APPLICATION OF SWAT MODEL

CHAPTER IV APPLICATION OF SWAT MODEL

4.1 INTRODUCTION

This chapter includes procedures used for delineation of watershed and sub­

watershed boundaries using DEM and generation of various maps (soil texture, drainage

and slope) using Geometica is discussed. Furthermore, methodologies for extracting

some of the model inputs such as area, slope, channel length and several others are

described in details. Detailed procedure for preparation of land use/land cover using

satellite imageries is also provided in this chapter. Procedures used for calibration ~

validation of the model and various criteria used for evaluating the mode!_p~~ Procedure for identification of critical sub-watersheds of study watershed and

justifications of treatments considered for management of identified critical sub­

watersheds are also discussed.

4.2 DATA PROCESSING FOR THE MODEL

4.2.1 Hydrological Data

Hydrological data has been processed using statistical and graphical procedures.

Observed rainfall data of Chhokranala and Arang watersheds for the year 2001-2005 and

2002-2005 respectively, are used to prepare precipitation file in DOS editor. Similarly

daily temperature data file is prepared and given to the model as input. Monthly mean of

daily precipitation, standard deviation, and skewness coefficient of 5 years rainfall data

(2001-2005) are determined by fitting frequency distributions. The 0.5 and 6.0 hours

rainfall amount forlO-year frequency is determined by the procedure suggested by Chow

(1964). The 10-year frequency of0.5 and 6.0 hours rainfall are found to be 15.79 mm and

79.37 mm, respectively.

A daily value of discharge (m3/sec) is converted to runoff depth (mm) using

drainage area of the watershed. The straight-line method, which is the simplest, is used

for base flow separation (Subramaniya, 1996). Daily values of observed sediment yield is

converted from gram per liter (g/1) to tons per hectare (tlha) using the watershed area and

the corresponding values of the runoff. These runoff and sediment yield values are used

to compare with the simulated values for evaluating the SWAT model.

4.2.2 Runoff and Sediment Samples

Procedure described by Rump and Krist (1988) is adopted and water samples are

collected randomly at the outlet of the Chhokranala and Arang watersheds during the

monsoon season. Sediment samples are taken with the use of bottle type USDH-48

sampler at the same time and place as that of runoff. Organic nitrogen and phosphorous

in sediment, nitrate nitrogen and soluble phosphorous in runoff are determined in the Soil

and Water Engineering Department, Indira Gandhi Krishi Vishwavidyalaya, Raipur.

4.2.3 Map Registration and Digitization

Geometica image processing and Arc GIS is used to process the data as per the

need in the input files of the SWAT model. All the maps including topographic, soil

texture and soil series maps are traced and scanned carefully. The scanned maps are

saved and exported to the Geometica for further processing. Then using Ground Control

Point (GCP) works, the second order registration with 24 m by 24 m resolution is

performed. Only topographic map is registered using this technique. For other maps and

images, image-to-image registration is performed. Using vector editor, contours are

carefully digitized and actual elevations of the contours are given as an attribute value.

Digitized contour map of the study watershed is shown in Fig. 3.1 and Fig. 3.2.

4.2.4 Generation ofDEM

A Digital Elevation Model (DEM) is a numerical representation of landscape

topography. DEM can be used to derive a wealth of information about the morphology of

land surface by means of algorithms in raster processing systems, which use

neighbourhood operations. These algorithms provide information such as flow direction,

85

flow accumulation, drainage network, slope aspect and overland flow path. The colored

area represents the zone of interpolation between two contour lines. Different colours

give different zones of interpolation. The accuracy of results obtained from a DEM

depends on the recent topography or contours and resolution. The DEM is prepared from

the contours traced from the two topographic sheets. The DEM of the watershed is

prepared in 24 m by 24 m resolution. Precision of the DEM depends on the interval of

elevation information. Small the interval, precision will be high.

Plate 4.1 and Plate 4.2 shows the original DEM of Chhokranala and Arang ' -~~

watershed, respectively. D~-~!()UL!Dap_of_f_!Jll()Jcranala watershed using y· topographic map of Survey of India having 2m contour inte:ryals. After that the DEM of

.-----/' Chhokranala watershed is generated using this digitized contour map. Similarly the

contour map of Arang watershed is digitized using topographic map of Survey of India

having I Om contour intervals. Digitized contour map is then used for preparing the DEM.

Many researchers have also used DEM of24 m by 24m resolution and found satisfactory

results (Bingner, 1996; Sharma et al., 1996; Tiwari et al., 1997; Wang and Hjelmfelt,

1998).

4.3 EXTRACTION OF WATERSHED PARAMETERS FOR THE MODEL

4.3.1 Morphological parameters

Various morphological parameters (Table 4.1) of both the watersheds have been

extracted using the standard equations and procedures applied on data extracted from

GIS. The values of some of the important morphological parameters are needed as inputs

to the SWAT model.

4.3.2 Generation of Drainage Network

The drainage maps are shown in Fig. 3.1 and Fig. 3.2. The drainage network gave

an idea about the location of streams of various orders and density of streams. The main

channel of the drainage network closely matched with the main stream as given in

topographic map.

86

Legend DEJA <VAlUE>

· . ..;

- 97 - 93 ?)

1\))

11,.1

tl>?

•·}.·

N - le-i - ·~· t -·~.,; - tOl

• • co CJtseU\ t 11-lh

« Ja. .nn t '\) ; ...... ___ .......,r:tar'tof

Plate 4.1 Digital Elevation Model (DEM) of the Chhokranala watershed

L..ti~g .. nd

<VALUE> ,............, 1&')

2.1C

2!1

2:r;

1H

:!J·1

:;!T-;

1iC

~Jt

2~

2~

- ':~5 - :8, :a:.

