Spatial Analysis of Surface Runoff Using SCS-CN Technique Integrated with GIS and Remote Sensing
Jakir Hussain K. N. *
Department of Soil Science and Agricultural Chemistry, College of Agriculture, University of Agricultural Sciences, Dharwad-580005, India.
Vijayakumari Raveendra Channavar
Department of Soil Science and Agricultural Chemistry, College of Agriculture, University of Agricultural Sciences, Dharwad-580005, India.
Nagaraj Malappanavar
Department of Soil and Water Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur, India.
Varsha Somaraddi Radder
Department of Soil Science and Agricultural Chemistry, College of Agriculture, University of Agricultural Sciences, Dharwad-580005, India.
Tejaswini Chandrakar
Department of Agronomy, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, India.
Jagadeesh B. R
Department of Soil Science and Agricultural Chemistry, College of Agriculture, University of Agricultural Sciences, Dharwad-580005, India.
Basavaraj D. B.
Department of Soil Science and Agricultural Chemistry, UAS, Dharwad, India.
*Author to whom correspondence should be addressed.
Abstract
The Soil Conservation Service (SCS) Curve Number (CN) method is a widely employed hydrological model for estimating surface runoff in watershed studies. This method utilizes land use, soil characteristics, and hydrologic soil grouping information to assign a CN that represents the potential for surface runoff of a specific area. The paper presents a comprehensive study on surface runoff estimation using the SCS Curve Number method integrated with Geographic Information System (GIS) and remote sensing technologies. The incorporation of GIS enhances the spatial representation and analysis of diverse influencing factors, contributing to more informed decision-making in water resource management. The Loose Coupling Model for Runoff Computation, combining GIS and simulation models, is appropriately employed. The study discusses the methodology, including the Thiessen polygon and the Improved Composite CN Computation Method, showcasing a meticulous approach. Results and discussions are supported by relevant studies, reinforcing the credibility of the research. Overall, the paper provides valuable insights for researchers and practitioners in the field of hydrology and water resource management. Future work in this field could focus on refining the SCS-CN method through improved data integration and model calibration. Additionally, exploring advanced machine learning techniques for enhancing the predictive capabilities of GIS-based surface runoff models could offer valuable insights for sustainable water resource management.
Keywords: SCS-CN, GIS, hydrologic soil group, runoff model