Rainfall-runoff data from small watersheds in Colorado, October 1977 through September 1980

Cochran, Betty J.; Minges, Donald R.; Jarrett, Robert D.; Veenhuis, Jack E.

doi:10.3133/ofr82873

Cited by 1 publication

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“…Regression-based hydrologic predictions are one of the oldest tools in predictive hydrology (e.g., Bridges 1982;Chow 1964;Cochran et al 1979;McCain and Jarrett 1976). Polyakov et al (2010) used the detailed observations of rainfall and runoff in Santa Rita Experimental Range watersheds to develop watershed-scale sediment yield predictive models.…”

Section: Introductionmentioning

confidence: 99%

Multiparameter Regression Modeling for Improving Quality of Measured Rainfall and Runoff Data in Densely Instrumented Watersheds

et al. 2019

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The Walnut Gulch Experimental Watershed is a semi-arid experimental watershed and long-term agro-ecosystem research (LTAR) site managed by the USDA-Agricultural Research Services (ARS) Southwest Watershed Research Center for which highresolution, long-term hydroclimatic data are available across its 149-km 2 drainage area. Quality control and quality assurance of the massive data set are a major challenge. We present the analysis of 50 years of data sets to develop a strategy to identify errors and inconsistencies in historical rainfall and runoff databases. A multiple regression model was developed to relate rainfall, watershed properties, and the antecedent conditions to runoff characteristics in 12 subwatersheds ranging in area from 0.002-94 km 2. A regression model was developed based on 18 predictor variables, which produced predicted runoff with correlation coefficients ranging from 0.4-0.94 and Nash efficiency coefficients up to 0.76. The model predicted 92% of runoff events and 86% of no-runoff events. The modeling approach is a complement to existing quality assurance and quality control (QAQC) procedures and provides a specific method for ensuring that rainfall and runoff data in the USDA-ARS Walnut Gulch Experimental Watershed database are consistent and contain minimal error. The model has the potential for making runoff predictions in similar hydroclimatic environments with available high-resolution observations.

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Section: Introductionmentioning

confidence: 99%