Search

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). Further, the choice of objective function (i.e. the criterion or combination of criteria for optimization) can significantly impact the parameter set values identified as optimal by the algorithm. 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 a hydrograph. We show in a catchment set that the impact of these transformations on the obtained time series can sometimes be different from their expected behaviour. Extreme transformations, such as squared or inverse 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 and the Box–Cox and logarithmic transformations, can be categorized as more generalist and show good performance for the medium range of streamflows, along with acceptable performance for extreme streamflows.

The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set.

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 consider 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 anthropogenic 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 objective functions, and two spatial frameworks, for a total of 78 distinct modelling options. A simple averaging method was used to combine the various simulated streamflow at the outlet of the catchments and sub-catchments. The lumped model with the highest efficiency score over the whole catchment set 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 approach in a variable spatial framework to simulate streamflow.

Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).

Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.

Abstract The seismometer onboard InSight NASA Mars mission discovered a seismically active planet. We focused on the strongest event named S1222a (4 May 2022, Mw ∼ 4.7), which was recorded by the Very Broad Band sensors and associated channel ELYSE and is located 37.2° away from InSight. We use two different methods based on a point source approach for an elastic, horizontally layered medium to retrieve source parameters of S1222a. In the first case, the seismic moment tensor inversion of high‐frequency seismogram data is calculated using a matrix method for the direct waves. The process includes the generation of records in displacement using the frequency‐wavenumber integration technique. A method of inversion of the moment tensor of direct P‐ and S‐waves, less sensitive to path effects than reflected and transformed waves, is presented, which significantly increases the accuracy and reliability of the method. In the second case, tensors were calculated using common low‐frequency full‐waveform inversion and the tests to verify the plausibility of this solution obtained from the single station calculation were performed and the uncertainty estimations for inversions can be useful in future research.

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.

In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower, etc.) and they can be strongly affected by water shortages. Therefore, there is a need for the early anticipation of low-flow periods to improve water management. This is strengthened by the perspective of having more severe summer low flows in the context of climate change. Several French institutions (Inrae, BRGM, Météo-France, EDF and Lorraine University) have been collaborating over the last years to develop an operational tool for low-flow forecasting, called PREMHYCE. It was tested in real time on 70 catchments in continental France in 2017, and on 48 additional catchments in 2018. PREMHYCE includes five hydrological models: one uncalibrated physically-based model and four storage-type models of various complexity, which are calibrated on gauged catchments. The models assimilate flow observations or implement post-processing techniques. Low-flow forecasts can be issued up to 90 d ahead, based on ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios. These climatic data (precipitation, potential evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis over the 1958–2017 period, which includes a wide range of conditions. The tool provides numerical and graphical outputs, including the forecasted ranges of low flows, and the probability to be under low-flow warning thresholds provided by the users. Outputs from the different hydrological models can be combined through a simple multi-model approach to improve the robustness of forecasts. Results are illustrated for the Ill River at Didenheim (northeastern France) where the 2017 low-flow period was particularly severe and for which PREMHYCE provided useful forecasts.

An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment set-up is based on (i) a large set of catchments in France, (ii) the GRP rainfall–runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they use (log, Box–Cox and log–sinh) to account for heteroscedasticity and the evolution of the other properties of the predictive distribution with the discharge magnitude. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation can be a reasonable choice for flood forecasting.

The InSight spacecraft landed on Mars on November 2018. Here, the authors characterize the surficial geology of the landing site and compare with observations and models derived from remote sensing data prior to landing and from ongoing in situ geophysical investigations of the subsurface.

Purpose: The Time to First Metastatic Recurrence (TFMR) could be considered as an indirect reflection of the tumour growth kinetics which plays an important role in cancer. Molecular subtypes such as expression of estrogen receptor are known predictive factors of TFMR. The CinéBreast study aimed to identify predictive factors of the time to TFMR. Methods: The French Epidemiological Strategy and Medical Economics (ESME) Metastatic Breast Cancer (MBC) Database (NCT03275311) was used, which contains data from a cohort of metastatic breast cancer patients from 2008 to 2016 using retrospective data collection. It is a national multi-centre database. The impact of TFMR on overall survival (OS) since first metastasis was also evaluated. Results: Among 16 702 patients recorded in the ESME MBC database, 10 595 had an initially localised breast cancer with hormone receptor (HR) and HER2 status available, with a metastatic recurrence. Median follow up was 56 months. Median TFMR was 59 months (120: 24%). HER2+ and TNBC were respectively 4 times and 12 times (p

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.

