Application of Swat

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Devaraj de Condappa, Jennie Barron,

Sat Kumar Tomer and Sekhar Muddu

Application of SWAT and a Groundwater Model for Impact

Assessment of Agricultural Water Management Interventions

in Jaldhaka Watershed: Data and Set Up of Models

Stockholm Environment Institute, Technical Report - 2012


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Application of SWAT and a GroundwaterModel for Impact Assessment of AgriculturalWater Management Interventions in JaldhakaWatershed: Data and Set Up of Models

Devaraj de Condappa, Jennie Barron,Sat Kumar Tomer and Sekhar Muddu


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Copyright © March 2012 by Stockholm Environment Institute


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ABSTRACT

This study contributes to the understanding of potential for Agricultural Water Management (AWM)

interventions in the watershed of Jaldhaka river, a tributary of the Brahmaputra river, located in Bhu-

tan, India and Bangladesh. An application of the Soil Water Assessment Tool (SWAT) and of a simple

lumped groundwater model was developed for the Jaldhaka watershed.

The rst stage of this work was to collect a large dataset to characterise the natural and agricul -

tural contexts of the Jaldhaka watershed. The watershed has a contrasting topography, with mountains

upstream and large plains downstream. It experiences high rainfall with a monsoonal pattern and an

average of 3,300 mm/year. The river ow is seasonal, with a sustained ow during the dry season, high

ows during the monsoon and recurrent ood events. The soils are sandy loam (upstream) to silty loam

(downstream), with little permeability. The aquifers in the region are alluvial and the groundwater lev-

els in the watershed are shallow and stable.

This study contributed to the development of a precise landuse map which identies the natural

vegetation, the water bodies, the settlements / towns, the tea plantations and the different cropping

sequences in the agricultural land. Agricultural statistics were gathered at administrative levels for

cropping sequences and crop yields. The irrigation in the watershed is predominantly from groundwa-

ter, with diesel pumps, to irrigate rice during summer and potatoes during winter.

SWAT and the groundwater model were adjusted in an interactive manner: SWAT was calibrated

against the observed streamows while the groundwater model was calibrated against the observed

groundwater levels and the interaction aimed at the convergence of both models. The performance

was satisfactory for modelling the watershed on an average monthly basis. However, the model set-up

failed to reproduce adequately the crop yields. This paper ends with a discussion of the modelling set-

up and data collection for agro-hydrological modelling.

This set-up was applied in an accompanying research report to study the current state of the hydrology

in the Jaldhaka watershed and the impacts of two types of AWM scenarios.


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CONTENTS

Abstract iii

List of abbreviations viii

1 Introduction 1

2 Introduction to the modelling softwares 3

2.1 Soil and Water Assessment Tool (SWAT) 3

2.2 Groundwater model 3

3 Biophysical data of the Jaldhaka watershed 6

3.1 Digital Elevation Model 6

3.2 Streamflow data 6

3.3 Climate data 123.4 Soils 16

3.5 Groundwater data 20

3.6 Land-use 23

3.7 Agricultural 29

3.8 Irrigation 37

4 Modelling set up 40

4.1 Initial setting of SWAT 40

4.2 Calibration of the groundwater model and SWAT 45

5 Discussion 56

5.1 … on the input dataset 56

5.2 … on the model set up 57

6 Conclusion 58

Acknowledgements 60

Annex 62

References 70


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LIST OF FIGURES

Figure 1: Location of the Jaldhaka / Dharla river watershed (in purple). The delineation

of the Jaldhaka / Dharla watershed were generated in this work. 1Figure 2: Scheme of the modelling 3

Figure 3: Digital Elevation Model from the Shuttle Radar Topography Mission and

locations where climatic and streamflow data was available 7

Figure 5: Topographic profile of the transect defined in Figures 3 and 4 7

Figure 4: Slope derived from the DEM, the two local meteorological and streamflow

gauge stations 7

Figure 7: Available time-series for streamflows measured at Taluk-Simulbari and

Kurigram stations, unfiltered (left) and average monthly streamflow, filtered

(right); the vertical error bars indicate the statistical standard deviation of daily

streamflows 9

Figure 8: Zoom around Kurigram on Google Earth where are visible the infrastructuresfor water diversion as well as the neighbouring rivers, in particular the

massive Brahmaputra. 10

Figure 9: Representative average rainfall for the Jaldhaka watershed, as calculated by

SWAT, and average streamflow at Kurigram (period 1998 – 2008) 11

Figure 10: Rainfall at Jalpaiguri and Cooch Behar stations (period 1988 - 2008). Top:

daily rainfall. Middle: annual rainfall. Bottom: average monthly rainfall, the

vertical error bars in red indicate the statistical standard deviation of daily

rainfall (in mm/day) 13

Figure 11: Average climatic data at Jalpaiguri and Cooch Behar stations (period 1988

- 2008). Top: temperature. Middle: wind. Bottom: humidity. The vertical error

bars indicate the statistical standard deviation of daily data 15

Figure 12: Distribution of the average annual rainfall in the sub-watersheds, as

represented in SWAT (period 1998 - 2008) 16

Figure 13: The georeferenced soil map in the region of the Jaldhaka watershed 17

Figure 14: The Harmonised World Soil Database and its soil units in the region of the

Jaldhaka watershed. 17

Figure 15: Plot in soil textural triangle of the United State Department of Agriculture 19

Figure 16: Location of the observation wells for groundwater level measurement. CGWB

stands for Central Ground Water Board and SWID for State Water 21

Figure 17: Measured groundwater levels in the Jaldhaka watershed. In pale: level of

different wells. In black: average of all the wells 22

Figure 18: Typical groundwater levels in the Jaldhaka watershed. The wells are located

on Figure 16. The vertical error bars indicate the statistical standard deviation 22Figure 19: Interpolation of average piezometric levels observed by the State Water

Investigation Directorate (SWID) (period 1994 - 2009). 23

Figure 20: Satellite images acquired for high resolution landuse mapping. Note the

demarcation between the north and south view 24

Figure 21: Location of the groundtruthing sites visited in April 2010 and draft

unsupervised classification of the landuse. Right: zoom on the transect (note

on this view the discrepancy 25

Figure 22: Calendar of the main cropping sequences in the Jaldhaka watershed 26

Figure 23: High resolution (10 m) landuse map of the Jaldhaka watershed (year 2008). 27

Figure 24: Photos of the spots identified on the landuse map (Figure 23) 28

Figure 25: Modified version of the landuse map (Figure 23, year 2008) entered in SWAT(90 m resolution) 29


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LIST OF TABLES

Table 1: Topographic regions of the Jaldhaka watershed 7

Table 2: Available number of measurements at Taluk-Simulbari and Kurigram stations.

Source of data: Bangladesh Water Development Board. 8

Table 3: Available climatic time-series and gaps in the datasets. RMC stands for

Regional Meteorological Centre (Kolkata) and NCC for National Climate

Centre. 12

Table 4: Annual rainfall at Jalpaiguri and Cooch Behar stations (period 1988 - 2008) 14

Table 5: Available measured groundwater levels in the Indian part of the watershed.

CGWB stands for Central Ground Water Board and SWID for State Water

Investigation Directorate. 20

Table 6: Distribution of the landuse categories (Figure 23) within the Jaldhaka

watershed. 28

Table 7: Distribution of the landuse categories entered in SWAT (Figure 25). 30

Table 8: Available agricultural statistics. 30Table 9: Average yields in the administrative blocks containing the Jaldhaka

watershed, period 1998 – 2008. In bracket the average dry yield of rice for

period 2004 to 2008. Source of data: Bureau of Applied Economics and

Statistics and Directorate of Agriculture. 31

Table 10: Typical cropping sequences and associated irrigation schedules in the

Jaldhaka watershed. 34

Table 11: Indicative distribution per sub-watershed of the cropping sequences within the

landuse units AAAJ and AWJJ. Derived with data from the Bureau of Applied

Figure 26: Area of the major crops in administrative blocks containing the Jaldhaka

watershed 32

Figure 27: Yield of the major crops in administrative blocks containing the Jaldhaka

watershed. Mind the different vertical scale 33Figure 29: Average monthly reference evapotranspiration calculated from difference

sources 46

Figure 30: Calibration with respect to the actual evapotranspiration ETa. Monthly value

of the different landuse vegetation categories (average over the calibration

period, 1998 – 2008). 47

Figure 31: Piezometric levels simulated at a monthly time-step by the groundwater model

vs. observations 48

Figure 32: Calibration with respect to the recharge of the shallow aquifer (GW_RCHG),

average for the Jaldhaka watershed over the calibration period (1998 – 2008) 49

Figure 33: Calibration with respect to the shallow groundwater baseflow (GW_Q),

average for the Jaldhaka watershed over the calibration period (1998 – 2008) 50Figure 34: Streamflow simulated (FLOW_OUT) at Kurigram in the initial run over the

calibration period (1998 – 2008) 51

Figure 35: Streamflow simulated (FLOW_OUT) in the final calibration (calibration run

n°100) over the calibration period (1998 – 2008). 53

Figure A.1: Example of the groundtruthing form (site GT 35) filled by the field assistants 69

