Accounting for three sources of uncertainty in ensemble hydrological forecasting

Abstract

Abstract. Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

Water Temperature Ensemble Forecasts: Implementation Using the CEQUEAU Model on Two Contrasted River Systems

“…Among other sources, the choice of particular hydrological and thermal models, initial conditions, and the level of model parameterization are known to induce uncertainty to the hydrological modeling/forecasting [59]. The combination of ensemble forecasting approaches (data assimilation, multi-model and ensemble meteorological forecasts) was shown to be effective to properly represent uncertainty over short-(one day) to long-term (10 days) hydrological forecasts [24]. River temperature forecasting would likely benefit from a similar, more complete approach.…”

“…Therefore, the thermal inertia of the system limits the deviation of the short-term forecasts from the initial conditions. Thiboult et al [24] showed the important influence of the initial conditions of the watershed on hydrological forecast accuracy and reliability through data assimilation. They also suggested that these initial conditions mostly influence the quality of the forecasts for shorter lead times.…”

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

“…In a study involving 20 catchments in Quebec, (Thiboult et al, 2016) showed that the uncertainty for initial conditions dominates the other sources of uncertainty for short-term (1 day to 3 days ahead) streamflow forecasts. Those catchments vary in size and other physical characteristics, but they are all subject to similar meteorological conditions, which are also shared by the Montmorency catchment.…”

“…Those catchments vary in size and other physical characteristics, but they are all subject to similar meteorological conditions, which are also shared by the Montmorency catchment. However, the Montmorency catchment has a smaller area than any of the 20 watersheds in (Thiboult et al, 2016) and has a shorter response time. Consequently, the uncertainty in the initial condition is expected to dominate for less than 1 day.…”

“…The behaviour and consequences of ice jams along the Montmorency River have been the focus of previous studies, such as forecasting river ice breakup (Turcotte and Morse, 2015). Risk analysis and technical solutions (Leclerc et al, 2001) have also been studied, but as of yet have not been implemented.…”