Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies

Abstract

Abstract. The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the postprocessing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts.

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

“…In hydrologic ensemble prediction systems, post-processing has become more and more popular in the last decade, particularly for medium-range ensemble forecasting (e.g. Weerts et al, 2011;Zalachori et al, 2012;Verkade et al, 2013;Madadgar et al, 2014;Roulin and Vannitsem, 2015). In seasonal forecasting, two popular bias correction methods are linear scaling and distribution mapping (Yuan et al, 2015).…”

Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

“…Our results confirm the findings of Wetterhall et al [39], who investigated the bias-correcting ECMWF System 4 seasonal precipitation forecast with the quantile method to improve the skill of forecasts. Zalachori et al [26] also demonstrated that applying a bias correction method for streamflow forecasts caused significant improvements in forecast reliability. Scatterplots of four deterministic scores, the Nash-Sutcliffe efficiency (NSE), the relative mean error (RME), the coefficient of variation of the root mean squared error (CV) and the correlation coefficient (CC) for different lead times.…”

“…Our results confirm the findings of Wetterhall et al [39], who investigated the bias-correcting ECMWF System 4 seasonal precipitation forecast with the quantile method to improve the skill of forecasts. Zalachori et al [26] also demonstrated that applying a bias correction method for streamflow forecasts caused significant improvements in forecast reliability. …”

An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

“…MORDOR is a conceptual lumped model developed in the early 1990s at EDF (Garçon, 1996), where it is used in operational hydrology in tasks as diverse as water resources forecasting, hydrological analysis, flood forecasting, low-flow forecasting, flood frequency estimation, and design of water regulation structures (see e.g., Mathevet et al, 2009;Bourqui et al, 2011;Zalachori et al, 2012;Paquet and Lawrence, 2013;Nicolle et al, 2014). The model represents the different components of the hydrological cycle through four reservoirs: superficial storage (contributes to actual evapotranspiration and direct runoff, and simulates soil moisture), evaporation matic stations for the mean elevation was only 5.4°C, with mean summer and winter temperatures of 14.5°C and -3.9°C, respectively.…”