Economics and Statistics and the Directorate of Agriculture. 36

Table 12: Estimated irrigation per crop. Sources of data: groundwater pumping duration

from Mukherji (2007) and diesel pump discharge from TERI (2007) 38


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LIST OF ABBREVIATIONS

ALAI_MIN SWAT parameter, minimum LAI for plant during dormant period [L2/L2]

ALOS Advanced Land Observing Satellite

ALPHA_BF SWAT parameter, baseflow alpha factor [-]

ArcSWAT ArcGIS interface for SWAT

AVNIR Advanced Visible and Near Infrared Radiometer

AWC SWAT parameter, available water capacity (AWC, [L3/L3])

AWM Agricultural water management

B Baseflow into the streams [L/T]

BLAI SWAT parameter, maximum potential LAI [L2/L2]

CGWB Central Ground Water Board

CH_K(1) SWAT parameter, effective hydraulic conductivity in tributary channel alluvium[L/T]

CH_K(2) SWAT parameter, effective hydraulic conductivity in main channel alluvium [L/T]

CH_N(1) SWAT parameter, Manning's value for the tributary channel [-]

CH_N(2) SWAT parameter, Manning's value for the main channel [-]

CHTMX SWAT parameter, maximum canopy height [L]

CN2 SWAT parameter, initial soil curve number for moisture condition II [-]

DEEPST SWAT parameter, initial depth of water in the deep aquifer [L]

DEM Digital elevation model

Dnet Net groundwater draft [L/T]

DPDWB Development & Planning Department - West Bengal

EPCO SWAT parameter, plant uptake compensation factor [-]

ESCO SWAT parameter, soil evaporation compensation factor [-]

Table 13: Indicative areas irrigated from surface sources in each sub-watershed,

derived from DPDWB (2005) for year 2004/5 38

Table 14: HRUs generation stages. 40

Table 15: Landuse distribution considered in SWAT after pre-processing by ArcSWAT,with respect to the discretisation in HRUs, and management operations for

each category. 42

Table 16: Estimation of irrigation areas and amount for 2008, with respect to the

discretisation in HRUs. 43

Table 17: Initial values for undetermined SWAT’s parameters. 44

Table 18: Average annual reference evapotraspiration calculated from different sources. 46

Table 19: Calibration with respect to the evapotranspiration ETa. Annual values of the

ratio ETa / ET0 for the different landuse vegetation categories (average over

the calibration period, 1998 – 2008). Aman: monsoon rice, Boro: summer

rice, Aus: pre-monsoon rice. 47

Table 20: Values of the calibration indicators defined by Eq. (10) to (13). 52Table 21: Watershed-average dry crop yields simulated by SWAT in the final calibration

(calibration run n°100) over the calibration period (1998 – 2008). 53

Table 22: Simplifications and limitations of the modelling. 55

Table A.1: Soil parameters. Light orange: data from the original soil map from the Indian

National Bureau of Soil Survey and Landuse Planning. 62

Table A.2: SWAT vegetation / crop parameters. 65

Table A.3: Sources of irrigation per administrative blocks containing the Jaldhaka

watershed, year 2004/5 66

Table A.4: SWAT calibration steps. 68


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ETa Actual evapotranspiration [L/T]

ET0 Reference evapotranspiration [L/T]

FLOW_OUT SWAT ouput, average daily streamflow out of reach during time step [L/T]

GIS Geographical Information SystemGPS Global Positioning System

GT Groundtruthing

GW_DELAY SWAT parameter, groundwater delay time [T]

GW_Q SWAT ouput, groundwater baseflow contribution to streamflow [L]

GW_RCHG SWAT ouput, recharge entering the shallow aquifer [L]

GW_REVAP SWAT parameter, groundwater evaporation coefficient [-]

GWQMIN SWAT parameter, threshold depth of water in the shallow aquifer required forbaseflow to occur [L]

h Groundwater piezometric level [L]

HRU Hydrologic response unit

HWSD Harmonised World Soil Database

I Irrigation [L/T]IDC SWAT parameter, land cover / plant classification

IWMI International Water Management Institute

LAI Leaf area index [L2/L2]

M Bias indicator [-]

masl Meter above sea level [L]

mbgl Meter below ground level [L]

NS Nash and Sutcliffe (1970) efficiency [-]

NShigh Modified version of NS to emphasise on high flows [-]

NSlow Modified version of NS to emphasise on low flows [-]

O Net groundwater underflow [L/T]

P Rainfall [L/T]

PGIS Participatory GISPHU SWAT parameter, total number of heat units or growing degree days needed to

bring plant to maturity

RCHRG_DP SWAT parameter, deep aquifer percolation fraction [-]

RDMX SWAT parameter, maximum root depth [L]

REVAPMN SWAT parameter, threshold depth of water in the shallow aquifer required forevaporation or percolation to the deep aquifer to occur [L]

RG Total groundwater recharge [L/T]

SEI Stockholm Environment Institute

SHALLST SWAT parameter, initial depth of water in the shallow aquifer [L]

SOL_K SWAT parameter, Soil conductivity, [L/T]

Sol_Z SWAT parameter, depth from soil surface to bottom soil layer [L]

SOL_ZMX SWAT parameter, Maximum rooting depth [L]SRTM Shuttle Radar Topography Mission

SURLAG SWAT parameter, surface runoff lag coefficient [-]

SWAT Soil Water Assessment Tool

SWID State Water Investigation Directorate (West Bengal)

Sy Specific yield [L3/L3]

t Time [T]

T_BASE SWAT parameter, minimum (base) temperature for plan growth [°C]

T_OPT SWAT parameter, optimal temperature for plan growth [°C]

WISE World Inventory of Soil Emission Potentials

WTF Water Table Fluctuation


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1 INTRODUCTION

Agricultural Water Management (AWM) interventions are often a rst step towards increasing small-

holder farmers’ yield levels, their incomes and household food security, in many developing countries.

Globally, smallholder farming systems may have the potential to increase current yield levels 2-4

times, and water productivity gains potentially more than double (Rockström 2003).

The AgWater Solution project (http://awm-solutions.iwmi.org/) is systematically assessing opportu-

nities to invest in agricultural water management interventions at local to continental scale, to enhance

smallholder farmers livelihoods. However, agricultural development and intensication can also unin-

tentionally impact various social and environmental dimensions where the interventions are adopted.

This report considers the AgWater Solution project watershed of the Jaldhaka river, also known as

Dharla, a tributary of the Brahmaputra river. It is a transboundary river originating in Bhutan, ow-

ing through India and joining the Brahmaputra in Bangladesh (Figure 1). The Jaldhaka watershed

is one of four project watershed sites, subject to a suite of assessments on agro-hydrological, liveli-hood and institutional contexts undertaken to identify what potential opportunities there are at a local

(watershed) scale and how potential interventions may impact the environment, in particular water

resources, and livelihoods.

Figure 1: Location of the Jaldhaka / Dharla river watershed (in purple). The delineation of theJaldhaka / Dharla watershed were generated in this work.

Images adapted from Google Earth


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Appl ication of SWAT and a Groundwater Model for Impact Assessment

The focus of this work is the development of an application of the Soil Water Assessment Tool

(SWAT) and of a simple lumped groundwater model to study the impacts on hydrological balance

and crop production under different scenarios of agricultural interventions. This working paper pre-

sents the methodology deployed for data collection and agro-hydrological modelling of the surface

and groundwater resource of the Jaldhaka watershed. The accompanying research report de Con-

dappa et al. (2011) applies the modelling to analyse the current state of the hydrology and agricul-

tural water management scenarios. The following sections introduce the chosen modelling software

(section II.), the input dataset (section III.), the set up of the models (section IV.) and will end with a

discussion (section V.).


