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Prediction of Sediment Erosion Pattern in Upper Tapi Basin, India


Affiliations
1 Central Water and Power Research Station, P.O. Khadakwasla, Pune 411 024, India
2 S.V. National Institute of Technology, Surat 395 007, India
3 608, Sai Regency, Ravinagar Square, Amravati Road, Nagpur 440 333, India
 

Physics-based distributed models are useful in identification of critical erosion-prone areas and planning soil conservation measures in the catchment. In this study, soil and water assessment tool (SWAT), a semidistributed hydrological model, is utilized for modelling sediment yield in Upper Tapi Basin, India. Twelve years of observed runoff and sediment yield data are used for calibration and validation of the aforesaid model. The performance indicators, viz. Nash- Sutcliffe efficiency and ratio of ischolar_main-mean-squared error to standard deviation showed good performance of calibrated model in prediction of sediment yield for independent datasets. The two adjoining subcatchments in the basin have shown contrasting behaviour with reference to sediment yield due to differences in their topography, land use-land cover, soil and climatic conditions. Also, simulated erosions at hydrological response units levels, enabled the investigators to demarcate the critical erosion-prone areas in the catchment. The SWAT model has further been used to assess the performance of various soil conservation measures, such as providing filter strips and stone bunds, in the critical erosion prone areas in reducing the sediment yield. Both soil conservations measures, being applied on equal areas, yielded comparative performance in controlling erosion from the catchment.

Keywords

Best Management Practices, Distributed Models, Sediment Yield, Soil Conservation Measures, Upper Tapi Basin.
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  • Wischmeier, W. H. and Smith, D. D., Predicting Rainfall Erosion Losses, Agriculture Handbook No. 537, USDA, Science and Education Administration, US Government, Washington DC, 1978.

  • Williams, J. R., Sediment Yield Predictions with the Universal Equation Using a Runoff Energy Factor, ARS-S-40, USDA, 1975, pp. 244–252.

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K. and Yoder, D. C., Predicting Soil Erosion by Water – A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE), Agricultural Handbook No. 703, US Government Printing Office, Washington DC, 1997.

  • Ferro, V. and Minacapilli, M., Sediment delivery processes at basin scale. Hydrol. Sci. J., 1995, 40(6), 703–717.

  • Kothyari, U. C. and Jain, S. K., Sediment yield estimation using GIS. Hydrol. Sci. J., 1997, 42(6), 833–843.

  • Ferro, V., Porto, P. and Tusa, G., Testing a distributed approach for modeling sediment delivery. Hydrol. Sci. J., 1998, 43(3), 425– 442.

  • Young, R. A., Onstad, C. A., Bossch, D. D. and Anderson, W. P., AGNPS: A non-point source pollution model for evaluating agricultural watersheds. J. Soil Water Conserv., 1987, 44(2), 168–173.

  • Wicks, J. M. and Bathurst, J. C., SHESED: A physically based, distributed erosion and sediment yield component for the SHE hydrological modeling system. J. Hydrol., 1996, 175, 213–238.

  • Shamsi, U. M., Storm-water management implementation through modeling and GIS. J. Water Resour. Plan. Manage. ASCE, 1996, 122(2), 114–127.

  • Rodda, H. J. E., Demuth, S. and Shankar, U., The application of a GIS based decision support system to predict nitrate leaching to ground water in south Germany. Hydrol. Sci. J., 1999, 44(2), 221– 236.

  • Foster, G. R., McCool, D. R., Renard, K. G. and Mobdenhaur, W.C., Conversion of universal soil loss equation to SI metric units. J. Soil Water Conserv., 1981, 36(6), 355–359.

  • Wilson, B. N., Barfield, B. J., Moore, I. D. and Warner, R. C., A hydrology and sedimentology watershed model. II: Sedimentology component. Trans. ASAE, 1984, 27, 1378–1384.

  • Beasley. D. B., Huggins, L. F. and Monke, E. J., ANSWERS: A model for watershed planning. Trans. ASCE, 1980, 23, 938–944.

  • Nearing, M. A., Foster, G. R., Lane, L. J. and Finkner, S. C., A process based soil erosion model for USDA water erosion prediction project technology. Trans. Am. Soc. Agric. Eng., 1989, 32(5), 1587–1593.

  • Arnold, J. G., Williams, J. R. and Maidment, D. R., Continuous – time water and sediment routing model for large basins. J. Hydraulic Eng. ASCE, 1995, 121(HY2), 171–183.

