Yu Jian CheongLloyd LingYu Heng Cheong0000-0001-5422-5123Chin Ren JieZulkifli YusopSteven Lim2026-10-202026-10-202025-1210.1016/j.wroa.2025.100414https://dspace-cris.utar.edu.my/handle/123456789/11795This study presents a statistically grounded reformulation of the Natural Resources Conservation Service (NRCS) Curve Number (CN) rainfall-runoff model by replacing the conventional linear initial abstraction (Ia) to retention (S) relationship (Ia = ?S, where ? is initial abstraction ratio) with a power law-based formulation (Ia = SL, where L is gradient of log-log graph) in order to restore mathematical correctness. A nonparametric bias-corrected and accelerated (BCa) bootstrap framework was employed to test the NRCS universal ? = 0.20 assumption, revealing its statistical invalidity (derived optimum ? value at 99 % BCa confidence interval: 0.032 - 0.079) for the urban Malaysian catchment studied. The proposed model achieved higher theoretical coherence and improved runoff estimate accuracy while preserving model parsimony. Importantly, it accommodates full rainfall-runoff datasets and dynamically captures catchment saturation-dependent retention behavior, addressing limitations of the conventional CN practices. The newly developed parsimonious two-parameter (S, L) runoff estimation model ensures practical adaptability by enabling catchment specific calibration without resorting to arbitrary CN selection. This study bridges traditional hydrology with modern statistical rigor, offering a scalable, data-driven alternative to conventional CN practices. The findings support a paradigm shift in runoff modelling by demonstrating the potential of nonparametric methods to refine legacy hydrological models and better capture real world nonlinearity and variability under changing climatic conditions. © 2025en-USBootstrapping BCaCurve numberSaturation condition-based modellingUrban rainfall-runoffAbstractingCalibrationConservationRainRunoffrainBased modellingBootstrapping bias-corrected and acceleratedCurve numbersNatural resources conservation servicesPower-lawRainfall runoffRunoff estimationSaturation condition-based modelingSaturation conditionscalibrationcatchmenthydrological modelingparadigm shiftrainfall-runoff modelingrunoffArticlebootstrappingcatchment area (hydrology)confidence intervalcurve numberdata accuracyevaluation studyFederal Territory of Kuala Lumpurflood forecastinghydrologyinferential statisticsKolmogorov Smirnov testmathematical modelnonparametric testpower law based calibrationpredictive modelrunoff model (hydrology)storm (weather)surface runofftheoretical modelurban areavalidityCatchmentsjournal-article