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Hydrological data uncertainty and its implications

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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
Example perceptual models of uncertainty for (a) watershed average soil moisture data, (b) aggregate pollutant loads. Red: Measurement uncertainty, Orange: Interpolation uncertainty in time; Yellow: Interpolation uncertainty in space
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Summary of data uncertainty sources, quantification methods, and benefits of use
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An example of how uncertain input data propagates through a hydrologic model to create uncertainty in predictions. In this illustration, an uncertain value of input x1 (represented by a normal distribution), and an uncertain value of input x2 (represented by a uniform distribution) are propagated through the model to give an uncertain value of model output y. [Figure reproduced from Morgan & Henrion, , figure 8.8]
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Schematic description of the Monte Carlo method for data uncertainty estimation. Graphics show a synthetic example of calculating uncertainty in a flow recession value by identifying and modeling the uncertainty in the stage–flow relationship, and then propagating this uncertainty into the flow series and the derived recession value
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Typical magnitudes of hydrologic data uncertainty from the literature, banded values of coefficient of variation. Gray boxes indicate that no quantitative estimates were found in the literature, although these sources can still be relevant
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Science of Water > Hydrological Processes
Engineering Water > Planning Water
Science of Water > Methods

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