Watershed as an entry point acts as a beginning to address the issues of sustainable rainwater management for improving livelihoods. Extraction of watershed parameters using Geographical Information System (GIS) and use of simulation models is the current trend for hydrologic evaluation of watersheds. In the present study, the open Source Tool Quantum GIS 2.2.0 was used for preparation of maps to verify the spatial extent of the area. The Soil and Water Assessment Tool (SWAT) having an interface with Arc-View GIS software (ArcGIS 10.1 with Arc SWAT 2012 extension) was selected for the estimation of runoff and sediment yield from Kaneri watershed, located in Western Maharashtra region. The coefficient of determination (R 2) for the monthly and yearly runoff was obtained as 0.849 and 0.951 respectively for the calibration period 1979 to 2000 and 0.801 and 0.950 respectively for the validation period 2001-2013. The R 2 value in estimating the monthly and yearly sediment yield during calibration period was computed as 0.722 and 0.788 respectively. The R 2 for monthly and yearly sediment yield values for validation period was observed to be 0.565 and 0.684 respectively.
Watershed as an entry point acts as a beginning to address the issues of sustainable rainwater management for improving livelihoods. To deal with water management issues, one must analyze and quantify the different elements of hydrologic processes taking place within the area of interest. Obviously, this analysis must be carried out on a watershed basis because all these processes are taking place within individual micro watersheds. Only after understanding the spatial and temporal variation and the interaction of these hydrologic components, one can scientifically formulate strategies for water and soil conservation. To achieve this goal, the choice and use of an appropriate watershed model is a must as stated by Sathian and Shyamala [
Kaneri village is located in south Maharashtra region and is dominated by undulating plateau. It is located south-east to Kolhapur city on Pune-Bangalore highway in Maharashtra state. It is located at 16.6055 and 16.6412N and 74.2535 and 74.2906E and is 11 kms away from Kolhapur. It is surrounded by small hills.
The water balance is the driving force for the simulation of hydrology. SWAT uses two steps for the simulation
of hydrology, land phase and routing phase. The land phase is the phase in which the quantity of water, sediment, nutrient and pesticides loadings in the main channel from each subbasin are calculated. Water balance equation for SWAT model is
where SWt is the final water content in millimeters, SWo is the initial soil water content on day i (mm), Pday is precipitation on day i (mm), Qsurf is surface runoff on day i (mm), AET is the actual evapo-transpiration on day i (mm), Qseep is the water flowing into the unsaturated zone form the soil profile on day i (mm) and Qgw is the return flow from the shallow aquifer and lateral flow on day i and t is time in days [
SWAT model uses the concept that whenever the rate of water application to the ground surface exceeds the rate of infiltration, surface runoff occurs. SWAT uses the Modified Universal Soil Loss Equation (MUSLE) to estimate the soil loss from each HRU. The peak runoff rate is the highest runoff rate that occurs with a given precipitation event. SWAT calculates the peak runoff rate with a modified rational method. The factors KUSLE, CUSLE, PUSLE, LSUSLE, and CFRG are taken and used based on previous studies on the watershed and the definition and calculations of the parameters presented in the SWAT documentation. Lateral flow is important in watersheds with soils with high hydraulic conductivities in surface layers and in impermeable or semi-permeable layer at a lesser depth. The water that collects above the impermeable layer is the spring of water for lateral subsurface flow [
SWAT calculates percolation for each layer in the profile and this process occurs only when the moisture content of the soil is more than field capacity. Recharge to an unconfined aquifer occurs by percolation to the water table from a major portion of the land surface. Depending on the water table height in the shallow aquifer, there is a base flow contribution to the main channel. This flow occurs only when the water stored in the shallow aquifer is greater than the threshold water level in the shallow aquifer for ground water input to the main channel to occur. This value is defined by the user or in the SWAT interface the variable is presented as GWQMN [
Ashok Mishra et al. [
To delineate the watershed and sub basins and to determine drainage networks SWAT uses the digital representation of the topographic surface. DEM is the digital representation of the topographic surface. A 30 m by 30 m resolution ASTERDEM was derived and re-sampled to 15 m × 15 m for ease in data acquisition.
