Identification of potential zones on the estimation of direct runoff and soil erosion for an ungauged watershed based on remote sensing and GIS techniques

Year 2023, Volume: 8 Issue: 3, 224 - 238, 15.10.2023

Manti Patıl Arnab Saha , Santosh Murlidhar Pıngale Devendra Singh Rathore Vikas Chandra Goyal

https://doi.org/10.26833/ijeg.1115608

Cited By: 3

Abstract

An investigation of soil and water resources is essential to determine the future scenario of water management and water resources to attain food and water security. The improper management of watersheds results in a huge amount of sediment loss and surface runoff. Therefore, the present study was carried out to estimate the surface runoff and soil erosion using the Soil Conservation Service Curve Number (SCS-CN) method and RUSLE approach, respectively. These have been estimated using geospatial technologies for the ungauged Mandri river watershed from the Kanker district of Chhattisgarh State in India. The runoff potential zones, which are defined by the area's impermeable surfaces for a given quantity of precipitation were identified based on curve numbers at the sub-watershed levels. The land use data were collected from LISS IV images of 2009. The results showed that the average volume of runoff generated throughout the 16 years (2000-2015) was 14.37 million cubic meters (mM3). While average annual soil loss was found to be 17.23 tons/ha/year. Most of the eroded area was found to be around the major stream in a drainage system of Mandri River and on higher slopes of the terrain in the watershed. This study revealed that surface runoff and soil erosion are primary issues, which adversely affected the soil and water resources in this watershed. Therefore, suitable water harvesting sites and structures can be constructed based on the potential runoff zone and severity of soil erosion to conserve the soil and water in the watershed.

