How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

McMillan, Hilary; Seibert, Jan; Petersen‐Øverleir, Asgeir; Lang, Michel; White, Paul; Snelder, Ton H.; Rutherford, James; Krueger, Tobias; Mason, Robert R.; Kiang, Julie E.

doi:10.1002/2016wr020328

Cited by 74 publications

(48 citation statements)

References 23 publications

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“…Rating curve uncertainty impacts any analysis that makes use of discharge time series: flood frequency analysis (e.g., Steinbakk et al, ), hydrological model calibration (e.g., Sikorska & Renard, ), real‐time flood forecasting (e.g., Ocio et al, ), hydrological change detection (e.g., Juston et al, ; Lang et al, ), hydrological signatures (e.g., Westerberg & McMillan, ), among many others. Consequently, quantifying rating curve uncertainty and using it in decision making may lead to better decisions, as illustrated by McMillan et al (). Nevertheless, discharge time series are usually provided without quantitative uncertainties (Hamilton & Moore, ).…”

Section: Introductionmentioning

confidence: 99%

Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times

Mansanarez

Renard²,

Coz³

et al. 2019

Water Resources Research

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Establishing streamflow time series in unstable rivers is challenged by changes in the stage‐discharge relation after floods. Then, the field hydrologist must develop a new stage‐discharge rating curve using new calibration data but also using some information from previous calibration data and previous rating curves. The process includes a large amount of informal expert knowledge and hydraulic assumptions seldom made explicit. This paper develops a stage‐period‐discharge (SPD) model based on the physical interpretation of changes in the stage‐discharge relation across a series of stability periods defined by known dates and times. Using simple hydraulic equations, the user provides prior knowledge on the controls, their static and varying parameters, and their possible changes. As a single model is used for all the periods, the estimation of all rating curves can be performed in one go: All gaugings hence provide information to estimate the static parameters and the varying parameters for the relevant periods. Bayesian inference is used, providing a natural way to include prior knowledge and to quantify uncertainty. The generality and some key properties of the method are demonstrated through application to two hydrometric stations, differing in hydraulic configuration and in number and type of changes. Specific experiments demonstrate the ability of the SPD model to transfer information across periods. Consequently, rating curves are more precisely estimated than by separately estimating them for each period. The SPD model provides a hydraulically based, transparent, and user‐friendly approach to replace manual shift corrections traditionally applied in operational practice, with a quantification of uncertainties.

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Section: Introductionmentioning

confidence: 99%

Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times

Mansanarez

Renard²,

Coz³

et al. 2019

Water Resources Research

Self Cite

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“…To account for this uncertainty, quantitative hydrologic storylines, in which key features of climate change impact are represented, can guide water managers in developing dynamic policy pathways (Haasnoot et al, 2013). In order to establish a meaningful range of quantitative hydrologic storylines, we need to reveal, reduce, and represent this uncertainty McMillan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Mapping (dis)agreement in hydrologic projections

Melsen

Addor

Mizukami

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Hydrologic projections are of vital socioeconomic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070-2100 compared to 1985-2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning.

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“…Their conclusions show that ignoring data uncertainty can cause error in hydrologic predictions, theory, and water resources management. Conversely, accounting for data uncertainty in water management has led to lower costs and better decisions (McMillan et al, 2017;Montgomery & Sanders, 1986). Uncertainty in the measured value, compared with the true hydrologic quantity at the same scale as the measurement Uncertainty in rainfall depth or soil moisture at a point.…”

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confidence: 99%

Hydrological data uncertainty and its implications

McMillan¹,

2018

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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

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How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

Cited by 74 publications

References 23 publications

Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times

Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times

Mapping (dis)agreement in hydrologic projections

Hydrological data uncertainty and its implications

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