A land use map was created by recording the crop type on each plot in the watershed and by identifying the land cover on areas other than cultivated fields. LULC map was acquired from LISS III (Linear Imaging and Self Scanning Sensors). The digital Google image was geo-referenced by taking control points around and inside the watershed. The shape file representing each plot and other land covers was created using the digitizing tools provided in ArcGIS, ArcMap. The soil map obtained from the NBSS&LUP was geo-metrically registered to the base data to match Landsat & IRS satellite imageries. The geo-referenced soil map was used to assist in visual classification of satellite imagery for obtaining soil categories. The final vector map was stored in a geo- database which is amenable to spatial analyze.
SWAT requires daily or sub-daily meteorological data. For Kaneri watershed the daily climate data from two rain gauge stations was used. Daily rainfall data was used for SWAT Model Run. Multiple Gauges were used for weather data input. The main inputs used for weather data in the model are gridded rainfall and temperature. The curve number method (USDA-SCS, 1986) was chosen for calculating runoff. Penman-Monteith method (Monteith, 1965) was chosen for calculating potential evapotranspiration. The variable storage method was chosen as the channel routing mechanism with the assumption that channel dimensions remain constant. The model was set to run from 1st January 1979 to 31st July 2014 with a monthly printout interval. By considering the drainage lines the stream network was prepared. The watershed outlet was manually added and selected for finalizing the watershed delineation (
The watershed of total area 535.48 Ha has been classified in following land uses (
Land use map is a critical input for SWAT model. Land use/land cover map was prepared using remote sensing data of Landsat ETM+. The classification of satellite data mainly follows two approaches i.e. supervised and unsupervised classification. The intent of the classification process is to categorize all pixels in a digital image into one of several land cover classes, or themes [
Sr. No. | Land Use Category | Code | Area (Ha) | % of Watershed Area |
---|---|---|---|---|
1 | Urban Residential Low density | URLD | 11.93 | 2.228 |
2 | Sugarcane | SUGC | 37.84 | 7.069 |
3 | Water bodies | WATR | 64.20 | 11.99 |
4 | Barren | BARR | 85.25 | 15.919 |
5 | Pasture | PAST | 5.72 | 1.069 |
6 | Agriculture Land | AGRR | 330.54 | 61.724 |
SWAT allows the user to delineate the watershed and sub basins using the Digital Elevation Model (DEM). Drainage network is also prepared which can be useful for delineation.
OID | SUBBASIN | URLD | SUGC | WATR | PAST | BARR | AGRR |
---|---|---|---|---|---|---|---|
1 | 1 | 12.27188 | 5.145494 | 6.739872 | |||
2 | 2 | 0.724717 | 8.261779 | ||||
3 | 3 | 0.917975 | 7.053916 | 0.338201 | |||
4 | 4 | 9.711213 | 0.096629 | 0.096629 | 14.97749 | ||
5 | 5 | 1.594378 | 7.005602 | 6.329199 | 26.25893 | ||
6 | 6 | 0.507302 | 4.56572 | 12.92413 | |||
7 | 7 | 2.19831 | 2.391567 | 0.70056 | |||
8 | 8 | 4.082575 | 4.855607 | 4.29999 | |||
9 | 9 | 0.26573 | 0.748875 | ||||
10 | 10 | 2.149995 | 0.144943 | 18.45614 | |||
11 | 11 | 0.773032 | 0.628088 | 9.590427 | |||
12 | 12 | 9.759528 | 18.72187 | ||||
13 | 13 | 2.174152 | 13.72132 | ||||
14 | 14 | 2.294939 | 0.869661 | 27.75668 | |||
15 | 15 | 1.956737 | 5.870211 | 0.26573 | |||
16 | 16 | 5.580324 | 1.256177 | 1.521907 | 0.144943 | 13.45559 | |
17 | 17 | 4.517405 | 8.817395 | 5.459538 | |||
18 | 18 | 6.280884 | 18.38367 | ||||
19 | 19 | 0.869661 | 2.439882 | 12.19941 | |||
20 | 20 | 4.106732 | 11.76458 | ||||
21 | 21 | 0.048314 | 16.30614 | ||||
22 | 22 | 0.169101 | |||||
23 | 23 | 1.811794 | |||||
24 | 24 | 14.32525 | |||||
25 | 25 | 5.483695 | 9.276383 | ||||
26 | 26 | 5.169651 | 24.49545 | ||||
27 | 27 | 33.02296 | 12.75503 | ||||
28 | 28 | 10.82245 | 44.83585 |
land cover map, soil map, slope map and weather data is provided as an input on which the processing is done through SWAT model.