  • Leonard, R. A., Knisel, W. G. and Still, D. A., GLEAMS: Groundwater loading effects on agricultural management systems.Trans. ASAE, 1987, 30(5), 1403–1418.

  • Knisel, W. G., CREAMS: A field-scale model for chemicals, runoff, and erosion from agricultural management systems. Conservation Research Report No. 26. USDA National Resources Conservation Service, Washington DC, 1980.

  • Williams, J. R., Arnold, J. G., Kiniry, J. R., Gassman, P. W. and Green, C. H., History of model development at temple, Texas. J. Hydrol. Sci., 2008, 53(5), 948–960.

  • Arnold, J. G. and Allen, P. M., Automated methods for estimating baseflow and ground water recharge from streamflow records. J. Am. Water Resour. Assoc., 1999, 35(2), 1010–1018.

  • Hao, F. H., Zhang, X. S. and Yang, Z. F., A distributed nonpointsource pollution model: Calibration and validation in the Yellow River basin. J. Environ. Sci., 2004. 16(4), 646–650.

  • Jha, M., Gassman, P. W. and Arnold, J. G., Water quality modeling for the Raccoon River watershed using SWAT. Trans. ASA-40 BE, 2007, 50(2), 479–493.

  • Betrie, G., Griensven, A. V., Mohammed, Y. A., Popescu, Mynett, A. E. and Hummel, S., Linking SWAT and SOBEK using modeling interface (OPENMI) for sediment transport simulation in Blue Nile river basin. Trans. ASABE, 2011, 54(5), 1749–1757.

  • Jeong, J., Kannan, N., Arnold, J. G., Glick, R., Gosselink, L. Srinivasan, R. and Harmel, R. D., Development of subdaily erosion and sediment transport models in SWAT. Trans. ASABE, 2011, 54(5), 1685–1691.

  • Park, J. Y., Park, M. J., Ahn, S. R., Park, G. A., Yi, J. E., Kim, G.S., Srinivasan, R. and Kim, S. J., Assessment of future climate changes impacts on water quality and quantity for a mountainous dam watershed using SWAT. Trans ASABE, 2011, 54(5), 1725– 1737.

  • Chandra, P., Patel, P. L., Porey, P. D. and Gupta, I. D., Estimation of sediment yield using SWAT model for Upper Tapi basin. J. ISH, 2014, 20(3), 291–300.

  • Bieger, K., Fohrer, N. and Hoermann, G., Detailed spatial analysis of SWAT – simulated surface runoff and sediment yield in a mountainous watershed in China. Hydrol. Sci. J., 2015, 60(5), 1–17.

  • Prabhanjan, A., Rao, E. P. and Eldho, T. I., Application of SWAT model and geospatial techniques for sediment-yield modeling in ungauged watersheds. J. Hydrol. Eng. ASCE, 2015, 20, C60140051–6.

  • Olivera, F., Valenzuela, M., Srinivasn, R., Choi, J., Cho, H., Koka, S. and Agrawal, A., ArcGIS-SWAT: A geodata model and GIS interface for SWAT. J. Am. Water Resour. Assoc., 2006, 42(2), 295–309.

  • Arabi, M., Govindaraju, R. S., Hantush, M. M. and Engel, B. A., Role of watershed subdivision on modeling the effectiveness of best management practices with SWAT. J. Am. Water Resour. Assoc., 2006, 42(2), 513–528.

  • Zhongwei, L., Water quality simulation and economic valuation of riparian land-use changes, Ph D, Arts and Sciences, Geography, University of Cincinnati, China, 2006.

  • Arabi, M., Frnkenberger, J. F., Engel, B. E. and Arnold, J. G., Representation of agricultural conservation practices with SWAT. J. Hydrol. Proc., 2007; doi:10.1002/hyp.6890.

  • Bracmort, K. S., Arabi, M., Frankenberger, J. R., Engel, B. A. and Arnold, J. G., Modeling long-term water quality impact of structural BMPs. J. Am. Soc. Agric. Biol. Eng., 2006, 49(2), 367– 374.

  • Ouyang, W., Hao, F. H. and Wang, X. L., Regional nonpoint source organic pollution modeling and critical area identification for watershed best environmental management. Water Air Soil Pollut., 2008, 187, 251–261.

