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Exploring Runoff Response to Simulated Rainfall: A Study of the Rising Limb of a Hydrograph on Sandy Slopes

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  • Radha S. Mohril

    (VNIT Nagpur)

  • Avinash D. Vasudeo

    (VNIT Nagpur)

Abstract

As climate change intensifies the variability of rainfall patterns, understanding runoff behavior and the trend of the rising limb of the hydrograph under diverse scenarios becomes increasingly essential. This research addresses the gap by examining the influence of rainfall intensity and duration on sandy slopes through controlled laboratory experiments. This study delves into the intricate relationship between rainfall characteristics and runoff dynamics, focusing on enhancing accurate runoff prediction and the hydrograph’s rising limb, a critical element for effective water and flood management. Current models, such as the Natural Resources Conservation Service Curve Number (NRCS-CN) method, simplify runoff estimation despite their complexity and accuracy variability. Artificially created and simulated rainfall was applied across a range of intensities (150 to 700 L per hour, LPH), durations (10, 20, and 30 min), and slope gradients of slight (1º), moderate (2º), and steep (3º), resulting in 108 simulations. The regression-based model developed in this study offers a robust framework for capturing Rainfall-Runoff interactions, demonstrating strong predictive performance with Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R²) values exceeding 0.8 and Percent Bias (PBIAS) below 10% during both calibration and validation. Nonlinear regression emerged as the most accurate and reliable prediction approach. These findings underscore the methodology’s potential to predict runoff and rising limb trends, providing valuable insights for hydrological modeling and water resource management. Furthermore, this study’s approach contributes to advancing predictive hydrology and supports the development of adaptive strategies for managing water resources in the face of climate change.

Suggested Citation

  • Radha S. Mohril & Avinash D. Vasudeo, 2025. "Exploring Runoff Response to Simulated Rainfall: A Study of the Rising Limb of a Hydrograph on Sandy Slopes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(7), pages 3199-3212, May.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:7:d:10.1007_s11269-025-04103-y
    DOI: 10.1007/s11269-025-04103-y
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    References listed on IDEAS

    as
    1. Mahesh Shelke & S. N. Londhe & P. R. Dixit & Pravin Kolhe, 2023. "Reservoir Inflow Prediction: A Comparison between Semi Distributed Numerical and Artificial Neural Network Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 6127-6143, December.
    2. Muhammad Ajmal & Muhammad Waseem & Jae-Hyun Ahn & Tae-Woong Kim, 2015. "Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1995-2010, April.
    3. Narayan C. Ghosh & Rahul Kumar Jaiswal & Shakir Ali, 2021. "Normalized Antecedent Precipitation Index Based Model for Prediction of Runoff from Un-Gauged Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1211-1230, March.
    4. Zahra Eslami & Khodayar Abdollahi & Ataollah Ebrahimi‬, 2023. "On the Role of Hydrological Losses in Estimating Event Runoff Coefficients Using the NRCS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4233-4252, September.
    5. Runxi Li & Chengshuai Liu & Yehai Tang & Chaojie Niu & Yang Fan & Qingyuan Luo & Caihong Hu, 2024. "Study on Runoff Simulation with Multi-source Precipitation Information Fusion Based on Multi-model Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 6139-6155, December.
    6. Hao Yang & Weide Li, 2023. "Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 557-581, January.
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