending 428800 $ will reduce the flood peak by 31 % is the most rational

alternative.

5.2.4 Sensitivity analyses

A ranking of alternatives is only certain if the given scores, priorities, value functions can be estimated

by complete certainty. Whenever the uncertainty goes up the ranking will be unstable and unreliable.

To investigate the impact of those effects on the decision-making procedure, one needs to have a clari-

fied perspective of those effects and their impacts on the purpose of the project.

Sensitivity analysis is a very good method to compare priority of alternatives regarding the variation of

the weights of effects.

For two main effects of interest, cost and flood peak reduction, this method has been carried out and

the results are given in figure 5.2.

It can be seen that by considering the given weight to the cost effect (w=0.45) on the multicriteria

analysis the first rank of alternatives is stable if the variation of cost weight is between 0.12-0.5. It

means that if the given weight gets smaller till 0.12 still the A5 is the first rank. Meanwhile if it in-

creases a little and comes to 0.5 the first rank (A5) will be reversed by A2 and will remain stable by

increasing the weight of cost by the end. Based on the weight of flood peak reduction the ranking will

be stable from 0.42 and greater. If the weight of flood peak reduction decrease by 0.42 and less the

ranks will be varied.


39

Figure 5.2. Sensitivity of ranking for cost and flood peak reduction criterions

6. CONCLUSION AND RECOMMENDATIONS


40

River gradient is an important factor among the many factors that influence the flood peak in a catch-

ment. It is obvious that for an individual stream the steep the stream gradient favours flash flow and

consequentially high flood peak at its outlet. However, considering a stream network catchment the

interaction between various streams should be taken in account.

Flow routing is an essential component of flood control studies. Regarding the results of various

methods, which were examined, it was found out that the HEC-HMS model was not be suitable to

investigate the role steep gradient and narrow channel beds. The reason is that the amount of river

storage is not considerable in comparison with the total flow volume and there is little sensitivity to

flow routing and gradient variations.

Especially for areas such as many parts of our country with a limited availability of input data and

insufficient information to permit proper parameterisation, this can be a problem. Use of more simple

models, such as convex routing method may offer a better approach than the application of a complex

model such as HEC –HMS or any other complex models.

Furthermore, it is found out that for hydrological investigations, it is not always necessary to

apply directly a complex one for several reasons.

- Depending on the purpose of the study, the upgrading of only a limited number of critical

processes in a simple model may be adequate to simulate satisfactorily the flow features of interest

- Not all process representations found in a complex model need to be present in a simple model be-

cause at particular spatial and temporal scales of investigation, runoff may be sensitive to only a lim-

ited number of processes. -

A compromise must be struck between the model demand and the availability of reliable input data

and this consideration is particularly pertinent for areas, which have relatively poor data.

The Manning equation can be used as an applicable method to compare just the peak flow in flow

routing.

As it does not deal with the stream length, could not be used as a perfect routing method. However it is

a powerful key to asses the results of other methods.

The Convex method is a simple and applicable model to rout the flow, since it deals with physically

based formulas and does not require detailed data about the rainfall and discharge in catchment.

In Kan catchment reducing the slope of all streams by check dams reduce the corresponding flood

peaks at the sub catchments out lets. But at the same time the flood peak at the main outlet of Kan does

not change or even increases a little in some cases.

This is due to the fact that the delayed hydrographs still add up to a high peak flow at the outlet of the

catchment.

The analyses show that by reducing the gradients of Solghan and Keshar tributaries but not other

cases flood peak at the main outlet of Kan catchment will increase by about 10%.


41

The largest reduction of the peak flow at catchment outlet (31%) was found by construction of check

dams in the main river of Rendan, if the number of check dams was so large that the overall gradient

was reduced from the present 9.6% to 2%.

General conclusion drawn from the study is that watershed management planning and flood control

measures, which mostly dealing with gradient reduction should focus on the sub catchments, which are

further from the main outlet. The flood runoff from sub catchments closer to the outlet will have

passed before the flood waves from the up stream catchments arrive.

It is not necessary to delay the hydrographs of those sub catchments, which are too close to the main

outlet. The amount of sub catchments discharge is another important factor, which should be taken in

account in this application.

Hence considering above conclusions should do selection of sub catchment to carry out the watershed

management measures.

To achieve more reduction on flood peak in Kan catchment, apart from engineering measures the other

biological and management measures should be carried out in various sub catchments to capture the

rainfall and reduce the amount of direct run off.

The taken results of multicriteria analysis indicate that by assigning equal weight to the cost of project

and flood peak reduction index the best alternative will be the alternative which cause more reduction

on flood peak. However assigning various weights to the different effects will change the selected al-

ternative. To make proper decisions national policy on flood control projects considering the different

technical and economical aspects should be prepared.

In the mountains there is much erosion and it is estimated that the small check dams in the Rendan

river, although numerous, will be filled in about five years, some big reservoirs are needed.

REFERENCES


42

1. Brouwer. H.D. Unit Hydrograph & Hydrograph Routing, Lecture note, ITC, 1995

2. Chow, V.T, Handbook of Applied Hydrology, Mc, Graw – Hill, Inc, 1964

3. Dingman, S. L. Physical Hydrology, Prentice-Hall, Inc. 1993

4. FAO.1976.Watershed Management Field Manual, Gully Control.FAO Conservation Guide

NO.13/2. Rome 1976

5. FAO.1983.Guidelines For Economic Appraisal Of Watershed Management Projects. FAO

Conservation Guide NO. 16. Rome 1983

6. ILWIS 3.11, 2002. User’s Manual, ITC

7. Meijerink A.M.J. & De Brouwer H.A.M. & Mannaerts. Chris M. &Valenzuela. Carlos R. In-

troduction to the Use of Geographic Information Systems for Practical Hydrology. ITC

Publication No.23. 1994

8. Mc Cuen, R. H, Hydrologic Analysis & Design, Prentic- Hall Inc, 1989.

9. Maidment. D. R. Handbook of Hydrology, Mc, Graw-Hill, Inc.1992

10. Sharifi, M .A. Introduction To Decision Support System And Multi Criteria Technique, Lec-

ture note, ITC, 2001

11. US Army Crops of Engineers, Technical Reference Manual of Hydrologic Modeling System

HEC-HMS, March 2000

12. Viessman, W.G, Lemi, Introduction to Hydrology, Happer & Row Publishers, 1989

13. Watershed Management Plan of Kan Catchment, Jihad Research Co. 1999 (In Persian).

14. Watershed Management Plan of Northern Tehran Catchment, Jihad Technical Services Co.

1992 (In Persian)

15. Weinmann, P.E., and E.Mlaurenson.1979. Approximate Flood Routing Methods: A Review.

American Society of Civil Engineeres Procceding, Journal of the Hydraulics Division 105

(HY-12): 1521-1526


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evaluating the effect of check dams on flood peaks to

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