Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE

Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE

Nensi A. Sachapara ^{a(0009-0000-9510-6198)}, Manan Patel ^{a(0009-0004-4712-3531)} , Hasti Dhameliya ^{b(0009-0003-8908-7906)}
Keval H Jodhani ^{c (0000-0002-3800-2402)}, Nitesh Gupta ^{c(0000-0003-0471-0133)} , Dhruvesh P. Patel ^{d (0000-0002-2074-7158)} Sudhir Kumar Singh ^{e  (0000-0001-8465-0649)}

^aUnder Graduate Student, Civil Engineering Department, Nirma University, Ahmedabad, 382481, Gujarat, India.  (nensisachapara16@gmail.com; mananrp07@gmail.com )

^bUnder Graduate Student, Biomedical Engineering Department, LD College of Engineering, Ahmedabad, 382481, Gujarat, India. (dhameliyahasti8@gmail.com)

^cAssistant Professor, Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, 382481, Gujarat, India. (jodhanikeval@gmail.com, niteshraz@gmail.com)

^dDepartment of Civil Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India (dhruvesh1301@gmail.com)

⁶ K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj-211002, Uttar Pradesh, India (sudhirinjnu@gmail.com)

 

Abstract: Flooding and soil erosion are major environmental challenges impacting the Sabarmati River Basin (SRB), adversely affecting its ecology, agriculture, and infrastructure. This study employs the Google Earth Engine (GEE) platform to comprehensively analyze flood-prone areas and soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model. High-resolution datasets from USGS Earth Explorer and GEE are integrated with remote sensing and geospatial technologies to assess the basin's vulnerabilities. Flood-prone regions were identified by analyzing historical rainfall (maximum annual rainfall of 1,667.15 mm in 2017), hydrological patterns, and topographic features. The RUSLE model quantified soil erosion, incorporating factors such as rainfall erosivity (R factor: 11,202.65–29,243.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹), soil erodibility (K factor: 0.20–0.20004 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), slope length and steepness (LS factor: 0–0.499), land cover (C factor: 0.327–1.078), and conservation practices (P factor: 1). Results indicate critical hotspots of soil erosion, with losses peaking at 1,232.33 t/ha/year in the northern SRB. Flood hazard mapping revealed that low-lying areas with recurrent flood events align with regions experiencing high rainfall and sediment transport. The overlap between high soil erosion and flood-prone zones highlights the need for integrated management strategies. These risks have significant socio-economic implications, including diminished agricultural productivity, infrastructure damage, and community displacement. This dual analysis underscores the efficacy of GEE for rapid environmental assessments, providing actionable insights for policymakers to prioritize interventions. The findings align with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), suggesting for adaptive strategies to mitigate flood and erosion risks and promoting sustainable resource management in vulnerable regions.

Keyword: RUSLE, GEE, Flood Hazard, SDG 13 & 15, Sabarmati Basin