  • Santhi, C., Srinivasan, R., Arnold, J. G. and Williams, J. R., A modeling approach to evaluate the impacts of water quality management plans implemented in a watershed in Texas. Environ. Model. Softw., 2006, 21(8), 1141–1157.

  • Vache, K., Eilers, J. and Santelmann, M., Water quality modeling of alternative agricultural scenarios in the US Corn Belt. J. Am. Water Resour. Assoc., 2002, 38(3), 773–787.

  • Tuppad, P., Kannan, N., Srinivasan, R., Rossi, C. G. and Arnold, J. G., Simulation of agricultural management alternatives for watershed protection. Water Resour. Manage., 2010, 24, 3115–3144.

  • White, M. J. and Arnold, J. G., Development of a simplistic vegetative filter strip model for sediment and nutrient retention at the field scale. Hydrol. Process, 2009, 23, 1602–1616.

  • Narsimhan, B., Allen, P. M., Srinivasan, R., Bednarz, S. T., Arnold, J. G. and Dunbar, J. A., Streambank erosion and best management practice simulation using SWAT. In Proceedings of the 4th Conference on Watershed Management to Meet Water Quality Standards and TMDLs, San Antonio, ASABE Publication, 2007, 701P0207.

  • Silva-Hidalgo, H., Martin-Domingue, I. R., Alarcon-Herrera, M. T. and Grandos-Olivas, A., Mathematical modeling for the integrated management of water resources in hydrological basins. Water Resour. Manage., 2009, 23(4), 721–730.

  • Ladhe, P. P., Sangita and Deshmukh, A., A critical problem of major irrigation projects – A case study of Hatnur Dam in Jalgaon District, Maharashtra. Proc. Sodh, Samiksha aur Mulyankan (International Research Journal), 2008, 350–351; ISSN-0974-2832.

  • Arnold, J. G., Srinivasan, R., Muttiah, R. S. and Williams, J. R., Large area hydrologic modeling and assessment part I: model development. J. Am. Water Resour. Assoc., 1998, 34, 73–89.

  • Neitsch, S. L., Arnold, J. G., Kiniry, J. and Williams, J. R., Soil and Water Assessment Tool Theoretical Documentation (Version 2005), USDA Agricultural Research Service and Texas A&M Blackland Research Center, Temple, Texas, 2005.

  • US Department of Agriculture-Soil Conservation Service (USDASCS), National Engineering Handbook, Section IV, Hydrology, 1972, pp. 4–102.

  • Green, W. H. and Ampt, C. A., Studies on soil physics: I. Flow of air and water through soils. J. Agric. Sci., 1911, 4, 1–24.

  • Priestley, C. and Taylor, R., On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev., 1972, 100, 81–92.

  • Monteith, J. L., Evaporation and environment. Symp. Soc. Exp. Biol., 1965, 19, 205–234.

  • Hargreaves, G. and Riley, J., Agricultural benefits for Senegal River basin. J. Irrig. Drain. E-ASCE, 1985, 111, 113–124.

  • Kannan, N., White, S. M., Worrall, F. and Whelan, M. J., Sensitivity analysis and identification of the best evapotranspiration and runoff options for hydrological modeling in SWAT-2000. J. Hydrol., 2007, 332(3–4), 456–466.

  • Williams, J. R., Flood routing with variable travel time or variable storage coefficients. T. ASAE, 1969, 12, 100–103.

  • Chow, V. T., Open Channel Hydraulics, McGraw-Hill Book Company, New York, 1959.

  • Williams, J. and Berndt, H., Sediment yield prediction based on watershed hydrology. T. ASAE, 1977, 20, 1100–1104.

  • Williams, J., SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. J. Am. Water. Resour.Assoc., 1980, 16, 843–848.

  • Waidler, D., White, M., Steglich, E., Wang, S., Williams, J., Jones, C. A. and Srinivasan, R., Conservation Practical Modeling Guide for SWAT and APEX, Texas Water Resources Institute, Texas, USA, 2009.

  • USEPA, Impact of best management practices of water quality of two small watersheds in Indiana: role of spatial scale (EPA/600/R05/080), 2004.

  • Arnold, J. G., Kiniry, J. R., Srinivasan, R., Williams, J. R., Haney, E. B. and Neitsch, S. L., Input/Output File Documentation. (http://www.swat.tamu.edu), 2011.

  • Kaini, P., Artita, K. and Nicklow, J. W., Optimizing structural best management practices using SWAT and genetic algorithm to improve water quality goals. Water Resour. Manage., 2012, 26, 1827–1845.

