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Considering the emergency to adapt agriculture to climate change, it seems essential to develop generic tools to build and evaluate potential solutions. We combine here a prospective based on stakeholders’ interviews and an integrated agro-hydrological model to evaluate the impacts of scenarios on the Seille catchment in 2050. The prospective and stakeholders’ interviews aim at understanding agriculture and water management drivers on the modelled catchment and designing appropriate inputs for future scenarios modelling. The model simulates flows at a daily time step with GR rainfall-runoff models, and simulates daily irrigation demand thanks the to single Kc method of FAO, allowing estimating hydrological and agronomic impacts of climate change and scenarios. The integration of the two models is made thanks to the airGRiwrm package. Depending of the research advancement, results of simulations in a climate change context and conclusions about climate change impacts for designed scenarios will be presented. more...

Accounting for the variability of hydrological processes and climate conditions between catchments and within catchments remains a challenge in rainfall–runoff modelling. Among the many approaches developed over the past decades, multi-model approaches provide a way to take into account the uncertainty linked to the choice of model structure and its parameter estimates. Semi-distributed approaches make it possible to account explicitly for spatial variability while maintaining a limited level of complexity. However, these two approaches have rarely been used together. Such a combination would allow us to take advantage of both methods. The aim of this work is to answer the following question: What is the possible contribution of a multi-model approach within a variable spatial framework compared to lumped single models for streamflow simulation? To this end, a set of 121 catchments with limited influence in France was assembled, with precipitation, potential evapotranspiration and streamflow data at the hourly time step over the period 1998–2018. The semi-distribution set-up was kept simple by considering a single downstream catchment defined by an outlet, and one or more upstream sub-catchments. The multi-model approach was implemented with 13 rainfall–runoff model structures, three calibration options and two spatial frameworks, for a total of 78 distinct modelling options. A simple average method was used to combine the various simulated streamflow at the outlet of the catchments and sub-catchments. The most efficient lumped model on a given catchment was taken as the benchmark for model evaluation. Overall, the semi-distributed multi-model approach yields better performance than the different lumped models considered individually. The gain is mainly brought about by the multi-model set-up, with the spatial framework providing a benefit on a more occasional basis. These results, based on a large catchment set, evince the benefits of using a multi-model in a variable spatial framework to simulate streamflow. more...

Mitigation of drought and flood rely on objectives that are often a combination of several flow thresholds to be respected for different locations downstream the reservoirs. In this context, multi-objective optimisation techniques quickly show their limits due to the curse of dimensionality (Bellman, 1957). To tackle this issue, we propose an approach in which we first evaluate the risk of non-achievement of each objective independently for a given climatology and a given state of the system. Then, we derive management rules by prioritising the riskiest objectives in the daily decision making.This approach is applied on the Seine catchment (located in the North of France), which is equipped with a system of four large reservoirs to protect against floods and water shortages multiple locations downstream including the Paris region.First, catchment naturalized flows are modelled at a daily time step with a semi-distributed GR4J model based on the R package airGRiwrm (Dorchies et al., 2022) and forced by 11 GCM/RCM scenarios for both RCP4.5 and RCP8.5 between 1950 and 2100.Then, these flows are used to assess the risk of non-achievement of each objective taking into account the current reservoir volume, the day of the year and a selection of climate scenarios and periods. This assessment is derived from the statistical distribution of the minimum (resp. maximum) volume required in the reservoirs for a given drought (resp. flood) objective calculated by a single objective dynamic programming optimisation. The result of this assessment is available to the public through an interactive Shiny interface (http://irmara.g-eau.fr) that allows to experiment management scenarios in real time.Finally, management rules are derived by prioritising the riskiest objectives and balancing proactive and reactive decisions taking into account a hedging policy. The performance of this management is compared to the current management of the reservoirs over the historical and future periods.This approach has the advantage of providing a decision based on a risk assessment and prioritisation process that allows the manager to justify the decision and paves the way for an operational application.ReferencesBellman, R. Dynamic programming. Princeton, N.J.: Princeton University Press, 1957.Dorchies, David, Olivier Delaigue, et Guillaume Thirel. « airGRiwrm: Modeling of Integrated Water Resources Management based on airGR. R Package version 0.6.1 ». Portail Data INRAE, 7 mars 2022. https://doi.org/10.15454/3CVD1I. more...

The calibration of hydrological models through the use of automatic algorithms aims at identifying parameter sets that minimize the deviation of simulations from observations (often streamflows). It is a widespread technique that has been the subject of much research in the past. Indeed, the choice of objective function (i.e. the criterion or combination of criteria to optimize) can significantly impact the parameter set values identified as optimal by the algorithm. Besides, the actual goal of the model application (flood or low-flow estimation, for instance) influences the way calibration is undertaken. This article discusses how mathematical transformations, which are sometimes applied to the target variable before calculating the objective function, impact model simulations. Such transformations, for example square root or logarithmic, aim at increasing the weight of errors made in specific ranges of the hydrograph. Typically, a logarithmic transformation tends to increase the fit of streamflows to lower values, compared to no transformation. We show in a catchment set that the impact of these transformations on the obtained time series can sometimes be different from what could be expected. Extreme transformations, such as squared or inverse of squared transformations, lead to models that are specialized for extreme streamflows, but show poor performance outside the range of the targeted streamflows and are less robust. Other transformations, such as the power 0.2, the Box–Cox and the logarithmic transformations, can be qualified as more generalist, and show a good performance for the intermediate range of streamflows, along with an acceptable performance for extreme streamflows. more...

