Abstract.Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue realtime forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).
Abstract. An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This paper evaluates the generalization ability of a simplification scheme of a 800-member HEPS formed by the combination of 16 lumped rainfall-runoff models with the 50 perturbed members from the European Centre for Mediumrange Weather Forecasts (ECMWF) EPS. Tests are made at two levels. At the local level, the transferability of the 9th day hydrological member selection for the other 8 forecast horizons exhibits an 82 % success rate. The other evaluation is made at the regional or cluster level, the transferability from one catchment to another from within a cluster of watersheds also leads to a good performance (85 % success rate), especially for forecast time horizons above 3 days and when the basins that formed the cluster presented themselves a good performance on an individual basis. Diversity, defined as hydrological model complementarity addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.
We are proposing to use the Nondominated Sorting Genetic Algorithm II (NSGA-II) for optimizing a hydrological forecasting model of 800 simultaneous streamflow predictors. The optimization is based on the selection of the best 48 predictors from the 800 that jointly define the "best" ensemble in terms of two probabilistic criteria. Results showed that the difficulties in simplifying the ensembles mainly originate from the preservation of the system reliability. We conclude that Pareto fronts generated with NSGA-II allow the development of a decision process based explicitly on the trade-off between different probabilistic properties. In other words, evolutionary multiobjective optimization offers more flexibility to the operational hydrologists than a priori methods that produce only one selection.
Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance)
An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This article evaluates a 94% simplification of an initial 800-member HEPS, forcing 16 lumped rainfall-runoff models with the European Center for Medium-range Weather Forecasts (ECMWF MEPS). More specifically, it tests the time (local) and space (regional) generalization ability of the simplified 50-member HEPS obtained using a methodology that combines 4 main aspects: (i) optimizing information of the short-length series using k-folds cross-validation, (ii) implementing a backward greedy selection technique, (iii) guiding the selection with a linear combination of diversified scores, and (iv) formulating combination case studies at the cross-validation stage. At the local level, the transferability of the 9th day member selection was proven for the other 8 forecast horizons at an 82% success rate. At the regional level, a good performance was also achieved when the 50-member HEPS was applied to a neighbouring catchment within the same cluster. Diversity, defined as hydrological model complementarities addressing different aspects of a forecast, was identified as the critical factor for proper selection applications
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