Comparing Frequency-Matched and Natural Data Approaches for Estimating the Curve Number from Rainfall-Runoff Data

Abstract

The curve number (CN) method, developed by the Natural Resources Conservation Service (NRCS), is one of the most widely used approaches worldwide to estimate runoff from rainfall events. However, one of the remaining uncertainties in the method remains whether to apply a frequency-matched or natural data approach for computing the CN from rainfall-runoff data. To address this knowledge gap, this study focuses on comparing CN estimations based on these two approaches. CN values were derived using three methods [asymptotic (ASY), least squares (LS), and Natural Engineering Handbook (NEH Median)] and the initial abstraction ratios (λ) 0.05 and 0.2, with a worldwide sample of 3,398 watersheds. The frequency-matched method for both λ provided greater CN values than natural data. The NEH Median approach yielded similar CN values for frequency-matched and natural data for both λ values. However, the ASY and LS methods showed variations of up to 15 and 5 units, respectively. Estimating CN using frequency-matched data improved runoff performance estimation for all methods, with the LS method providing the most accurate runoff estimations. Furthermore, λ=0.05 outperformed in terms of runoff estimation, with an accuracy further enhanced when paired with the frequency-matched approach. These results offer a broad perspective on the NRCS-CN method, reducing potential regional biases found in local studies. Adopting event ranking based on return period improves the method’s accuracy and therefore has the potential to enhance engineering practices through more accurate runoff estimations.