Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data
https://orcid.org/0000-0002-0751-1474
https://orcid.org/0000-0001-9748-0140
https://orcid.org/0000-0002-7143-5912
A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.
NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data
https://orcid.org/0000-0002-0751-1474
https://orcid.org/0000-0001-9748-0140
https://orcid.org/0000-0002-7143-5912
A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.
NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data
https://orcid.org/0000-0002-0751-1474
https://orcid.org/0000-0001-9748-0140
https://orcid.org/0000-0002-7143-5912
A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.
NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data
J. Hydrol. Eng.
Moglen, G. E. (Autor:in) / Sadeq, H. (Autor:in) / Hughes, L. H. (Autor:in) / Meadows, M. E. (Autor:in) / Miller, J. J. (Autor:in) / Ramirez-Avila, J. J. (Autor:in) / Tollner, E. W. (Autor:in)
01.10.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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Integrating the NRCS Runoff Curve Number in Delineation of Hydrologic Homogeneous Regions
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