The estimation of irrigation water requirements (IWR) amount and timing is crucial for designing water management strategies at the regional scale. Irrigation requirements can be estimated with different types of models: very complex and detailed crop models, agent-based models, or simplified modeling approaches. Because simplified approaches are often preferred, in this study, we evaluate the consequences of using simplified approaches for IWR assessment at a catchment scale and the consequences of various modeling choices, providing information on the uncertainties. To this end, different simple modeling approaches based on the CropWat model are compared with an agent-based approach (MAELIA), which serves as a benchmark. To assess simulations in detail, partial variance is calculated for several indicators characterizing daily simulated irrigation. Our sensitivity analysis, applied over a sub-catchment of the Aveyron River (southwestern France), shows a high variability in simulations produced by CropWat between the modeling assumptions tested, principally explained by the rules for irrigation triggering and the quantification of daily irrigation. The analysis also shows that several simplified approaches are able to reproduce the irrigation simulated by the high-accuracy MAELIA model, but not necessarily corresponding to an optimal irrigation scheme. Hence, this study confirms the possibility of assessing daily irrigation with simplified approaches, but warns about high modeling uncertainties, reflecting uncertainty in effective irrigation practices. This uncertainty can be taken into account by water managers and modelers through the combination of a set of irrigation models. more...

Hydrological modelling is at the core of most studies related to water, especially for anticipating disasters, managing water resources, and planning adaptation strategies. Consequently, teaching hydrological modeling is an important, but difficult, matter. Teaching hydrological modeling requires appropriate software and teaching material (exercises, projects); however, although many hydrological modeling tools exist today, only few are adapted to teaching purposes. In this article, we present the airGRteaching package, which is an open-source R package relying on the GR rainfall-runoff models. In this package, thanks to a graphical user interface and a limited number of functions, numerous hydrological modelling exercises representing a wide range of hydrological applications are proposed. To ease its use by students and teachers, the package contains several vignettes describing complete projects that can be proposed to investigate various topics such as streamflow reconstruction, hydrological forecasting, and assessment of climate change impact. more...

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decisionmaking process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models., European Commission European Commission Joint Research Centre 690462 more...

Water management practices (dam management, uptakes for irrigation, industry and domestic water supply or water sanitation) can strongly influence river flow at the catchment scale. If water withdrawals and releases are important (for example in the case of a large dam), this influence can strongly impact the performance of bucket-type rainfall-runoff models whose structure do not explicitly consider water management practices. However, the influence of water management can also be compensated by the calibration of parameters, which is difficult to perceive when the model is conceptual, as the performance may not necessarily be negatively affected. This work aims at evaluating the effect of these compensations over the parameters of the GR4J bucket-type rainfall-runoff model over an influenced catchment. To do so, the model was applied in a semi-distributed way over the rainfed Sèvre Nantaise catchment in Western France. For this catchment, we used uptakes, sanitation and reservoir management data estimated at a daily time step, as well as a dense network of daily flow measurement stations. Water management practices were explicitly taken into account in the GR4J model through the airGR and airGRiwrm R packages that allow injecting or removing water from the river depending on water management practices. The model was calibrated on the different flow stations both with and without taking water withdrawals and releases into account. The obtained parameter values were compared in terms of reliability, robustness, plausibility and spatial homogeneity. more...

Subsurface drainage is a hydraulic technique currently used on agricultural soils showing strong waterlogging issues. This technique is applied on near 10% of French arable soils. Consequently, assessing its future in a context of climate change is relevant since subsurface drainage may have a major influence on the environment, specifically on agricultural water pollution. Here, the purpose is to analyse the evolution of hydrological indicators of subsurface drainage under climate change by 2100, on the La Jaillière plot being representative of French drainage in the Great West region. We define seventeen hydrological indicators to best describe the main aspects of subsurface drainage: drained water balance, temporality of the drainage season, flood events… These indicators are simulated by modelling subsurface drained discharge on the projected future using three hydrological subsurface drainage models: DRAINMOD, MACRO and SIDRA-RU. These three models are fed by twelve climate projections CPs provided by the EURO-CORDEX project, being selected by Météo-France to ensure the representativeness of future conditions in France. We consider three climatic scenarios RCPs (Representative Concentration Pathways): the RCP 2.6 (optimistic), the RCP 4.5 (intermediate) and the RCP 8.5 (pessimistic). The propagation of uncertainties on the hydrological indicators from the modelling chain is assessed by variance decomposition using the QUALYPSO method, from the various potential sources of uncertainties (hydrological models, CPs and RCPs). Preliminary results obtained with the SIDRA-RU model alone showed that subsurface drainage will be exposed to more extreme events such as more intense flood events over shorter periods by 2100. Depending on RCPs, the sustainability of the current network may no longer be ensured. more...

