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<p>Understanding rainfall–runoff dynamics in data-scarce regions remains a critical challenge under increasing hydro-climatic variability. This study investigates event-scale runoff behaviour across four gauged watersheds (212–1878&#xa0;km²) in semi-arid to sub-humid western India using long-term hydro-meteorological observations (2005–2016). Watershed characteristics and rainfall patterns were analysed to estimate Curve Numbers (CN) and assess runoff response under varying antecedent moisture conditions (AMC I–III). Runoff volumes were computed using the SCS–Curve Number approach, while hydrograph timing and peak discharge were simulated through the SCS dimensionless unit hydrograph. Model performance showed satisfactory to high agreement with observations (NSE = 0.63–0.98) across diverse events and watershed scales. A total of 91 screened storm events from four gauged watersheds (212–1878&#xa0;km²) formed the basis for calibration, validation and sensitivity assessment. The results indicate pronounced temporal variability in CN, strong non-linear dependence of runoff generation on antecedent moisture, and systematic attenuation of peak discharge with increasing catchment size. The study demonstrates that event-specific Curve Numbers cannot be reliably represented by a single watershed value, with runoff sensitivity increasing markedly under wetter antecedent conditions and across larger catchments. Comparative analysis across four natural watersheds further reveals that watershed structure and antecedent moisture jointly govern runoff uncertainty and hydrograph response. These findings provide practical guidance for uncertainty-aware application of the SCS-CN method in data-scarce, monsoon-dominated basins and contribute new evidence on the transferability of event-scale runoff modelling across contrasting watershed scales.</p> Graphical abstract <p>Note: Event-scale analysis across four monsoon-dominated watersheds demonstrates that runoff generation is governed by dynamic curve number variability, antecedent moisture conditions, and watershed structure rather than rainfall magnitude alone. The study highlights the non-linear sensitivity of runoff response and supports the use of uncertainty-aware, parsimonious modelling approaches for reliable hydrological prediction in data-scarce environments</p>

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Event-scale variability and uncertainty in curve number-based runoff modelling under data-scarce conditions

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Murari Lal Gaur,
  • Nirav K. Butani

摘要

Understanding rainfall–runoff dynamics in data-scarce regions remains a critical challenge under increasing hydro-climatic variability. This study investigates event-scale runoff behaviour across four gauged watersheds (212–1878 km²) in semi-arid to sub-humid western India using long-term hydro-meteorological observations (2005–2016). Watershed characteristics and rainfall patterns were analysed to estimate Curve Numbers (CN) and assess runoff response under varying antecedent moisture conditions (AMC I–III). Runoff volumes were computed using the SCS–Curve Number approach, while hydrograph timing and peak discharge were simulated through the SCS dimensionless unit hydrograph. Model performance showed satisfactory to high agreement with observations (NSE = 0.63–0.98) across diverse events and watershed scales. A total of 91 screened storm events from four gauged watersheds (212–1878 km²) formed the basis for calibration, validation and sensitivity assessment. The results indicate pronounced temporal variability in CN, strong non-linear dependence of runoff generation on antecedent moisture, and systematic attenuation of peak discharge with increasing catchment size. The study demonstrates that event-specific Curve Numbers cannot be reliably represented by a single watershed value, with runoff sensitivity increasing markedly under wetter antecedent conditions and across larger catchments. Comparative analysis across four natural watersheds further reveals that watershed structure and antecedent moisture jointly govern runoff uncertainty and hydrograph response. These findings provide practical guidance for uncertainty-aware application of the SCS-CN method in data-scarce, monsoon-dominated basins and contribute new evidence on the transferability of event-scale runoff modelling across contrasting watershed scales.

Graphical abstract

Note: Event-scale analysis across four monsoon-dominated watersheds demonstrates that runoff generation is governed by dynamic curve number variability, antecedent moisture conditions, and watershed structure rather than rainfall magnitude alone. The study highlights the non-linear sensitivity of runoff response and supports the use of uncertainty-aware, parsimonious modelling approaches for reliable hydrological prediction in data-scarce environments