• •e;. -m Plate 4.2 Digital Elevation Model (DEM) of the Arang watershed

87

Table 4.1: Values of morphological parameters of both the watersheds

S.No. Watershed Parameters Chhokranala Arang I Watershed area (A), km" 17.31 54.50 2 Length of all the streams, km 21.73 44.08 3 Length of main stream, (Lru,) km 6.10 8.56 4 Maximum length of watershed (Lb), km 5.80 11.85 5 Length of overland flow (L0), km 0.397 0.618 6 Drainage density (Dd) kmlkm• 1.247 1.237 7 Average slope of the watershed 0.016 0.015 8 Main channel slope 0.002 0.005

4.3.3 Automatic Delineation of Watershed and Sub-watersheds •

It can be seen that the al!!2.mati£ally_ delin~!.!~d water~5/]9§e]y_matched _with

the manually delineated watershed. The boundary almost superimposed with the

manually traced watershed. It is also checked with the ground truth data. For

Chhokranala watershed, the area delineated by the algorithm is 17.31 km2 against the '1.­

manually judged area of 19.52 krn2. The error of calculation is found to be 2.21~ ~

Similarly, for Arang watershed, the area delineated by the algorithm is 54.50 km2 against 'ttj the manually judged area of 56.74 km2 and the error of calculation is found to be 2.24lJ>.~\­

Hence automatic delineated watershed is used for further study. Similar agreement

between manually and automatic delineated watershed were found by several researchers

in the previous studies (Jenson and Domingue, 1988; Stuebe and Johnston, 1990; Smith

and Brilly, 1992; Smith and Vidmar, 1994).

The SWAT model can work on sub-watershed basis, so;hiit'both watersheds are /

divided into number of sub-watersheds on the basis of drainage and elevation information

of corresponding watershed. The Chhokranala watershed is subdivided into 7 sub­

watersheds whereas Arang watershed is subdivided into 10 sub-watersheds. Sub­

watershed boundaries are carefully digitized using GIS and area corresponding to

different sub-watersheds of Chhokranala and Anmg watershed is determined. Plate 4.3

and Plate 4.4 shows the sub-watersheds of the Chhokranala and Arang watershed,

respectively. The sub-watersheds are given different colours for easy identification.

88

N

h Legend

-~ .. 111 - '!:'In - S'V/3

-~

Plate 4.3 Sub-watershed map of the Chhokranala watershed

., J.

Legend ld / ld

-~.'·\~ - s·,,~

- Sf\7

- SWJ

• Otllte

Plate 4.4 Sub-watershed map of the Arang watershed

89

4.3.4 Determination of Average Slope

Average watershed slope is essential in calculating topographic factor (LS) for

sediment yield prediction. A grid-contour method described by Linsley et al. (1949) and

modified by Williams and Berndt (1976) is used to determine the average slope of the

sub-watersheds. The length of each grid line within the watershed is measured, and the

contours crossing or tangent to the line are counted. The land slope (Sd) in any direction

is computed by the equation:

S =NdH a D

d

(4.1)

where H is contour interval, Nd is total number of contour crossing for all lines in one

direction, and Dd is total length of lines in that direction. The average watershed slope

can be determined by computing the slope in both grid directions with equation (4.1) and

calculating the resultant:

(4.2)

where SL is the average per cent slope of the watershed along its length, and Sw is the

average per cent slope of the watershed along its width.

4.4 LAND USE/LAND COVER CLASSIFICATION

Classification of satellite data is mainly accomplished by two techniques. The first

technique is a supervised classification method. This is an interactive approach in which

the operator classifies an area or group of pixels that belong to one or more categories of

specific land use/land cover. The other classification technique is an unsupervised

classification method. In this method, the operator determines how many different classes

are desirable and classifies all data into the statistically created classes using sophisticated

set of algorithms.

90

Most common method, the supervised classification, has been adopted for

classifying the land use of the study watershed. Maximum Likelihood Classifier (MLC)

module is used for classifying the land uses classes of the watershed. Various land uses

are given different colours for easy identification. All the three levels of land use/land

cover classification system (NRSA, 1995) have been adopted for classifying the land use

of the study watershed. The report generated by MLC called Maximum Likelihood

Report (MLR) is presented in tabular form in Table 4.2 and Table 4.3 for Chhokranala

and Arang watershed, respectively. It gives the segment name, number of pixels, area

and per cent of image occupied by each land use class. Plates 4.5 and 4.6 show the land

use maps for Chhokranala and Arang watershed, respectively.

Table 4.2: Area under different land use classes in the Chhokranala watershed

Segment Name No. of pixels Area (ha) %Image Water body 591 8.81 0.51 Grasses & shrubs 177 2.64 0.15 Orchard 254 3.79 0.22 Crop land 51605 769.49 44.45 Settlement 7837 116.87 6.75 Barren land 10183 151.84 8.77 Fallow land 45438 677.56 39.15 Total 116085 1731.00 100.00

Table 4.3: Area under different land use classes in the Arang watershed

Segment Name No. of pixels Area (ha) %Image Deepwater 390 5.67 0.10 Shallow water 8408 121.08 2.22 Upland paddy 152742 2199.48 40.-36 Lowland paddy 150504 2167.26 39.77 Barren land 19371 278.94 5.12 Fallow land 2099 30.23 0.55 Grasses & shrubs 30993 446.30 8.20 Settlement 13945 200.81 3.68 Unclassified 20 0.29 0.01 Total 378472 5450.00 100.0

\

\

\

91

{

N

A

Legend

ws_class.lmg Cl•s_Names

Botrtn land

-Cropland

- Folowlond

- On:hord

c:J Sellement

- Shrubs

- Undossified

- wotorbody

e Outlet

Plate 4.5 Land use/cover map of the Chhokranala watershed

Legend Class_Names

-Bar~nland

N

A

- Lowland Paddy

- Upland Paddy

- DeepWater

0 Falowland

c::J orassLand

- Settlement - Shntls - Unclas9fied

- ShalaN Water

• O.kf

Plate 4.6 Land use/cover map of the Arang watershed

92

4.5 SOIL TEXTURE MAP

The soil map of the watershed is traced, scanned and exported to the Geometica.