Semi-distributed hydrological models have the potential to improve the efficiency of flood forecasting chains. Such models take into account the spatial distribution of both meteorological forcings and soil moisture states to predict streamflow along the river network and allow the assimilation of streamflow observations on multiple internal flow gauges. How to update the model states on ungauged upstream sub-catchments remains however a challenge. Indeed, the actual relative contribution of each upstream sub-catchment to the observed streamflow at the outlet cannot be observed but simply estimated by the hydrological model from the simulated upstream streamflow and routing-lag. In this work, we test the following hypotheses:the simultaneous assimilation of streamflow observations at internal gauges should improve streamflow predictions at the main downstream outlet accounting for time lags between flow gauges is needed to efficiently update model states in cases where no streamflow observations are available at internal gauges The analysis is performed with a semi-distributed version of the hourly GR5H model (de Lavenne et al., 2019; Peredo-Ramirez et al., 2021) on a large dataset of French gauged catchments, each one having at least one internal gauged station. Several experiments were set up in gauged and pseudo-ungauged contexts to test both hypotheses. Two updating schemes were used: a particle filter (Piazzi et al., 2021) and a direct insertion method used in the operational flood forecasting model GRP (Furusho et al., 2016). Referencesde Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H., & Perrin, C. (2019). A regularization approach to improve the sequential calibration of a semidistributed hydrological model. Water Resources Research, 55, 8821–8839, 2018WR024266.Furusho, C., Perrin, C., Viatgé, J., Lamblin, R., and Andréassian, V. (2016). Collaborative work between operational forecasters and scientists for better flood forecasts, La Houille Blanche, 102:4, 5-10.Peredo-Ramirez, D., Ramos, M.-H., Andréassian, V., and Oudin, L. (2021). Investigating hydrological model versatility to simulate extreme flood events. Hydrological Sciences Journal, in review.Piazzi, G., Thirel, G., Perrin, C., and Delaigue, O. (2021). Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale. Water Resources Research, 57(4), e2020WR028390. more...

Extreme rainfall and associated river floodings are important concerns in modern human societies. Despite a recent increase of extreme rainfall, there is no evidence of an increase of the intensity and frequency of flash floods in Southern Europe, and future projections of flash-floods are quite uncertain in part due to the coarse resolution of available climate model simulations.The recent development of convection-permitting climate models allow a better representation of precipitation extremes. This new generation of climate models have been little employed in combination with hydrological models up to now, and their added value for flash flood modeling remains to be identified.In this work, a 2.5-km convection-permitting climate model (CNRM-AROME) simulation is used to force two hydrological models (CREST and GR5H). This new modeling chain is tested in a French mediterranean catchment, the Gardon at Anduze, that experienced severe flash flooding episodes over the last decades. Hydrological models are calibrated using the COMEPHORE 1 km observed precipitation dataset merging radar and rain gauge rainfall at the hourly time step. We compare the CNRM-AROME-based hydrological simulation to a benchmark run driven by a conventional CORDEX 12-km CNRM-ALADIN simulation. The analysis of the peak discharges simulated by both hydrological models driven by the different meteorological inputs allows to determine how higher resolution precipitation could improve the simulation of flash floods. more...

Accounting for the variability of processes and climate conditions between catchments and within catchments remains a challenge in hydrological modelling. To address this issue, various approaches were developed over the past decades. Among them, multi-model approaches provide a way to quantify and reduce the uncertainty linked to the choice of model structure, and semi-distributed approaches propose a good compromise to account for spatial variability of the processes by dividing the catchment in sub-catchments while maintaining a limited level of complexity. However, these two approaches were barely applied together. The aim of this work is to answer the following question: can we improve the efficiency of hydrological models by implementing a multi-model approach within a semi-distributed framework? In this work, the benchmark considered is a lumped model with a fixed structure.To this end, a large set of 147 catchments in France was assembled, with precipitation, evapotranspiration and flow data at an hourly time step over the 1998-2018 period. The semi-distribution set-up was kept simple by considering a single intermediate catchment between a downstream station and one or more upstream catchments. The multi-model approach was implemented with two versions of the GR model (namely GR4H and GR5H). Within a semi-distributed framework, the two models were either used individually, i.e. applied on all sub-catchments (called GR4H-SD and GR5H-SD respectively), or combined using a simple and a weighted mean.The first step of this work was to check whether past conclusions published in the scientific literature, obtained with lumped multi-models, were the same in a semi-distributed framework. In other words, does the multi-model approach generate better performance than individual models in a semi-distributed context?Another possible combination of the semi-distributed and the multi-model approaches would be to make different choices of model structures or combinations on each sub-catchment. Intuitively, it makes sense to propagate the flow simulated by the best model from upstream to downstream. The second analysis therefore focuses on the following question: is the best upstream model always the most useful downstream?The results and the operational implications of this work will be analyzed in the case of the Rhône basin. more...

Climate warming and evolution water demand for anthropogenic activities are two related ongoing changes that may increase existing pressure on water resources. Adaptation strategies are usually thought on the basis of hydrological projections. These projections, however, rarely incorporate changes in water and land management, issues that are then addressed by the adaptation plans within a sequential process. The objective of this study is to develop a modelling framework that incorporates water and land management evolution within a spatially distributed hydrological model in order to produce projections that are more realistic on the water resources shortages. We applied this methodology on the Moselle River (North-East France) basin using climate projections derived from various representative concentration pathway scenarios (RCPs), general circulation models (GCM), and regional climate models (RCM) in a multi-scenario multi-model approach. Hydrological projections are computed from a conceptual hydrological model, and a recent R package (airGRiwrm) was used to account for anthropogenic water withdrawals. Through contrasting scenarios of water use evolution built in partnership with the water stakeholders, we are able to explore different evolutions of water demand for each type of use present in the Moselle basin (withdrawals for domestic and industrial uses, energy production, and waterway navigation). These scenarios allow us to feed the hydrological model with different water withdrawals, in addition to the climate scenarios. The results of this multi-scenario and multi-model impact study on the water resources will be used to evaluate the water management sustainability and the adaptation levers. more...

