In Meenachil River Basin of Kerala, India 1Celine George and 2E
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Scholars Journal of Engineering and Technology (SJET) ISSN 2321-435X Sch. J. Eng. Tech., 2013; 1(2):68-77 ©Scholars Academic and Scientific Publisher (An International Publisher for Academic and Scientific Resources) www.saspublisher.com Research Article Simulation of streamflow using soil and water assessment tool (SWAT) in Meenachil river basin of Kerala, India 1Celine George and 2E. J. James 1 Scientist –E2 & Head, CWRDM Sub Centre, Manimalakunnu, Oliyappuram P. O., Koothattukulam – 686 679, Kerala, India. 2Vice Chancellor and Director (Water Institute), Karunya University, Coimbatore – 641 114, Tamilnadu, India. *Corresponding author Celine George Email: Abstract: The main objective of the study was to test the performance and feasibility of SWAT 2005 model for prediction of streamflow in Meenachil river basin, Kerala. The model was calibrated and validated for three gauging stations, viz., Peroor, Pala and Cheripad. The model was autocalibrated for a period of 13 years (1982 – 1994) using Swat Cup software. The SuFi2 algorithm in Swat Cup was adopted for autocalibration of the model. The calibrated model was validated for the three gauging stations for a period of 10 years (1995 – 2004). The landuse map used for the calibration period was for the year 1990 and that for the validation period was for the year 2000. The simulated monthly streamflow has Nash Sutcliffe efficiency value of 0.80, 0.78 and 0.80 for the calibration period for the Peroor, Pala and Cheripad stations respectively. The model was successful in simulating streamflow during validation period as indicated by Nash Sutcliffe efficiency value of 0.75, 0.78 and 0.80 and R2 value of 0.84, 0.83 and 0.85 respectively for Peroor, Pala and Cheripad stations. The model results in good performance showing that it is feasible for predicting streamflow in Meenachil river basin under changing landuse and climate conditions. Keywords: SWAT2005, Swat Cup, SuFi2 algorithm, Meenachil river basin . INTRODUCTION agricultural chemical yields in large complex Meenachil river basin suffers from water scarcity watersheds. Only a few applications of SWAT model during six months in a year. The available land and are made to Kerala conditions [1, 2, 3]. The main water resources are to be effectively utilized to improve objective of the present study was to test the the livelihood and socio economic conditions of the performance and feasibility of the SWAT2005 model inhabitants. The existing land and water resources for prediction of flow in Meenachil river basin of system of the area is adversely affected by the rapid Kerala. growth of population and change in landuse/landcover. There is a need for hydrological research in the STUDY AREA Meenachil river basin that can support improved catchment management programs which can better The Meenachil river basin in Kerala safeguard degradation of soil and water resources in the encompasses approximately 1272 km2 of drainage area, area. The lack of decision support tools and limitation extending from Vagamon in the east at an elevation of of data concerning weather, hydrological, topography, 1195 m above mean sea level to Vembanad lake on the soil and landuse are factors that significantly hinder southwest coast of India. It lies between 09º26’24” and research and development in the area. The decision 09º51’00” N and 76º 22’12” and 76º55’12” E. The support tools are the various hydrological and erosion mean annual rainfall of the catchment area is about models. Some of the watershed models developed 3510 mm. Figure 1 shows the drainage map of the during the last two decades are WMS (Watershed Meenachil river basin with the locations of rain gauge Modeling System), CREAMS (Chemicals, Runoff and and streamflow stations. The streamflow data at Peroor, Erosion from Agricultural Management Systems), EPIC Pala and Cheripad are used in the present study; areas (Erosion Productivity Impact Calculator), AGNPS of the sub basins covered by these stations are 768, 438 (Agricultural Non Point Source model), SWAT (Soil and 147 km2 respectively. and Water Assessment Tool) and HSPF (Hydrologic Simulation Program – Fortran). Many of these models METHODOLOGY are applied for runoff and soil loss prediction, water quality modeling, landuse change effect assessment and The present study concerns the application of a climate change impact assessment. Among these physically based watershed model SWAT2005 in models, physically based distributed model SWAT is a Meenachil river basin to examine the influence of well established model for analyzing the impact of land topographic, landuse, soil and climatic condition on management practices on water, sediment and streamflow. Model application involved calibration, 68 Celine George et al., Sch. J. Eng. Tech., 2013; 1(2):68-77 sensitivity and