Image to image registration is performed using the registered topographic maps. The

scanned map is loaded in a channel and the boundaries demarcating the different soils are

carefully traced and the polygons representing various soils are filled with different

colours for identification. Different grey level values are given for different soil types

while preparing the map. There are mainly four series of soil found in the watersheds;

they are the Bhata, Matasi, Dorsa and Kanhar series. Areas under each soil texture and

soil series in each sub-watershed are also determined for use. Soil texture and soil series

and soil resources data are given in section 3.4.3 (Table 3.1) of Chapter III. Areas

occupied by different soil texture in the both watersheds are given in Table 4.4. Plate 4. 7

and Plate 4.8 shows the soil texture map of the Chhokranala and Arang watershed,

respectively.

Table 4.4: Area under different soil texture prevailing in the watersheds

Particulars Bhata Matasi Dorsa Kanhar

(Sandy Loam) (Sandy clay loam) (Loam) (Clay)

Chhokranala watershed

Area, ha 203.88 655.50 231.23 640.39

%total area 11.78 37.87 13.36 36.99

Arang watershed

Area, ha 174.62 520.20 711.78 4043.40

%total area 3.21 9.54 13.06 74.19

93

N

A Legend 10

.. !IIIIa

.. Malasi

.. Dorsa

C:::J Kanhar

2,(Dl

Meters

Plate 4. 7 Soil texture map of the Chhokranala watershed

I~

I

Legend • •:rt\1!

·· ;r~ - !'1\,'ll

c::Ji:.t)ol

Plate 4.8 Soil texture map of the Arang watershed

94

4.6 CALCULATION OF CURVE NUMBERS

Land use map, soil map and sub watershed maps are used for determination of

curve numbers. These maps are overlaid using GIS and then statistical information

(number of pixels corresponding to various land use and soil texture) is extracted. This

information is then used as input to get the weighted average curve numbers for each sub­

watershed based on the standard table of curve numbers for the Indian conditions

(Narayana, 1993). The programme algorithm calculated the hydrologic condition of the

watershed based on drainage network, the hydrological soil group based on soil

properties, and the antecedent moisture condition (AMC-II) as described by Singh

(1995). The standard reference table for curve numbers is shown in Table 4.5. Estimated

sub-watershed wise AMC-ll curve numbers are given in Table 4.6 and Table 4.7 for

Chhokranala and Arang watershed, respectively which are used for calibration and

validation of the SWAT model, respectively.

Table 4.5: Runoff curve numbers (for AMC-Il) for the Indian conditions

Land use Treatment/practices Hydrologic Hydrological soil group condition A IB IC ID

Cultivated Straight row --- 76 86 90 93 Contoured Poor 70 79 84 88

Good 65 75 82 86 Contoured & terraced Poor 66 74 80 82

Good 62 71 77 81 Bunded Poor 67 75 81 83

Good 59 69 76 79 Paddy (rice) --- 95 95 95 95

Orchards With under stony cover --- 39 53 67 71 Without under stony cover --- 41 55 69 73

Forest Dense --- 26 40 58 61 Open 28 44 60 64 Shrubs 33 47 64 67

Pasture --- Poor 68 79 86 89 Fair 49 69 79 84 Good 39 61 74 80

Waste land --- --- 71 80 85 88

Hard surface --- --- 77 86 91 93

95

Table 4.6: Sub-watershed wise data for Chhokranala watershed

Sub- Area Slope Curve Av. slope Channel Channel K p

watershed (ha) (%) Numbers length (m) length (km) slope(%) :value value

SWS-1 185.45 1.2 79.32 140.3 2.13 .001 0.18 0.60

SWS-2 290.71 1.6 88.16 143.8 3.75 .003 0.20 0.50

SWS-3 119.71 1.4 87.37 142.6 3.70 .002 0.14 0.50

SWS-4 316.71 1.3 89.52 145.4 3.15 .002 0.15 0.50

SWS-5 277.74 2.0 81.63 149.8 2.13 .005 0.21 0.60

SWS-6 280.71 1.8 89.23 142.3 3.70 .004 0.23 0.60

SWS-7 259.97 1.7 83.31 135.0 3.75 .003 0.21 0.50

WS* 1731.00 1.6 85.02 146.7 6.10 .005 0.20 0.50

* Entire Chhokranala watershed

Table 4.7: Sub-watershed wise data for Arang watershed

Sub- Area Slope Curve Ave. slope Channel Channel K p

watershed (ha) (%) Numbers length (m) length (krn) slope(%) value value

SWS-1 345.14 1.3 91.91 136.3 1.50 .001 0.18 0.60

SWS-2 148.67 1.3 87.98 139.8 1.50 .001 0.18 0.60

SWS-3 355.17 1.5 83.40 118.6 1.60 .002 0.18 0.60

SWS-4 934.78 1.6 89.00 142.4 2.10 .003 0.18 0.60

SWS-5 838.88 2.0 89.74 145.8 2.40 .005 0.20 0.50

SWS-6 577.55 1.6 90.00 132.3 2.60 .003 0.18 0.60

SWS-7 385.30 1.7 86.91 117.0 2.50 .004 0.22 0.50

SWS-8 674.36 1.2 90.64 124.3 2.60 .002 0.18 0.60

SWS-9 634.48 1.3 89.60 137.8 2.20 .001 0.18 0.60

SWS-10 555.67 1.3 90.77 124.7 1.50 .001 0.18 0.60

WS* 5450.00 1.5 89.29 I 41.7 8.56 .005 0.18 0.60

* Entire Arang watershed

96

4.7 OTHER WATERSHED PARAMETERS

Watershed parameters such as average channel slope, average slope length,

average surface steepness, drainage density, saturated hydraulic conductivity, soil

erodibility factor, channel depth, channel width and conservation practice factors are

detennined as follows:

Average channel slope: The average channel slope is computed by dividing the

difference in elevation between the sub-watershed outlet and the most distant point in the

sub-watershed.

Conservation practice factor: Conservation practice factor (P) is the ratio of soil loss

with a specific support practice to the corresponding loss with up and down slope culture.

The typical values of conservation practice factor (P) suggested by Wischmeier and

Smith (1978) for down slope rows, contouring strip cropping, and terracing (Table Bl of

Appendix B) are referred and used in this study (Table 4.6 and Table 4. 7).

