Estimation of Surface Runoff from Dapoli Watershed Using Remote Sensing and GIS

Y. S. Tsopoe *

Department of Soil and Water Conservation Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India.

H. N. Bhange

Department of Soil and Water Conservation Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India.

B. L. Ayare

Department of Soil and Water Conservation Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India.

P. M. Ingle

Department of Irrigation and Drainage Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India.

P. B. Bansode

Department of Soil and Water Conservation Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India.

*Author to whom correspondence should be addressed.


Abstract

Soil and water are the two basic natural resources for the survival of living organisms and the future of the world depends largely on the effective management, utilization and development of these resources. In this present study, Dapoli watershed located in Ratnagiri District of Maharashtra, has been considered as the study area for the estimation of surface runoff by SCS Curve Number method using remote sensing and GIS. SRTM DEM of 30m resolution and SENTINEL 2 satellite imagery of 10m resolution were used to generate thematic maps such as elevation map, HSG map, stream order map and LULC map. The results of this study showed that the highest rainfall was observed in the year 2021 and the lowest rainfall was observed in the year 2015. The maximum and minimum annual runoff depth from 1993-2022 were in the years 2021 (2505.14 mm) and 2001 (734.81 mm) respectively. The study revealed that in the past 30 years, 41.68% of the rainfall was contributed to runoff and SCS-CN method coupled with remote sensing and GIS can serve as a useful tool for estimating surface runoff in the coming years for similar watersheds.

Keywords: Surface runoff, curve number, DEM, LULC, HSG, AMC, remote sensing, GIS


How to Cite

Tsopoe, Y. S., H. N. Bhange, B. L. Ayare, P. M. Ingle, and P. B. Bansode. 2024. “Estimation of Surface Runoff from Dapoli Watershed Using Remote Sensing and GIS”. International Journal of Environment and Climate Change 14 (9):44-53. https://doi.org/10.9734/ijecc/2024/v14i94391.

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