Enhanced surface water flood forecasts: User‐led development and testing

Journal of Flood Risk Management

Charlie Pilling¹  Murray Dale²  Neil Hunter³  Kay L. Shelton³  Rob Lamb⁴  Adrian Hines⁵  Mark A. Trigg⁶  Benjamin L. Rabb⁷  Steven J. Böing⁷  Cathryn E. Birch⁷  Andrea L. Taylor⁷ 

[1] Flood Forecasting Centre Exeter UK;JBA Consulting Exeter UK;JBA Consulting Skipton UK;JBA Trust Skipton UK;Met Office Exeter UK;School of Civil Engineering University of Leeds Leeds UK;School of Earth and Environment University of Leeds Leeds UK;

关键词: convective rainfall;    engagement;    ensemble forecasting;    flood forecasting;    intense rainfall;    pluvial flooding;   

DOI  :  10.1111/jfr3.12691

来源: DOAJ

【 摘 要 】

Abstract The risk of surface water flooding (SWF) in England is already high and its frequency and severity is projected to increase in the future. SWF generally occurs due to intense, highly localised rainfall, which is challenging to forecast with sufficient accuracy to take proactive action ahead of flood events. Being able to manage the risk effectively lies in improved rainfall and flood forecast products, better communication of uncertainty and building the capacity of local responders. This study utilises state‐of‐the‐art high‐resolution ensemble rainfall forecasts and hydraulic modelling tools alongside a novel post‐processing method to develop and trial new SWF forecast products within an incident workshop attended by forecast producers and regional forecast users. Twenty‐two of 24 workshop participants reported that the new information would be useful to their organisation but more product development and training in its interpretation is required. Specific recommendations to improve SWF forecast provision include increased support for local government through a single government organisation responsible for SWF, making more use of existing static SWF mapping in a real‐time context and employing the process of user‐based consultation, as outlined in this study, to guide the future development of future SWF forecast information and processes.

【 授权许可】

Unknown