Benchmarking hydrological models for low-flow simulation and forecasting on French catchments

Abstract

Abstract. Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing lowflow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storagetype models -Gardenia, GR6J, Mordor and Presages -and one distributed physically oriented model -SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average.

Quantifying human impacts on hydrological drought using a combined modelling approach in a tropical river basin in central Vietnam

“…Our softly linked model set-up shows good results in terms of statistical efficiency performances and provides reliable simulations for both reconstructed and naturalized streamflow. This applies also to the low-flow simulations and hydrological drought periods which usually pose the greatest challenges to hydrological modelling (Pilgrim et al, 1988;Nicolle et al, 2014). This method presents several advantages compared to statisticsbased approaches such as Budyko curves or double mass curves.…”

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

“…In all, 16 catchments spread over France were selected from the database used by Nicolle et al (2014). Using a set of catchments helps getting more general conclusions (see e.g.…”

Moselle Nehri̇’ndeki̇ Düşük Debi̇leri̇n Benzeti̇mi̇ İçi̇n Çoklu Geneti̇k Programlama Modeli̇ni̇n Kali̇bre Edi̇lmesi̇

“…Recently, a neural network model and two well-known conceptual models (GR4J and HBV) are used to simulate low flows in the Moselle River (Demirel et al, 2015). GR4J model has been used in other low flow studies too (Nicolle et al, 2014;Pushpalatha et al, 2011). However, to our knowledge, the new data-driven method, i.e.…”