Uncertainty in hydrological signatures for gauged and ungauged catchments

Reliable information about hydrological behavior is needed for water‐resource management and scientific investigations. Hydrological signatures quantify catchment behavior as index values, and can be predicted for ungauged catchments using a regionalization procedure. The prediction reliability is affected by data uncertainties for the gauged catchments used in prediction and by uncertainties in the regionalization procedure. We quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted‐pooling‐group approach. Discharge uncertainty was estimated using Monte Carlo sampling of multiple feasible rating curves. For each sampled rating curve, a discharge time series was calculated and used in deriving the gauged signature uncertainty distribution. We found that the gauged uncertainty varied with signature type, local measurement conditions and catchment behavior, with the highest uncertainties (median relative uncertainty ±30–40% across all catchments) for signatures measuring high‐ and low‐flow magnitude and dynamics. Our regionalization method allowed assessing the role and relative magnitudes of the gauged and regionalized uncertainty sources in shaping the signature uncertainty distributions predicted for catchments treated as ungauged. We found that (1) if the gauged uncertainties were neglected there was a clear risk of overconditioning the regionalization inference, e.g., by attributing catchment differences resulting from gauged uncertainty to differences in catchment behavior, and (2) uncertainty in the regionalization results was lower for signatures measuring flow distribution (e.g., mean flow) than flow dynamics (e.g., autocorrelation), and for average flows (and then high flows) compared to low flows.Key Points:We quantify impact of data uncertainty on signatures and their regionalizationMedian signature uncertainty ±10–40%, and highly variable across catchmentsNeglecting gauging uncertainty causes overconditioning of regionalizationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/1/wrcr21917-sup-0001-2015WR017635-s01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/2/wrcr21917.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137249/3/wrcr21917_am.pd

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

Streamflow data are used for important environmental and economic decisions, such asspecifying and regulating minimum flows, managing water supplies, and planning for flood hazards.Despite significant uncertainty in most flow data, the flow series for these applications are oftencommunicated and used without uncertainty information. In this commentary, we argue that properanalysis of uncertainty in river flow data can reduce costs and promote robust conclusions in watermanagement applications. We substantiate our argument by providing case studies from Norway and NewZealand where streamflow uncertainty analysis has uncovered economic costs in the hydropower industry,improved public acceptance of a controversial water management policy, and tested the accuracy of waterquality trends. We discuss the need for practical uncertainty assessment tools that generate multiple flowseries realizations rather than simple error bounds. Although examples of such tools are in development,considerable barriers for uncertainty analysis and communication still exist for practitioners, and futureresearch must aim to provide easier access and usability of uncertainty estimates. We conclude that flowuncertainty analysis is critical for good water management decisions

Uncertainty in streamflow records – a comparison of multiple estimation methods

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAUInternational audienceStage-discharge rating curves are used to relate streamflow discharge to continuously measured river stage readings in order to create a continuous record of streamflow discharge. The stage-discharge relationship is estimated and refined using discrete streamflow gaugings over time, during which both the discharge and stage are measured. The resulting rating curve has uncertainty due to multiple factors including the curve-fitting process, assumptions on the form of the model used, the changeable nature of natural channels, and the approaches used to extrapolate the rating equation beyond available observations. A number of different methods have been proposed for estimating rating curve uncertainty, differing in mathematical rigour, in the assumptions made about the component errors, and in the information required to implement the method at any given site. This study compares several methods that range from simple LOWESS fits to more complicated Bayesian methods that consider hydraulic principles directly. We evaluate these different methods when applied to a single gauging station using the same information (channel characteristics, hydrographs, and streamflow gaugings). We quantify the resultant spread of the stage-discharge curves and compare the level of uncertainty attributed to the streamflow record by the different methods