Identification of Sensitive Parameters for Runoff Simulation in Santrod Watershed Using QSWAT
DOI:
https://doi.org/10.52151/jae2025622.1931Keywords:
calibration, curve number, hydrological modelling, sensitivity analysis, SWAT-CUP, water resource managementAbstract
In this study, Soil and Water Assessment Tool (SWAT) was employed in conjunction with remote sensing and geographic information system to simulate runoff in Santrod watershed, located in Mahi River basin, in the north-west region of India. The model calibration was performed on a monthly time-step for 2000-2014 period with an initial 2-year warm-up period (1998-1999). Following calibration, the model was validated using observed runoff data for 2015-2019. Model calibration and parameter sensitivity analysis were performed using automatic calibration feature available in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) software. Sensitivity analysis showed that hydrological processes of the watershed are highly influenced by soil evaporation compensation factor, threshold depth for shallow aquifer and runoff curve number factor. The model performance was found good during calibration with values of coeffi cient of determination (R²) as 0.91, Nash-Sutcliffe efficiency (NSE) as 0.91, percent bias (PBIAS) as 7.2 and root mean square error to standard deviation ratio (RSR) as 0.3. Similarly, the model performance was found satisfactory during validation with values of R², NSE, PBIAS and RSR as 0.86, 0.89, 8.4 and 0.24, respectively. Findings of this study provide valuable insights
for policymakers, water resource managers and environmental planners. The findings can further guide in sustainable water allocation strategies and watershed conservation efforts, to ensure long-term health of Santrod watershed and support in effective water management practices in the region.
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Aawar, T., & Khare, D. (2020). Assessment of climate change impacts on streamflow through hydrological model using SWAT model: A case study of Afghanistan. Modeling Earth Systems and Environment, 6(3), 1427-1437. https://doi.org/10.1007/s40808-020-00759-0
Abbaspour, K. C. (2015). SWAT Calibration and Uncertainty Programs-A User Manual. Swiss Federal Institute of Aquatic Science and Technology: Eawag, Switzerland. pp. 100. Aavialble at: https://swat.tamu.edu/media/114860/usermanual_swatcup.pdf (accessed on 31 May 2025).
Abbaspour, K. C., Johnson, C. A., & van Genuchten, M. T. (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3(4), 1340-1352. https://doi.org/10.2136/vzj2004.1340
Abeysingha, N. S., Islam, A., & Singh, M. (2020). Assessment of climate change impact on flow regimes over the Gomti River basin under IPCC AR5 climate change scenarios. Journal of Water and Climate Change, 11(1), 303–326. https://doi.org/10.2166/wcc.2018.039
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew, M. W., Kannan, N., & Jha, M. K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508. https://doi.org/10.13031/2013.42256
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
Baker, T. J., & Miller, S. N. (2013). Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed. Journal of Hydrology, 486, 100-111. https://doi.org/10.1016/j.jhydrol.2013.01.041
Boithias, L., Sauvage, S., Taghavi, L., Merlina, G., Probst, J.L., & Pérez, J.M.S. (2011). Occurrence of metolachlor and trifluralin losses in the Save river agricultural catchment during floods. Journal of Hazardous Materials, 196, 210-219. https://doi.org/10.1016/j.jhazmat.2011.09.012
Bouraoui, F., Benabdallah, S., Jrad, A., & Bidoglio, G. (2005). Application of the SWAT model on the Medjerda river basin (Tunisia). Physics and Chemistry of the Earth, 30(8-10), 497-507. https://doi.org/10.1016/j.pce.2005.07.004
Buonocore, C., Pascual, J. J. G., Cayeiro, M. L. P., Salinas, R. M., & Mejías, M. B. (2021). Modelling the impacts of climate and land use changes on water quality in the Guadiana basin and the adjacent coastal area. Science of the Total Environment, 776, 146034. https://doi.org/10.1016/j.scitotenv.2021.146034
Desai, S., Singh, D. K., Islam, A., & Sarangi, A. (2021). Impact of climate change on the hydrology of a semi-arid river basin of India under hypothetical and projected climate change scenarios. Journal of Water and Climate Change,12(3), 969–996. https://doi.org/10.2166/wcc.2020.287
Di Luzio, M., Srinivasan, R., & Arnold, J.G. (2004). A GIS-coupled hydrological model system for the watershed assessment of agricultural nonpoint and point sources of pollution. Transactions in GIS, 8(1), 113-136. https://doi.org/10.1111/j.1467-9671.2004.00170.x
FAO (Food and Agriculture Organization). 2003. Digital soil map of the world and derived soil properties. Land and Water Development Division, FAO, Rome, Italy. Available at: http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases (accessed on 2/9/2020).
Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G., (2007). The soil and water assessment tool: Historical development, applications, and future research directions. Transactions of the ASABE, 50(4), 1211-1250. https://doi.org/10.13031/2013.23637
Gassman, P. W., Sadeghi, A. M., & Srinivasan, R. (2014). Applications of the SWAT model special section: overview and insights. Journal of Environmental Quality, 43(1), 1-8. https://doi.org/10.2134/jeq2013.11.0466
Ghoraba, S. M. (2015). Hydrological modeling of the Simly Dam watershed (Pakistan) using GIS and SWAT model. Alexandria Engineering Journal, 54(3), 583-594. https://doi.org/10.1016/j.aej.2015.05.018
Khatun, S., Sahana, M., Jain, S. K., & Jain, N. (2018). Simulation of surface runoff using semi distributed hydrological model for a part of Satluj Basin: parameterization and global sensitivity analysis using SWAT CUP. Modeling Earth Systems and Environment, 4(1), 1111-1124. https://doi.org/10.1007/s40808-018-0474-5
Lee, J., Lee, J. E., Kim, C. G., & Chung, I. M. (2024). Evaluation of individual impacts of human activities on streamflow changes using SWAT Model.Water,16(23), 3455. https://doi.org/10.3390/w16233455
Li, C., & Fang, H. (2021). Assessment of climate change impacts on the streamflow for the Mun River in the Mekong Basin, Southeast Asia: Using SWAT model. CATENA, 201, 105199. https://doi.org/10.1016/j.catena.2021.105199
Molina-Navarro, E., Trolle, D., Martínez-Pérez, S., Sastre-Merlín, A., & Jeppesen, E. (2014). Hydrological and water quality impact assessment of a Mediterranean limno-reservoir under climate change and land use management scenarios. Journal of Hydrology, 509(1), 354-366. https://doi.org/10.1016/j.jhydrol.2013.11.053
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/10.13031/2013.23153
Moriasi, D. N., Steiner, J. L., & Arnold, J. G. (2011). Sediment measurement and transport modeling: impact of riparian and filter strip buffers. Journal of Environmental Quality, 40(3), 807-814. https://doi.org/10.2134/jeq2010.0066
Munoth, P., Gehlot, L. K., Patel, P. L., Khandelwal, S., Timbadiya, P.V ., & Goyal, R. (2023). Integrated hydrological modelling of two contrasting watersheds with a terminal reservoir in the Upper Tapi River basin, India. Water Supply, 23(12), 4891-4907. https://doi.org/10.2166/ws.2023.314
Muthee, S.W., Kuria, B.T.O., Mundia, C. N., Sichangi, A.W., Kuria, D.N., Goebel, M., & Rienow, A. (2023). Using SWAT to model the response of evapotranspiration and runoff to varying land uses and climatic conditions in the Muringato basin, Kenya. Modeling Earth Systems and Environment, 9(2), 1531-1543. https://doi.org/10.1007/s40808-022-01579-0
Narsimlu, B., Gosain, A. K., & Chahar, B. R., 2013. Assessment of future climate change impacts on water resources of Upper Sind River Basin, India using SWAT model. Water Resources Management, 27, 3647-3662. https://doi.org/10.1007/s11269-013-0371-7
Neitsch S. L., Arnold J. G., Kiniry J. R., & Williams J. R. (2011). Soil and water assessment tool Theoretical documentation version 2009. Texas Water Resources Institute Technical Report No 406, Texas A & M University Systems, College Stations, Texas.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J.R. (2002) Soil and Water Assessment Tool User’s Manual- Version 2000. TWRI Report TR-191. Texas Water Resources Institute, College Station, Texas.