  • Winchell, M., Srinivasan, R., Di Luzio, M. and Arnold, J. G., ArcSWAT Interface for SWAT2005 User’s Guide, USDA Agricultural Research Service and Texas A&M Blackland Research Center, Temple, Texas, 2007.

  • Santhi, C., Arnold, J. G., Williams, J. R., Dugas, W. A., Srinivasan, R. and Hauck, L. M., Validation of the SWAT model on a large river basin with point and nonpoint sources. J. Am. Water Resour. Assoc., 2001, 37(5), 1169–1188.

  • Engel, B., Storm, D., White, M., Arnold, J. and Arabi, M., A hydrologic/water quality model application protocol. J. Am. Water Resour. Assoc., 2007, 43(5), 1223–1236.

  • Van Griensven, A. and Bauwens, W., Multiobjective autocalibration for semidistributed water quality models. Water Resour. Res., 2003, 39(12), 1348–1356.

  • Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M. and Srinivasan, R., A global sensitivity analysis tool for the parameters of multi-variable catchment models. J. Hydrol., 2006, 324, 10–23.

  • Abbaspour, K., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J. and Srinivasan, R., Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol., 2007, 333(2–4), 413–430.

  • Abbaspour, K. C., Johnson, C. A. and van Ganuchten, M. Th., Estimating uncertainty and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J., 2004, 3, 1340– 1352.

  • Rouholahnejada, E., Abbaspour, K. C., Vejdani, M., Srinivasan, R., Schulin, R. and Lehmann, A., A parallelization framework for calibration of hydrological models. J. Environ. Model. Softw., 2012, 31, 28–36.

  • Gorgan, D., Bacu, V., Mihon, D., Rodila, D., Abbaspour, K. and Rouholahnejad, E., Grid based calibration of SWAT hydrological models. J. Nat. Hazards Earth Syst. Sci., 2012, 12(7), 2411–2423.

  • Nash, J. E., and Sutcliffe, J. E., River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol., 1970, 10(3), 282–290.

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmenl, R. D. and Veith, T. L., Model evaluation guidelines for systematic quantification of accuracy in Watershed simulations. Am. Soc. Agric. Biol. Eng., 2007, 50(3), 885–900.

  • Jain, S. K., India’s water balance and evapotranspiration, Curr.Sci., 2012, 102(7), 964–967.

  • Brown, C. B., Discussion of sedimentation in reservoirs. J. Witzig.Proc. ASCE, 1943, 69, 1493–1500.

  • www.india-wris.nrsc: India – WRIS Web GIS Water Resources Information System of India.


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  • Prediction of Sediment Erosion Pattern in Upper Tapi Basin, India

Abstract Views: 384  |  PDF Views: 175

Authors

Prabhat Chandra
Central Water and Power Research Station, P.O. Khadakwasla, Pune 411 024, India
P. L. Patel
S.V. National Institute of Technology, Surat 395 007, India
P. D. Porey
608, Sai Regency, Ravinagar Square, Amravati Road, Nagpur 440 333, India

Abstract


Physics-based distributed models are useful in identification of critical erosion-prone areas and planning soil conservation measures in the catchment. In this study, soil and water assessment tool (SWAT), a semidistributed hydrological model, is utilized for modelling sediment yield in Upper Tapi Basin, India. Twelve years of observed runoff and sediment yield data are used for calibration and validation of the aforesaid model. The performance indicators, viz. Nash- Sutcliffe efficiency and ratio of ischolar_main-mean-squared error to standard deviation showed good performance of calibrated model in prediction of sediment yield for independent datasets. The two adjoining subcatchments in the basin have shown contrasting behaviour with reference to sediment yield due to differences in their topography, land use-land cover, soil and climatic conditions. Also, simulated erosions at hydrological response units levels, enabled the investigators to demarcate the critical erosion-prone areas in the catchment. The SWAT model has further been used to assess the performance of various soil conservation measures, such as providing filter strips and stone bunds, in the critical erosion prone areas in reducing the sediment yield. Both soil conservations measures, being applied on equal areas, yielded comparative performance in controlling erosion from the catchment.

Keywords


Best Management Practices, Distributed Models, Sediment Yield, Soil Conservation Measures, Upper Tapi Basin.

References





DOI: https://doi.org/10.18520/cs%2Fv110%2Fi6%2F1038-1049