Channel length: The channel length is the distance along the channel from the watershed

outlet to the most distant point in the watershed. Histogram programme is used to

calculate average channel length of the watershed and each sub-watershed is given in '

Table 4.6 and Table 4. 7.

Soil erodibility factor: The soil erodibility factor (K) describes the inherent erodibility

of the soil expressed in the same units as the annual erosion losses in tones per ha.

Numerous factors control the erodibility of cohesive soils such as grain size distribution,

texture, permeability and organic matter content. The computed values of K for different

soil textures are taken from Singh et al. (1981). For each sub-watershed, weighted K

value is determined as follows (Williams and Berndt, 1977):

n

L K 1 xDA 1

K = -'1.=.·''------DA

(4.3)

97

where K is the soil erodibility factor for the watershed; K1 is the soil erodibility for an

individual soil, i; DA is the total drainage area of the watershed; and n is number of

different soils in the watershed.

Average watershed slope length: The contour-extreme point method developed by

Williams and Berndt (1976) is being used for determination of average slope length of

each sub-watershed. The contour-extreme point method is faster and more accurate.

The average slope length (A.) can be computed by the contour-extreme point

method for any combination of watershed and channel slopes on a linear surface. For use

on a complete contour, the contour length, LC is measured and divided by twice the

number of extreme points. Also, the length around the base of the contour, LB is

measured and divided by twice the number of extreme points (EP). The average slope

lengths of the each sub-watershed are given in Table 4.6 & 4.7 and the equation used for

determination of average slope length is as follows:

A.- (LCxLB)

- (2EP-J LC' - LB' ) (4.4)

Channel depth and width: The model requires average main channel depth and width

for determining the transmission losses. Both these parameters are estimated from general

knowledge of the watershed and personal communication.

Saturated hydraulic conductivity: Selection of saturated hydraulic conductivity

prediction technique depends upon availability and the level of information on physical

and hydraulic properties of soil (Mualem, 1986). Since soil texture classes are available

the saturated hydraulic conductivity at !00 % moisture content is obtained using

unsaturated hydraulic conductivity curves as given by Rawls et al. (1982) and Saxton et

a!. (1986) (Fig. 4.1 ).

98

0 " 60 70 80 90 100 0

" .. 30

" g a " ,, c

70 .. " ...

Fig. 4.1 Saturated hydraulic conductivity (cmlhr) for USDA texture triangle

4.8 DESCRIPTIONS OF INPUT/OUTPUT FILES OF THE MODEL

SWAT input files are split into separate files by subbasin and data type. SWAT

reads a file name, opens that file, reads and stores the input data, and then closes the file.

This eliminates the problem of having more files open than the operating system will

allow. There is one important file called file.cio (Control Input Output file), which

contains all of input and output file names that are used by the model. A brief description

of input output files and input data which are being considered for the model in the study

is given below.

4.8.1 Input Files

Control Code File (.cod): The input control code (.cod) file contained the number of

years of simulation, beginning year of simulation, number of subbasin, weather

generation control codes, print codes, and several others. All the inputs are common to

the entire basin and not subbasin dependent. There is a provision to take output on daily,

monthly or annual basis by providing different codes. Monthly output provides necessary

information for evaluating model performance within growing season or for examining

monthly runoff and sediment yields. Daily output is mainly used for error detection,

although it may be useful in simulating for growing season conditions. Single set of

99

rainfall and temperature input files can be specified for entire basin or different input files

for each sub basin. These input data files can be based on either observed data or model­

simulated data.

For the statistical analysis of monthly output the model requires the measured

monthly water and sediment yield as input to compare with the simulated runoff and

sediment yield. If measured monthly water yields are available, model can compute

relevant statistics on the measured and predicted values. If the measured values are not

available, statistical comparisons can be skipped. Statistical calculations in the existing

SWAT model was not properly working, hence statistical calculations are made using

Microsoft Excel. Hargreaves method is used for potential ET calculation.

Rainfall and Temperature Data Files: The measured rainfall (.pep) input file contains

daily rainfall values in mm. Each day is stored on one line. The measured temperature

(.tmp) input file contains daily maximum and minimum temperature values in Degree

Celsius. Each day's maximum and minimum temperature is stored on one line.

The Basin File (.bsn): The general basin (.bsn) input file contains inputs for the entire

basin. It includes drainage area, base flow factor and initial soil water content. The base

flow factor is used for computing subsurface flow when return flow travel time is not

input. The field capacity soil water content (33 kPa) is multiplied by fraction of field

capacity to obtain the initial soil water storage. SWAT calculates FFC as a function of

average armual precipitation by setting FFC code to zero. A sample of basin input file is

given in Table B2 of Appendix B.

The Reservoir File (.res): The reservoir (.res) input file contains reservoir-input data

including total reservoir surface area at emergency and principle spillway, runoff required

to fill up to emergency and principle spillways, release rates, initial reservoir volume,

normal sediment concentration in the reservoir and hydraulic conductivity of reservoir

bottom.

100

Watershed Configuration File (.fig): The watershed configuration (.fig) input file

contains the routing commands SWAT uses to route and adds flows through a watershed.

The commands include subbasin, route, routers, and transfer, add, routsub, recall, save,

and finish.

Crop Data File ( crop.dat): The crop.dat is a crop database input file contains crop

specific parameters. When a crop is specified to be planted in the management (.mgt) file,

the crop parameters for that crop are taken from crop.dat file. The crop parameters

include biomass conversion factor, harvest index, optimum and base temperatures,

maximum leaf area, maximum root depth and several others. One crop data file namely

CROPPARM.DAT is provided in the model that contains information for 66 crops

(Arnold et al., 1996). In this study four crops (rice, maize, groundnut and soybean) are

considered for developing the management scenarios. Input parameters of this file can be

adjusted based on research results (Arnold et a!., 1996). Optimal and minimum

temperatures for plant growth for all the crops are adjusted as per the available research

results. Other parameters are considered as ·such.