Modeling process once over, gives the output in the form of surface runoff, PET (Potential Evapo Transpiration), Evapo Transpiration, Percolation, Ground water flow, Soil Moisture, Water Yield and Sediment Yield. In this study by Ashok Mishra et al. [
Multiple HRUs were defined within a sub basin by ignoring land uses less than 2% of the subbasin and also ignoring soil types in a subbasin covering less than 5% of the subbasin. A total of 76 HRUs for 28 sub basins were created.
SWAT tool having an interface with Arc View GIS software (AVSWAT 2000) was used by Jain S. K. et al. [
Narayan K. Shreshtha et al. [
Calibration and validation of a SWAT model applied by Arun Babu et al. [
For calibration and validation, different techniques are being used. In the present study, conventional method along Arc SWAT tool was used. The main function of an interface is to provide a link between the input/output of a calibration program and the model. The simplest way of handling the file exchange is through text file formats. The model was run for thirty six years 1979 to 2014. Surface runoff and sediment calibration for the Kaneri watershed was conducted for the years 1979 to 2000. Similarly, surface runoff and sediment validation for the Kaneri watershed was carried out for the years 2001 to 2013. The most widely used criteria, for testing performance of a model is coefficient of determination R2
where,
R2 = coefficient of determination;
Qobs i = Observed value at time step i;
Qsim i = Simulated value at time step i;
Qaobsi = Average of observed value at time step i;
Qasim i = Average of simulated value at time step i.
R2 describes the percentage of the variance in calculated data experienced by the model. According to the criteria developed by Sameh et al. [
Performance rating | R2 |
---|---|
Very Good | R2 > 0.70 |
Good | 0.60 < R2 ≤ 0.70 |
Satisfactory | 0.50 < R2 ≤ 0.60 |
Unsatisfactory | R2 < 0.50 |
The model goodness-of-fit was evaluated on a yearly and monthly basis. The linear graphs for the measured and simulated values both for flow and sediment on yearly basis for calibration and validation are presented. The linear graphs for the measured and simulated values both for flow and sediment on monthly basis for calibration and validation are also produced.
The SWAT model was calibrated using the yearly data of runoff and sediment yield recorded at the outlet of the study watershed for the years 1979 to 2000. Several simulation runs were applied to achieve the model calibration. The time series of the observed and simulated monthly sediment yield for the calibration period were plotted for visual comparison (
The observed and predicted values were plotted against each other in order to determine the goodness-of fit criterion of coefficient of determination (R2) both for runoff and sediment yield. The R2 for yearly and monthly values was obtained as 0.951 and 0.849 respectively for runoff (
The model validation was carried out for yearly and monthly surface runoff and sediment yield for the years 2001 to 2013. A graphical comparison of the observed and simulated yearly and monthly flows and sediment yield are shown in
The total monthly surface runoff computed by the model was, found to be 3228.04 mm against the observed runoff of 3027.38 mm during 2001 to 2013. The sediment yield computed by the model during respective months was obtained as 60.06 t/ha against the observed sediment yield of 66.40 t/ha. The observed and predicted values were plotted against each other in order to determine the goodness-of fit criterion of coefficient of determination (R2) both for runoff and sediment yield. The R2 value for monthly and yearly surface runoff was obtained as 0.801 and 0.950 respectively (
In the present study, The Soil and Water Assessment Tool (SWAT) having an interface with Arc-View GIS software (ArcGIS 10.1 with Arc SWAT 2012 extension), was applied to the hilly watershed for modeling runoff and sediment yield. After preparing all the thematic maps and database as per the format of AVSWAT model,
the model was calibrated for the yearly and monthly surface runoff and sediment yield using the observed data of 1979 to 2000. The model validation was carried out for a data set of thirteen years of 2001 to 2013. The simulation performance of the model for calibration and validation was evaluated using graphical and statistical methods.
The coefficient of determination (R2) for the yearly and monthly runoff was obtained as 0.849 and 0.951 respectively for the calibration period and 0.801 and 0.950 respectively for the validation period. The R2 value in estimating the yearly and monthly sediment yield during calibration was computed as 0.722 and 0.788 respectively. The R2 for yearly and monthly sediment yield values during validation period was observed to be 0.565 and 0.684. Thus, the values of R2 can be considered satisfactory for estimating runoff and sediment yield from a hilly watershed with available data.
Vidula A. Swami,Sushama S. Kulkarni, (2016) Simulation of Runoff and Sediment Yield for a Kaneri Watershed Using SWAT Model. Journal of Geoscience and Environment Protection,04,1-15. doi: 10.4236/gep.2016.41001