Estimating the benefits of single value and probability forecasting for flood warning

Verkade, Jan; Werner, Micha

doi:10.5194/hessd-8-6639-2011

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Solomatine and make use of the classical QR approach, without considering quantile crossing and NQT. Weerts et al (2011), Verkade and Werner (2011), and Roscoe et al (2012) apply QR to various deterministic hydrologic forecasts. The QR configuration investigated in these studies uses the water level or discharge forecasts as predictors to estimate the distribution quantiles of the model error.…”

Section: Introductionmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

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Section: Introductionmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Unlike value of perfect information approaches, CVSI uses a Bayesian framework to estimate the value of the information obtained from real observations with finite sample sizes. CVSI approaches have been extensively used in the water sciences to assist decisions, particularly related to the design of sampling networks and observation campaigns (Alfonso & Price, 2012;Borisova et al, 2005;Bouma et al, 2009;Feyen & Gorelick, 2005;James & Gorelick, 1994;Khader et al, 2013) and investment in flood mitigation infrastructure (Alfonso et al, 2016;Davis et al, 1972;Roberts et al, 2009;Thiboult et al, 2017;Verkade & Werner, 2011). Yet to our knowledge, CVSI has not been used for value-based evaluation of model predictions.…”

Section: Introductionmentioning

confidence: 99%

A Value‐Based Model Selection Approach for Environmental Random Variables

Müller

Thompson

2019

Water Resources Research

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Environmental decisions with substantial social and environmental implications are regularly informed by model predictions, incurring inevitable uncertainty. The selection of a set of model predictions to inform a decision is usually based on model performance, measured by goodness‐of‐fit metrics. Yet goodness‐of‐fit metrics have a questionable relationship to a model's value to end users, particularly when validation data are themselves uncertain. For example, decisions based on flow frequency models are not necessarily improved by adopting models with the best overall goodness of fit. We propose an alternative model evaluation approach based on the conditional value of sample information, first defined in 1961, which has found extensive use in sampling design optimization but which has not previously been used for model evaluation. The metric uses observations from a validation set to estimate the expected monetary costs associated with model prediction uncertainties. A model is only considered superior to alternatives if (i) its predictions reduce these costs and (ii) sufficient validation data are available to distinguish its performance from alternative models. By describing prediction uncertainties in monetary terms, the metric facilitates the communication of prediction uncertainty by end users, supporting the inclusion of uncertainty analysis in decision making.

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“…Flood forecasting and warning systems are non-structural measures to reduce the residual flood risk by reducing the flood related impacts by timely and effective warning for an emergency response ( [2]). Within the emergency response a specific warning is converted into specific measures ( [3]). Adapted reservoir operation, enforcing existing flood defences by temporary measures or building new temporary flood defences, temporary object protection and horizontal or vertical evacuation of persons and assets are some examples of operationally available flood risk reduction measures (emergency measures).…”

Section: Introductionmentioning

confidence: 99%

Prototypes of risk-based flood forecasting systems in the Netherlands and Italy

et al. 2016

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Abstract. Flood forecasting, warning and emergency response are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within emergency response. However, the information provided for decision support is often restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in current early warning and response systems. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. This paper presents the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the first results from two prototype applications of the new developed concept: The first prototype is applied to the Rotterdam area situated in the western part of the Netherlands. The second pilot study focusses on a rural area between the cities of Mantua and Ferrara along the Po river (Italy).

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Estimating the benefits of single value and probability forecasting for flood warning

Cited by 14 publications

References 22 publications

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

A Value‐Based Model Selection Approach for Environmental Random Variables

Prototypes of risk-based flood forecasting systems in the Netherlands and Italy

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