The Tillage File (till.dat): Other than tillage reference number and name, the mixing

efficiency of operation is the input to this file. The mixing efficiency of the operation is

the fraction of materials (crop residue and nutrients) that is mixed uniformly in the

ploughing depth of the implement. The till.dat file that is included in the model contains

mixing efficiencies for over 70 tillage operations that can be selected in the management

file (Arnold eta!., 1996). However the mixing efficiency of the bullock drawn country

plough is also added in this file to evaluate the model performance under existing tillage

practices. A value of 0.5 of mixing efficiency is assigned for the conventional tillage

(country plough) practices.

101

Subbasin Input Files: Files such as sub, rte, clun, sol, mgt, mco, gw and wgn are

required for each subbasin. These files contain inputs that are specific to each subbasin.

Brief description about these files is given as follows:

The general subbasin (.sub) input file, which contains general inputs specific to

each subbasin such as area, curve number, carbon dioxide (C02) concentration, land and

channel slopes and lengths, USLE P factor, and initial residue cover. The runoff curve

numbers for the Indian conditions are used as input to the model for the AMC-II

condition. C02 concentration (ppm) is used in ET and biomass calculations. If C02 value

is left blank, default of 330 ppm is assumed in SWAT. Average main channel depth and

width are required to determine the transmission losses. The effective hydraulic

conductivity of the channel alluvium for various channel bed materials used in the study

is given in Table B3 of Appendix B for both the watersheds.

Return flow travel time is required for subsurface flow from the centroid of the

subbasin to reach the subbasin. The SWAT model calculates the return flow travel time

from soil hydraulic properties and flow characteristics if zero value is assigned. SWAT

provides this feature in which the percolated water can return to surface streams through

lateral flow from the soil profile and/or return flow from shallow aquifer. Moreover water

that has joined the deep aquifer is considered lost from the system and cannot return.

Sediment concentration in return flow is usually very low and does not contribute

significantly to total sediment yields unless return flow is very high. As the sediment

concentration in return flow was not known, a value of 500 ppm is taken as suggested by

Arnold ef a!. (1996). USLE erosion control practice factor (P) given by Wischmeier and

Smith (1978) was considered (Table Bl of Appendix B). The P factor values for different

sub-watersheds are given in Table 4.6 and Table 4.7 for Chhokranala and Arang

watershed, respectively.

102

The average slope length is estimated for each subbasin with the use of

contour-extreme point method (Williams and Berndt, 1976). The average slope steepness

is also estimated by using the method described by Williams and Berndt (1976). Initial

residue cover (kg/ha) at the start of simulation was not known; a value of zero is assigned

to the model. The subbasin input data file is given in Table B4 and Table B5 of Appendix

B for Chhokranala and Arang watershed, respectively.

The channel 'n' value is the Manning's 'n' value for various types of channel and

the surface roughness factor is the Manning's 'n' value for various conditions. The 'n'

values those considered for overland and channel flows are taken from Table 4.8 and 4.9,

respectively (Arnold et al., 1996). The overland and channel 'n' values are calibrated and

are given in Chapter V (Table 5.1 ).

Table 4.8: Values of Manning's 'n' for overland flow under various conditions

Overland flow Value chosen Range

Fallow, no residue 0.0100 0.008-0.012

Conventional tillage, no residue 0.0900 0.06-0.12

Conventional tillage, residue 0.1900 0.16-0.22

Chisel plough, no residue 0.0900 0.06-0.12

Chisel plough, residue 0.1300 0.10-0.16

Fall disking, residue 0,4000 0.30-0.50

No-till, no residue 0.0700 0.04-0.10

No-till, (0.5-1.0 tlha) 0.1200 0.07-0.17

No-till (2.0-9.0 tlha) 0.3000 0.17-0.47

Range land (20% cover) 0.6000 0.03-0.05

Short grass prairie 0.1500 0.10-0.20

Dense grass 0.2400 0.17-0.30

Bermuda grass 0.4100 0.30-0.48

103

Table 4.9: Values of Manning's 'n' for the channels under various conditions

Channel flow Value chosen Range

Excavated or dredged

Earth, straight and uniform 0.0250 0.016-0.033

Earth, winding and sluggish 0.0350 0.023-0.05

Not maintained, weeds and brush 0.0750 0.04-0.14

Natural streams

Few trees, stones, or brush 0.0500 0.025-0.065

Heavy timber and brush 0.1000 0.05-0.15

The routing input file ( .rte) contains information on channel dimensions (length,

slope, width, depth, etc.) for the main channel through the subbasin. Some of the input

parameters required for routing file were similar to the subbasin file. Parameters such as

channel width, channel depth, charmel slope, channel length, channel 'n' value, effective

hydraulic conductivity of the charmel and other data considered in this file are given in

Table B6 and B7 of Appendix B for Chhokranala and Arang watershed, respectively. The

USLE soil erodibility factor (K) for channel ranged from 0 to I. A value of 0.5 for K is

taken for each sub-watershed. Since there is no resistance to erosion in the channel and

channel is erosive, a value of one for cover factor (C) is taken in the routing file.

The chemical (.chm) input file, contains data on initial pesticide concentrations in

the soil and in the foliage along with initial nutrient concentrations in the soil. Since in

this study no pesticide is considered, initial organic nitrogen, phosphorous and soluble

phosphorous concentrations in upper layer (g/t) are given as input to the model to

simulate nutrient losses.

The soil (.sol) input file contains soil data including bulk density, available water

capacity, saturated conductivity, particle sizes, organic carbon, and maximum rooting

depth. Each soil can have a maximum of I 0 layers. Soil input data used for each sub-

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watershed are given in Table B8 and B9 of Appendix B for Chhokranala and Arang

watershed, respectively. Saturated conductivity value for all the soil layers are

determined by the procedure described earlier in this chapter (section 4.7). The mean

values for specific soil texture classes are taken into consideration in the model. For each

sub-watershed weighted value of soil is considered for the study. The data related to

organic carbon (%) for every layer, clay content(%), USLE soil erodibility factor K and

initial N03 concentration (g/t of soil) are used as input.

