Revisiting the Curve Number Approach for Improving Surface Runoff Estimation - A State-of-the-Art Review

Oct 21, 2026

Estimation of runoff is central to watershed drainage studies and the design of hydraulic structures and erosion-control measures. This state-of-the-art review examines the curve number (CN) approach—originally the Soil Conservation Service (SCS) CN—its common modifications, and how parameters such as initial abstraction ratio (λ), slope (θ), storm characteristics, and antecedent moisture condition (AMC) have been revisited to improve accuracy while retaining simplicity. It also highlights recent methods for retrieving gridded soil and LULC data, and briefly discusses probabilistic CN determination, proposing a versatile modeling framework to support improved runoff estimation for water-resource management and flood forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Curve number (CN) approach and its role

Models for runoff estimation and comparative context

Accuracy debates and evolving data needs

Downloaded on 10/20/26from ascelibrary .org by The University of Manchester . Copyright ASCE; all rights reserved, including rights for text and data mining and training of artificial intelligence or similar technologies . State-of-the-Art Review Revisiting the Curve Number Approach for Improving Surface Runoff Estimation: A State-of-the-Art Review Anubhab Panigrahi 1 and Meenu Ramadas, Ph. D.2 Abstract: Estimation of runoff is an important aspect of watershed drainage studies and the design of hydraulic structures and erosion control measures. For the calculation of surface runoff, the curve number (CN) approach originally known as the Soil Conservation Service (SCS) CN has stood the test of time as one of the most popular methods. Over the years, numerous modifications have been proposed to this approach to improve its accuracy without hampering its inherent simplicity. The presented review discusses how the different parameters of the CN model such as initial abstraction ratio (λ), slope (θ), storm characteristics, and antecedent moisture condition (AMC) of the catchment that affect the runoff generation process have been revisited, updated, and/or modified over the years. The review also focuses on the recent research and advancements in the retrieval of gridded soil and land use–land cover (LULC) data sets that can potentially improve the CN model for runoff estimation. In this state-of-the-art review, alternative approaches including probabilistic methods for the determination of CN are also briefly discussed. Finally, a versatile modeling framework that incorporates the major modifications to the CN method is proposed, so as to guide interested researchers and engineers toward techniques for an improved runoff estimation model for effective water resource management and flood forecasting studies. DOI: 10.1061/JIDEDH.IRENG-10173. © 2025 American Society of Civil Engineers. Author keywords: Surface runoff; Curve number; Initial abstraction ratio; Runoff modeling; Antecedent moisture condition. Introduction Surface runoff or the hydrologic response at the catchment scale is a major element of the hydrologic cycle and its estimation plays an important role in hydraulic drainage design. Numerous models such as MIKE-Système Hydrologique Européen (MIKE-SHE), Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV), topography-based hydrological model (TOPMODEL), and variable infiltration capacity (VIC), among others, have been developed throughout the last century to make excess runoff calculation as simple and effective as possible using hydrometeorological inputs and catchment attributes. Comparative studies of these models have been carried out over the years (Franchini et al. 1996 ; Smith et al. 1995 ; Johnson et al. 2003 ; Jain 2012 ; Devi et al. 2015 ; Sitterson et al. 2018) . Besides these, the curve number (CN) approach, popularly known as the Soil Conservation Service (SCS) CN, is a simple model that has been extensively used because of its fewer input requirements as compared to other conceptual as well as physical models (Sitterson et al. 2018) . This approach, developed by the USDA Natural Resources Conservation Service (NRCS) in the 1950s (SCS 1973), is also regarded as versatile enough to account formost of the watershed characteristics (Mishra and Singh 2003) . The method allows the partitioning of input rainfall into losses and runoff with the help of a dimensionless number—the curve number, a function of storm characteristic, hydrologic soil group 1Ph.D. Candidate, School of Infrastructure, Indian Institute of Technology Bhubaneswar, Khordha, Odisha 752050, India (corresponding author) . ORCID: [https://orcid.org/0009-0009-2912-9952. Email:](https://orcid.org/0009-0009-2912-9952. Email:) anubhabpanigrahi@ [gmail.com](gmail.com); [a23ce09011@iitbbs.ac.in](a23ce09011@iitbbs.ac.in) 2Associate Professor, School ofInfrastructure, Indian Institute ofTechnology Bhubanes

FAQs

What is the curve number (CN) approach used for in runoff estimation?

The CN approach partitions rainfall into losses and direct runoff using a dimensionless curve number linked to storm characteristics, hydrologic soil group, antecedent moisture condition, and land use–land cover factors.

Which CN model parameters have been revisited to improve runoff estimation?

Key parameters reviewed include the initial abstraction ratio (λ), slope (θ), storm characteristics, and the antecedent moisture condition (AMC) of the catchment.

How can gridded soil and land use–land cover (LULC) data improve the CN model?

Recent research on retrieving gridded soil and LULC datasets can enhance how inputs are represented for CN-based runoff estimation, potentially improving model performance by leveraging improved data availability across spatial scales.

Oct 21, 2026

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