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2 INTRODUCTION TO THE MODELLING SOFTWARES

The primary hydrological model selected for this purpose was the Soil and Water Assessment Tool

(SWAT) developed by the United State Department of Agriculture and Texas A & M University. SWAT

simulates the different surface and ground hydrological components as well as crop yields. Since its

modelling of the groundwater is extremely simplied and the groundwater is a prominent water resource

for agriculture in the Jaldhaka watershed, the groundwater model developed by Tomer et al. (2010)

was also employed to specically describe the groundwater processes. The groundwater model inter -

preted the available groundwater levels, which is not possible with the used version of SWAT , and

guided subsequently the setting of SWAT’s groundwater parameters. The strategy of the modelling

that will be detailed in the following sections is illustrated in Figure

Figure 2: Scheme of the modelling

2.1 Soil and Water Assessment Tool (SWAT)For the application to the Jaldhaka watershed, version 433 of SWAT 2009 was used and it was oper-

ated through the interface ArcSWAT version 2009.93.4. General information on this model can be

found on the website http://swatmodel.tamu.edu and in the references Arnold et al. (1993), Srini-

vasan and Arnold (1994), Arnold et al. (1995) and Arnold et al. (1998)

2.2 Groundwater modelThe groundwater model considered here was developed by Tomer et al. (2010). It is based on a combi-

nation of groundwater budget and the Water Table Fluctuation (WTF) technique. The WTF technique

has widely been applied to link the change in ground water storage with resulting water table uctua-

tions through the storage parameter (specic yield). The WTF is a lumped model based approach suited

when limited hydraulic head measurements made at a nite number of observation wells and also lit-

tle hydrological, geological and meteorological information is available. It was rst used to estimate

ground water recharge (e.g., in West Africa by Leduc et al. (1997), in Korea by Moon et al. (2004)) and

has been extended to estimate change in groundwater storage (e.g., in California by Ruud et al. (2004))

or the ground water recharge and the specic yield (e.g., in India by Maréchal et al. (2006)) with the

combined use of groundwater budget.


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The main limitations of the WTF modelling method are: (i) it requires the knowledge of the spe-

cic yield of the saturated aquifer at a suitable scale, (ii) its accuracy depends on both the knowledge

and representativeness of water table uctuations and (iii) it does not explicitly take into account the

spatial variability of inputs, outputs, or parameters and considers the catchment as an undivided entity

and uses lumped values of input variables and parameters. This approach was however relevant to this

work as observed time-series of groundwater levels were available at different locations while very

few other hydrogeological data were obtained. As SWAT’s groundwater module does not simulate pie-

zometric levels, the groundwater model enabled the use of the available measured groundwater levels.

The mathematical expressions at the core of the groundwater model developed by Tomer et al. (2010)

is the groundwater budget:

(1)

where Sy [L3/L3] is the specic yield, h [L] is the groundwater piezometric level, t [T] is the time,

R G [L/T] is the total groundwater recharge due to rainfall and other sources including irrigation and

recharge from streams, Dnet

[L/T] is the net groundwater draft, B [L/T] is the baseow into the streams

and O [L/T] is the net groundwater underow from the area across the watershed boundary. The term

represents the total discharge (Tomer et al., 2010).

In this work, we assumed that O represented regional deep aquifer processes and was nil at the scale

of the Jaldhaka watershed. Moreover, following the approach of Park and Parker (2008), a linear rela-

tionship was assumed between the baseow B and the level h:

(2)

where λ [1/T] is a rate coefcient. Replaced in Eq. (1) it gives:

(3)

The equation (3) is a linear ordinary differential equation, which can be solved analytically. Follow-

ing the guidance of Simon (2006), the analytical solution was converted into a discrete equation for

the ease of modelling, which can be written as:

(4)

where A [-] is called the discharge parameter, k is the index for time and the discharge was equal to:

(5)

As commonly assumed, the recharge R G is calculated linearly from rainfall P [L/T] and irrigation I

[L/T]:


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(6)

where r [-] is the recharge factor. Note that the irrigation I is only equal to Dnet if groundwater is

the only source of irrigation water (e.g., no irrigation from river). As in the Jaldhaka watershed the

groundwater data did not show a constant recharge factor, a time varying recharge factor was assumed,

calculated from the rainfall P and irrigation I:

(7)

where a [-] and b [T/L] are recharge parameters. Finally, the total recharge RG is expressed as:

(8)

and Eq. (4) and (8) are used during the calculations of the groundwater model.


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3 BIOPHYSICAL DATA OF THE JALDHAKA WATERSHED

Biophysical data were gathered by conducting eld work, request to relevant organisations and using

publicly available data on internet.

3.1 Digital Elevation Model

3.1.a Source

The source of the Digital Elevation Model (DEM) is the Shuttle Topography Mission (SRTM), as

pre-processed by Jarvis et al. (2008).

3.1.b Analysis

In Figures 3 and 4, three topographic regions can be identied in the watershed (Table 1):

• mountainous upstream (18 per cent of the watershed), where elevation ranges from 500 to more

than 4,000 meter above sea level (masl) and slope from 3 to 40 degrees (within the watershed),

• piedmont upstream (22 per cent of the watershed), where elevation ranges from 100 to 500 masl

and slope from 1 to 3 degree,

• and plain middle and downstream (60 per cent of the watershed), where elevation ranges from 100

to 18 masl and slope less than 1 degree.

The prole of transect dened on Figures 3 and 4 is placed in Figure 5.

A striking characteristic of this watershed is the atness in the plain which:

• makes the delineation of the watershed boundary downstream highly uncertain,

• and entails invasive oods from neighbouring rivers, in particular from the Teesta river bordering

the watershed in the west; during these events, the delineation is further uncertain as rain falling

outside of the watershed’s border contributes to the ood of the Jaldhaka river, hence the dened

watershed boundaries varies with rainfall and ood events.

3.2 Streamflow data

3.2.a SourceObtaining streamow from Indian organisations (Central Water Commission, Irrigation and Water -

ways Department) was impossible during the span of this study. Instead the Bangladesh Water Devel-

opment Board provided ow of the Jaldhaka at two gauges stations, downstream in the Bangladeshi

part: Taluk-Simulbari and Kurigram (Figure 6). The measurements were instantaneous readings of

height, i.e., no average over a time-span, from August 1998 to June 2009 with variable frequencies:

• at Taluk-Simulbari: almost daily up to June 2002, then weekly,

• at Kurigram: about twice a month.


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Figure 3: Digital Elevation Model from theShuttle Radar Topography Mission and locationswhere climatic and streamflow data was

available

Figure 4: Slope derived from the DEM, the twolocal meteorological and streamflow gaugestations

0 20 40 60 80 100 120 140 160 180 2000

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Distance (km)

E l e v a

t i o n ( m a s l )

Crest of the watershed

Mountains

P i e d m o n t Plains

Boundary of the watershed

Figure 5: Topographic profile of the transect defined inFigures 3 and 4

Topography Area

(km²)Share (%)

Mountains 1,031 18

Piedmont 1,270 22

Plains 3,494 60

Total 5,795 100

Table 1: Topographic regions ofthe Jaldhaka watershed


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3.2.b Inspection of the data

The number of measurements is higher at Taluk-Simulbari, especially during the low ow season

(Table 2). The hydrographs of both stations were compared and some measurements at Kurigram

appeared suspicious (Figure 7); these were removed. In total, 878 measurements at Taluk-Simulbari

and 197 at Kurigram were used to calibrate the SWAT application for the Jaldhaka watershed.

Original data Filtered data

Kurigram Taluk-Simulbari Kurigram Taluk-Simulbari

#% of

days#

% of

days#

% of

days#

% of

days

Low flowsNovember to April

112 6% 674 34% 97 5% 674 34%

High flowsMay to Octo-ber

112 5% 204 9% 100 5% 204 9%

Total 224 5% 878 21% 197 5% 878 21%

Table 2: Available number of measurements at Taluk-Simulbari and Kurigram stations. Source ofdata: Bangladesh Water Development Board.

As Kurigram is located downstream of Taluk-Simulbari, the ow at Kurigram should be greater

than at Taluk-Simulbari. The average monthly ow during the months July to March seems to satisfy

this criteria (Figure 7). However during April to June, when the rst major rain events occur, this is

not any longer the case. Without a detailed knowledge of the local conditions, we speculated that this

may be due to diversion infrastructures that are visible on Google Earth, which divert part of the rais-

ing water (Figure 8).