The groundwater (.gw) input file contains aquifer data including initial

groundwater height (m), initial groundwater flow contribution to streamflow (mrnlday),

alpha factor (coefficient for estimation of groundwater return flow to stream) for

groundwater, specific yield, groundwater delay (days), the revap coefficient (coefficient

for estimation of evaporation from the shallow aquifer), deep aquifer percolation

coefficient, revap storage (shallow aquifer storage) and initial deep aquifer storage. Due

to lack of groundwater information of the study area only few of the known parameters

are entered in this file to run the model (Table B 10 in Appendix B).

The management (.mgt) input file contains input data for management operations

such as planting, harvesting and tillage operations; and irrigation, pesticide and nutrient

applications. There is facility to schedule the operations by month and day or by heat

units. Inputs in this file include dates, tiliage code, crop code and pesticide code and

application amounts.

The crop code value of zero indicates no crop, whereas a value of one indicates a

crop is present at the beginning of the simulation. The number of years of crop rotation

may vary from 1 to 10. It is unusual for rotations to exceed four years in decision­

making applications. However, in model tests involving research results, it is common

for the rotation to be equal to the entire period of record because any change in

management (crop, fertilizer application, planting and harvest dates, or tillage) requires a

105

separate year in the rotation. The total number of pesticides to be used in the simulation

could be entered to know the effect of pesticides on crop yield and also losses could be

assessed. Since in this study no pesticides are used therefore this input is zero.

Plant, fertilizer, harvest & kill and tillage operations are used in this study. Plant

operation requires heat units up to maturity, crop identification number, harvest index

override (optional) (tlha), biomass override (tlha) (optional) and runoff curve number as

input. Fertilizer operation requires amount of total nitrogen applied (kglha), amount of

phosphorus applied (kglha), soil layer to apply fertilizer, fraction of organic N in total N

applied, fraction of organic P in total P applied as input. Harvest and kill operation

requires heat units to maturity, leaf area index and biomass at start of simulation as input.

Tillage operation requires tillage identification number and runoff curve number as input.

A sample management input file is given in Table B11 of Appendix B.

The weather generator (.wgn) input file contains monthly parameters that are

required for generating daily amounts of precipitation, maximum and minimum

temperatures, and solar radiation. All the parameters required by this file are given in

Table Bl2 of Appendix B. Rainfall depths for 0.5 and 6 hours duration for 10-year

frequency are taken after fitting the frequency distribution as the procedure suggested by

Chow (1964). Monthly mean of daily rainfall, standard deviation and skewness

coefficients are calculated by fitting the frequency distribution. Inputs such as number of

rainy days in a month and average value of temperature, solar radiation and wind velocity

are given as input to the model.

4.8.2 Output Files

The main SWAT output files are the std, sbs, rch, and rsv files. The std file was

described in Arnold eta!. (1990). The subbasin (.sbs) output file reports output for over

50 variables related to water, sediment, nutrients, and crops. The channel reach (.rch)

output file reports output for each strean1 channel routing reach. Output variables in this

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file include water, sediment, and pollutants entering and leaving the reach. The reservoir

(.rsv) output file provides information on water and sediment entering, leaving and being

deposited in the reservoirs. More than 100-output variables could be written on daily,

monthly, or annual basis for each subbasin in output files. The output variables that are

used to evaluate the model performance and to develop management scenarios for the

Chhokranala and Arang watershed include surface runoff (mm), sediment yield (t/ha),

organic nitrogen in sediment (kglha), phosphorous in sediment (kg/ha), N03 and soluble

Pin surface runoff {kg/ha) and crop yield (kg/ha).

4.9 CRITERIA FOR MODEL EVALUATION

Out of many advisable calibration aids graphical comparisons are extremely

useful. Continuous time series plot of the recorded and simulated series and a scattergrarn

of recorded data plotted against simulated flows are therefore used in this study. Several

types of statistics provide useful numerical measures of the degree of agreement between

models simulated and recorded quantities. The numerical and graphical performance

criteria used in this study are described below:

Martinec and Rango (1989) recommended that the criteria should be as simple as

possible. The deviation of runoff volumes, Dv, is one goodness-of-fit criterion.

v-v'wo v (4.5)

where V is the measured yearly or seasonal runoff volume; V' is the model computed

yearly or seasonal runoff volume. Dv can take any value; however, smaller the number

better the model results are. Dv would equal zero for a perfect model. The use of Dv

provided an immediate compliment to a visual inspection of the continuous hydrographs.

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The second basic goodness-of-fit criterion recommended by ASCE Task

Committee (1993) is the Nash-Sutcliffe coefficient or coefficient of simulation efficiency

(COE) (Nash and Sutcliffe, 1970):

~JQ,-Q',Y COE = 1- -'-'1

"':-1 ---

I(Q, -Q)' (4.6)

i:ol

where Q1 is the measured daily discharge; Q'1 is the computed daily discharge; Q is the

average measured discharge values. The COE values can be varies from 0 to 1, with 1

indicating a perfect fit. A value of COE = 0 indicates that the mpdel is simulating no

better than using the average of the observed data. Furthermore, a shortcoming of the

Nash-Sutcliffe statistics occurs in periods of low flow. If the daily measured flows

approach the average value, the denominator of the equation (3.125) goes to zero and

COE approaches minus infinity with only minor model miss predictions. This statistic

works best when the coefficient of variation for the observed data set is large. The

average measured discharge is determined either for the year or period in question or

from earlier years as long-term average. Martinec and Rango (1989) recommended using

Q for the year or season to avoid unrealistically high values ofCOE in low runoff years.

4.10 CALIBRATION OF THE MODEL

Parameters required for the model calibration are extracted from the analysis of

DEM, soil map and satellite imagery. Those parameters are then used in the SWAT

model run. The SWAT model has been calibrated satisfactorily by Tripathi et al., (2003).

The manual calibration procedure as described by Sorooshian and Gupta (1995) and used

by Tripathi eta/., (2003) have been adopted. In manual procedure trial-and-error process

of parameter adjustments is used. After each parameter adjustment, the simulated and

observed hydrographs are visually compared to see if the match had improved.

