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Estimation of rainfall–runoff using SCS-CN method and GIS techniques in drought-prone area of Upper Kangsabati Watershed, India

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Abstract

Purulia is one of the most intense drought-prone districts of the western part of West Bengal, India. Acute form of water scarcity is a common phenomenon in this area during the hot-summer period. The water scarcity in this study region is due to the presence of monsoonal vagaries, unfavorable lithological condition and availability of poor groundwater. Therefore, watershed management is the primary concern for sustainable development of natural resources like water and land for optimal development of watershed and economic activities. Therefore, optimal measurement of rainfall-induced runoff is indeed necessary to understand the hydrological behavior. Several traditional statistical and advanced machine learning methods has been used previously to measure surface runoff among the researchers. It is difficult to simulate the required runoff with physical-based models due to the complexity and non-linear behavior of the runoff phenomena, as well as the absence of relevant historical data in all places. Thus, in the present research study of gravelly dominated drought-prone area of Upper Kangsabati Watershed (UKW) is considered to assess rainfall-induced surface runoff using most reliable method of Soil Conservation Service Curve Number (SCS-CN), and considering remote sensing and geographic information system platform. The SCS-CN method is very much reliable and till now has been frequently used among the global hydrological community to optimal assessment of surface runoff and adaptation of proper watershed management strategies. Henceforth, in this study SSC-CN method is used in the hard rock terrain landscape of extended plateau fringe of western West Bengal. The estimated result of runoff depth and runoff volume is 979.45 mm and 280.85 m3, respectively, and the rainfall–runoff is strongly positively correlated with (r) value being 0.98. Additionally, the applied statistical methods and the outcomes of this study will be helpful among the hydrological communities, different stakeholders and policy makers for sustainable watershed management in terms of optimal conserve of water resources and reduced threating of drought condition.

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References

  • Abdalla EMH, Pons V, Stovin V et al (2021) Evaluating different machine learning methods to simulate runoff from extensive green roofs. Hydrol Earth Syst Sci 25:5917–5935. https://doi.org/10.5194/hess-25-5917-2021

    Article  Google Scholar 

  • Adnan RM, Petroselli A, Heddam S et al (2021) Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stoch Environ Res Risk Assess 35:597–616. https://doi.org/10.1007/s00477-020-01910-0

    Article  Google Scholar 

  • Alcamo J, Henrichs T, Rösch T et al (2000) World Water in 2205: Global modeling and scenario analysis for the World Commission on Water for the 21st Century. Kassel, Germany

    Google Scholar 

  • Al-Ghobari H, Dewidar A, Alataway A (2020) Estimation of surface water runoff for a semi-arid area using RS and GIS-based SCS-CN method. Water 12:1924. https://doi.org/10.3390/w12071924

    Article  Google Scholar 

  • Amutha R, Porchelvan P (2009) Estimation of surface runoff in Malattar sub-watershed using SCS-CN method. J Indian Soc Remote Sensing 37:291–304

    Article  Google Scholar 

  • Askar MK (2013) Rainfall-runoff model using the SCS-CN method and geographic information systems: a case study of Gomal River watershed. WIT Trans Ecol Environ 178:159–170

    Article  Google Scholar 

  • Bansode A, Patil KA (2014) Estimation of runoff by using SCS curve number method and arc GIS. Int Sci Eng Res 5(7):1283–1287

    Google Scholar 

  • Beran M, Rodier JA (1985) Hydrological aspects of drought. Studies and reports in hydrology 39. UNESCOWMO, Paris, France

  • Bhunia P, Das P, Maiti R (2020) Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst Environ 4:43–55

    Article  Google Scholar 

  • Bhuyan SJ, Mankin KR, Koelliker JK (2003) Watershed–scale AMC selection for hydrologic modeling. Trans ASAE 46:303