uncertainty analysis. For this the SuFi2 t (1) calibration and uncertainty algorithm in Swat Cup was SWt SWo (Rday Qsurf Ea wseep Qgw ) used. i 1 where, SWAT Model Description SWt = soil water content at time t, SW = initial soil water content, The SWAT model is a watershed scale, continuous- o t = time (in days), time model with a daily time step. SWAT is capable of R = amount of precipitation on day i, simulating long-term yields for determining the effect day Q = amount of surface runoff on day i, of land-management practices [4]. SWAT components surf E = amount of evapotranspiration on day i, include hydrology, weather, soil, temperature, sediment a w = water percolation to the bottom of the soil yield, agricultural management practices, nutrients, seep profile on day i and, pesticides etc. SWAT simulation is based on the water Q = amount of water returning to the ground water balance equation (1). gw on day i. Figure-1:Drainage Map of Meenachil River Basin For the estimation of surface runoff the SCS curve number (CN) is used in the model. This method The second equation relates retention parameter to uses two equations for runoff computation. The first curve number as : relates runoff to rainfall and retention parameter as : 1,000 S 25.4 10 (3) (R 0.2S)2 CN Q , R 0.2S (2) R 0.2S where, where, CN = curve number ranging from 0 ≤ CN ≤ 100 Q = daily surface runoff (in mm), The SCS curve number depends on the R = daily rainfall (in mm), infiltration characteristics of the soil, landuse and the S = retention parameter, the maximum potential antecedent soil moisture condition. The SCS defines difference between rainfall and runoff (in mm) three antecedent soil moisture conditions. I – dry starting at the time the storm begins (wilting point), II – average moist, and III – wet. The 69 Celine George et al., Sch. J. Eng. Tech., 2013; 1(2):68-77 moisture condition I curve number is the lowest value within a sub watershed flows to the sub watershed that the daily curve number can assume in dry outlet. The land area in a sub watershed may be divided conditions. The standard values of curve number shown into Hydrologic Response Units (HRUs). These in SCS tables for various land cover and soils are based portions of a sub watershed possess unique on antecedent soil moisture condition II. The standard landuse/management/soil attributes. The number of values for curve number can be adjusted for drier or HRUs in a sub watershed is determined by threshold wetter antecedent conditions using the following value for landuse and soil delineation in the sub equations : watershed. The use of HRUs generally simplifies a simulation run because all similar soil and landuse areas 20 100 CN are lumped into a single response unit. SWAT2005 CN CN 2 (4) 1 2 100 CN exp 2.533 0.0636 100 CN version using ArcGIS platform was used for the study. 2 2 The ArcGIS platform provides the user with a complete set of GIS tools for developing, running and editing hydrologic and management inputs and finally CN CN exp 0.00673 100 CN (5) 3 2 2 calibrating the model. The spatially distributed data required for ArcSWAT include the Digital Elevation where, Model (DEM), soil data and landuse data layers either as shape files or grid data. Weather data and measured CN1 = moisture condition I curve number, streamflow data are also required as input for CN2 = moisture condition II curve number, calibration and prediction purposes. CN3 = moisture condition II curve number. Digital Elevation Model SWAT uses typical curve numbers for various soils Topography was defined by a DEM that describes the with moisture condition II and a set slope of 5 percent. elevation of any point in a given area at a specific To adjust the curve number to different slopes an spatial resolution. For this the contour map of the area equation developed by William (1995) was used was prepared using ArcGIS and the DEM prepared. (equation 6). This DEM (Figure 2) was used to delineate the watershed using automated delineation tool in SWAT. CN CN CN 3 2 1 2 exp 13.86 slp CN (6) The entire watershed was divided into 17 sub 2s 3 2 watersheds, each of which were again divided into several HRUs. A total of 307 HRUs were created. Where, Climate Data CN2s = moisture condition II curve number adjusted The climate data required are precipitation, for the slope, maximum/minimum air temperature, wind speed, relative humidity and solar radiation. Values for these CN3 = moisture condition III curve number for parameters may be read from records of observed data default 5 percent slope, or they may be generated. The weather generator input file contains the statistical data needed to generate CN2 = moisture condition II curve number for representative daily climate data for the subbasins. default 5 percent slope, Climate data will be generated in two instances : when user specifies that simulated weather will be used or slp = average percent slope of the sub-watershed.