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Estimated curve number (CN) is given as input to the model. The soil series and

soil texture are different for different sub watersheds. Therefore, weighted average of all

the soil resource data are given as input to the model. Since most of the input parameters

are available for the Chhokranala and Arang watershed, those parameters were not

calibrated and considered as such.

Parameters such as Manning's 'n' value for overland flow and channel flow and

initial soil water storage as a Fraction of Field Capacity (FFC) are taken into

consideration for calibration. First of all, prescribed range for the conventional tillage

without residue is selected. Since the Chhokranala and Arang watershed is a natural

stream having few trees, stones and brush, a range of 0.025-0.065 for the channel 'n'

value is selected (Arnold et al., 1996). Different values between lower limit and upper

limit are chosen and model was run to simulate runoff and sediment yield. Several

simulation runs were performed to get the channel 'n' value. Similarly, overland flow 'n'

values are chosen for the conventional tillage without residue condition in which the

value ranged between 0.03 and 0.09 and simulation runs are performed.

Runoff is generally sensitive to the initial soil water storage. Therefore, attempts

are also made to calibrate FFC value to get adequate runoff. The range of FFC value is

from 0 to 1. The zero value for FFC meant that the SWAT calculated the fraction of field

capacity on the basis of average annual precipitation. The zero value of FFC gave good

response in terms of runoff and sediment yield during preliminary runs of the model.

The effective hydraulic conductivity of the alluvium for the surface is chosen for

the bed material group of moderate loss rate in which the bed material characteristics is

the mixture of sand and gravel with significant amount of silt-clay. The prescribed range

6.4 to 25 mmlhr is considered for effective hydraulic conductivity (Table 83 of Appendix

B). Very low loss rate of bed material having consolidated formation with high silt clay

content is chosen for channel in which the value ranged between 0.025-2.5 mm/hr for all

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the sub-watersheds. Model did not respond to the change in the effective field capacity

values. Therefore values chosen by Arnold et al. (1996) are considered appropriate for

both the watersheds.

ET methods are also evaluated for the available data set of both watersheds. The

model default method that is Priestley-Taylor. Other methods such as Penman Monteith

and Hargreaves method are tested for the both watersheds. It was noticed that for the

available data set of study watershed the Hargreaves method performed better than the

other methods because this method requires air temperature only as input to calculate ET.

While other methods required relative humidity, wind velocity and solar radiation as

input besides temperature.

Some other parameters such as base flow factor, specific yield, alpha factor and

revap coefficient, revap storage are tried to calibrate but there was no significant effect

shown by the model. Therefore either model default or range of values suggested by the

model developers is used. Calibration results are compared with the observed data set for

the monsoon period.

4.11 SENSITIVITY ANALYSIS

The sensitivity analysis is important for the model in order to determine the

relative change in model output with respect to the change in model input variables. First

of all base data file is established and base output variables are determined. Each variable

is varied within the prescribed range keeping others constant. The output values are then

analysed to determine their variation with respect to the base values. As each variable is

varied about the base value, the mean output is compared with the mean of the base value

predictions as a measure of sensitivity. From the sensitivity analysis it is possible to

determine which variables need to be closely estimated to make accurate predictions of

the watershed yields. The inputs that are varied during the sensitivity analysis are given

in Table 5.1 of Chapter V.

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4.12 VALIDATION OF THE MODEL

Validation is the essential part of a model testing after calibration. Adequately

calibrated model should be validated properly to know the model performance without

any changes in the input file except climatic parameters. After calibration, the SWAT

model is validated for daily as well as for monthly runoff, sediment yield and nutrient

losses from the both watersheds. Daily runoff and sediment yield validation has been

done for the monsoon season of the year 2004 for Chhokranala watershed. Monthly

validation of the model has been performed for the year 2003 to 2004 for Arang

watershed and 2004 to 2005 for Chhokranala watershed. The model is validated for the

nutrient losses for the several events of 2003 to 2005 for both the watersheds. The model

is also used to test the capability of simulating daily rainfall using weather generator

(wgn). Using generated daily rainfall, runoff and sediment yields are also simulated and

performance of the wgn was evaluated. For this purpose, the model is run for five years

(2001 to 2005) for Chhokranala watershed and four years (2002 to 2005) for Arang

watershed and the results are compared with observed data by using standard procedures.

4.13 IDENTIFICATION AND PRIORITISATION OF CRITICAL SUB­

WATERSHEDS

Watershed prioritisation is the ranking of different critical sub-watersheds of a

watershed according to the order in which they have to be taken up for treatment and soil

conservation measures. It is always better to start management measures from the most

critical sub-watershed, which makes it mandatory to prioritise the sub-watershed

available. A particular sub-watershed may get top priority due to various reasons but

often, the intensity of land degradation is taken as the basis. This approach of prioritising

watersheds based on actual sediment yield rates may be possible only when the number

of sub-watersheds to be prioritised is less and necessary data is available. Further, this

method will be helpful when the sediment yield potentials of different sub-watersheds do

not have considerable variation.

Ill

The critical sub-watersheds are identified on the basis of average annual sediment

and nutrient losses from the sub-watersheds during the period of 2003 to 2005. In this

context, annual sediment yields are simulated for each sub-watershed of both the

watersheds using SWAT model. Priorities are fixed on the basis of ranks assigned to each

critical sub-watershed according to ranges of soil erosion classes described by Singh et

a!. (1992) (Table 4.10). Also for nutrient losses a threshold value of !0 mg/1 for nitrate

nitrogen and 0.5 mg/l for dissolve phosphorous as described by EPA (1976) is considered

as criterion for identifying the critical sub-watersheds. Identified critical sub-watersheds

are arranged in descending order and then priorities were fixed for their management.

Table 4.10: Area under different classes of soil erosion by water in India

Soil erosion Slight Moderate High Very Severe Very

classes high severe

Soil erosion range 0-5 5-10 10-20 20-40 40-80 >80

(t/ha/yr)

Area 801,350 1,405,640 805,030 160,050 83,300 31,895

(km2)

4.14 EFFECTIVE MANAGEMENT OF WATERSHEDS

After adequate calibration and validation, using the weather generator, the model

along with the calibrated parameters can be used to analyze future scenarios of the

management practices for the critical sub-watersheds. Srinivasan et al. (1998) also

suggested that the best management practices (BMPs) could be evaluated for critical

erosion prone areas using SWAT and recommended for reducing the soil erosion.

