How do I know if I've improved my continental scale flood early warning system?

• Published in: Environmental research letters Vol. 12; no. 4; pp. 44006 - 44015
• Main Authors: Cloke, Hannah L, Pappenberger, Florian, Smith, Paul J, Wetterhall, Fredrik
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.04.2017
• Subjects: copernicus; Early warning systems; Emergency communications systems; european flood awareness system; flood early warning systems; Flood forecasting; flood resilience; Floods; forecast skill; Mathematical models; monetary value; multi-forcing ensemble; Numerical prediction; Optimization; Spatial variations; Warning systems; Weather forecasting
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/aa625a

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.