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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 RAINFALL RUNOFF MODELLING IN EPHEMERAL RIVER BASIN USING SWAT

Anurag Bandi1, Y. R. Satyaji Rao2, Sanjeet Kumar3

1Post graduate student, department of civil engineering, koneru lakshmaiah Education foundation, Deemed to be university, Vaddeswaram, , -522502, 9704120520 2Scientist G and Head, National Institute of Hydrology, Kakinada, India, 3Associate Professor, Department of Civil Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Vaddeswaram, Andhra Pradesh, india-522502 [email protected], [email protected], [email protected]

Received: 11 May 2020 Revised and Accepted: 09 July 2020

ABSTRACT: Ephemeral river is surface drainage network, normally stays dry and that occasionally may drain related discharge due to rainfall events. Present study focus on Sarada river; ephemeral river where flow occurs mostly during monsoon season, located in Andhra Pradesh, India. In this study SWAT model was used to study hydrological behaviour of the river. The sarada river watershed was split into 26 sub-watersheds; land use & land cover consists mainly of five Landuse classes (with an agricultural land covering more than 62%); the slope ranges mostly 0-10 m (more than 62%). The model was calibrated (1999 to 2005) and validated (2006 to 2008) with 1996 to 1998 as warm-up period at time scale of monthly at gauging station. Monthly calibration and validation shows significant matching between observed and simulated values when evaluated with statistical parameters like coefficient of determination (R2), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) fit of 0.83 & 0.85, 0.83 & 0.67, 9.8 & 15.9 respectively. The graphical representation also shows close match with observed and simulated values. Overall, efficiency of the SWAT model generating stream flow at Anakapalle gauge station has been classified as reasonably good. Optimized SWAT model can be implemented in watershed dominating with agriculture for runoff modelling. The model is demonstrated good execution on Ephemeral River and it will be help for the other rivers also. KEYWORDS: Ephemeral, SWAT, Watershed, Model, Sarada River.

I. INTRODUCTION Hydrological modelling is a crucial and efficient tool for planning and execution of water resources management by both research hydrologists and the practicing engineers with an integrated approach (Sui et al., 1999; Schultz, G.A., 1993). Among various hydrological models the Soil & Water Assessment Tool (SWAT) (Arnold et al., 1998), is one such hydrological model that works on a time step of daily or monthly established by the department of Agriculture (USDA) used as one of most popular, has been widely regarded and computationally effective models for simulating water hydrology (Douglas et al., 2010). It is a catchment scale model that is commonly utilised worldwide and is capable of runoff simulation, impact of anthropogenic factors on stream flow, LULC impact, global climate impacts, and management practices for controlling reservoir sedimentation (Singh & Ahmad 2019). The SWAT model is commonly used in several countries in recent years and extensively developed for hydrological modelling at various spatial scales to examine management approaches for hydrological watersheds and water quality response (Luo et al., 2012; Bosch et at., 2013; Kumar et al 2015; Gravit et al., 2017). In India for different river basin SWAT model has been implemented to simulate rainfall runoff, sediment yield, nutrient loss etc., in those situations in which most of the rivers generated from the extreme storm in monsoon season, the findings were considered to be reasonably satisfactory (Santhi et al., 2002; Mishra et al., 2007; Panhalkar., 2014; Jain et al., 2014; Diwakar et al., 2014; Anand et al., 2018; Das et al., 2019). In India witnessed reasonably satisfactory results for different basins (Kannan et al., 2007; Narasimlu et al., 2015). Apart from the

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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 physical based model execution, there are various problems will challenge the model performance, like uncertainty analysis within input parameters, nonlinear relationship between hydrological input characteristics and hydrological response, and the approaches considered for calibrating various sensitive parameters of model (Griensven et al., 2006). To determine correct values for model parameters which is achieved through trail-and- error procedure and essential parameters were adjusted to obtain the suitable outcome. Manual calibration approach is tedious, times consuming and requires skill. The main objective of the study is to know the rainfall runoff behaviour of the Sarada river basin using SWAT model.