For evaluating the management scenarios of the critical sub-watersheds the

recorded rainfall and temperature data for the year 2003 through 2005 are used. Climatic

parameters such as relative humidity, solar radiation and wind velocity are generated and

used for evaluating the best management practices. The model takes only the generated

112

values of these climatic parameters as input. To reduce the sediment and nutrient losses

from these critical sub-watersheds, an attempt is made to find out the best management

practices for the identified critical sub-watersheds. The management files of critical sub­

watersheds are taken into consideration for evaluating the management scenarios.

Several simulations are performed considering seventy and sixty-six combinations

for Chhokranala and Arang watershed respectively, of the different treatments for the

management of the critical sub-watersheds. Four numbers of crops (rice, maize,

groundnut and soybean), three fertilizer doses (existing, half of recommended and

recommended) and five tillage practices (conventional, MB plough, field cultivator,

conservation tillage and zero tillage) have been considered.

The Chhokranala and Arang watersheds are almost well treated with soil

conservation measures such as graded and contour bunds. Hence these measures for

reducing the sediment and nutrient losses have not been considered. Major parts of the

watershed are under agronomic practices, which are feasible. Therefore crop based

agronomic measures are only considered for management purpose. Based on the

available field data and existing practices of cultivation, the treatments of watershed are

decided for evaluating the best management practices. Justifications for each treatment

including tillage, crop and fertilizer are presented below:

Tillage: Other than conventional tillage (country plough) four tillage treatments are

considered viz. zero tillage, conservation tillage, field cultivator and mold board (M. B.)

plough. Farmers of the watershed do not use M. B. plough, zero tillage and conservation

tillage. However, very few farmers use cultivator occasionally. These treatments are

selected on the basis of previous studies for evaluating management practices by the

researchers all over the world. Tillage treatments and their respective mixing efficiencies

are given in Table 4.11. Mixing efficiencies are considered as suggested by Arnold et al.

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(1996) for all the tillage treatments except for country plough for which it has been

determined on the basis of other tillage implements.

Table 4.11: Tillage treatments and their mixing efficiencies

Tillage treatments Code Mixing efficiency

Zero tillage T1 0.05

Conservation tillage T2 0.25

Field cultivator T3 0.30

M. B. plough T4 0.90

Country plough (Conventional) T5 0.50

Crops: The rice (Oryza sativa) crop in the Chhokranala and Arang watershed is mostly

grown under both upland and low land situations with high seed rate (150-200 kg/ha) and

low doses of fertilizer (20-25 kg Nlha and 10-15 kg P/ha). This crop predominantly

occupied maximum area (about 36% of the total area) in the both the watersheds. The

crop is normally sown during June-July and harvested during September-October.

Maize (Zea mays) is the second crop occupying about 8% area of the Chhokranala

and Arang watershed. Maize is generally grown during monsoon season (June to

October) in upland situation only. Some of the farmers are also growing groundnut

(Arachis) in few patches of uplands. Soybean (Glycine max) may be suitable for the

prevailing agro-climatic condition of the Chhokranala and Arang watershed. This is a

cash crop, therefore considered in this study.

Timely crop establishment with adequate plant population is a pre-requisite for

improved management of the crop. Selected crops along with fertilizer level considered

are shown in Table 4.12.

114

Table 4.12: Level ofN: P (kg/ha) of various crops taken for management

Fertilizer level (code) Rice Maize G-nut Soybean

Existing (Fl) 25:15 20:15 10:20 10:20

(5:5) (5:5) (5:5) (5:5)

Y, of the recommended (F2) 40:30 50:30 20:40 30:30

(10:10) (10:5) (10:5) (10:5)

Recommended (FJ) 80:60 100:60 30:60 60:60

(20:20) (20:10) (15:10) (15:10)

NB: Values ofN: P kglha present in manure are given in parentheses.

Fertilizer levels: All soils of the watershed are low in fertility in terms of availability of

nitrogen and phosphorus. Aerobic nitrogen is present in soil predominantly in the form of

nitrate (N03). In the semi wet situations with intermittent aerobic and anaerobic

conditions, leaching and denitrification of N03 further depletes the N status. Since soils

of the watershed are acidic in nature, availability of phosphorous is limited due to

fixation in acidic soils.

Crop responses to fertilizers in upland fields are often dramatic, as most of the

upland soils are inherently poor in fertility. Organic manure in conjunction with different

chemical sources of nutrients for various crops has been evaluated to identify suitable

combinations to maintain soil fertility and productivity on a sustained basis under various

tillage practices. Fertilizer levels along with manure for different crops are given in Table

4.12.

4.15 CONCLUDING REMARK

In this chapter collected data are processed through different types of standard

formula and methodologies for extracting some of the model inputs such as area, slope,

channel length and several others. Generation of the watershed and sub-watershed

115

boundaries using DEMand generation of various maps (soil texture, drainage and slope)

using Geometica. After delineation the Chhokranala watershed is subdivided into 7 sub­

watersheds whereas Arang watershed is subdivided into 10 sub-watersheds. Supervised

classification is adopted for classifying the land use of the study watershed. With the help

of soil texture maps four soil texture classes are identified in the watersheds; they are the

sandy loam, sandy clay loam, loam and the clay series. Estimated sub-watershed wise

AMC-II curve numbers for both the watersheds are used for calibration and validation of

the SWAT model. The manual calibration procedure as described by Sorooshian and

Gupta (1995) is adopted. The sensitivity analysis has been done which is important for

the model in order to determine the relative change in model output with respect to the

change in model input variables. Validation procedure is also discussed because it is the

essential part of a model testing after calibration. Procedure for identification of critical

sub-watersheds of study watershed and justifications of treatments considered for

management of identified critical sub-watersheds are also discussed. How the input files

are prepared using extracted and other readily available data has been discussed in details

under this Chapter.

116