Is the Curve Number (CN) Method Climate-Ready? Implications for Future Rainfall–Runoff Prediction

¹ Centre for Climate Change and Disaster Risk Reduction, Department of Civil Engineering, Department of Mathematical and Actuarial Sciences. Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
² ASEAN Academy of Engineering and Technology, Singapore
³ Centre for Mathematical Sciences, Department of Mathematical and Actuarial Sciences. Department of Electrical and Electronic Engineering. Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
⁴ Department of Electrical and Electronic Engineering. Lee Kong Chian Faculty of Engineering & Science, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
⁵ School of Engineering, Monash University Malaysia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The Soil Conservation Service Curve Number (SCS-CN) method remains one of the most widely used rainfall–runoff models due to its simplicity and low data requirements. However, increasing hydro-climatic non-stationarity, intensified rainfall extremes, and climate change raise critical questions regarding the climate readiness of the conventional Curve Number (CN) formulation. This study critically examines the suitability of the conventional CN methodology for future rainfall–runoff prediction, with particular emphasis on its underlying mathematical derivation and embedded assumptions. The analysis reveals fundamental limitations in the conventional CN framework, notably its fixed key parameter, which inadequately represents evolving rainfall–runoff dynamics under altered climatic conditions. As a result, the conventional approach exhibits systematic bias and reduced robustness when applied to non-stationary rainfall regimes. To address these deficiencies, this study adopts a data-driven non-parametric calibration strategy to optimise key model parameters of the conventional model framework. The study demonstrates that new approach substantially improves runoff estimation accuracy, reduce prediction bias, and enhance adaptability across varying rainfall conditions. The findings indicate that although the conventional Curve Number method is not inherently climate ready, its predictive performance can be significantly strengthened through targeted recalibration, offering important implications for climate resilient hydrological modelling and flood risk assessment.