Abstract Sox9 is a member of the gene family of SOX transcription factors, which is highly conserved among vertebrates. It is involved in different developmental processes including gonadogenesis. In all amniote species examined thus far, Sox9 is expressed in the Sertoli cells of the male gonad, suggesting an evolutionarily conserved role in testis development. However, in the anamniotes, fishes and amphibians, it is also expressed in the oocyte but the significance of such an expression remains to be elucidated. Here, we have investigated the nuclear localization of the SOX9 protein in the oocyte of three amphibian species, the urodelan Pleurodeles waltl, and two anurans, Xenopus laevis and Xenopus tropicalis. We demonstrate that SOX9 is associated with ribonucleoprotein (RNP) transcripts of lampbrush chromosomes in an RNA-dependent manner. This association can be visualized by Super-resolution Structured Illumination Microscopy (SIM). Our results suggest that SOX9, known to bind DNA, also carries an additional function in the posttranscriptional processes. We also discuss the significance of the acquisition or loss of Sox9 expression in the oocyte during evolution at the transition between anamniotes and amniotes.

In many conceptual rainfall–runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall–runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall–runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).

Predicting streamflow hydrographs in ungauged catchments is challenging, and accompanying the estimates with realistic uncertainty bounds is an even more complex task. In this paper, we present a method to transfer global uncertainty estimates from gauged to ungauged catchments and we test it over a set of 907 catchments located in France, using two rainfall–runoff models. We evaluate the quality of the uncertainty estimates based on three expected qualities: reliability, sharpness, and overall skill. The robustness of the method to the availability of information on gauged catchments was also evaluated using a hydrometrical desert approach. Our results show that the method presents advantageous perspectives, providing reliable and sharp uncertainty bounds at ungauged locations in a majority of cases.

Les changements climatiques projetés à l'horizon du milieu du 21e pourraient avoir des impacts importants sur la disponibilité en eau en France. L'étude prospective Explore 2070, pilotée par le Ministère de l'Ecologie, a ainsi mis en évidence la nécessité de quantifier et d'anticiper ces changements, et de construire des stratégies d'adaptation pour limiter leurs conséquences négatives sur les hydrosystèmes et les activités humaines. Cet article analyse dans quelle mesure ces travaux ont pu contribuer à la sensibilisation des acteurs de l'eau et à la réflexion sur l'adaptation au changement climatique en France.

Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing low-flow 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 storage-type 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.

This paper investigates the robustness of rainfall–runoff models when their parameters are transferred in time. More specifically, we propose an approach to diagnose their ability to simulate water balance on periods with different hydroclimatic characteristics. The testing procedure consists in a series of parameter calibrations over 10 yr periods and the systematic analysis of mean flow volume errors on long records. This procedure was applied to three conceptual models of increasing structural complexity over 20 mountainous catchments in southern France. The results showed that robustness problems are common. Errors on 10 yr mean flow volume were significant for all calibration periods and model structures. Various graphical and numerical tools were used to investigate these errors and unexpectedly strong similarities were found in the temporal evolutions of these volume errors. We indeed showed that relative changes in simulated mean flow between 10 yr periods can remain similar, regardless of the calibration period or the conceptual model used. Surprisingly, using longer records for parameters optimisation or using a semi-distributed 19-parameter daily model instead of a simple 1-parameter annual formula did not provide significant improvements regarding these simulation errors on flow volumes. While the actual causes for these robustness problems can be manifold and are difficult to identify in each case, this work highlights that the transferability of water balance adjustments made during calibration can be poor, with potentially huge impacts in the case of studies in non-stationary conditions.

A holy grail of hydrology is to understand catchment processes well enough that models can provide detailed simulations across a variety of hydrologic settings at multiple spatiotemporal scales, and under changing environmental conditions. Clearly, this cannot be achieved only through intensive place-based investigation at a small number of heavily instrumented catchments, or by empirical methods that do not fully exploit our understanding of hydrology. In this opinion paper, we discuss the need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall–runoff process, thereby balancing depth with breadth. We examine the history of such investigations, discuss the benefits (improved process understanding resulting in robustness of prediction at ungauged locations and under change), examine some practical challenges to implementation and, finally, provide perspectives on issues that need to be taken into account as we move forward. Ultimately, our objective is to provoke further discussion and participation, and to promote a potentially important theme for the upcoming Scientific Decade of the International Association of Hydrological Sciences entitled Panta Rhei.

Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in a hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasting and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole country of France at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semidistributed mode using three unit sizes of subcatchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of French catchments (181) representing a variety of sizes and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semidistributed model in western France, where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. By contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.

