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• W2890843468 abstract "Hydrologic data are at the core of our understanding of physical hydrologic processes, our simulation models and forecasts of water resources and hazards, and our monitoring of water quantity and quality. However, hydrologic data are subject to multiple sources of uncertainty that can introduce bias and error into our analyses and decision‐making if not properly accounted for. In this article, we summarize five categories of data uncertainty: measurement uncertainty, derived data uncertainty, interpolation uncertainty, scaling uncertainty, and data management uncertainty. Hydrologic data uncertainty magnitudes are typically in the range 10–40%. To quantify data uncertainty, hydrologists should first construct a perceptual model of uncertainty that itemizes uncertainty sources. The magnitude of each source can then be estimated using replicates (repeated, nested or subsampled measurements), or information from the literature (in‐depth uncertainty results from experimental catchments, colocated gauges or method comparisons). Multiple uncertainty sources can be combined using Monte Carlo methods to determine total uncertainty. Data uncertainty analysis improves hydrologic process understanding by enabling robust hypothesis testing and identification of spatial and temporal patterns that relate to true process differences rather than data uncertainty. By quantifying uncertainty in data used for input or evaluation of hydrologic models, we can prevent parameter bias, exclude disinformative data, and enhance model performance evaluation. In water management applications, quantifying data uncertainty can lead to robust risk analysis, reduced costs, and transparent results that improve the trust of the public and water managers. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Engineering Water > Planning Water" @default.
• W2890843468 created "2018-09-27" @default.
• W2890843468 creator A5031688554 @default.
• W2890843468 creator A5066929105 @default.
• W2890843468 creator A5068686112 @default.
• W2890843468 date "2018-09-20" @default.
• W2890843468 modified "2023-10-14" @default.
• W2890843468 title "Hydrological data uncertainty and its implications" @default.
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• W2890843468 doi "https://doi.org/10.1002/wat2.1319" @default.
• W2890843468 hasPublicationYear "2018" @default.
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