II. MATERIALS AND METHODS Study Area The Sarada River originates in the Lakshmipuram village in of Andhra Pradesh. Catchment area of the Sarada river basin is 2672 Sq km. The central water commission has installed a gauging station at Anakapalle. The study area lies pin the district in Andhra Pradesh having North latitude 17°25' to 18°17' and East longitude 82°32' to 83°06'. It is one of the minor watersheds lying between the Eastern Ghats and the Eastern coast line. Agriculture is the principal occupation. Paddy, jowar, bajra, maize, groundnut, black gram, horse gram and sugar cane are the main crops in the basin. The Sarada River gathers its head waters in the high mountains of Eastern Ghats and flows from north to south receiving a few tributaries and finally joins the . The drainage pattern is dendtric type. The northern and northeastern parts of the basin have mountains with a maximum relief of 1631 meters. The study area is between coast and foothills of the Eastern Ghats enjoys semi-arid climate. The annual rainfall of the Sarada River basin is found to be between 700 to 1000 mm.

Fig. 1-Location Map: Study area of Sarada River

III. DATA USED FOR THE STUDY Land use & Land cover map was prepared using supervised classification from the LANDSAT 7 images having 30 m resolution and acquired from the USGS site https://earthexplorer.usgs.gov/ the acquired image include seven bands and bands 5, 4 and 3 were layer stacked using Erdas Imagine 2014 for FCC image to perform supervised classification. Supervised classification is done for the image Erdas Imagine 2014 identifying the different signature classes that were present in the Sarada basin. Majority of classes classified are water, urban land, forest, barren land and agriculture. The FCC image is matched with projection system of DEM with

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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 similar datum in Arc GIS. The Landuse of the Sarada basin and the percentage of area under different landuse type are showed in the fig 2.

Figure 2: Sarada Rives Watershed LULC Map

The soil map published by National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) was obtained then digitized for the necessary study region & soils present in the basin are red loamy soils, red sandy soils, coastal sands and alluvial soils. Soil classification for study area illustrated in fig 3.

Figure 3: Sarada River Basin Soil Map.

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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 Digital elevation model for the study area was downloaded from https://opentopography.org/ for delineating the watershed into sub-watershed & for slope variations. The elevation data acquired from Shuttle Radar Topographic Mission (SRTM) and the resolution of the data is 30 m. the DEM is projected into WGS _1984_UTM_Zone _44N.

Fig 4: Sarada River Basin Digital Elevation Model.

Meteorological data (observed) like solar radiation, relative humidity, wind speed, and temperature data is acquired from https://globalweather.tamu.edu/. Precipitation data is acquired from the Water Resources Information System India (WRIS). The discharge data was originally measured at the Anakapalle, Vishakhapatnam district of Andhra Pradesh which is supervised by Central Water Commission (CWC). For period over 13 years daily data (1996-2008) acquired from the Water Recourses Information System of India (WRIS).

IV. METHODOLOGY Arc SWAT 2012 version was used for the present study to perform simulations. SWAT is built for measuring the effect of land & water management activities on runoff and soil erosion. Model deals with the corresponding elements: surface water, weather, return flow, water supply, transmission losses, drainage, storage of ponds & reservoirs, nutrient & pesticide filling, passageway, irrigation, etc., SWAT utilizes the Hydrological Response Unit (HRU) to describes about local variation, with the specific landuse characteristics, soil properties & land slope variations. And defining the meteorological data into each HRU’s allows to run model in daily and monthly time step basis.

