Development and evaluation of flood forecasting models for forecast-based financing using a novel model suitability matrix. (April 2020)

• Record Type: Journal Article
• Authors: Hagen, Jenny Sjåstad; Cutler, Andrew; Trambauer, Patricia; Weerts, Albrecht; Suarez, Pablo; Solomatine, Dimitri
• Is Part Of: Progress in disaster science. Volume 6(2020)
• Publication Date: 2020-04
• Subjects: Model suitability -- Forecast-based financing -- Flood forecasting -- Neural network -- Open data -- Delft-FEWS; Disasters -- Periodicals; Disaster relief -- Planning -- Periodicals; Emergency management -- Periodicals; 363.3405
• DOI: 10.1016/j.pdisas.2020.100076
• Languages: English
• ISSNs: 2590-0617

Abstract

Abstract: Forecast-based financing is a financial mechanism that facilitates humanitarian actions prior to anticipated floods by triggering release of pre-allocated funds based on exceedance of flood forecast thresholds. This paper presents a novel model suitability matrix that embeds application-specific needs and contingencies at local level on a pilot project of forecast-based financing. The added value of this flexible framework is demonstrated on a set of hydrological and machine learning models. The model suitability matrix facilitates transparency and traceability of subjectivity in model evaluation. This paper advocates a stronger interface between model developers and end users for upscaling of forecast-based financing. Highlights: A model suitability matrix is developed for forecast-based financing. The flexible framework is intended to guide model selection and underpin upscaling. Trade-offs between forecast skill and model suitability are discussed. A wider discussion on forecast model evaluation is opened.