Another apparent anomaly is that peaks of the discharge at Kurigram are much greater than at Taluk-

Simulbari. Three possible explanations could be:

• there are errors in measurements of peak ows at Taluk-Simulbari and / or at Kurigram; we think

that these errors are most likely at Kurigram where the standard deviation is very important for

measurements in July (Figure 7),

• in this at zone the tributaries of the Brahmaputra (in particular the Teesta and Torsa rivers) con-

verge, hence it is possible that ood water from these neighbouring rivers invade the part of the

watershed between Taluk-Simulbari and Kurigram, creating much higher ows at Kurigram,

• Kurigram is just 20 km upstream from the massive Brahmaputra, hence it could be that ood

water from the Brahmaputra travel upstream; however, according to the DEM, the difference in

elevation from Kurigram and the conuence is about 9 m, which does not favour this possibility.

3.2.c Analysis of the ltered data

The ow of the Jaldhaka is perennial at both locations, although there is an important seasonal

contrast (Figure 7). The low ow season is between November to April, the average base discharge is


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Jun-98 Oct-99 Mar-01 Jul-02 Dec-03 Apr-05 Aug-06 Jan-08 May-090

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000Kurigram Taluk-Simulbari

D i s c h a r g e ( m 3 / s )

1 2 3 4 5 6 7 8 9 10 11 120

500

1,000

1,500

2,000

2,500

3,000Kurigram

Taluk-Simulbari

Month

D i s c h a r g e ( m 3 / s )

Figure 7: Available time-series for streamflows measured at Taluk-Simulbari and Kurigram stations,unfiltered (left) and average monthly streamflow, filtered (right); the vertical error bars indicate thestatistical standard deviation of daily streamflows

Source: Bangladesh Water Development Board


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114 m3/s (coefcient of variation of 52 per cent) at Kurigram and about 11 per cent of the annual ow

occurs during this period.

The high ow season occurs between May to October with a sharp raise of the discharge in June.

Occurrence of ood is common, often in the month of July, with a maximum of 3,700 m3/s measured

at Kurigram in July 2005. According to a personal communication from the Irrigation and Waterways

Department – Cooch Behar, the maximum ow measured at Mathabhanga (the inlet of sub-watershed

13 on Figure 6) is 10,120 m3/s, about three times more the maximum observed during the time period

August 1998 to June 2009 (3,700 m3/s). The force of the oods can be such that the main river of the

region, the Teesta, shifted its course from the Ganges to the Brahmaputra only in the last three hundred

years (Kundu and Soppe 2002).

The comparison of rainfall and streamow patterns (Figure 9) shows that both signals reach their

maximum in the same month (July) but there is a lag: the increase in streamow at the beginning of

the monsoon is not as rapid as for rainfall and the decrease of ows is buffered at the end of the rainseason. Moreover, there is a noticeable baseow during the dry season.

3.2.d Processing for modelling – generation of the sub-watersheds

The DEM (section III.1.) was used as such in ArcSWAT and the Automatic Watershed Delineation

routine of ArcSWAT generated the set of sub-watersheds for the modelling. As the downstream part of

the watershed is very at, the tributaries downstream were digitised on Google Earth and included in

the Automatic Watershed Delineation so as to carve their river beds in the DEM. The same was done

with tributaries of the neighbouring Teesta river system (Figure 1) for a correct demarcation.

As we had no data on the ow of the Jaldhaka at the conuence with the Brahmaputra, the outlet

of the watershed chosen in this work was not this conuence (6,140 km²) but the Kurigram station

(5,795 km²) (Figures 2 and 3).

Figure 8: Zoom around Kurigram on Google Earth where are visible the infrastructures for waterdiversion as well as the neighbouring rivers, in particular the massive Brahmaputra.


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1 2 3 4 5 6 7 8 9 10 11 120

200

400

600

800

1,000

1,200 Jaldhaka wa-tershed rainfall

Streamflow atKurigram

Month

( m m /

m o n t h )

Figure 9: Representative average rainfall for the Jaldhaka watershed, as calculated by SWAT, and

average streamflow at Kurigram (period 1998 – 2008)The vertical error bars indicate the statistical standard deviation of monthly values (in mm/month).

An important parameter of the delineation routine is the minimum area of the sub-watersheds. As

sub-watersheds carry the climatic characteristics of the watershed, it is advisable to consider a suf-

cient number of sub-watersheds (S. L. Neitsch et al. 2005). However the observed streamow was

only available at two gauge stations and climatic data about every 0.5° (cf. section III.3. and Figure 3),

the minimum area threshold was chosen equal to 200 km², which generated a sufcient number of 14

sub-watersheds.

Four additional sub-watersheds were manually created (Figure 6):

• two having the Indian streamow gauge stations as outlet (NH-31 and Mathabhanga), in case data

from these stations become available at a later stage (sub-watersheds n°3 and 10),

• one for the Taluk-Simulbari station (sub-watershed n°17),

• and a last to represent climatic data from Jalpaiguri meteorological station (sub-watershed n°8).


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3.3 Climate data

3.3.a Sources

Two sources of climate data were obtained (Figure 3):

• daily rainfall, min and max temperatures, wind and humidity at Jalpaiguri and Cooch Behar sta-

tions, period 1988 – 2008, from the Regional Meteorological Centre, Kolkata;

• gridded daily rainfall at a resolution of 0.5°, period 1971 – 2005, from the National Climate Cen-

tre, Pune (Rajeevan and Bhate 2008); 8 pixels from this dataset are within and near the watershed.

The coverage of these dataset is summarised in Table 3.

Table 3: Available climatic time-series and gaps in the datasets. RMC stands for Regional

Meteorological Centre (Kolkata) and NCC for National Climate Centre.

Vari-

able

Time

period

Reso-

lution

Available

measure-

ments (days)

Missing measure-

ments Location Source

(days) ( per cent)

Rainfall1988 -2008

Daily 6,040 1,631 21 Cooch Behar

RMC7,034 637 8 Jalpaiguri

Rainfall1971 -2005

Daily 12,784 0 0 Whole India,resolution 0.5°

NCC

Min

temper-ature

1988 -2008

Daily

6,036 1,635 21 Cooch Behar

RMC5,868 1,803 24 Jalpaiguri

Maxtemper-ature

1988 -2008

Daily

6,039 1,632 21 Cooch Behar

RMC7,027 644 8 Jalpaiguri

Humid-ity

1988 -2008



Wind1988 -2008



3.3.b Analysis

The annual rainfall is important in the Jaldhaka watershed with values uctuating from 2,000 to

almost 5,000 mm/year, with an average of 3,500 mm/year at Cooch Behar station, during period

1988 to 2008 (Figure 10 and Table 4). The rainfall has a monsoonal seasonal pattern, with a relatively

dry season from November to March and a rainy season from April to October (Figure 10). Almost

all of the annual rain falls in the rainy season (98 per cent), especially between June to September

(80 per cent). Daily rainfall intensities can be very high during the peak of the monsoon, with in aver-

age about 33 mm/day per rain event, occurrence every year of events greater than 100 mm/day and a

maximum recorded in the magnitude of 470 mm/day in 1999. Some daily rain events usually occur

every month of the dry season, but of much lesser magnitude.


