Verification of Medium- to Long-Range Hydrological Forecasts

Abstract

Hydrological forecasting is crucial for hydropower production and risk manage- ment related to extreme events. Since uncertainty cannot be eliminated from such a process, forecasts should be probabilistic in nature, taking the form of proba- bility distributions over future events. However, verification tools adapted to probabilistic hydrological forecasting have only been recently considered. How can such forecasts be verified accurately? In this chapter a simple theoretical framework proposed by Gneiting et al. (2007) is employed to provide a formal guidance to verify probabilistic forecasts. Some strategies and scoring rules used to measure the performance of hydrological forecasting systems, namely, Hydro- Québec, are presented. Monte Carlo simulation experiments and applications to a real archive of operational medium-range forecasts are also presented. An exper- iment is finally performed to evaluate long-range hydrological forecasts in a decisional perspective, by employing hydrological forecasts in a stochastic mid- term planning model designed for optimizing electricity production. Future research perspectives and operational challenges on diagnostic approaches for hydrological probabilistic forecasts are given.