Keywords

SCS-CN, RUSLE, Rainfall-Runoff modeling, Soil erosion, Ungauged watershed

References

• Mishra, S. K., Pandey, A., & Singh, V. P. (2012). Special issue on soil conservation service curve number (SCS-CN) methodology. Journal of Hydrologic Engineering, 17(11), 1157-1157. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000694
• Marshall, E. J. P., West, T. M., & Kleijn, D. (2006). Impacts of an agri-environment field margin prescription on the flora and fauna of arable farmland in different landscapes. Agriculture, ecosystems & environment, 113(1-4), 36-44. https://doi.org/10.1016/j.agee.2005.08.036
• Swain, S., Mishra, S. K., & Pandey, A. (2021). A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin, India. Environmental Earth Sciences, 80, 1-15. https://doi.org/10.1007/s12665-021-09523-8
• Patil, M. (2016). Stream flow modeling for ranganadi hydropower project in India considering climate change. Current World Environment, 11(3), 834. https://doi.org/10.12944/CWE.11.3.19
• Ramana, G. V., Viswanadh, G. K., & Gautam, N. C. (2011). Rainfall and Runoff process using by overland Time of Concentration Model and GIS Modules. In 12th ESRI India User Conference, New Delhi.
• Mishra, S. K., & Singh, V. P. (2002). SCS-CN method. Part I: derivation of SCS-CN-based models. Available electronically from http://hdl.handle.net/1969.1/164640
• Mishra, S. K., & Singh, V. P. (2013). Soil conservation service curve number (SCS-CN) methodology (Vol. 42). Springer Science & Business Media
• Rajurkar, M.P., Kothyari, U.C., & Chaube, U.C. (2004). Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285(1-4), 96-113. https://doi.org/ 10.1016/j.jhydrol.2003.08.011
• Singh, V. P., Frevert, D. K., Rieker, J. D., Leverson, V., Meyer, S., & Meyer, S. (2006). Hydrologic modeling inventory: cooperative research effort. Journal of irrigation and drainage engineering, 132(2), 98-103. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:2(98)
• Guru, B. G. (2015). Critical Evaluation of MS (Mishra and Singh) Model for Runoff Estimation. Journal of Civil Engineering and Environmental Technology, 2(10), 11-14.
• Aron, K., & Johnson, P. M. (1977). The multiphoton ionization spectrum of xenon: interatomic effects in multiphoton transitions. The Journal of Chemical Physics, 67(11), 5099-5104. https://doi.org/10.1063/1.434737
• Chen, C. L. (1982). An evaluation of the mathematics and physical significance of the soil conservation service curve number procedure for estimating runoff volume. In Proc., Int. Symp. on Rainfall-Runoff Modeling, Water Resources Publ., Littleton, Colo (pp. 387-418).
• Hjelmfelt Jr, A. T. (1980). Curve-number procedure as infiltration method. Journal of the Hydraulics Division, ASCE, 106(HY6), 1107-1111. https://doi.org/10.1061/JYCEAJ.0005445
• Ponce, V.M., & Hawkins, R.H., 1996. Runoff curve number: has it reached maturity? Hydrol. Eng. ASCE 1 (1), 11–19. https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11)
• Siddiraju, R., Sudarsanaraju, G., & Rajsekhar, M. (2018). Estimation of rainfall-runoff using SCS-CN Method with RS and GIS Techniques for Mandavi Basin in YSR Kadapa District of Andhra Pradesh, India. Hydrospatial Analysis, 2(1), 1-15p. https://doi.org/10.21523/gcj3.18020101
• Köylü, Ü. & Geymen, A. (2016). GIS and remote sensing techniques for the assessment of the impact of land use change on runoff. Arabian Journal of Geosciences, 9(7), 484. https://doi.org/10.1007/s12517-016-2514-7
• Liu, X., & Li, J. (2008). Application of SCS model in estimation of runoff from small watershed in Loess Plateau of China. Chinese Geographical Science, 18(3), 235. https://doi.org/10.1007/s11769-008-0235-x
• Rawat, K. S., & Singh, S. K. (2017). Estimation of surface runoff from semi-arid ungauged agricultural watershed using SCS-CN method and earth observation data sets. Water Conservation Science and Engineering, 1(4), 233-247. https://doi.org/ 10.1007/s41101-017-0016-4
• Zelelew, D. G. (2017). Spatial mapping and testing the applicability of the curve number method for ungauged catchments in Northern Ethiopia. International Soil and Water Conservation Research, 5(4), 293-301. https://doi.org/10.1016/j.iswcr.2017.06.003
• Hawkins, R. H. (1973). Improved prediction of storm runoff in mountain watersheds. Journal of the Irrigation and Drainage Division, 99(4), 519-523. https://doi.org/10.1061/JRCEA4.0000957
• Hawkins, R. H. (1978). Runoff curve numbers with varying site moisture. Journal of the irrigation and drainage division, 104(4), 389-398. https://doi.org/ 10.1061/JRCEA4.0001221
• Meshram, S. G., Powar, P. L., Singh, V. P., & Meshram, C. (2018). Application of cubic spline in soil erosion modeling from Narmada Watersheds, India. Arabian Journal of Geosciences, 11(13), 362. https://doi.org/ 10.1007/s12517-018-3699-8