The estimation of irrigation amounts and timings is crucial for the design of water management strategies at the regional scale. However, simplified modelling approaches are often preferred even though very complex and high-accuracy crop models or agent-based models exist. In this study, we develop a sensitivity analysis to evaluate the impacts of simplifications and hypotheses in irrigation modelling. For this, different simple modelling approaches based on the CropWat model were compared to a multi-agent based approach (Maelia), which served as a benchmark. To make an in-depth comparison between simulations, several indicators characterizing daily simulated irrigation were calculated and a decomposition of variance was carried out to measure impacts of diverse factors on irrigation. Applied over a downstream portion of the Aveyron River (southern France), the sensitivity analysis shows a high variability between simulations in function of modelling assumptions. It also shows that several simplifying approaches were able to reproduce the high-accuracy model estimation of irrigation. Decisive variation factors we identified are rules of triggering and quantification of daily irrigation, irrigation period definition and evapotranspiration estimation. Recommendations to take into account highlighted variability linked to farmers’ irrigation practices are introduced in this work, consisting in a combining a set of irrigation models. In function of advancement, a complete integrated agro-hydrologic modelling chain might be presented. more...

Air temperature increase due to climate change can lead to an increase in evapotranspiration, depending on whether the atmospheric demand is water or energy limited. This results in the intensification of the transfer from the continental hydrological compartment to the atmosphere. Investigating the uncertainties in the representation of potential evapotranspiration (PE) can provide a better understanding of the sensitivity of hydrological projections to atmospheric demand. This work summarizes our findings on different aspects about PE evolution under climate change and the uncertainties associated. First, we explored the relative importance of the contribution of PE formulations to the total uncertainty in PE projections, compared to the other steps in the modeling chain. Second, we evaluated the importance of the negative feedback of atmospheric CO2 concentration on evapotranspiration for hydrological projections by modifying the stomatal resistance equation in the Penman-Monteith equation according to three formulations proposed in the literature to evaluate the sensitivity of hydrological projections to vegetation responses.Regarding the importance of PE formulations on PE projections, it appears that the relative role of formulations is minor compared to other sources of uncertainty (namely climate models and greenhouse gases emission scenarios). Regarding the impact of CO2, results highlight a zone of uncertain change from decreasing to increasing average annual runoff, depending on the emission scenario and stomatal resistance equation. Thus, in the north of France, where PE fluxes are energy-limited, various levels of runoff response are showed with three tested formulations. The use of a PE formulation with stomatal resistance as a function of atmospheric CO2 could be used as forcing of a conceptual hydrological model, to include vegetation responses. more...

Many hydrological models use the concept of potential evapotranspiration (PE) to simulate actual evapotranspiration (AE). PE formulations often neglect the effect of carbon dioxide (CO2), which challenges their relevance in a context of climate change and rapid changes in CO2 atmospheric concentrations. In this work, we implement three options from the literature to take into account the effect of CO2 on stomatal resistance in the well-known Penman–Monteith PE formulation. We assess their impact on future runoff using the Budyko framework over France. On the basis of an ensemble of Euro-Cordex climate projections using the RCP 4.5 and RCP 8.5 scenarios, we show that taking into account CO2 in PE formulations largely reduces PE values but also limits projections of runoff decrease, especially under an emissive scenario, namely, the RCP 8.5, whereas the classic Penman–Monteith formulation yields decreasing runoff projections over most of France, taking into account CO2 yields more contrasting results. Runoff increase becomes likely in the north of France, which is an energy-limited area, with different levels of runoff response produced by the three tested formulations. The results highlight the sensitivity of hydrological projections to the processes represented in the PE formulation. more...

Real-time flood forecasting and other hydrological applications in mountainous areas require a good understanding and accounting of snow accumulation and melt. The CemaNeige snow model is currently used with the GRP hydrological model by most regional operational services in France to produce floods forecasts with lead times varying from a few hours to a few days. The snow model is based on a degree-day approach and needs limited inputs (precipitation and air temperature) spatialized on altitude bands over the catchment. It was initially developed on snow-dominated catchments by Valéry et al. (2014) using only streamflow series as calibration information. The model was then adapted by Riboust et al. (2019) to better simulate snow-covered areas as estimated by MODIS satellite images. These data were used as a secondary source of information for parameter calibration. All these developments were made at the daily time step. However, for real-time purposes, the outputs from the snow model are often needed at a finer time step, typically hourly or sub-hourly.Here the transposability and the consistency of the CemaNeige model were studied across a range of time steps, from hourly to daily, on a set of snow-influenced catchments. This follows previous works on the hydrological model to improve its consistency across time scales (see e.g. Viatgé et al., 2019). Several questions were addressed:To which extent are the outputs of the snow model and its parameters consistent across various time steps? Is the current snow model complexity (structure and parameters) sufficient to simulate the snow influence at the catchment scale at sub-daily time steps? Can we expect better results by running the snow model at sub-daily time steps than by disaggregating the outputs of the snow model run at the daily time? The answers to these questions will be presented based on a comprehensive testing scheme and a set of numerical criteria. Perspectives in terms of operational use will be discussed. more...

