Strategies for hydrologic ensemble generation and calibration: On the merits of using model-based predictors

Tiberi-Wadier, Anne-Laure ; Goutal, Nicole ; Ricci, Sophie ; Sergent, Philippe ; Taillardat, Maxime ; Bouttier, François ; Monteil, Céline

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<p align=justify>This paper investigates the hydrometeorological chain with an ensemble approach. The objective is the generation of Hydrologic Ensemble Forecasts (HEF) on the Odet catchment (France, Brittany), using the Quantile Regression Forest (QRF) method usually applied for the ensemble calibration of meteorological forecats. First, a Global Sensitivity Analysis (GSA) in the distributed MORDOR-TS model is carried out taking into account uncertainty in forecasted rain with AromeEPS-RR1 and in model parameters. GSA highlights the role and importance of the different hydrologic model parameters during rain events and allows to only take into account the most influent parameters for the generation of an Hydrologic Ensemble Forecast (HEF). Three strategies for the generation of HEF are then compared. First (i), a raw ensemble is built with a model-based only approach using the deterministic forecast rainfall Expert-RR3 and perturbed model parameters, without further statistical calibration. Then, the QRF calibration method is used to generate two ensembles of quantiles: (ii) the observation-based approach uses only predictors that are independent from hydrology, whereas (iii) the combined model and observation approach combines these predictors with statistics of the raw hydrologic ensemble (mean, standard deviation). This latter approach was shown to outperfom the previous ones, enhancing the importance of the choice of the predictors in the QRF method. In the prospect of using the hydrologic ensemble as input for hydraulic simulation, the Ensemble Copula Coupling method (ECC) and a trajectory smoothing procedure is then applied on (iii). This step slightly deteriorates the reliability of hourly streamflows, yet Continuous Ranked Probability Score (CRPS) and forecast skills on the cumulated or maximum streamflows are improved.</p>
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