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Figure 10: Rainfall at Jalpaiguri and Cooch Behar stations (period 1988 - 2008). Top: dailyrainfall. Middle: annual rainfall. Bottom: average monthly rainfall, the vertical error bars in redindicate the statistical standard deviation of daily rainfall (in mm/day)

Source: Regional Meteorological Centre, Kolkata

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 090

50

100

150

200

250

300

350400

450

500Jalpaiguri

Year

D a i l y r a i n f a l l ( m m /

d a y )

87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 090

50

100

150

200

250

300

350400

450

500Cooch Behar

Year

D a i l y r a i n f a l l ( m m /

d a y )

88 89 90 91 92 93 94 95 96 97 98 99 0040102 03 04 05 06 07 080

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000 Jalpaiguri

Year

A n n u a l r a i n f a l l ( m m /

y e a r )

88 89 90 91 92 93 94 95 96 97 98 99 0040102 03 04 05 06 07 080

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000 Cooch Behar

Year

A n n u a l r a i n f a l l ( m m /

y e a r )

1 2 3 4 5 6 7 8 9 10 11 12

0

100

200

300

400

500

600

700

800

900

1,000

0

10

20

30

40

50

60

70

80

Jalpaiguri

Monthly

Dailyevent

Month

M

o n t h l y r a i n f a l l ( m m /

m o n t h )

A v e r

a g e d a i l y r a i n e v e n t ( m m /

d a y )

1 2 3 4 5 6 7 8 9 10 11 120

100

200

300

400

500

600

700

800

900

1,000

0

10

20

30

40

50

60

70

80

Cooch Behar

Monthly

Dailyevent

Month

M

o n t h l y r a i n f a l l ( m m /

m o n t h )

A v e r

a g e d a i l y r a i n e v e n t ( m m /

d a y )

Temperature in the watershed is moderately warm with a winter season from November to February,

where minimum monthly temperature is about 10°C in the plain, and a summer season from March to

October, where maximum monthly temperature is about 32°C in the plain (Figure 11). Wind measure-

ments from both local stations show some differences and after comparing with average data available


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on the website of the Indian Meteorological Department , measurements from Jalpaiguri station appear

erroneous. Values from Cooch Behar indicate that there is a clear variation of wind with time, with a

maximum of about 6 m/s in April followed by a continuous decrease to 2 m/s in December. Monthly

humidity is high and does not vary much monthly, taking a minimum of 60 per cent in March and a

maximum of 85 per cent during several months in winter and summer.

High humidity and moderately warm temperature imply that the reference evapotranspiration (R. G.

Allen et al. 1998) is modest in the watershed, with an average value of 1,300 mm/year (period 1998 –

2008). As a consequence, the ratio [rainfall] / [reference evapotranspiration] is particularly high as it

equals 2.5, which is a distinguishable characteristic of this watershed as compared to the other water-

sheds studied by the AgWater Solution Project.

With respect to daily variability of the climate data within a month, unsurprisingly rainfall is the

most variable followed by the wind, and less variable is the humidity and almost stable monthly-wise

are the temperatures (Figures 10 and 11).

3.3.c Processing for modelling

Modelling requires a continuous climate input dataset. As the data from the two local stations has

some gaps (Table 3) and the daily rainfall gridded data from NCC ends in 2005, we tried to comple-

ment both dataset to have a continuous coverage in the period 1998 – 2008, which was the calibration

period of the groundwater model and SWAT. More precisely we used multivariate regression method:

• during period 1998 – 2005 to ll gaps in rainfall data from the two local stations Jalpaiguri and

Cooch Behar using gridded data as predictor, without considering any time lag in the rainfall of

neighbouring stations,

• during 2006 – 2008, on the contrary, gridded data at the 8 pixels were predicted from measure-

ments at the two local stations.

As for temperatures, humidity and especially wind, a daily missing value was replaced by the aver-

age of contiguous days. Gaps of several days were replaced by the average value of the given month.

During the processing of input data, the ArcSWAT interface selects for each sub-watershed the

meteorological station which is the closest to the centroid of the sub-watershed. In our case, ArcSWAT

chose for the whole Jaldhaka watershed the two local stations and three pixels of the gridded data.

The equivalent distribution of annual rainfall is mapped on Figure 12. There is no clear pattern of the

rainfall with this method of distributing rainfall, with a succession of higher or lower rainfall amount

while one moves from upstream to downstream.

Station Range (mm/year) Average (mm/year) CV ( per cent)

Jalpaiguri 2,000 – 4,800 3,375 20

Cooch Behar 2,500 – 4,900 3,500 19

Table 4: Annual rainfall at Jalpaiguri and Cooch Behar stations (period 1988 - 2008)

Source: Regional Meteorological Centre, Kolkata


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3.4 Soils

3.4.a SourcesFive sources were used:

• the scanned soil map of West Bengal prepared by the Indian National Bureau of Soil Survey and

Landuse Planning, courteously provided by the Indian Space Research Organisation;

• general data on soil texture and fertility for Cooch Behar district, courteously provided by the

Principal Agricultural Ofcer , Cooch Behar;

• the Harmonised World Soil Database which is an international database that combines regional

and national soil information worldwide (SOTER, ESD, Soil Map of China, WISE) with the infor -

mation contained within the FAO-UNESCO Soil Map of the World (HWSD 2009);

Figure 12: Distribution of the average annual rainfall in the sub-watersheds, as represented inSWAT (period 1998 - 2008)


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• the qualitative description in Kundu and Soppe (2002);

• and discussions with local soil scientists from the Agricultural University of Cooch Behar.

3.4.b Analysis

The soil map of West Bengal was clipped to the zone of the Jaldhaka watershed, georeferenced and

digitised in GIS (Figure 13). Unfortunately we did not have the detailed notice attached to the soil

map and only the brief qualitative information from the legend of the map was available (Table A.1,

Annex). Three topographical zones are dened in the soil map:

• the mountainous soils, W001 to W004, shallow, coarse sandy loam,

• soils in the piedmont, W006 to W008, deep, sandy loam to loam,

• soils in the plain, W010 to W028, deep, sandy loam to silty loam.

The unit Riv additionally describes soils of the river beds.

Figure 13: The georeferenced soil map in theregion of the Jaldhaka watershed

Source: soil map of West Bengal, Indian National Bu-

reau of Soil Survey and Landuse Planning

Figure 14: The Harmonised World SoilDatabase and its soil units in the region of theJaldhaka watershed.


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The soil spatial units dened by the HWSD in the region of the Jaldhaka watershed (Figure 14) are

those of the Digital Soil Map of the World , from the FAO-UNESCO. The HWSD reports additionally

some quantitative information from the WISE database (FAO/IIASA/ISRIC/ISS-CAS/JRC 2009), like

the textural percentage in sand, silt, clay (Table A.1 in Annex).

The information from the Principal Agricultural Ofcer , Cooch Behar, were used to interpret the

soil map (Table A.1, Annex). Local soil scientists indicated the same texture for these soils (Loamy

Sand) and insisted that generally the clay percentage is low. Kundu and Soppe (2002) report that the

mountainous soils (units W001 to W004) are sandy with high inltration rates – this information was

used while entering the soil data in SWAT’s database. They also mention that in the plains top-soils are

usually Sandy Loam with rather low inltration rates and that there is a textural transition at about 50

cm in depth for a sandier sub-soil.

3.4.c Pcrocessing for modelling

The soil information had to be processed before entering it in SWAT. In particular, the qualitativedescription had to be transformed into equivalent quantitative values for the soil database of SWAT.

The Table A.1 in Annex summarises the values entered in the soil database.

The textural percentages from HWSD were plotted in the United State Department of Agricul-

ture soil textural triangle to check if they were in accordance with the information from the soil map

(Table A.1) and Kundu and Soppe (2002) (Figure 15). This was not the case as the top soils’ texture

from HWSD was ner (Loam) and sub soils were even ner instead of getting coarser. Corrections

were as follows:

• For soils in mountainous regions (W001 to W004), the percentages of clay and silt from the

HWSD for top soils were too high while percentage for sand too low, hence 10 per cent was

deducted to the percentage of clay and silt and 20 per cent was added to the percentage in sand.

For sub-soils, original values from the HWSD were ignored and instead the corrected values of

top-soils were considered, reducing further the percentages of silt and clay in favour of the sand

content.

• For soils in piedmont and plains (W006 to W028), the HWSD clay percentage for top soils were

too high (more than 20 per cent) compared to the information from local soil scientists, hence the

top soil textures of Loamy Sand units were modied by deducing 10 to the clay percentage and

adding it to the sand content; silt content was not modied. For the sub-soil, original values from

the HWSD were ignored as well and the textures were calculated by deducing 10 per cent and

5 per cent to the silt and clay content of the top soil, adding it to the sand percentage.

The corrected soil textures were indeed matching with description from the various sources (Figure

15). The texture of the unit Riv was not modied. Eventually the qualitative information of the soil

map (Figure 13) was translated into equivalent quantitative values using the HSWD and local soil

knowledge.

The soil hydrologic group, required by the ArcSWAT interface, was derived from the soil texture:

• group A: coarse sandy loams, units W001, W002, W004 and Riv,

• group B: ne sandy loams, units W003, W006, W008 to W025,

• group C: loamy soils, units W007, W026 and W028.