Fig 5: Work flow Diagram for the Study V. MODEL SETUP

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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 For the present study area Arc SWAT version 2012.10_3.19 released on 01/23/2017 was utilized to fulfil the aim of the study. Arc SWAT uses DEM to automatic delineates a Basin into watersheds, stream definition and outlet points. Watershed delineation tool is used to delineate sub-watersheds based on an automated process using the DEM of the region. The Basin must be delineated into adequate number Hydrological Response units which will be considering the changes in climate, landuse & soil types. Overall basin is split into 26 Sub- watersheds. The delineated watershed of study area is found to be 2676 km2. The hydrological analysis in swat is conducted on hydrologic response units (HRU), on monthly step. Overlaying of landuse and landcover map, soil map, and slope map created from DEM generates HRU’s (Hydrological Response Unit). HRU’s are lumped land area with specific combinations of landuse, soil and management with in each sub-basin runoff is calculated separately for each HRU, and routed to get the overall runoff. The study area’s landuse/ landcover, soil and slope maps were overlaid on each other to obtain HRUs. In the present study 133 HRUs were formed and spread across over 26 sub-watersheds. As for the weather data and daily precipitation data files are created to link with required files for that location. Data on minimum & maximum temperatures, Wind speed, Relative humidity and Solar radiation are model generated based on weather generated tool due to non-availability of observed data. For initial SWAT run loading input data will generate required files from database and then it can be run on the basis of daily and monthly time step basis. Calibration & validation model was done based on the obtained discharge data by dividing it into 2 parts; period from 1999-2005 was utilized for calibration to obtain a calibrated model and data from 2006-2008 utilized for validation.

VI. RESULTS AND DISCUSSION Comparisons of the observed & simulated time series were shown in fig 8 for the calibration period, for monthly and cumulative runoff at Anakapalle gauging station. The simulated average runoff as closely matched the pattern of the observed average runoff. Although the simulated average runoff was slightly higher than observed average runoff. During high rainfall events the magnitudes of the simulated average monthly runoff usually exceed the observed average runoff values. The observed runoff initially shows slow rate of runoff release this may have been due to the soil’s initial dry condition which results in the agricultural land, channels, minor check dams and reservoir retaining a substantial part of the rainfall. This response sustains after saturation of the soil & homogenous conditions were formed. The variation in the peak events may be caused by the variation in the rainfall and manual measuring error in the observed average runoff data. However, statistical analysis indicated that simulated average runoff usually matched with the corresponding observed average values, as shown by R2 & NSE values estimated for monthly time step basis, comparisons between observed and simulated values were shown in table 2. The R2 and NSE value for monthly comparisons illustrates the fact that the model appears to be good in predicting the measurements of the monthly surface runoff.

VII. CALIBRATION AND UNCERTAINTY ANALYSIS Latin Hypercube Sampling (LHS) in which One-Factor-At-A-Time (OAT) analysis method (Griensven et al., 2006), was used one of the feature available in Arc SWAT tool. For the present study calibration and validation was carried out by the Latin Hypercube sampling (LHS). To find sensitive parameters in the basin One-Factor- At-A-Time simulated for the best outcomes. The analysis for present study was done on monthly time step basis. For model use warmup period has been set for Initial three years of data (1996-1998). For calibration and finding out the sensitive parameters of model, data from the period 1999 to 2005 has been used. To get an adequate calibrated SWAT model several simulations were done with different values for each parameter at a time as an input. Estimations of the calibrated sensitive parameters are picked inside the recommended run characterized in the SWAT model. The sensitive parameters values from the calibration are given in table 1. On the trial and error basis calibration was performed manually by adjusting a single parameter at a time. The simulated and observed average runoff is graphically compared after each parameter change to determine the progress in model simulation. The calibration results of runoff were represented graphically in fig 6 and 8.

Table 1: Fitted sensitive parameters after calibration

S No Parameters and description Min value Max value Calibrated value

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1 SOL_Z (mm) (Depth from soil surface to bottom of 0 3500 300 layer.) 2 SOL_K (mm/hr) 0 2000 150 (Saturated hydraulic conductivity.) 3 CH_K1 (mm/hr) (Effective hydraulic conductivity in 0 300 90 tributary channel alluvium.) 4 CH_N1 (Manning's "n" value for the tributary 0.01 30 5 channels.) 5 SHALLST (mm) (Initial depth of water in the shallow 0 5000 500 aquifer.) 6 DEEPST (mm) (Initial depth of water in the deep 0 10000 700 aquifer.) 7 GW_DELAY (days) 0 500 5 (Groundwater delay.)

