Moving beyond the cost–loss ratio : economic assessment of streamflow forecasts for a risk-averse decision maker

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost–loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts’ uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts’ quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid overforecasting could help improve both the quality and the value of forecasts

Les modèles de prévision opérationnels d’aujourd’hui auraient-ils été fiables sur la crue de 1910 ? Analyse rétrospective critique sur une base de données de 1910

La crue de janvier 1910 survenue sur le bassin de la Seine constitue un mythe pour le Service de Prévision des Crues Seine Moyenne-Yonne-Loing (SPC SMYL) de la DIREN Ile-de-France, une référence quant à sa capacité à être opérationnel et performant sur un tel événement et un défi pour les modèles. Sur la base de données d’époque, issues de l’exploitation récente d’archives, un exercice en temps réel de simulation de la crue a été proposé aux prévisionnistes, munis seulement d’outils de prévision rudimentaires. Les prévisions produites dans ce mode dégradé répondent de façon satisfaisante aux attentes, tant en anticipation qu’en précision, pour l’Ile-de-France. Les modèles de prévision opérationnels du SPC ont eux aussi été testés, mettant en évidence de bons résultats pour la partie hydraulique de la modélisation, mais de faibles performances pour la partie hydrologique. Ces déficiences trouvent une explication dans la faible quantité et le format des données disponibles, mais surtout dans les processus physiques exceptionnels qui ont généré cette crue

Calage et application opérationnelle du modèle de prévision de crue GRP - Manuel d'utilisation (v2022.r3046)

Manuel d'utilisation du modèle de prévision des crues GR