• Mishra, S. K., & Singh, V. P. (1999). Another look at SCS-CN method. Journal of Hydrologic Engineering, 4(3), 257-264. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(257)
• Mishra, S. K., & Singh, V. P. (2003). Derivation of SCS-CN parameter S from linear Fokker-Planck equation. Acta Geophys Pol, 51(2), 180-202. Available electronically from http://hdl.handle.net/1969.1/164631
• Mishra, S. K., & Singh, V. P. (2004). Long‐term hydrological simulation based on the Soil Conservation Service curve number. Hydrological Processes, 18(7), 1291-1313. https://doi.org/10.1002/hyp.1344
• Mockus, V. (1949). Estimation of total (and peak rates of) surface runoff for individual storms. Exhibit A of Appendix B, Interim Survey Rep. Grand (Neosho) River Watershed, USDA, Washington, DC.
• Rallison, R. E. (1980) Origin and evolution of the SCS runoff equation. Proceedings of ASCE irrigation and drainage division symposium on watershed management, ASCE, New York, NY, 2, 912–924.
• Williams, J. R., & LaSeur, W. V. (1976). Water yield model using SCS curve numbers. Journal of the hydraulics division, 102(9), 1241-1253. https://doi.org/10.1061/JYCEAJ.0004609
• Guptha, G. C., Swain, S., Al-Ansari, N., Taloor, A. K., & Dayal, D. (2021). Evaluation of an urban drainage system and its resilience using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 23, 100601. https://doi.org/ 10.1016/j.rsase.2021.100601
• Guptha, G. C., Swain, S., Al-Ansari, N., Taloor, A. K., & Dayal, D. (2022). Assessing the role of SuDS in resilience enhancement of urban drainage system: A case study of Gurugram City, India. Urban Climate, 41, 101075. https://doi.org/10.1016/j.uclim.2021.101075 Nayak, T., Verma, M. K., &Bindu, S. H. (2012). SCS curve number method in Narmada basin. International Journal of Geomatics and Geosciences, 3(1), 219-228.
• Sharma, I., Mishra, S. K., Pandey, A., Kumre, S. K., & Swain, S. (2020). Determination and verification of antecedent soil moisture using Soil Conservation Service Curve Number method under various land uses by employing the data of small Indian experimental farms. In Watershed Management 2020 (pp. 141-150). Reston, VA: ASCE. https://doi.org/ 10.1061/9780784483060.013 Ibrahim-Bathis, K., & Ahmed, S. A. (2016). Rainfall-runoff modelling of Doddahalla watershed—an application of HEC-HMS and SCN-CN in ungauged agricultural watershed. Arabian Journal of Geosciences, 9(3), 170. https://doi.org/10.1007/s12517-015-2228-2
• Singh, A., Malik, A., Kumar, A., & Kisi, O. (2018). Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arabian Journal of Geosciences, 11(11), 261. https://doi.org/ 10.1007/s12517-018-3614-3
• Mishra, S. K., Tyagi, J. V., Singh, V. P., & Singh, R. (2006). SCS-CN-based modeling of sediment yield. Journal of Hydrology, 324(1-4), 301-322. https://doi.org/10.1016/j.jhydrol.2005.10.006
• Lal, M., Mishra, S. K., Pandey, A., Pandey, R. P., Meena, P. K., Chaudhary, A., ... & Kumar, Y. (2017). Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots. Hydrogeology Journal, 25(1), 151-167. https://doi.org/10.1007/s10040-016-1460-5
• Swain, S., Mishra, S. K., Pandey, A., & Dayal, D. (2022). Spatiotemporal assessment of precipitation variability, seasonality, and extreme characteristics over a Himalayan catchment. Theoretical and Applied Climatology, 147, 817-833. https://doi.org/10.1007/s00704-021-03861-0
• Kumar, S., & Kushwaha, S. P. S. (2013). Modelling soil erosion risk based on RUSLE-3D using GIS in a Shivalik sub-watershed. Journal of Earth System Science, 122(2), 389-398. https://doi.org/10.1007/s12040-013-0276-0
• Tyagi, J. V., Mishra, S. K., Singh, R., & Singh, V. P. (2008). SCS-CN based time-distributed sediment yield model. Journal of hydrology, 352(3-4), 388-403. https://doi.org/10.1016/j.jhydrol.2008.01.025
• Rather, M. A., Kumar, J. S., Farooq, M., & Rashid, H. (2017). Assessing the influence of watershed characteristics on soil erosion susceptibility of Jhelum basin in Kashmir Himalayas. Arabian Journal of Geosciences, 10(3), 59. https://doi.org/10.1007/s12517-017-2847-x
• Haiyan, F., & Liying, S. (2017). Modelling soil erosion and its response to the soil conservation measures in the black soil catchment, Northeastern China. Soil and Tillage Research, 165, 23-33. https://doi.org/10.1016/j.still.2016.07.015
• Kinnell, P. I. A. (2010). Event soil loss, runoff and the Universal Soil Loss Equation family of models: A review. Journal of Hydrology, 385(1-4), 384–397. https://doi.org/10.1016/j.jhydrol.2010.01.024
• Mosbahi, M., Benabdallah, S., & Boussema, M. R. (2013). Assessment of soil erosion risk using SWAT model. Arabian Journal of Geosciences, 6(10), 4011-4019. https://doi.org/10.1007/s12517-012-0658-7
• Pradeep, G. S., Krishnan, M. N., & Vijith, H. (2015). Identification of critical soil erosion prone areas and annual average soil loss in an upland agricultural watershed of Western Ghats, using analytical hierarchy process (AHP) and RUSLE techniques. Arabian Journal of Geosciences, 8(6), 3697-3711. https://doi.org/10.1007/s12517-014-1460-5