Noori, N., & Kalin, L. (2016). Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology, 533, 141-151. https://doi.org/10.1016/j.jhydrol.2015.11.050
Oeurng, C., Sauvage, S., & Sánchez-Pérez, J. M. (2010). Temporal variability of nitrate transport through hydrological response during flood events within a large agricultural catchment in south-west France. Science of the Total Environment, 409(1), 140-149. https://doi.org/10.1016/j.scitotenv.2010.09.006
Patel, D. P., & Srivastava, P. K. (2013). Flood hazards mitigation analysis using remote sensing and GIS: correspondence with town planning scheme.Water Resources Management, 27(7), 2353-2368. https://doi.org/10.1007/s11269-013-0291-6
Patel, D. P., & Srivastava, P. K. (2014). Application of geo-spatial technique for flood inundation mapping of low lying areas. In: Srivastava, P.K., Mukherjee, S. and Gupta, M. (Eds.), Remote sensing applications in environmental research (pp. 113–130). Springer, Cham., https://doi.org/10.1007/978-3-319-05906-8_7.
Patel, D. P., Dholakia, M. B., Naresh, N., & Srivastava, P. K. (2012). Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the lower Tapi basin. Journal of the Indian Society of Remote Sensing, 40(1), 299-312. https://doi.org/10.1007/s12524-011-0147-6
Rasool, T., & Kumar, R. (2019). Application of Soil and Water Assessment Tool for runoff simulation in a data scarce Himalayan watershed. Journal of Agricultural Engineering (India), 56(2), 136-146. https://doi.org/10.52151/jae2019562.1685
Ray, S. L., Sahu, A. P., Paul, J. C., Das, D. M., Raul, S. K., & Kundu, S. K. (2024). Climate change impact on hydro-climatic fluxes in Kantamal catchment of the middle Mahanadi River basin, India. Journal of Agricultural Engineering (India), 61(6), 890-909. https://doi.org/10.52151/jae2024616.1894
Santhi, C., Arnold, J.G., Williams, J. R., Dugas, W. A., Srinivasan, R., & Hauck, L. M. (2001). Validation of the SWAT model on a large rwer basin with point and nonpoint sources. Journal of the American Water Resources Association, 37(5), 169-1188. https://doi.org/10.1111/j.1752-1688.2001.tb03630.x
Sellami, H., Benabdallah, S., La Jeunesse, I., & Vanclooster, M. (2016). Quantifying hydrological responses of small Mediterranean catchments under climate change projections. Science of the Total Environment, 543(Part B), 924-936. https://doi.org/10.1016/j.scitotenv.2015.07.006
Sharma, P., & Machiwal, D. (2021). Streamflow forecasting: Overview of advances in data-driven techniques. In: Sharma, P. and Machiwal, D. (Eds), Advances in streamflow forecasting - from traditional to modern approaches. Elsevier, Netherlands, pp. 1-50. https://doi.org/10.1016/B978-0-12-820673-7.00013-5
Shivhare, V., Goel, M. K., & Singh, C .K. (2014). Simulation of surface runoff for upper Tapi subcatchment Area (Burhanpur Watershed) using swat. Remote Sensing and Spatial Information Sciences, 40, 391-397. https://doi.org/10.5194/isprsarchives-XL-8-391-2014
Shrestha, M. K., Recknagel, F., Frizenschaf, J., & Meyer, W. (2016). Assessing SWAT models based on single and multi-site calibration for the simulation of flow and nutrient loads in the semi-arid Onkaparinga catchment in South Australia. Agricultural Water Management, 175, 61-71. https://doi.org/10.1016/j.agwat.2016.02.009