As they are useful and convenient, rainfall-runoff models are widely used in research and engineering. Applications of rainfall-runoff models range from flood risks estimation, to water resources management and low-flow related issues. The Catchment Hydrology Group at INRAE (Antony, France) has developed a set of conceptual GR models over the past 30 years with the main objective of designing models that are as efficient as possible in terms of streamflow simulation, and are applicable to a wide range of catchments with low data requirements.In recent years, in order to provide access to these hydrological models, INRAE has developed an open-source package named airGR for the R free software environment. This tool embeds the GR4H, GR4J, GR2M and GR1A models, among others, which operate at the hourly, daily, monthly and annual time steps. This package also includes a snow accumulation and melt module, a calibration tool, efficiency criterion calculations and plotting facilities.Recently, a galaxy of tools (fig. 1) has formed around airGR (hydroGR.github.io/airGR). airGRteaching R package is designed for simple applications and requires limited coding knowledge. It also offers a graphical user interface particularly useful for educational purposes. airGRiwrm R package (for integrated water resources management) provides tools to integrate human influences in a semi-distributed hydrological model, namely local flow injections or withdrawals based on predefined flows, or on user-defined decision algorithms given model outputs during simulation. airGRdatassim R package allows assimilating uncertain observed data to constrain the GR model predictions. Two data assimilation methods are available: the ensemble Kalman Filter & the Particle Filter. It also includes a model inputs perturbation function to generate probabilistic meteorological forcings. In addition to these R packages, the airGR constellation includes two web applications available on sunshine.irstea.fr. First, the airGRteaching GUI (fig. 2) is a demo of the tool embedded in the R package. Second, the airGRmaps GUI (fig. 3) provides regionalized parameters for GR daily hydrological models from geographical coordinates or by browsing on the map, over France.Figure 1: airGR galaxy tools.Figure 2: airGRteaching GUI.Figure 3: airGRmaps GUI. more...

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Many hydrological models use the concept of potential evaporation (PE) to simulate actual evaporation. PE formulations often neglect the effect of carbon dioxide (CO2), which challenges their relevance in a context of climate change and rapid changes in CO2 atmospheric concentrations. In this work, we implement three options from the literature to take into account the effect of CO2 on stomatal resistance in the well-known Penman–Monteith PE formulation. We assess their impact on future runoff using the Budyko framework over France. On the basis of an ensemble of Euro-Cordex climate projections using the RCP 4.5 and RCP 8.5 scenarios, we show that taking into account CO2 in PE formulations largely reduces PE values but also limits projections of runoff decrease, especially under an emissive scenario, namely, the RCP 8.5. Whereas the classic Penman–Monteith formulation yields decreasing runoff projections over most of France, taking into account CO2 yields more contrasting results. Runoff increase becomes likely in the north of France, which is an energy-limited area, with different levels of runoff response produced by the three tested formulations. The results highlight the sensitivity of hydrological projections to the processes represented in the PE formulation. more...

Drainage systems are currently implemented on agricultural plots subjected to temporary or permanent waterlogging issues. Drained plots account for 9 % of all arable soils in France. As such, the need for accurate hydrological modeling is crucial, especially in an unstable future context affected by climate change. The aim of this paper is to assess the capacity of the SIDRA-RU hydrological drainage model to represent the variability in pedoclimatic conditions within French metropolitan areas and to demonstrate the utility of this model as a long-term management tool. The model is initially calibrated using the KGE′ criterion as an objective function (OF) on a large and unique database encompassing 22 plots spread across France and classified according to three main soil textures (silty, silty–clay, and clayey). The performance of SIDRA-RU is evaluated by monitoring both the set of KGE′ calibration values and the quality of simulations on each plot with respect to high and low discharges, as well as the annual drained water balance. Next, the temporal robustness of the model is assessed by conducting, on selected plots, the split-sample test capable of satisfying the data requirements. Results show that the SIDRA-RU model accurately simulates drainage discharge, especially on silty soils. The performance on clayey soils is slightly weaker than that on silty soils yet remains acceptable. Similarly, the split-sample test indicates that SIDRA-RU is temporally robust on all three soil textures. Consequently, the SIDRA-RU model closely replicates the diversity of French drained soil and could be used for its long-term management potential. more...

The MOSARH21 project evaluated the future impacts of climate change on streamflows from the French tributaries of the River Rhine based on recent climate projections (CMIP5 experiment). The adopted methodology relies on two hydrological models (GRSD and LARSIM), used jointly with an ensemble of climate projections disaggregated at the local scale. The impacts were quantified with several indicators describing rivers regimes, floods and low flows. The evolutions of mean interannual streamflow indicate a slight increase, which could even become important for the radiative scenario RCP 8.5 (the most pessimistic one in terms of climate warming). Flood could intensify in the near future (2021-2050). In the far future (2071-2100), the evolution of floods indicators is more uncertain, as the hydrological projections diverge. Low flows could be decreasing in the near future. Their evolution in the far future is more uncertain, going from drastic decrease to sensible rise depending on the chosen scenario. The results were compared to those from previous impact studies performed on the basin. Given the methodological differences and the obtained results, we can conclude that the MOSARH21 project is consistent with the conclusions of the previous studies. more...

Climate change might cause regional modifications of precipitation regimes and increase of air temperature and evaporative demand. As a consequence, the potential increase in evapotranspiration has been determined as a key risk, which could lead to a decrease in runoff and water resources. In many hydrological models, evapotranspiration is determined by a preliminary computation of the evaporative demand, potential evapotranspiration (PET). Estimating PET for future climate is still subject to extensive research, due to the multiplicity of PET formulations and the uncertainties associated with the climatic variables used within these formulations. Physically-based PET formulations use several climatic variables whose simulations come with large uncertainties, while more simple empirical PET formulations rely on limited climatic variables. However, their empiric development questions their robustness for transient climatic conditions.In this work, we examined the evolution of PET under future climate conditions. We also investigated to what extent seven different classical PET formulations could modify the partitioning of uncertainty associated with climate projections.The importance of PET formulation on the total uncertainty of the potential evapotranspiration changes was evaluated within a multiscenario multimodel ensemble (Euro-CORDEX climate projections from CMIP5 experiment) over the whole France. This approach was used to account for the uncertainty on the unknown future greenhouse gas emissions trajectories, and differences coming from climate models (GCMs and RCMs). An analysis of the variance (ANOVA), allowed us to determine the contribution of each modelling step to the total uncertainty of PET estimates over entire France. The ANOVA was applied on an ensemble completed by a Bayesian process, to have a balance set of projections to analyze.The results showed that the relative importance of PET formulations was found to be minor compared with other uncertainty sources (GCMs and RCMs) in the future. We also found that divergences of PET among the different formulations were highly dependent on the temperature anomaly. Based on our experimental design, we concluded that the choice of PET formulation might not constitute a major element of uncertainty reduction for hydrological projections. more...

