A.V. et al. (2026) A Novel Approach for the SCS-CN Method by Updating Curve Numbers for the Antecedent Soil Moisture Conditions (ASMCs) and Improving its Performance

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-01-01
• Authors: Ajith A.V., Dillip Kumar Barik
• DOI: 10.1007/s11269-025-04452-8

Research Groups

• School of Civil Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Short Summary

This study investigated the effect of antecedent soil moisture conditions (ASMC) on runoff generation using the SCS-CN method in the Kidangoor watershed, proposing novel equations to update Curve Numbers (CNs) for varying ASMCs, which significantly improved runoff estimation accuracy with an average error of -2.63%.

Objective

• To assess the SCS-CN method’s efficacy in calculating runoff by assuming a constant CN for ASMC II and by varying CN values based on ASMCs, utilizing CN values from the standard NEH 4 table.
• To update the CN for ASMC I, ASMC II, and ASMC III using observed rainfall and discharge data, and to provide equations for converting CN from ASMC II to ASMC I and III.
• To compare surface runoff resulting from the CNs obtained from the proposed equations to the tabular CNs for ASMC I, ASMC II, and ASMC III.

Study Configuration

• Spatial Scale: Kidangoor watershed, Meenachil Taluk, Kottayam district, Kerala state, India, covering an area of 615 square kilometers.
• Temporal Scale: Daily rainfall and runoff data collected for 18 years, from 2000 to 2017.

Methodology and Data

• Models used:
  • Soil Conservation Service Curve Number (SCS-CN) method.
  • Equations for runoff (Q) and potential maximum retention (S): Q = (P - 0.3S)^2 / (P + 0.7S) and S = (25400/CN) - 254.
  • Hawkins (1993) equation for updating CN based on observed rainfall (P) and runoff (Qo).
  • Proposed empirical equations for converting CN from ASMC II to ASMC I and ASMC III:
    • CNI = (8.55 * CNII) / (10 + 0.039 * CNII)
    • CNIII = (8.55 * CNII) / (10 - 0.039 * CNII)
  • Performance evaluation metrics: Nash-Sutcliffe model efficiency (E) and percent bias (PBIAS).
  • Tools: Remote Sensing and Geographic Information System (GIS) (ArcGIS 10.5).
• Data sources:
  • Daily rainfall data (2000-2017) from six rain gauge stations: India Meteorological Department (IMD) and Irrigation Design and Research Board (IDRB), Thiruvananthapuram, Kerala.
  • Daily runoff data (2000-2017) from Kidangoor gauging station: Central Water Commission (CWC) of India, Kochi.
  • Digitized topographical map (1:50,000 scale): Survey of India, Thiruvananthapuram, Kerala.
  • Satellite images for Land Use/Land Cover (LULC) classification: USGS Earth Explorer portal (Landsat-7, IRS, Landsat-5).
  • Soil map: Department of Soil Survey and Soil Conservation, Thiruvananthapuram, Kerala.
  • Standard Curve Number (CN) values: NEH 4 table.

Main Results

• Initial runoff estimation using the standard SCS-CN method with constant ASMC II overestimated runoff by -5.8%.
• Considering the event-day ASMC (using standard tabular CNs) reduced the runoff overestimation error to -4.3%.
• The proposed empirical equations for converting CNs from ASMC II to ASMC I and ASMC III resulted in the most accurate runoff estimations, with an average error of -2.63% and a percent bias (PBIAS) of -2.58%.
• The Nash-Sutcliffe model efficiency (E) was consistently high (> 0.99) across all three scenarios (constant ASMC II, event-day ASMC, and proposed CNs).
• Land Use/Land Cover (LULC) changes had minimal impact on runoff generation, while rainfall and Antecedent Soil Moisture Conditions (ASMCs) were identified as the key influencing factors.
• A strong correlation between rainfall and runoff was established for all ASMCs in the Kidangoor watershed, with coefficients of determination (R^2) of 0.898 for ASMC-I, 0.921 for ASMC-II, and 0.999 for ASMC-III.

Contributions

• Improved the classic SCS-CN approach by integrating long-term observed rainfall and runoff data from a humid tropical watershed.
• Developed novel empirical equations for converting Curve Numbers (CNs) between different Antecedent Soil Moisture Conditions (ASMC I, II, and III), leading to more accurate surface runoff estimations.
• Demonstrated that considering event-day ASMCs and using updated CNs significantly enhances runoff prediction compared to assuming a constant ASMC II.
• Established robust rainfall-runoff relationships for various ASMCs, providing a reliable tool for water resource managers and decision-makers in similar regions.

Funding

The authors declare that no funding was received to assist with the preparation of this manuscript.

Citation

@article{AV2026Novel,
  author = {A.V., Ajith and Barik, Dillip Kumar},
  title = {A Novel Approach for the SCS-CN Method by Updating Curve Numbers for the Antecedent Soil Moisture Conditions (ASMCs) and Improving its Performance},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-025-04452-8},
  url = {https://doi.org/10.1007/s11269-025-04452-8}
}