Models with a fixed structure are widely used in hydrological studies and operational applications. For various reasons, these models do not always perform well. As an alternative, flexible modelling approaches allow the identification and refinement of the model structure as part of the modelling process. In this study, twelve different conceptual model structures from the SUPERFLEX framework are compared with the fixed model structure GR4H, using a large set of 237 French catchments and discharge-based performance metrics. The results show that, in general, the flexible approach performs better than the fixed approach. However, the flexible approach has a higher chance of inconsistent results when calibrated on two different periods. When analysing the subset of 116 catchments where the two approaches produce consistent performance over multiple time periods, their average performance relative to each other is almost equivalent. From the point of view of developing a well-performing fixed model structure, the findings favour models with parallel reservoirs and a power function to describe the reservoir outflow. In general, conceptual hydrological models perform better on larger and/or wetter catchments than on smaller and/or drier catchments. The model structures performed poorly when there were large climatic differences between the calibration and validation periods, in catchments with flashy flows, and in catchments with unexplained variations in low flow measurements.

This paper investigates the temporal transposability of hydrological models under contrasted climate conditions and evaluates the added value of using an ensemble of model structures for flow simulation. This is achieved by applying the Differential Split Sample Test procedure to twenty lumped conceptual models on a catchment in the Province of Québec (Canada) and another one in the State of Bavaria (Germany). First, a calibration/validation procedure was applied on four historical non-continuous periods with contrasted climate conditions. Then, model efficiency was quantified individually (for each model) and collectively (for the model ensemble). The individual analysis evaluated model performance and robustness. The ensemble investigation, based on the average of simulated discharges, focused on the twenty-member ensemble and all possible model subsets. Results showed that using a single model may provide hazardous results when the model is to be applied in contrasted conditions. Overall, some models turned out as a good compromise in terms of performance and robustness, but generally not as much as the twenty-model ensemble. Model subsets offered yet improved performance over the twenty-model ensemble, but at the expanse of spatial transposability (i.e. need of site-specific analysis).

An operational hydrological ensemble forecasting system based on a meteorological ensemble prediction system (M-EPS) coupled with a hydrological model searches to capture the uncertainties associated with the meteorological prediction to better predict river flows. However, the structure of the hydrological model is also an important source of uncertainty that has to be taken into account. This study aims at evaluating and comparing the performance and the reliability of different types of hydrological ensemble prediction systems (H-EPS), when ensemble weather forecasts are combined with a multi-model approach. The study is based on 29 catchments in France and 16 lumped hydrological model structures, driven by the weather forecasts from the European centre for medium-range weather forecasts (ECMWF). Results show that the ensemble predictions produced by a combination of several hydrological model structures and meteorological ensembles have higher skill and reliability than ensemble predictions given either by one single hydrological model fed by weather ensemble predictions or by several hydrological models and a deterministic meteorological forecast.

This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

As all hydrological models are intrinsically limited hypotheses on the behaviour of catchments, models – which attempt to represent real-world behaviour – will always remain imperfect. To make progress on the long road towards improved models, we need demanding tests, i.e. true crash tests. Efficient testing requires large and varied data sets to develop and assess hydrological models, to ensure their generality, to diagnose their failures, and ultimately, help improving them.

This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall-runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

Les progrès scientifiques et techniques pourraient laisser croire que les connaissances et données existantes sont suffisantes pour comprendre et modéliser tous les processus physiques qui régissent les phénomènes naturels sur Terre. Or, la prévision des crues demeure une mission difficile, avec des marges d'amélioration importantes, et dont les forts enjeux socio-économiques mobilisent scientifiques et acteurs opérationnels. Dans cet article, nous présentons le processus de développement d'un outil hydrologique largement utilisé dans le réseau de la prévision des crues en France. La genèse de cet outil et les principales étapes de son développement sont fondées sur des résultats de recherche, mais aussi sur l'interaction entre les scientifiques et les acteurs opérationnels. Nous décrivons les structures mises en place pour favoriser les échanges autour de l'outil, et deux exemples illustrent le rôle moteur des utilisateurs dans les développements scientifiques qui ont été réalisés récemment, ainsi que leur traduction concrète en termes de solutions innovantes pour la prévision. Les résultats encourageants obtenus dans l'utilisation de ces outils montrent tout l'intérêt de ces synergies entre acteurs.

Les crues entraînent des dommages humains et des dégâts matériels importants. Pouvoir prédire avec précision les crues est donc précieux afin d'intervenir en temps utile pour protéger les biens et les personnes. Cet article nous présente le travail de mise au point et d'application, sur le bassin versant expérimental de l'Orgeval, du modèle hydrologique GRP « Génie rural pour la prévision » développé par l'équipe Hydrologie d'Irstea à Antony.