Singh, A., & Gosain, A. K. (2011). Climate-change impact assessment using GIS-based hydrological modelling. Water International, 36(3), 386-397. https://doi.org/10.1080/02508060.2011.586761
Singh, L., & Saravanan, S. (2020). Impact of climate change on hydrology components using CORDEX South Asia climate model in Wunna, Bharathpuzha, and Mahanadi, India. Environmental Monitoring and Assessment, 192(11), 678. https://doi.org/10.1007/s10661-020-08637-z
Sinha, J., Sahu, R.K., Agarwal, A., Pali, A. K., & Sinha, B. L. (2013). Rainfall-runoff modelling using multi layer perceptron technique - A case study of the upper kharun catchment in Chhattisgarh. Journal of Agricultural Engineering (India), 50(2), 43-51. https://doi.org/10.52151/jae2013502.1512
Sophocleous, M. (2002). Interactions between groundwater and surface water: The state of the science. Hydrogeology Journal, 10(1), 52-67. https://doi.org/10.1007/s10040-001-0170-8
Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-set metrics in market response models: An integrative approach. Journal of Marketing Research, 47(4), 672-684. https://doi.org/10.1509/jmkr.47.4.672
Srivastava, P. K., Han, D., Ramirez, M. A. R., & Islam, T. (2013a). Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology, 498, 292-304. https://doi.org/10.1016/j.jhydrol.2013.06.021
Srivastava, P. K., Han, D., Ramirez, M. R.,& Islam, T. (2013b). Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27, 3127-3144. https://doi.org/10.1007/s11269-013-0337-9
Strayer, D. L., Ewing, H. A., & Bigelow, S. (2003). What kind of spatial and temporal details are required in models of heterogeneous systems. Oikos, 102(3), 654-662. https://doi.org/10.1034/j.1600-0706.2003.12184.x
Todini, E. (2007). Hydrological catchment modelling: Past, present and future. Hydrology and Earth System Sciences, 11(1), 468-482. https://doi.org/10.5194/hess-11-468-2007
van Griensven, A, Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., & Srinivasan, R. (2006). A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324(1-4), 10-23. https://doi.org/10.1016/j.jhydrol.2005.09.008
Van Liew, M.W., Arnold, J.G., & Garbrecht, J.D. (2003). Hydrologic simulation on agricultural watersheds: Choosing between two models. Transactions of the ASAE, 46(6), 1539-1551. https://doi.org/10.13031/2013.15643
Varughese, A., Praveena, K. K., Sruthakeerthi, P., Rachana, V. V., & Anjali, C. V. (2022). Runoff prediction of Bharathapuzha River basin using artificial neural network and SWAT model. Journal of Agricultural Engineering (India), 59(4), 404-416. https://doi.org/10.52151/jae2022594.1791
Wang, G., & Xia, J. (2010). Improvement of SWAT2000 modelling to assess the impact of dams and sluices on streamflow in the Huai River basin of China. Hydrological Processes, 24(11), 1455-1471. https://doi.org/10.1002/hyp.7606
Wang, Z., He, Y., Li, W., Chen, X., Yang, P., & Bai, X. (2023). A generalized reservoir module for SWAT applications in watersheds regulated by reservoirs. Journal of Hydrology, 616, 128770. https://doi.org/10.1016/j.jhydrol.2022.12877
Yang, J., P. Reichert, & K. C. Abbaspour (2007), Bayesian uncertainty analysis in distributed hydrologic modeling: A casestudy in the Thur River basin (Switzerland), Water Resources Research, 43(10), W10401. https://doi.org/10.1029/2006WR005497