    Article  Google Scholar 

  • Bo X, Qing-Hai W, Jun FAN et al (2011) Application of the SCS-CN model to runoff estimation in a small watershed with high spatial heterogeneity. Pedosphere 21:738–749

    Article  Google Scholar 

  • Chow VT, Maidment DK, Mays LW (2002) Applied hydrology. McGraw-Hill Book Company, New York, USA

    Google Scholar 

  • CWC, NRSC (2014) Watershed Atlas of India. New Delhi

  • Das B, Pal SC, Malik S, Chakrabortty R (2019) Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geol Ecol Landscapes 3:223–237

    Article  Google Scholar 

  • Das S, Behera SC, Kar A et al (1997) Hydrogeomorphological map** in ground water exploration using remotely sensed data - a case study in keonjhar district, orissa. J Indian Soc Remote Sensing 25:247–259. https://doi.org/10.1007/BF03019366

    Article  Google Scholar 

  • Ditthakit P, Pinthong S, Salaeh N et al (2021) Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin. Sci Rep 11:19955. https://doi.org/10.1038/s41598-021-99164-5

    Article  Google Scholar 

  • Gitika T, Ranjan S (2014) Estimation of surface runoff using NRCS curve number procedure in Buriganga Watershed, Assam, India - a geospatial approach. Int Res J Earth Sci 2:1–7

    Google Scholar 

  • Haroon MA, Zhang J, Yao F (2016) Drought monitoring and performance evaluation of MODIS-based drought severity index (DSI) over Pakistan. Nat Hazards 84:1349–1366

    Article  Google Scholar 

  • Kumar Mishra S, Gajbhiye S, Pandey A (2013) Estimation of design runoff curve numbers for Narmada watersheds (India). J Appl Water Eng Res 1:69–79

    Article  Google Scholar 

  • Kumari N, Srivastava A, Sahoo B et al (2021) Identification of suitable hydrological models for streamflow assessment in the Kangsabati River Basin, India, by using different model selection scores. Nat Resour Res 30:4187–4205

    Article  Google Scholar 

  • Leopardi M, Scorzini AR (2015) Effects of wildfires on peak discharges in watersheds. iForest-Biogeosci Forestr 8:302

    Article  Google Scholar 

  • Li J, Liu C, Wang Z, Liang K (2015) Two universal runoff yield models: SCS vs. LCM J Geograph Sci 25:311–318

    Article  Google Scholar 

  • Martz LW, Garbrecht J (1999) An outlet breaching algorithm for the treatment of closed depressions in a raster DEM. Comput Geosci 25:835–844. https://doi.org/10.1016/S0098-3004(99)00018-7

    Article  Google Scholar 

  • Mishra SK, Chaudhary A, Shrestha RK et al (2014) Experimental verification of the effect of slope and land use on SCS runoff curve number. Water Resour Manage 28:3407–3416

    Article  Google Scholar 

  • Mishra SK, Gajbhiye S, Pandey A (2013) Estimation of design runoff curve numbers for Narmada watersheds (India). J Appl Water Eng Res 1:69–79. https://doi.org/10.1080/23249676.2013.831583

    Article  Google Scholar 

  • Mittal N, Bhave AG, Mishra A, Singh R (2016) Impact of human intervention and climate change on natural flow regime. Water Resour Manage 30:685–699

    Article  Google Scholar 

  • Mohammadi B (2021) A review on the applications of machine learning for runoff modeling. Sustain Water Resour Manag 7:98. https://doi.org/10.1007/s40899-021-00584-y

    Article  Google Scholar 

  • Myronidis D, Ioannou K (2018) Forecasting the urban expansion effects on the design storm hydrograph and sediment yield using artificial neural networks. Water 11:31

    Article  Google Scholar 

  • Nalbantis I, Lymperopoulos S (2012) Assessment of flood frequency after forest fires in small ungauged basins based on uncertain measurements. Hydrol Sci J 57:52–72

