Ida Westerberg

A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations

Abstract Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques to assess discharge uncertainty. However, few studies have developed generalized approaches to quantify discharge uncertainty. This study presents a generalized framework for estimating discharge uncertainty at many gauging stations with different errors in the stage‐discharge relationship. The methodology utilizes a nonparametric LOWESS regression within a novel framework that accounts for uncertainty in the stage‐discharge measurements, scatter in the stage‐discharge data and multisection rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place‐specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10–397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage‐discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses.

Comment on "Pursuing the method of multiple working hypotheses for hydrological modeling" by P. Clark et al.

Reference EPFL-ARTICLE-184119doi:10.1029/2012Wr012282View record in Web of Science Record created on 2013-02-27, modified on 2016-08-09

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 over-conditioning 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. This article is protected by copyright. All rights reserved.

The Role of Rating Curve Uncertainty in Real-Time Flood Forecasting

Data assimilation has been widely tested for flood forecasting, although its use in operational systems is mainly limited to a simple statistical error correction. This can be due to the complexity involved in making more advanced formal assumptions about the nature of the model and measurement errors. Recent advances in the definition of rating curve uncertainty allow estimating a flow measurement error that includes both aleatory and epistemic uncertainties more explicitly and rigorously than in the current practice. The aim of this study is to understand the effect such a more rigorous definition of the flow measurement error has on real-time data assimilation and forecasting. This study, therefore, develops a comprehensive probabilistic framework that considers the uncertainty in model forcing data, model structure, and flow observations. Three common data assimilation techniques are evaluated: 1) Autoregressive error correction, 2) Ensemble Kalman Filter, and 3) Regularised Particle Filter, and applied to two locations in the flood-prone Oria catchment in the Basque Country, northern Spain. The results show that, although there is a better match between the uncertain forecasted and uncertain true flows, there is a low sensitivity for the threshold exceedances used to issue flood warnings. This suggests that a standard flow measurement error model, with a spread set to a fixed flow fraction, represents a reasonable trade-off between complexity and realism. Standard models are therefore recommended for operational flood forecasting for sites with well-defined stage–discharge curves that are based on a large range of flow observations.

Adaptation of water resources systems to changing society and environment: a statement by the International Association of Hydrological Sciences

ABSTRACT We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends, inter alia, on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned

Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example

ABSTRACT Characterizing, understanding and better estimating uncertainties are key concerns for drawing robust conclusions when analyzing changing socio-hydrological systems. Here we suggest developing a perceptual model of uncertainty that is complementary to the perceptual model of the socio-hydrological system and we provide an example application to flood risk change analysis. Such a perceptual model aims to make all relevant uncertainty sources – and different perceptions thereof – explicit in a structured way. It is a first step to assessing uncertainty in system outcomes that can help to prioritize research efforts and to structure dialogue and communication about uncertainty in interdisciplinary work.

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment

Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, suc ...

Five Guidelines for Selecting Hydrological Signatures

The aim of this paper is to encourage a more rigorous approach to hydrological signature selection, which considers the ability of signatures to represent hydrological behaviour and underlying processes for the catchment and application in question. To this end, we propose a set of guidelines for selecting hydrological signatures. We describe five criteria that any hydrological signature should conform to: Identifiability, Robustness, Consistency, Representativeness, and Discriminatory Power. We explain why each criterion is important and give examples of design or adaption of signatures to meet the guidelines.

A Comparison of Methods for Streamflow Uncertainty Estimation

Streamflow time series are commonly derived from stage-discharge rating curves, but theuncertainty of the rating curve and resulting streamflow series are poorly understood. While differentmethods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding ofhow uncertainty estimates differ between methods due to different assumptions and methodologicalchoices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at threeriver locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a widerange of estimates, particularly for high and low flows. At the simplest site on the Isere River (France), fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast,uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of therating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (UnitedKingdom), where the hydraulic control is unstable at low flows. Differences between methods result fromdifferences in the sources of uncertainty considered, differences in the handling of the time-varying nature ofrating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptionswhen extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of anuncertainty method requires a match between user requirements and the assumptions made by theuncertainty method. Given the signi ficant differences in uncertainty estimates between methods, we suggestthat a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.