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After the soil texture, another important soil characteristic in SWAT is the soil Available Water

Capacity (AWC, [L3/L3]). The HWSD provides approximate AWC but we preferred to enter values

considering the texture of each soil unit (Figure 15) and adapting the capacities advised by Kundu

and Soppe (2002). Moreover, there is a sort of continuity between of the soil AWC and the specicyield used by the groundwater model. The value of specic yield was often 0.15 in the plain, less in

mountainous sub-watersheds and this was reected in the AWC, as it will be detailed in section IV.1.d..

Ultimately:

• unit Riv: top soil AWC = 0.08,

• unit W001: top soil AWC = 0.10,

• units W002 and W004: top soil AWC = 0.15, sub-soil AWC = 0.10,

• units W003, W006, W008 to W025: top soil AWC = 0.20, sub-soil AWC = 0.15,

Top soil

Original texture from the HWSD

Top soil

Corrected texture

Sub soil

Original texture from the HWSD

Sub soil

Corrected texture

Figure 15: Plot in soil textural triangle of the United State Department of Agriculture


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• units W007 and W026: top soil AWC = 0.25, sub-soil AWC = 0.20,

• unit W028: top soil AWC = 0.30, sub-soil AWC = 0.25.

The soil depths were also specied combining the information from the soil map and Kundu and

Soppe (2002):

• shallow soils: top soil 50 cm, no sub-soil,

• moderate shallow: top soil 30 cm, sub-soil 70 cm,

• deep: top soil 40 cm, sub soil 100 cm,

• very deep: top soil 50 cm, sub-soil 200 cm.

The parameter SOL_ZMX was given the value of 300 cm.

The soil conductivity (SOL_K) was chosen with respect to the soil hydrologic group as advised by

Neitsch et al. (2010).

Finally, the soil map (Figure 13) was approximatively extended using the satellite imageries to cover

all the delineation of the watershed.

3.5 Groundwater data

3.5.a Source

Groundwater levels in the Indian part of the watershed, from Jalpaiguri and Cooch Behar districts,

were obtained from two organisations (Table 5):

• IWMI provided reading from the Central Ground Water Board (CGWB),

• and the State Water Investigation Directorate (SWID) of West Bengal.

Table 5: Available measured groundwater levels in the Indian part of the watershed. CGWBstands for Central Ground Water Board and SWID for State Water Investigation Directorate.

Monitoring

organisa-

tion

Number of observation

wells

Period Measurement fre-

quency

Total Jaldhaka watershed

Total Jaldhaka watershed

CGWB 49 12 1996 –2006

1996 –2006

Every 3 months

SWID 112 43 1988 –2009

1994 –2009

Every 3 months

3.5.b Analysis

These measurements are mainly located in the plain, with some few in the piedmont (Figure 16).

The spatial resolution of the measurement in the Indian part of the watershed is satisfactory with a

sufcient number of the observation wells. However, the time resolution is scattered as measurements

are not monthly but almost 3 months with some irregularities, hence we may miss some time variation


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of the groundwater levels. In particular we may not know exactly when the groundwater levels are the

deepest and the shallowest.

Leaving aside the groundwater system in the mountains upstream where no observations are avail-

able, the groundwater regime can be categorized in two categories: ( i) in the plain downstream and

(ii) in the piedmont area middle stream. In the plain downstream, the aquifer system is alluvial and

composed of ancient sediments from succession of the Ganga – Brahmaputra river systems (Kundu

and Soppe 2002; CGWB 2009). The groundwater levels are shallow. According to the scattered time-

series of groundwater levels, the water table is apparently deepest in April before the monsoon, uc-

tuating from 1 to 5 meter below ground level (mbgl), and shallowest in August during the monsoon,uctuating from 0 to 3 mbgl (Figure 17). Typical groundwater levels are those of wells PTC-25 and

PTC-9 (Figure 18).

In the piedmont area, the aquifer is composed of more recent sediments carried by the tributaries

from the mountainous upstream areas (Kundu and Soppe 2002; CGWB 2009). The groundwater lev-

els are also shallow, although some wells show deeper level. The levels are apparently the deepest

between February to April before the monsoon, uctuating from 2 to 15 mbgl, and the shallowest in

August during the monsoon, uctuating from 1 to 11 mbgl (Figure 17). Typical groundwater levels are

those of wells D-10 and D-12 (Figure 18).

Watershed-wise, the average groundwater level is between 2 to 4 mbgl (Figure 17). The last 16 years

of the groundwater levels time-series show little inter-annual water table uctuations (Figure 17 and

Figure 16: Location of the observation wells for groundwater level measurement. CGWB standsfor Central Ground Water Board and SWID for State Water


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Source: Central Groundwater Board (CGWB) Source: State Water Investigation Directorate (SWID)

Figure 17: Measured groundwater levels in the Jaldhaka watershed. In pale: level of differentwells. In black: average of all the wells

In piedmont In piedmont

In plainIn plain

Figure 18: Typical groundwater levels in the Jaldhaka watershed. The wells are located on Figure16. The vertical error bars indicate the statistical standard deviation

Source of data: State Water Investigation Directorate (SWID)


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Figure 18): there is no noticeable trend to increase or to decrease, i.e., watershed-wise the groundwa-

ter levels have been stable during the period 1994 – 2009. This is consistent with observations from

Shamsudduha et al. (2009).

Interpolation of observed groundwater piezometric levels in India using the Inverse Distance

Squared Weighting method shows that the levels follow the topography, which is expected for an allu-

vial aquifer system (Figure 19). The gradient is along the North to South orientation and direction in

piedmont region and changes towards the South – East in the plain. The groundwater ow is therefore

in the same orientation and direction of the surface water and converges towards plains in Bangladesh,

in particular towards the Brahmaputra river. This is again consistent with Shamsudduha et al. (2009).

If we compare the cases when the available observed piezometric levels are the shallowest (August)

versus the deepest (April), there is a general shift of the contours along the ow direction, i.e., the

topography, but the relative distribution does not change signicantly.

3.5.c Pre-processingThe dataset from SWID contained more measurements than the one from CGWB (Figure 17), hence

only readings from SWID were considered afterwards. Moreover the groundwater model focused on

the wells located within the watershed, which amounted to 33 wells.

3.6 Land-useA high spatial resolution landuse map of the Jaldhaka was generated within the context of the AgWater

Solution Project. Satellites images were acquired and we contributed to the development of the lan-

duse map by conducting the groundtruthing and helping in the landuse classication.

Figure 19: Interpolation of average piezometric levels observed by the State Water InvestigationDirectorate (SWID) (period 1994 - 2009).


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3.6.a Satellite images

Six images from the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) aboard the

Advanced Land Observing Satellite (ALOS) were acquired by IWMI. The images are of 10 m spatial

resolution and have four bands, three in visible and one in near infrared. The images do not cover the

Jaldhaka watershed completely (Figure 20). The six images were for two locations with three replica-

tions for each location. The purpose of having multiple images from different seasons is to enable to

better extract crop type and rotation information (Cai, personal communication, 2010).

Among the three dates, October (31/10/2008) have the clearest views. The two views of January

(31/01/2009) are slightly hazy while views of March (i) are from different dates (15/03/2008 and

18/03/2009) and (ii) there is a spatial discrepancy between both views. This combination of dates was

representative of the year 2008 and thus an additional difculty was that groundtruthing was carried-

out in April 2010, at a date different to the satellite images.

3.6.b GroundtruthingBefore the eld work, a draft unsupervised classication was produced on a southern view, to choose

the location the groundtruthing (GT) sites (Figure 21). In total, 90 GT sites were visited in April 2010

and two types of observations were carried-out:

• precise in 40 (GT points 1 to 40) of these sites,

• brief in the remaining 50 sites (GT points 41 to 90).

Taking inspiration from Cai and Sharma (2010), a GT form pertaining to the vegetation distribution

and the crop system (sequence, growing period etc.) was prepared. The choices of the sites were as

follows (Figure 21):

Date North view: 31/01/2009

Date South view: 31/01/2009





Figure 20: Satellite images acquired for high resolution landuse mapping. Note the demarcationbetween the north and south view


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views (mainly crops) are taken into account while the pixels are clustered. The northern and southern

classications created each 20 classes (Cai, personal communication, 2010).

As this study focuses primarily on agricultural land, IWMI’s GIS / remote sensing expert tried to

identify crop types as well as crop rotations. Temporal spectral changes of each agricultural class were

analysed and the trend in vegetation development was assessed. They were then compared to the crop

calendar (Table 10) to match with dominant crop types. GT points and Google Earth were used for to

aid crop type determinations (Cai, personal communication, 2010). The fact that groundtruthing was

conducted at a date different from the original satellite images hindered the classication.

