EWASE—Early Warning Systems Efficiency

Schröter, Kai; Ostrowski, Manfred W.; Gocht, Martin; Kahl, B; Nachtnebel, Hans Peter; Corral, Carles; Sempere‐Torres, Daniel

doi:10.1201/9780203883020.ch85

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…In addition to the BLND, the MCFFG uses a lead-time-correlation function to associate 𝜌 𝑦𝑦 ̂ with a forecasting lead time τ; this reflects what is known about forecasting model performance, and aims to represent the trade-off between τ and PU. This function, which is analogous to that found by Schröter et al (2008) and is depicted graphically in Fig. 2a, describes the common behaviour of forecasting models when forced with precipitation.…”

Section: The Monte Carlo Flood and Forecast Generator (Mcffg)supporting

confidence: 75%

“…That is, it represents the fact that, for τ values lower than basin lag time (L), with a well-calibrated hydrological model, and forecast updating in real-time, the performance of a forecasting model is relatively high as forecasts are based on observed precipitation by using, for example, gauge-based quantitative precipitation estimation (QPE). Past this value L, the forecasting model has to be forced with quantitative precipitation forecasts (QPFs), and forecasting performance is assumed to drop monotonically (Schröter et al, 2008). The slope of this function for a lead-time less than L defines the quality of forecasting models based on gauge-based QPE or gauge-radarbased QPE, and the slope beyond L defines the quality of the forecasts based on QPFs.…”

Section: The Monte Carlo Flood and Forecast Generator (Mcffg)mentioning

confidence: 99%

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A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Duque

O’Connell

O’Donnell

2023

Journal of Hydrology

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Section: The Monte Carlo Flood and Forecast Generator (Mcffg)supporting

confidence: 75%

Section: The Monte Carlo Flood and Forecast Generator (Mcffg)mentioning

confidence: 99%

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Duque

O’Connell

O’Donnell

2023

Journal of Hydrology

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“…Using a real‐life study of economic costs for the case of flooding, an optimal trade‐off between time to impact and model skill is found by Schröter et al. (2008). Recently, Regnier (2020) analyzed the role of lead time for evacuations associated to hurricanes forecasts.…”

Section: Earliness/timelinessmentioning

confidence: 99%

Early warning systems and end‐user decision‐making: A risk formalism tool to aid communication and understanding

de Elía,

Ruiz,

Francce

et al. 2023

Risk Analysis

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In this work, we introduce a formalism to highlight the role of decision‐making implicit in the setup of early warning systems (EWSs) and its consequences with respect to loss avoidance for end users. The formalism, a close relative of the cost/loss approach, combines EWS verification scores with traditional expressions of risk from the point of view of the user. This formalism articulates in mathematical format many well‐known issues surrounding EWS usage, offering a conceptual anchor for concepts that otherwise may seem to wobble among the multidisciplinary perspectives participating in the EWS chain.This decision model is visually represented in a variation of the popular “performance diagram” used in forecast and warning verification. Our diagram adds to this the perspective of a generic user, in an effort to gain insight into how choices made regarding EWS settings may determine which users benefit from warnings and which do not. Although these results are based on a conceptual model, they are useful to better understand the actual benefits experienced by users and to highlight aspects that may temper unrealistic expectations on EWSs.The recent United Nations initiative to extend EWSs for natural hazards to all nations within 5 years will make EWSs more common and more public. The approach proposed here can be a tool to promote greater transparency and improve the necessary dialog between warning issuers and users in order to reduce loss.

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“…During this time period, additional data and information are collected and predictions become increasingly accurate (see e.g. Grasso et al, 2007;Schröter et al, 2008). The reliability analysis in part II is therefore conducted as a function of the lead time.…”

Section: Reliability Analysis Ii: Non-automated Ewsmentioning

confidence: 99%

“…If the EWS detects a hazard event, timely warnings can initiate preventive actions, such as an evacuation of endangered persons to prevent damage. However, frequent false alarms can lead to excessive intervention costs or reduce compliance with future warnings (Pate-Cornéll, 1986;Grasso et al, 2007;Schröter et al, 2008;Rogers and Tsirkunov, 2011;Ripberger et al, 2014). To account for the probability that events are correctly detected (hit) and the probability that false alarms are issued ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effectiveness of early warning systems for natural hazards

Sättele¹,

Bründl²,

Straub³

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

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EWASE—Early Warning Systems Efficiency

Cited by 6 publications

References 1 publication

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Early warning systems and end‐user decision‐making: A risk formalism tool to aid communication and understanding

Quantifying the effectiveness of early warning systems for natural hazards

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