Runoff Analysis Using the SCS CN Method: Implications for Hydrological Modeling and Sustainable Development

Abstract

This study emphasizes the pivotal role of the Soil Conservation Service Curve Number (SCS CN) method in hydrological modeling for runoff analysis. The method, based on land use, soil type, and antecedent moisture conditions, accurately predicts direct runoff, facilitating water resource management and sustainable development. The simplicity and efficiency of the SCS CN method make it invaluable for hydrologists, planners, and policymakers. Through case studies, it demonstrates adaptability and reliability in diverse geographical areas, even with limited data. The research underscores the method\'s contribution to informed decision-making, effective stormwater management, and risk mitigation. By integrating the SCS CN method, this study promotes sustainable development through improved water resource management, enabling responsible water use in agriculture, urban development, and ecosystem preservation. In conclusion, the SCS CN method significantly enhances hydrological models, fostering better water resource management and contributing to sustainable development goals. The study involved the calculation of the Antecedent Moisture Condition (AMC) using rainfall data and curve numbers of microwatersheds, along with their corresponding hydrological soil groups. These findings underscore the necessity of localized assessments for effective watershed management, emphasizing the importance of tailored strategies to address varying rainfall patterns and enhance sustainable water resource management practices Results revealed significant variations in rainfall distribution across different blocks, with Mandalgarh block recording the highest accumulation at 48,263 TCM (thousand cubic meters), indicative of robust runoff generation and water availability.

Introduction

Runoff and its Importance:
Surface runoff occurs when water flow exceeds the soil’s infiltration capacity. It plays a vital role in water supply by replenishing rivers, lakes, and groundwater. Runoff also helps with erosion control by transporting sediments and nutrients, supports aquatic ecosystems, regulates floods, facilitates transportation and navigation, generates hydropower, and promotes recreation and tourism. However, excessive runoff can cause soil erosion, flooding, water pollution, habitat disruption, and these impacts are worsened by climate change.

Challenges in Runoff Estimation:
Estimating runoff in ungauged watersheds is difficult and resource-intensive, requiring extensive hydrological and meteorological data for accurate modeling.

Role of Remote Sensing and GIS:
The integration of remote sensing and Geographic Information Systems (GIS) has transformed runoff analysis and water resource management. Remote sensing provides spatial data on land cover, soil moisture, vegetation, and topography through satellites and drones, which are essential for understanding runoff processes. GIS integrates these data layers to model and visualize runoff pathways, volumes, and flow dynamics using hydrological models like SWAT and HEC-HMS. This combination improves the accuracy, efficiency, and scalability of runoff predictions, supporting better watershed management and environmental protection.

Hydrological Modeling and Runoff Estimation:
Runoff modeling uses various techniques, from simple empirical models like the Soil Conservation Service Curve Number (SCS-CN) method to complex distributed models. These models simulate runoff generation and flow based on factors such as rainfall, soil, land use, and antecedent moisture.

Soil Conservation Service Curve Number (SCS-CN) Method:
Developed by the USDA in the 1950s, the SCS-CN method estimates direct runoff from rainfall using an empirical curve number that accounts for soil type, land use, surface conditions, and moisture levels. It is widely used due to its simplicity, reliability, and suitability for ungauged watersheds. The method is based on water balance equations linking rainfall, runoff, infiltration, and retention.

References

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Copyright

Copyright © 2025 Monika Jain, Dr. Lokesh Kumar Tripathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.