This generated 40 classes: 20 for the northern view and another 20 for the southern view. In an

attempt to validate, this classication was queried in a buffer of about 100 m radius around each GT

location and extracted landuse was compared with GT observations. Discrepancies were importantwith, for instance, an over-representation of Wheat, an under-representation of Jute and the absence

of a category for towns. Hence we tried to reduce the number of classes and enhance their representa-

tiveness by:

• merging equivalent northern and southern classes,

• using Google Earth to recognise the GT observations and associate them with classication clus-

ters,

• differentiate more precisely Tea from Shrublands,

• creating manually a new class for towns.

1 2 3 4 5 6 7 8 9 10 11 12

Month

Irrigated Monsoon_RiceWheat

Irrigated Monsoon_RicePotatoJute

Irrigated Monsoon_Rice

Summer_Rice

Irrigated Monsoon_RicePre-monsoon_Rice

Rainfed Monsoon_RiceJute

Rainfed Monsoon_RiceRainfed Monsoon_Rice

Rainfed Monsoon_Rice





Jute

Jute

Pre-monsoon_Rice

Potato Potato

Summer_Rice

Wheat

Figure 22: Calendar of the main cropping sequences in the Jaldhaka watershed


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Eventually, a high resolution (10 m) landuse map with 11 classes representative of the year 2008

was generated (Figure 23):

• 8 classes for non-agricultural lands with in particular three categories of habitation zones, from

the smallest to the largest: (i) settlements, with few habitations around trees, (ii) villages with a

greater number of habitations and sparse vegetation and (iii) towns with urbanised areas,

• and 3 categories of agricultural practices:

- Monsoon_Rice → [Pre-monsoon_Rice], i.e., Monsoon_Rice possibly followed by Pre-

monsoon_Rice,

- Monsoon_Rice → [Winter Crop] → [Jute or Pre-monsoon_Rice], i.e., Monsoon_Rice, pos-

sibly followed by a Winter Crop, possibly followed by Jute or Pre-monsoon_Rice,

- Monsoon_Rice → [Winter Crop] → Summer_Rice, i.e., Monsoon_Rice, possibly followed

by a Winter Crop, followed by Summer_Rice.

Locally, the monsoon, summer and pre-monsoon rices are called Aman, Boro and Aus respectively.

The schedule of each crop is placed in Figure 22. The photos taken at the locations identied on the

Figure 23 are placed in Figure 24.

Figure 23: High resolution (10 m) landuse map of the Jaldhaka watershed (year 2008).

Photos of the identified observation sites are placed in Figure 24.


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Table 6: Distribution of the landuse categories (Figure 23) within the Jaldhaka watershed.

Landuse category Area

(km²) ( per cent)

Forest 740 16.2

Tea or Light Forest 510 11.1

Small Trees or Shrubland or Settlement 786 17.2

Monsoon_Rice –> [Pre-monsoon_Rice] 174 3.8

Monsoon_Rice –> [Winter Crop] –> [Jute or Pre-monsoon_Rice] 1,356 29.6

Monsoon_Rice –> [Winter Crop] –> Summer_Rice 309 6.8

Village or Fallow 254 5.6

Town 8 0.2

Water 104 2.3

River bed 182 4.0

Cloud 151 3.3

Total 4,574 100.0

Figure 24: Photos of the spots identified on the landuse map (Figure 23)

3.6.d Processing for modelling

The landuse map (Figure 23) cannot be used as such by the interface ArcSWAT as its cloud category

has to be replaced by a relevant landuse and it does not cover all of the watershed. The cloud category

was replaced referring to the landcover visible on Google Earth, ie., generally Forest upstream in Bhu-

tan and Monsoon_Rice → [Winter Crop] → [Jute or Pre-monsoon_Rice] in India and Bangladesh.

Before extending the landuse map to part of the watershed not covered, as the category River bed is

a mixture of sand and gravels bare soil class (visible on satellite images) where hardly any vegetation

grows, it cannot be matched to a SWAT land use class, hence River bed was replaced by the category

Water . Finally, the map was extended using Google Earth and matching with the soil map:


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• upstream it was expanded as Forest,

• while downstream it was a mixture of Monsoon_Rice –> [Winter Crop] –> Summer_Rice, Mon-

soon_Rice –> [Winter Crop] –> [Jute or Pre-monsoon_Rice] and Water.

As the GIS raster of the landuse map was to be processed by ArcSWAT, the raster was matched spa-

tially with the DEM, which is the fundamental GIS information for ArcSWAT. This implied in particu-

lar that the spatial resolution of the enlarged landuse map (10 m) was downgraded to 90 m.

The resulting landuse map is placed in Figure 25 and the tabulated areas are in Table 7: these are

the landuse information eventually entered in SWAT. We dened 9 landuse categories for SWAT (e.g.,

FRSJ for Forest , AAAJ for Monsoon_Rice → [Pre-monsoon_Rice]) that were associated to a crop/

vegetation of SWAT’s database. The agricultural units AAAJ, AWJJ and AWBJ were generic crop

classes, their associated crop sequences, that will be presented in section III.7., were entered in SWAT

at a later stage, while dening SWAT’s management tables (section IV.1.b.).

3.7 Agricultural

3.7.a Sources

Data on crop yields, area extent and productivity were obtained from 3 sources (Table 8):

• the website of the Development & Planning Department - West Bengal (DPDWB 2005)

• the Bureau of Applied Economics and Statistics, Kolkata, India,

Figure 25: Modified version of the landuse map (Figure 23, year 2008) entered in SWAT (90 mresolution)


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• and the Directorate of Agriculture, Kolkata, India.

Additionally, typical crop growing seasons were noted during the eld work described in sec -

tion III.6.b. (Figure 22) and are reported in Table 10.

3.7.b Analysis

The major crops in the region of the Jaldhaka watershed are (Figure 22):

• Summer_Rice, called locally Boro: irrigated rice grown before the onset of the monsoon, from

February to May.

• Pre-monsoon_Rice, called locally Aus: partly irrigated rice grown at the onset of the monsoon,

from April to June.

Table 7: Distribution of the landuse categories entered in SWAT (Figure 25).

Landuse category In

SWAT

Area

(km²) (%)

Forest FRSJ 1338 23.1

Tea or Light Forest TEAB 510 8.8

Small Trees or Shrubland or Settlement FRMJ 785 13.5

Monsoon_Rice –> [Pre-monsoon_Rice] AAAJ 174 3.0

Monsoon_Rice –> [Winter Crop] –> [Jute or Pre-monsoon_Rice]

AWJJ 1,841 31.8

Monsoon_Rice –> [Winter Crop] –> Summer_Rice AWBJ 342 5.9

Village or Fallow VIFA 256 4.4

Town URMD 8 0.1

Water WATR 541 9.3

Total 5,795 100.0

Table 8: Available agricultural statistics.

Source Time-

period

Spatial resolution Vari-

ables

Crops

Development& PlanningDepartment -

West Bengal

2003 –2004

Administrative Blocksof Cooch Behar andJalpaiguri districts

Area,Yield,Produc-

tion

Monsoon_Rice, Pre-mon-soon_Rice, Summer_Rice,Potato, Jute, Wheat, vari-

ous pulses

Bureau of Applied Eco-nomics andStatistics

1988 –2009


Area,Yield,Produc-tion

Monsoon_Rice, Pre-mon-soon_Rice, Summer_Rice,Jute, Wheat, Maize, vari-ous pulses

Directorate of Agriculture

1998 –2009


Area,Yield,Produc-tion

Potato


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• Monsoon_Rice, called locally Aman: rice grown during the rain season, from June/July to Sep-

tember/October, rainfed or partly irrigated depending on the case.

• Jute: rainfed vegetable bre grown at the onset of the monsoon, from April to June.

• Winter Crop: irrigated crop following the rain season, which is Potato (predominantly), Tobacco

or Vegetables, from November to February; from now we will consider this crop to be Potato.

• Wheat: irrigated during winter, from January to April.

The irrigation schedule of these crops are described in the following section III.8.. Although Tobacco

is an important cash crop in the watershed, in particular in Cooch Behar district, no data could be gath-

ered as this crop is not monitored by governmental organisations. The data from the Development &

Planning Department - West Bengal were ignored as these were only for the year 2003/04, however

their utility were to assure the homogeneity with statistics from the Bureau of Applied Economics andStatistics and the Directorate of Agriculture.