VIII. SWAT MODEL VALIDATION After a valid calibration the SWAT model was validated for the runoff in watershed. Model validation is a process of using different meteorological time series data, and performing the simulation using the parameters that was used in altered over the model calibration procedure. The main aim is that with the predefined parameters used for the calibration of model, weather the calibrated SWAT model can generate the outputs for the validation period. From the results it has been observed that run off is slightly under predicting over the month of heavy rainfall. Overall the statistical methods adopted showed that the distribution of observed & simulated monthly average runoff reasonably good and uniform for the period (2006-2008). During the validation period slight variations in correlation is due to the variations in real-world and model representing preliminary water and soil conditions. Statistical elevation methods were used to compare the observed and model computed runoff for visual comparison which provides matching peeks, general agreement in hydro graph characteristics and trends of recession were plotted for the period (2006-2008) are presented in table 2.

Fig 6: Scatter Plot for observed and simulated discharge over calibration period

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Fig 7: Scatter Plot for observed and simulated discharge over validation period

Fig 8: Correlation of Monthly average flow for Sarada river basin over Calibration period

Fig 9: Correlation of Monthly average flow for Sarada river basin over Validation period

IX. EVALUATION OF THE RUNOFF SIMULATION The model performance for different hydrological variables were evaluated by statistical methods like coefficient of determination (R2), Nash Sutcliffe coefficient efficiency (NSE), & Root mean square error (RMSE) (Nash et al., 1970) and used graphical methods. Coefficient of determination (R2) is one of the regularly implemented statistical parameter. R2 value more than 0.5 are considered to be satisfactory and higher the values indicates well acceptance. Nash Sutcliffe coefficient of efficiency (NSE) was utilized to determine

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ISSN- 2394-5125 VOL 7, ISSUE 13, 2020 the magnitude between observed data variance & simulated data variance. Root Mean Square Error (RMSE) describes the difference between the simulated and observed values in the units of variable.

Table 2: Calibration and validation values for the Sarada river basin

Parameter Calibration (Monthly) Validation (Monthly) Correlation coefficient (R2) 0.831 0.855 Nash Sutcliffe Coefficient (NSE) 0.83 0.66 Root Mean Square Error (RMSE) 9.83 15.99

X. CONCLUSIONS To acknowledge hydrological process & status of water resources in Sarada river basin SWAT model was applied. Hydrological and metrological datasets on a daily basis at Anakapalle gauging site was utilized as an input to simulate the SWAT model. At gauging station both observed discharge & SWAT simulated discharge were compared. The datasets of 13 years (1996-2008) has been arranged for the model setup, initial 3 years (1996-1998) was utilised as warm up period, next 7 years (1999-2005) set for calibration period and left 3 years (2006-2008) kept for the validation period. For calibration of SWAT model, the sensitive & ranges various hydrological parameters for the Sarada basin was identified by using the Latin Hypercube Sampling (LHS) One- Factor-At-A-Time (OAT) analysis technique was utilized which was inbuilt in the Arc SWAT model. Based on the analysis the most sensitive parameters found were shown in table 1. The model’s performance was analysed with statistical parameters like NSE, R2 and RMSE, the values were observed during the period of calibration and validation were 0.83, 0.81 and 9.83, 0.85, 0.66 and 15.99 respectively, at gauging station. At the gauging station this represents the good performance of SWAT model. The model accuracy and precision can be improved by better meteorological data. On the perception that SWAT as an important tool for integrated management of the basin, based on water flow and its availability.

XI. ACKNOWLEDGEMENT Authors are highly acknowledged to National Institute of Hydrology, Kakinada, Andhra Pradesh, for supporting for providing all the required facility during the project and project and providing the data for the project. The authors also acknowledged to the K.L.E.F Deemed to be University, Guntur for allowing the student to do the project at NIH.

(Anurag Bandi)

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