On the Role of Hydrological Losses in Estimating Event Runoff Coefficients Using the NRCS Method

Zahra Eslami, Khodayar Abdollahi () and Ataollah Ebrahimi‬
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Zahra Eslami: Shahrekord University
Khodayar Abdollahi: Shahrekord University
Ataollah Ebrahimi‬: Shahrekord University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 11, No 2, 4233-4252

Abstract: Abstract The NRCS-CN method is widely used to estimate runoff depth. It is based on a key parameter called the Curve Number (CN), which is used to estimate the runoff coefficients of rainfall-runoff events. Since both storm and catchment characteristics for each event are dynamically variable, treating the CN of a basin as constant seems to be an unrealistic assumption. To take this variability into account, we have developed an empirical equation that estimates the event-based runoff coefficients using a correction factor obtained from the 24-h and maximum daily intensity of rainfall as well as from a minimum CN. The proposed CN method was tested in both calibration and validation stages using an event-based rainfall-runoff dataset from the Kuhsookhteh Watershed, Iran. The results showed good performance for both the calibration and validation datasets, with NS = 0.75 and R2 = 0.77 for the calibration set, and acceptable with NS = 0.6 and R2 = 0.61 for validation set. This study also highlights a variable threshold in form of the slope intercept equation which its initial abstraction could be seen as a descriptor of the loss component. The results for storm events with multiple continuous rainfalls show that the basin infiltration capacity reaches its minimal value and excess rainfall is expected to be controlled by the interaction between potential retention and λ. The obtained runoff coefficient from this methodology was encouraging however such relations for long rainy events should be tested in more catchments.

Keywords: Initial abstraction; Surface runoff; Temporal variability; Rainfall-runoff modelling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03550-9

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