Area and yield of these crops during the period 1998 – 2008 in the administrative block containing

the Jaldhaka watershed is shown on Figures 26and 27. There is a gap in the data from the Bureau of

Applied Economics and Statistics for the years 2001/02. A striking feature is that the area under Mon-

soon_Rice is much greater than the other cultivations and is quite stable. The tendency for area under

Pre-monsoon_Rice was to gently decrease while potato to increase. The area under Summer_Rice

increased sharply in the recent years in the administrative blocks located downstream. Extent of Jute

and Wheat cultivation is relatively stable with a small area under Wheat as compared with the other

crops. Among the yields, those of Potato are the most varying.

The average crop yields in the watershed for the period 1998 – 2009 is estimated by calculating the

average yields of the administrative blocks containing the watershed (Table 9). It is noteworthy that

the yield of Summer_Rice is greater than Monsoon_Rice and Pre-monsoon_Rice. The agricultural sta-

tistics obtained for rice from the Bureau of Applied Economics and Statistics were in two parts: one for

the period 1998/99 – 2003/04, which only mentioned for the rice the clean yield, and another for the

period 2004/05 to 2008/09, which mentioned for the rice the dry yield and clean yield. Hence only the

clean rice yield was available throughout the period 1998 – 2009 but the dry yield was also reported in

Table 9 as it was required to compare with SWAT’s outputs (cf. section IV.2.f.).

3.7.c Processing for modelling

We derived typical cropping sequences that will be considered in SWAT (Figure 22 and Table 10)

from (i) observations during the landuse groundtruthing (cf. section III.6.b.), (ii) Participatory GIS

Table 9: Average yields in the administrative blocks containing the Jaldhaka watershed, period1998 – 2008. In bracket the average dry yield of rice for period 2004 to 2008. Source of data:Bureau of Applied Economics and Statistics and Directorate of Agriculture.

Monsoon rice

(Aman)

Clean yield

(T/ha)

Pre-mon-

soon rice

(Aus)

Clean yield

(T/ha)

Summer rice

(Boro)

Clean yield

(T/ha)

Jute

Dry yield

(T/ha)

Wheat

Dry yield

(T/ha)

Potato

Yield

(T/ha)

1.5 (2.4) 1.4 (2.0) 2.2 (2.9) 1.9 1.8 18.8


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98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

A r e a ( 1 , 0

0 0 h a )

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

A r e a ( 1 , 0

0 0 h a )

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata IISitai

Sitalkuchi

Years

A r e a ( 1 , 0

0 0 h a )

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata IISitai

Sitalkuchi

Years

A r e a ( 1 , 0

0 0 h a )

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

A r e

a ( 1 , 0

0 0 h a )

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

A r e

a ( 1 , 0

0 0 h a )

Monsoon_Rice (a.k.a. Aman) Pre-monsoon_Rice (a.k.a. Aus)

Summer_Rice (a.k.a. Boro) Jute

Wheat Potato

Figure 26: Area of the major crops in administrative blocks containing the Jaldhaka watershed

Source of data: Bureau of Applied Economics and Statistics and Directorate of Agriculture


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98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0Watershed

Rajganj

Mal

Matiali

NagrakataMadarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

C l e a n r i c e y i e l d ( T / h a

)

Average of administrativeblocks in the Watershed

98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Watershed

Rajganj

Mal

Matiali

NagrakataMadarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

C l e a n r i c e y i e l d ( T / h a

)


98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0Watershed

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata IDinhata II

Sitai

Sitalkuchi

Years

C l e a n r i c e y i e l d ( T / h a )


98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Watershed

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

Falakata

Dhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata IDinhata II

Sitai

Sitalkuchi

Years

D r y y i e l d ( T / h a )


98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0Watershed

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

FalakataDhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

D r y y i e l d

( T / h a )


98-99 99-00 00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-090

5

10

15

20

25

30

35

40

Watershed

Rajganj

Mal

Matiali

Nagrakata

Madarihar

Kalchine

Kumargram

Alipuduar I

Alipuduar II

FalakataDhupguri

Mainaguri

Mekhliganj

Mathabhanga I

Mathabhanga II

Cooch Behar I

Dinhata I

Dinhata II

Sitai

Sitalkuchi

Years

Y i e l d (

T / h a )


Monsoon_Rice (a.k.a. Aman) Pre-monsoon_Rice (a.k.a. Aus)

Summer_Rice (a.k.a. Boro) Jute

Wheat Potato

Figure 27: Yield of the major crops in administrative blocks containing the Jaldhaka watershed.Mind the different vertical scale

Source of data: Bureau of Applied Economics and Statistics and Directorate of Agriculture


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34


Table 10: Typical cropping sequences and associated irrigation schedules in the Jaldhakawatershed.

Crop-

ping sys-tems

Lan-

dusecat-

egory

in

SWAT

Start End Grow-

ingdays

Total

pumped(mm)

Irriga-

tiondura-

tion

(days)

Chosen

irriga-tion

events

RainfedMon-soon_Rice

AAAJand AWJJ

June Sep-tember

122 -

RainfedMon-soon_

Rice

AAAJand AWJJ

July Octo-ber

123 - 100

Jute April June 91 -

IrrigatedMon-soon_Rice

AAAJand AWJJ

July Octo-ber

123 247 100 12 mmevery 5days

Pre-mon-soon_Rice

April June 91 617 80 15 mmevery 2days

IrrigatedMon-

soon_Rice

AAAJand

AWJJ

July Octo-ber

123 247 100 12 mmevery 5

days

Potato Novem-ber

Febru-ary

120 247 100 10 mmevery 4days

Jute April June 91 -


AWBJ June Sep-tember

122 247 100 12 mmevery 5days

Summer_Rice

February May 120 1,233 100 25 mmevery 2days

Wheat AAAJand AWJJ

January April 120 247 100 12 mmevery 5days


June Sep-tember

122 247 100 12 mmevery 5days


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(PGIS) analysis conducted by SEI (de Bruin et al. 2010) and (iii) the landuse map (Figure 25). The

irrigation of these sequences will be explained in the forthcoming section III.8.

The next critical steps was to estimate the area of each cropping sequences from the agricultural sta-

tistics at administrative block level. These areas were indeed used prior to the modelling to differenti-

ate the agricultural units AAAJ and AWJJ of the landuse map (Figure 25); the unit AWBJ was assigned

the sequence Irrigated Monsoon_Rice - Summer_Rice, hence no winter crop was eventually supposed

to grown in this unit. As the area growing Monsoon_Rice is much greater than the other crops (Figure

26), we took the assumption that all the elds of units AAAJ and AWJJ grow Monsoon_Rice during

the rain season, which may be followed by fallow or crop(s). We did as follows in each administrative

block:

• We calculated the average area of each crops (i.e., Monsoon_Rice, Pre-monsoon_Rice, Sum-

mer_Rice, Jute, Wheat, Maize, various pulses, Potato) over the last 5 years.

• The areas of Maize and various pulses were small, hence Maize was combined with Wheat and

various pluses with Potato (approximative same growing season).

• As this differentiation concerns units AAAJ and AWJJ, where there is no Summer_Rice, for each

administrative block we subtracted to the Monsoon_Rice area the Summer_Rice extent.

• We took the additional assumption that each cropping sequence introduced in Table 10 are dis-

tinct, hence the area calculate in step 3 is composed of all the cropping sequences dened in Table

10, except Irrigated Monsoon_Rice - Summer_Rice.

• We expressed the area growing Pre-monsoon_Rice, Jute, Wheat and Potato (calculated in step 1

& 2) as a percentage of Monsoon_Rice’s area estimated in step 3. This percentage was always less

than 100 per cent.

• Then it was decided to distribute the cropping sequences as follows:

Area ( per cent) of... … was equal to...

Irrigated Monsoon_Rice→ Winter Crop→ Jute

Percentage of Potato computed in step 5

Irrigated Monsoon_Rice→ Wheat Percentage of Wheat computed in step 5

Irrigated Monsoon_Rice→ Pre-monsoon_Rice

Percentage of Pre-monsoon_Rice computed in step5

Rainfed Monsoon_Rice→ Jute Maximum [0,Percentage of Pre-monsoon_Rice com-puted in step 5 - percentage of Irrigated Monsoon_Rice→ Winter Crop→ Jute]

Documents

Application of Swat