IWRM modeling aims at representing interactions between humans and their environment (Badham et al. 2019), which can involve hydrological, surface-hydraulic, and groundwater models. Semi-distributed models implementing a simplified hydraulic propagation between sub-catchments are often used as IWRM model (Ficchi et al. 2014, Dorchies et al. 2016) because of the good trade-off they offer between simplification and result relevancy.The R-package airGR (Coron et al., 2017, 2020) is widely used in the R language hydrology community and its recent development with semi-distributive (see Abstract EGU21-1371) capabilities allows to use it for IWRM modeling. The R-package airGRiwrm has been developed for multiple purposes linked to IWRM. First, it proposes a simplified network description for building semi-distributed models containing several sub-basins with diverse connections, which greatly simplifies the calibration and modeling steps. Then, it allows to easily integrate predefined flows (feedforward control) into the model, namely local flow injections or withdrawals. Finally, it integrates controllers that apply user-defined decision algorithms given model outputs during simulation (feedback control). The controllers allows for example to apply withdrawal restriction in case of drought, or to simulate a reservoir behaviour with complex management rules.In this presentation, we will introduce the airGRiwrm possibilities and we will demonstrate its use on the case of the Seine River basin in France. References:Badham, J., et al., 2019. Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities. Environmental Modelling & Software 116, 40–56. https://doi.org/10.1016/j.envsoft.2019.02.013Coron, L., Delaigue, O., Thirel, G., Perrin, C., Michel, C., 2020. airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.4.3.65. https://doi.org/10.15454/EX11NACoron, L., Thirel, G., Delaigue, O., Perrin, C., Andréassian, V., 2017. The suite of lumped GR hydrological models in an R package. Environmental Modelling & Software 94, 166–171. https://doi.org/10.1016/j.envsoft.2017.05.002Dorchies, D., Thirel, G., Perrin, C., Bader, J.-C., Thepot, R., Rizzoli, J.-L., Jost, C., Demerliac, S., 2016. Climate change impacts on water resources and reservoir management in the Seine river basin (France). La Houille Blanche 32–37. https://doi.org/10.1051/lhb/2016047Ficchi, A., Raso, L., Malaterre, P.-O., Dorchies, D., Jay-Allemand, M., 2014. Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control. Presented at the International Conference on Hydroinformatics, New York. more...

International audience; The Mediterranean region is a climate change hotspot for water resources. However, uncertainty analyses of hydrological projections are rarely quantified. In this study, an in-depth analysis of projections and uncertainties for high and low flows is performed. Climatic projections derived from a recent downscaling method were used, for two representative concentration pathway scenarios (RCPs), five general circulation model/regional climate model (GCM/RCM) couples, three hydrological models (HMs), and 29 calibration schemes. A quasi-ergodic analysis of variance was used to evaluate the contribution of each impact modelling step to the total uncertainty. For high flows, the results show a mean increase of 30% by 2085, and RCPs make the highest contribution to the total uncertainty, followed by GCMs. For low flows, 50% of projections indicate a decrease of 7% or more by 2085, and HM structures, hydrological model parameters, and GCMs are the most important uncertainty sources. These results contribute to raise awareness among water managers regarding future hydrological extreme events. more...

International audience

International audience; Abstract. Prior to their use under future changing climate conditions, all hydrological models should be thoroughly evaluated regarding their temporal transferability (application in different time periods) and extrapolation capacity (application beyond the range of known past conditions). This note presents a straightforward evaluation framework aimed at detecting potential undesirable climate dependencies in hydrological models: the robustness assessment test (RAT). Although it is conceptually inspired by the classic differential split-sample test of Klemeš (1986), the RAT presents the advantage of being applicable to all types of models, be they calibrated or not (i.e. regionalized or physically based). In this note, we present the RAT, illustrate its application on a set of 21 catchments, verify its applicability hypotheses and compare it to previously published tests. Results show that the RAT is an efficient evaluation approach, passing it successfully can be considered a prerequisite for any hydrological model to be used for climate change impact studies. more...

Hydrological models are increasingly used under evolving climatic conditions. They should thus be evaluated regarding their temporal transferability (application in different time periods) and extrapolation capacity (application beyond the range of known past conditions). In theory, parameters of hydrological models are independent of climate. In practice, however, many published studies based on the Split-Sample Test (Klemeš, 1986), have shown that model performances decrease systematically when it is used out of its calibration period. The RAT test proposed here aims at evaluating model robustness to a changing climate by assessing potential undesirable dependencies of hydrological model performances to climate variables. The test compares, over a long data period, the annual value of several climate variables (temperature, precipitation and aridity index) and the bias of the model over each year. If a significant relation exists between the climatic variable and the bias, the model is not considered to be robust to climate change on the catchment. The test has been compared to the Generalized Split-Sample Test (Coron et al., 2012) and showed similar results.Here, we report on a large scale application of the test for three hydrological models with different level of complexity (GR6J, HYPE, MIKE-SHE) on a data set of 352 catchments in Denmark, France and Sweden. The results show that the test behaves differently given the evaluated variable (be temperature, precipitation or aridity) and the hydrological characteristics of each catchment. They also show that, although of different level of complexity, the robustness of the three models is similar on the overall data set. However, they are not robust on the same catchments and, then, are not sensitive to the same hydrological characteristics. This example highlights the applicability of the RAT test regardless of the model set-up and calibration procedure and its ability to provide a first evaluation of the model robustness to climate change. ReferencesCoron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, doi:10.1029/2011WR011721Klemeš, V., 1986. Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13–24, doi:10.1080/02626668609491024 more...

