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.
Forecast reliability and accuracy is a prerequisite for successful hydrological applications. This aim may be attained by using data assimilation techniques such as the popular Ensemble Kalman filter (EnKF). Despite its recognized capacity to enhance forecasting by creating a new set of initial conditions, implementation tests have been mostly carried out with a single model and few catchments leading to case specific conclusions. This paper performs an extensive testing to assess ensemble bias and reliability on 20 conceptual lumped models and 38 catchments in the Province of Québec with perfect meteorological forecast forcing. The study confirms that EnKF is a powerful tool for short range forecasting but also that it requires a more subtle setting than it is frequently recommended. The success of the updating procedure depends to a great extent on the specification of the hyper-parameters. In the implementation of the EnKF, the identification of the hyper-parameters is very unintuitive if the model error is not explicitly accounted for and best estimates of forcing and observation error lead to overconfident forecasts. It is shown that performance are also related to the choice of updated state variables and that all states variables should not systematically be updated. Additionally, the improvement over the open loop scheme depends on the watershed and hydrological model structure, as some models exhibit a poor compatibility with EnKF updating. Thus, it is not possible to conclude in detail on a single ideal manner to identify an optimal implementation; conclusions drawn from a unique event, catchment, or model are likely to be misleading since transferring hyper-parameters from a case to another may be hazardous. Finally, achieving reliability and bias jointly is a daunting challenge as the optimization of one score is done at the cost of the other.
Abstract. Seeking for 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, multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims at untangling the sources of uncertainty by studying the combination of these tools and assessing their 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 stage in the forecasting process by using different means. Their combination outperforms any of the tool used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition 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 improve the general forecasting performance and prevents 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 Ensemble Kalman Filter tuning to avoid overlapping in error deciphering.
In an operational context, efficient decision-making is usually the ultimate objective of hydrometeorological forecasts. Because of the uncertainties that lay within the forecasting process, decisions are subject to uncertainty. A better quantification of uncertainties should provide better decisions, which often translate into optimal use and economic value of the forecasts. Six Early Warning Systems (EWS) based on contrasted forecasting systems are constructed to investigate how the quantification of uncertainties affects the quality of a decision. These systems differ by the location of the sources of uncertainty, and the total amount of uncertainty they take into account in the forecasting process. They are assessed with the Relative Economic Value (REV), which is a flexible measure to quantify the potential economic benefits of an EWS. The results show that all systems provide a gain over the case where no EWS is used. The most complex systems, i.e. those that consider more sources of uncertainty in the forecasting process, are those that showed the most reduced expected damages. Systems with better accuracy and reliability are generally the ones with higher REV, even though our analysis did not show a clear-cut relationship between overall forecast quality and REV in the context investigated.
Successful data assimilation depends on the accurate estimation of forcing data uncertainty.Forcing data uncertainty is typically estimated based on statistical error models. In practice, the hyper-parameters of statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of forcing data uncertainty estimation, this study proposes the direct use of existing ensemble climate products to represent climate data uncertainty in the ensemble Kalman filter (EnKF) of flow forecasting. Specifically, the Newman et al. ( 2015) dataset (N15 for short), covering the contiguous United States, northern Mexico, and southern Canada, is used here to generate the precipitation and temperature ensemble in the EnKF application. This study for the first time compares the N15 generated climate ensemble with the carefully tuned hyper-parameters generated climate ensemble in a real flow forecasting framework. The forecast performance comparison of 20 Québec catchments shows that the N15 generated climate ensemble yields improved or similar deterministic and probabilistic flow forecasts relative to the carefully tuned hyper-parameters generated climate ensemble. Improvements are most evident for short lead times (i.e., 1-3 days) when the influence of data assimilation dominates. However, the analysis and computational time required to use N15 is much less compared to the typical trial-and-error hyper-parameter tuning process. KEY WORDS: Climate data uncertainty; hyper-parameter tuning; ensemble Kalman filter (EnKF); short-term ensemble flow forecasting; Newman et al. (2015) dataset Highlights EnKF-based flow forecasts use an existing historical climate ensemble product (N15) N15 gives more realistic climate uncertainty than tuned statistical error models N15 yields improved or similar flow forecast quality N15 saves users from tuning precipitation and temperature relevant hyper-parameters N15 enables disaggregating hydrologic model uncertainty sources in the EnKF
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