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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs40899-022-00731-z/MediaObjects/40899_2022_731_Fig12_HTML.png)
Similar content being viewed by others
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
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
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
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
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
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
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
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
Bhuyan SJ, Mankin KR, Koelliker JK (2003) Watershed–scale AMC selection for hydrologic modeling. Trans ASAE 46:303
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
Chow VT, Maidment DK, Mays LW (2002) Applied hydrology. McGraw-Hill Book Company, New York, USA
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
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
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
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
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
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
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
Leopardi M, Scorzini AR (2015) Effects of wildfires on peak discharges in watersheds. iForest-Biogeosci Forestr 8:302
Li J, Liu C, Wang Z, Liang K (2015) Two universal runoff yield models: SCS vs. LCM J Geograph Sci 25:311–318
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
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
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
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
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
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
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
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
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
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
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
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
Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district, India. Nat Hazards 84:1905–1920
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Thiessen AH (1911) Precipitation averages for large areas. Mon Weather Rev 39(7):1082–1089
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
Tripathi MP (1999) Hydrological modeling for effective management of a small watershed India, variously paged. Indian Institute of Technology Kharagpur
USDA (1972) Soil Conservation Service. National Engineering Handbook. USDA, Washington DC, USA
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
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
Wang G, Mang S, Cai H et al (2016) Integrated watershed management: evolution, development and emerging trends. J Forestry Res 27:967–994
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
Xu AL (2006) A new curve number calculation approach using GIS Technology. ESRI 26th Int’l User Conference 2006. Dallas, USA, pp 1–7
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
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
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
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest between the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40899-022-00731-z