Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets
AffiliationSchool of Natural Resources and the Environment, University of Arizona
long-term data curation
quality hydrologic data
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CitationMeles, M. B., Demaria, E. M. C., Heilman, P., Goodrich, D. C., Kautz, M. A., Armendariz, G., Unkrich, C., Wei, H., & Perumal, A. T. (2022). Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets. Water (Switzerland), 14(14).
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AbstractThe curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets. © 2022 by the authors.
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Except where otherwise noted, this item's license is described as Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).