The evaluation of inflow forecast quality and value is essential in hydroelectric reservoir management. Forecast value can be quantified by the economic gains obtained when optimizing hydroelectric reservoir operations informed by weather and hydrological forecasts. This study [1] investigates the impact of 7-day streamflow forecasts on the optimal management of hydroelectric reservoirs and the associated economic gains. Flows from ten catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to ten hydroelectric reservoirs, which are conceptually parametrized. Each reservoir is associated to a downstream power plant with yield 1 which produces electricity valued with a price signal. The system is modelled using linear programming. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with under-dispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1% to 3% of the revenue over the study period. Besides revenue, the forecast quality also impacts spillage, stock evolution, production hours and production rates. For instance, forecasting systems that present a positive bias result in a tendency of operations to keep the storage at lower levels so that the reservoir can be able to handle the high volumes expected. This impacts the optimal placement of production at the best hours (i.e. when prices are higher) and the opportunity to produce electricity at higher production rates. Our study showed that when using biased forecasting systems, hydropower production is not only planned during more hours at lower rates but also at hours with lower median prices of electricity. The modelling approaches adopted in our study are certainly far from representing all the complexity of hydropower management under uncertainty. However, they proved to be adapted to obtaining the first orders of magnitude of the value of inflow forecasts in elementary situations.[1] https://doi.org/10.5194/hess-2020-410 more...

airGR (Coron et al., 2017, 2020) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea). It allows running seven hydrological models (including GR4J) dedicated to different time steps (hourly to annual) that can be combined to a snow accumulation and melt model (CemaNeige).Thanks to the success of the airGR package, that was downloaded 45,000 times so far among 50 countries in the world and was used in dozen of publications since its release[1], its development team carries on its efforts to offer new features and improve the computer codes. This is how after offering a first add-on, the airGRteaching package, expressly developed for educational purposes, the team now offers tools dedicated to semi-distribution and data assimilation.Using (semi-)distributed models is often necessary to explicitly represent spatial climatic and physiographic heterogeneities and to allow an analysis of their impact on the watershed response. Consequently, in the latest version of the airGR package, we introduced the semi-distribution of GR models, which are traditionally lumped, on a sub-basin basis. This development will also ultimately enable possibilities of implementing on a modular way different transfer functions as well as integrated water resource management (see package airGRiwrm in Abstract EGU21-2190).In addition, a new package, called airGRdatassim, was recently proposed (Piazzi et al., 2021a, b) as an add-on to the airGR package. airGRdatassim enables the user to assimilate discharge observations via both Ensemble Kalman filter (EnKF) and particle filter (PF) schemes. Besides improving the simulations of GR models, this new package extends the potential applications of airGR to forecasting purposes by allowing for a reliable assessment of the initial conditions of streamflow forecasts. References:Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2020). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.4.3.65. URL: https://CRAN.R-project.org/package=airGR.Piazzi, G., Delaigue, O. (2021a). airGRdatassim: Suite of Tools to Perform Ensemble-Based Data Assimilation in GR Hydrological Models. R package version 0.0.3.13. URL: https://gitlab.irstea.fr/HYCAR-Hydro/airgrdatassim.Piazzi, G., Thirel, G., Perrin, C., Delaigue, O. (2021b, accepted). Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model. Water Resources Research. [1] https://hydrogr.github.io/airGR/page_publications.html more...

International audience; Abstract. The improvement of a forecasting system and the continuous evaluation of its quality are recurrent steps in operational practice. However, the systematic evaluation of forecast value or usefulness for better decision-making is less frequent, even if it is also essential to guide strategic planning and investments. In the hydropower sector, several operational systems use medium-range hydrometeorological forecasts (up to 7–10 d ahead) and energy price predictions as input to models that optimize hydropower production. The operation of hydropower systems, including the management of water stored in reservoirs, is thus partially impacted by weather and hydrological conditions. Forecast value can be quantified by the economic gains obtained with the optimization of operations informed by the forecasts. In order to assess how much improving the quality of hydrometeorological forecasts will improve their economic value, it is essential to understand how the system and its optimization model are sensitive to sequences of input forecasts of different quality. This paper investigates the impact of 7 d streamflow forecasts of different quality on the management of hydroelectric reservoirs and the economic gains generated from a linear programming optimization model. The study is based on a conceptual approach. Flows from 10 catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to 10 hydroelectric reservoirs, which are conceptually parameterized. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with underdispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1 % to 3 % of the revenue over the study period. Besides revenue, the quality of the forecasts also impacts spillage, stock evolution, production hours and production rates, with systematic over- and underestimations being able to generate some extreme reservoir management situations. more...

International audience

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research, and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall-runoff modelling from a user perspective, contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated in these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps inform selection of what packages could/should be used depending on the problem at hand. In this regard, technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R. more...