    Article  Google Scholar 

  • Nath A, Mthethwa F, Saha G (2020) Runoff estimation using modified adaptive neuro-fuzzy inference system. Environ Eng Res 25:545–553. https://doi.org/10.4491/eer.2019.166

    Article  Google Scholar 

  • Ningarahu HJ, Ganesh Kumar SB, Surendra HJ (2016) Estimation of Runoff Using SCS-CN and GIS method in ungauged watershed: a case study of Kharadya mill watershed, India. Int J Adv Eng Res Sci 3:2349–6495

    Google Scholar 

  • Oppel H, Schumann AH (2020) Machine learning based identification of dominant controls on runoff dynamics. Hydrol Process 34:2450–2465. https://doi.org/10.1002/hyp.13740

    Article  Google Scholar 

  • Pal SC, Chakrabortty R (2019) Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Adv Space Res 64:352–377. https://doi.org/10.1016/j.asr.2019.04.033

    Article  Google Scholar 

  • Pal SC, Chakrabortty R, Roy P et al (2021) Changing climate and land use of 21st century influences soil erosion in India. Gondwana Res 94:164–185. https://doi.org/10.1016/j.gr.2021.02.021

    Article  Google Scholar 

  • Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district, India. Nat Hazards 84:1905–1920

    Article  Google Scholar 

  • Psomiadis E, Soulis KX, Efthimiou N (2020) Using SCS-CN and earth observation for the comparative assessment of the hydrological effect of gradual and abrupt spatiotemporal land cover changes. Water 12:1386

    Article  Google Scholar 

  • Rizeei HM, Pradhan B, Saharkhiz MA (2018) Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region. Arab J Geosci 11:1–16

    Article  Google Scholar 

  • Saha A, Ghosh M, Chandra Pal S (2021a) Identifying suitable sites for rainwater harvesting structures using runoff model (SCS-CN), remote sensing and GIS techniques in Upper Kangsabati Watershed, West Bengal, India. Geostatistics and geospatial technologies for groundwater resources in India. Springer International Publishing, pp 119–150

    Chapter  Google Scholar 

  • Saha A, Ghosh M, Pal SC (2021b) Forest health assessment using advanced geospatial technology in Buxa reserve forest, sub-Himalayan West Bengal, India. Forest resources resilience and conflicts. Elsevier, pp 49–61

    Chapter  Google Scholar 

  • Saha A, Pal SC, Santosh M et al (2021c) Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: the present and future scenarios. J Cleaner Prod 320:128713

    Article  Google Scholar 

  • Saini KM, Deb TK, Mitra PP et al (1999) Assessment of degraded lands of Puruliya district, West Bengal using remotely sensed data. J Indian Soc Remote Sensing 27:23–30. https://doi.org/10.1007/BF02990772

    Article  Google Scholar 

  • Santra A, Mitra SS (2020) Space-time drought dynamics and soil erosion in Puruliya district of West Bengal, India: a conceptual design. J Indian Soc Remote Sensing 48:1191–1205

    Article  Google Scholar 

  • Sarangi A, Bhattacharya AK (2005) Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India. Agric Water Manag 78:195–208. https://doi.org/10.1016/j.agwat.2005.02.001

    Article  Google Scholar 

  • Sarkar D, Gangopadhyay SK, Sahoo AK (2006) Soil resource appraisal towards land use planning using satellite remote sensing and gis a case study in patloinala micro-watershed, district Puruliya, West Bengal. J Indian Soc Remote Sensing 34:245–260

    Article  Google Scholar 

  • Satheeshkumar S, Venkateswaran S, Kannan R (2017) Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India. Modeling Earth Syst Environ 3:1–8. https://doi.org/10.1007/s40808-017-0301-4

    Article  Google Scholar 

  • Schulze R, Schmidt E, Smithers J (1992) SCS-SA User Manual PC Based SCS Design Flood Estimates for Small Catchments in Southern Africa. Pietermaritzburg

