Publications

Abstract : Long-term stream low-flow forecasting is one of the emerging issues in hydrology due to the escalating demand of water in dry periods. Reliable long-lead (a few weeks to months in advance) streamflow forecasts can improve the management of water resources and thereby the economy of the society and the conditions for aquatic life. The limited studies on low flows in the literature guided us to address some of the existing issues in low-flow hydrology, mainly on low-flow simulation and forecasting. Our ultimate aim to develop an ensemble approach for long-term low-flow forecasting includes several prior steps such as characterisation of low flows, evaluation of some of the existing model's simulation efficiency measures, development of a better model version for low-flow simulation, and finally the integration of an ensemble forecasting approach. A set of catchments distributed over France with various hydrometeorological conditions are used for model evaluation. This data set was first analysed and low flows were characterized using various indices. Our objective to better evaluate the models' low-flow simulation models resulted in the proposition of a criterion based on the Nash-Sutcliffe criterion, but calculated on inverse flows to put more weight on the errors on extreme low flows. The results show that this criterion is better suited to evaluate low-flow simulations than other commonly used criteria. Then a structural sensitivity analysis was carried out to develop an improved model structure to simulate stream low flows. Some widely used models were selected here as base models to initiate the sensitivity analysis. The developed model, GR6J, reaches better performance in both low- as well as high-flow conditions compared to the other tested existing models. Due to the complexity of rainfall-runoff processes and the uncertainty linked to future meteorological conditions, we developed an ensemble modelling approach to issue forecasts and quantify their associated uncertainty. Thus the ensemble approach provides a range of future flow values over the forecasting window. Here observed (climatological) rainfall and temperature were used as meteorological scenarios fed the model to issue the forecasts. To reduce the level of uncertainty linked to the hydrological model, various combinations of simple updating procedures and output corrections were tested. A straightforward approach, similar to what can be done for flood forecasting, was selected as it proved the most efficient. Last, attempts were made to improve the forecast quality on catchments influenced by dams, by accounting for the storage variations in upstream dams. Tested on the Seine and Loire basins, the approach showed mixed results, indicating the need for further investigations.