Streamflow estimation from rain events is a delicate exercise. Watersheds are complex natural systems and their response to rainfall events is influenced by many factors. Hydrological rainfall-runoff modelling is traditionally used to understand those factors by predicting discharges from precipitation data. These models are simplified conceptualisations and thus still struggle when facing some particular processes linked to the catchment. Among those processes, the tide influence on river discharges is rarely accounted for in hydrological modelling when estimating streamflow series at river mouth areas. Instead, estimated streamflow series are sometimes corrected by coefficients to account for the tide effect.In this presentation, we explored a semi-distributed hydrological model by adapting it to account for tidal-influence in the river mouth area. This model uses observed spatio-temporal rainfall and potential evapotranspiration databases to predict streamflow at gauged and ungauged locations within the catchment. The hydrological model is calibrated using streamflow observations and priors on parameter values to calibrate each model parameters of each sub-catchments. A drift procedure in the calibration process is used to ensure continuity in parameter values between upstream and downstream successive sub-catchments.This novel approach was applied to a tidal-affected catchment: the Adour’s catchment in southern France. Estimated results were compared to simulations without accounting for the tidal influence. Results from the new hydrological model were improved at tidal-affected locations of the catchment. They also show similar estimations in tidal-unaffected part of the catchment. more...

The Mediterranean region is a hot spot for climate change impact on the water cycle where water resources are anticipated to decrease and hydrological extremes to intensify while population and water use conflicts growth would keep rising. However, the analysis of the uncertainty related to hydrological projections is generally poorly quantified and difficult to translate to decision-makers. In this study, an in-depth analysis of projections and uncertainties for extreme high- and low-flows was performed. Climatic projections derived from a recent downscaling method over France (Adamont, Verfaillie et al., 2017) were used, and hydrological projections were produced on the Hérault River catchment based on two different Radiative Concentration Pathways (RCPs), five global and regional climate model (GCM/RCM) couples, three hydrological models (HMs), and twenty-nine calibration schemes (Lemaitre-Basset et al., sub). This ensemble was analysed with the QUALYPSO approach (Evin et al., 2019) that allows transient uncertainty analysis of ensembles derived from incomplete GCM/RCM matrix. The quasi-ergodic analysis of variance (QE-ANOVA) used in QUALYPSO evaluates the contribution of each impact modelling step to the total uncertainty. For high-flows, GCMs and RCPs contribute the most to the total uncertainty at the short and long lead-time, respectively. For low-flows, HMs structure and calibration period are the most important sources of uncertainty across 2006-2100. While high-flow projections show a significant mean increase of 30% by 2085 compared to the historical period (confidence intervals: [-1%; +64%]), low-flows would slightly decrease (-7%) by 2085, but with a higher uncertainty (confidence interval: [-24%; +13%]). The time horizons for which a change (e.g. -50, -20, -10, …, +10, +20, +50%) in high- and low-flows intensity becomes robust (i.e. when more than 66% of the ensemble is above/below a given threshold) were also assessed. This provides strong messages to water managers of the Hérault River catchment who can then anticipate the time needed to prepare and adapt to climate change impacts for extreme hydrological hazards.References:Evin, G., Hingray, B., Blanchet, J., Eckert, N., Morin, S., & Verfaillie, D. (2019). Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation. JOURNAL OF CLIMATE, 32, 18. https://doi.org/10.1175/JCLI-D-18-0606.1Lemaitre-Basset, T., Collet, L., Thirel, G., Parajka, J., Evin, G., Hingray, B. (submitted) Climate change impact and uncertainty analysis on hydrological extremes in a Mediterranean catchment. Hydrological Sciences JournalVerfaillie, D., Déqué, M., Morin, S., & Lafaysse, M. (2017). The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models. Geoscientific Model Development, 10(11), 4257–4283. https://doi.org/10.5194/gmd-10-4257-2017 more...

Log-transformed discharge is often used to calculate performance criteria to better focus on low flows. This prior transformation limits the heteroscedasticity of model residuals and was largely applied in criteria based on squared residuals, like the Nash–Sutcliffe efficiency (NSE). In the recent years, NSE has been shown to have mathematical limitations and the Kling–Gupta efficiency (KGE) was proposed as an alternative to provide more balance between the expected qualities of a model (namely representing the water balance, flow variability and correlation). As in the case of NSE, several authors used the KGE criterion (or its improved version KGE′) with a prior logarithmic transformation on flows. However, we show that the use of this transformation is not adapted to the case of the KGE (or KGE′) criterion and may lead to several numerical issues, potentially resulting in a biased evaluation of model performance. We present the theoretical underpinning aspects of these issues and concrete modelling examples, showing that KGE′ computed on log-transformed flows should be avoided. Alternatives are discussed. more...

The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application. more...

Hydrological models are valuable tools for short-term forecasting of river flows, long-term predictions for water resources management and to increase our understanding of the complex interactions of water storage and release processes at the catchment scale. Hydrological models provide relatively robust estimates of streamflow dynamics, as shown by the countless applications in many regions across the world. However, various model structures can lead to similar aggregated outputs, i.e. model equifinality. To provide reliable estimates, it is of critical importance that not only the aggregated response but also the internal behaviors are consistent with their real-world equivalents. In a previous international comparison study (de Boer-Euser et al., 2017), eight research groups followed the same protocol to calibrate their twelve models on streamflow for several catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar runoff performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of actual evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar runoff performance represent internal states and fluxes differently. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, the plausibility of process representation could only be evaluated to some extent as many variables remain unknown, highlighting the need for more experimental research. The study further emphasizes the value of multi-model, multi-parameter studies to reveal to decision-makers the uncertainty inherent to the lack of evaluation data and the heterogeneous hydrological landscape.References:de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., and Willems, P.: Looking beyond general metrics for model comparison – lessons from an international model intercomparison study, Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, 2017. more...