  • Shrestha MN (2003) Spatially distributed hydrological modelling considering land-use changes using remote sensing and GIS Map Asia 2003. Map Asia 2003: Water Resources 1–9

  • Sindhu D, Shivakumar BL, Ravikumar AS (2013) Estimation of surface runoff in Nallur Amanikere. Int J Res Eng Technol 2(13):404–409

    Article  Google Scholar 

  • Soulis KX (2021) Soil conservation service curve number (SCS-CN) method: current applications, remaining challenges, and future perspectives. Water 13:192. https://doi.org/10.3390/w13020192

    Article  Google Scholar 

  • Soulis KX, Valiantzas JD (2012) SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds–the two-CN system approach. Hydrol Earth Syst Sci 16:1001–1015

    Article  Google Scholar 

  • Srivastava A, Sahoo B, Raghuwanshi NS, Singh R (2017) Evaluation of variable-infiltration capacity model and MODIS-terra satellite-derived grid-scale evapotranspiration estimates in a River Basin with Tropical Monsoon-Type climatology. J Irrig Drain Eng 143:04017028

    Article  Google Scholar 

  • Thiessen AH (1911) Precipitation averages for large areas. Mon Weather Rev 39(7):1082–1089

    Google Scholar 

  • Tikhamarine Y, Souag-Gamane D, Ahmed AN et al (2020) Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs particle swarm optimization. J Hydrol 589:125133

    Article  Google Scholar 

  • Tripathi MP (1999) Hydrological modeling for effective management of a small watershed India, variously paged. Indian Institute of Technology Kharagpur

    Google Scholar 

  • USDA (1972) Soil Conservation Service. National Engineering Handbook. USDA, Washington DC, USA

    Google Scholar 

  • USDA-SCS (1974) Soil survey of Travis County. Washington DC, USA

  • Verma S, Mishra SK, Singh A et al (2017) An enhanced SMA based SCS-CN inspired model for watershed runoff prediction. Environ Earth Sci 76:1–20

    Article  Google Scholar 

  • Wallace JS, Gregory PJ (2002) Water resources and their use in food production systems. Aquat Sci 64:363–375. https://doi.org/10.1007/PL00012592

    Article  Google Scholar 

  • Wang G, Mang S, Cai H et al (2016) Integrated watershed management: evolution, development and emerging trends. J Forestry Res 27:967–994

    Article  Google Scholar 

  • Wu H, Zhang J, Bao Z et al (2022) Runoff modeling in ungauged catchments using machine learning algorithm-based model parameters regionalization methodology. Engineering. https://doi.org/10.1016/j.eng.2021.12.014

    Article  Google Scholar 

  • Xu AL (2006) A new curve number calculation approach using GIS Technology. ESRI 26th Int’l User Conference 2006. Dallas, USA, pp 1–7

    Google Scholar 

  • Yilmaz AG, Muttil N (2014) Runoff estimation by machine learning methods and application to the Euphrates Basin in Turkey. J Hydrol Eng 19:1015–1025. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000869

    Article  Google Scholar 

  • Zade M, Ray SS, Dutta S, Panigrahy S (2005) Analysis of runoff pattern for all major basins of India derived using remote sensing data. Curr Sci 88(8):1301–1305

    Google Scholar 

  • Zhan X, Huang ML (2004) ArcCN-Runoff: An ArcGIS tool for generating curve number and runoff maps. Environ Model Softw 19:875–879. https://doi.org/10.1016/j.envsoft.2004.03.001

    Article  Google Scholar 

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Saha, A., Ghosh, M. & Pal, S.C. Estimation of rainfall–runoff using SCS-CN method and GIS techniques in drought-prone area of Upper Kangsabati Watershed, India. Sustain. Water Resour. Manag. 8, 130 (2022). https://doi.org/10.1007/s40899-022-00731-z

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