• Tirkey, A. S., Pandey, A. C., & Nathawat, M. S. (2013). Use of satellite data, GIS and RUSLE for estimation of average annual soil loss in Daltonganj watershed of Jharkhand (India). Journal of Remote Sensing Technology, 1(1), 20-30.
• Soulis, K. X., & Valiantzas, J. D. (2012). SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds–the two-CN system approach. Hydrology and Earth System Sciences, 16(3), 1001-1015. https://doi.org/10.5194/hess-16-1001-2012
• Brady, S.J. (1985). Conservation compliance and wetlands conservation provisions of the omnibus farm acts of 1985, 1990, and 1996. A comprehensive review of Farm Bill contributions to wildlife conservation, 2000, 5-17.
• Subramanya, K. (2013). Engineering Hydrology, 4e. Tata McGraw-Hill Education.
• Suresh, R. (2012). Soil and water conservation engineering. Standard Publishers Distributors.
• Lal, D., Patil, M., Kumar, S., Gotekar, Y., Karwariya, S., & Kumar, R. (2017) Land Degradation and Soil Loss Estimation by Rusle and GIS Technique: A Case Study. Journal of Climate Change and Water, 2(1), 34-46
• Chow, V. T. (1964). Handbook of applied hydrology: a compendium of water-resources technology.
• Miller, D. A., & White, R. A. (1998). A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth interactions, 2(2), 1-26. https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2
• Das, G. (2008). Hydrology and Soil Conservation Engineering: Including Watershed Management. PHI Learning Pvt. Ltd, New Delhi.
• Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) (Vol. 703). Washington, DC: United States Department of Agriculture.
• Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.
• Kowal, J.M., & Kassam, A.H., (1976). Energy and instruments intensity of rainstorms at Samary, northern Nigeria. Tropical Agriculture (UK). 53, 185–198.
• Stone, R. P., & Hilborn, D. (2000). Universal Soil Loss Equation (USLE). Ontario. Ministry of Agriculture. Food and Rural Affairs, 9.
• RUSLE - an online soil erosion assessment tool. (2022). Msu.edu. http://www.iwr.msu.edu/rusle/kfactor.htm
• Mishra, A., Kar, S., & Singh, V. P. (2007). Prioritizing structural management by quantifying the effect of land use and land cover on watershed runoff and sediment yield. Water Resources Management, 21(11), 1899-1913. https://doi.org/10.1007/s11269-006-9136-x
• Le Roux, J. J. (2005). Soil erosion prediction under changing land use on Mauritius (Doctoral dissertation, University of Pretoria). URI: http://hdl.handle.net/2263/25468 Roose, E. J. (1977). Application of the universal soil loss equation of Wischmeier and Smith in West Africa. In Soil Conservation and Management in the Humid Tropics; Proceedings of the International Conference. In: Greenland, D.J.
• De Jong, S. M. (1994). Derivation of vegetative variables from a Landsat TM image for modelling soil erosion. Earth Surface Processes and Landforms, 19(2), 165-178. https://doi.org/10.1002/esp.3290190207 Pandey, A., Chowdary, V. M., & Mal, B. C. (2009). Sediment yield modelling of an agricultural watershed using MUSLE, remote sensing and GIS. Paddy and Water Environment, 7(2), 105-113. https://doi.org/10.1007/s10333-009-0149-y
• Shinde, V., Tiwari, K. N., & Singh, M. (2010). Prioritization of micro watersheds on the basis of soil erosion hazard using remote sensing and geographic information system. International Journal of Water Resources and Environmental Engineering, 5(2), 130-136. https://doi.org/10.5897/IJWREE.9000046
• Sharma, A., Tiwari, K. N., & Bhadoria, P. B. S. (2011). Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environmental monitoring and assessment, 173(1-4), 789-801. https://doi.org/10.1007/s10661-010-1423-6
• Lal, D., Patil, M., Kumar, S., Gotekar, Y., Karwariya, S., & Kumar, R. (2017) Land Degradation and Soil Loss Estimation by Rusle and GIS Technique: A Case Study. Journal of Climate Change and Water, 2(1), 34-46
• Ahmad, I., Vera, V., & Vera, M. K. (2015). Application of curve number method for estimation of runoff potential in GIS environment. In 2nd International Conference on Geological and Civil Engineering, IPCBEE (Vol. 80, pp. 16-20). https://doi.org/10.7763/IPCBEE
• Chakraborty, S., Pandey, R.P., Mishra, S. K., & Chaube, U. C. (2015). Relation between Runoff Curve Number and Irrigation Water Requirement. Agricultural research, 4(4), 378-387. https://doi.org/10.1007/s40003-015-0184-4.
• Singh, A., Chen, E. Y., Lugovoy, J. M., Chang, C. N., Hitzeman, R. A., &Seeburg, P. H. (1983). Saccharomyces cerevisiae contains two discrete genes coding for the α-factor pheromone. Nucleic acids research, 11(12), 4049-4063. https://doi.org/10.1093/nar/11.12.4049
• Gajbhiye, S., Mishra, S. K., & Pandey, A. (2014). Relationship between SCS-CN and sediment yield. Applied Water Science, 4(4), 363-370. https://doi.org/10.1007/s13201-013-0152-8