Reconstruction of missing daily streamflow data using dynamic regression models

International audienceRiver discharge is one of the most important quantities in hydrology. It provides fundamentalrecords for water resources management and climate change monitoring. Even very short data-gaps in thisinformation can cause extremely different analysis outputs. Therefore, reconstructing missing data ofincomplete data sets is an important step regarding the performance of the environmental models, engineering,and research applications, thus it presents a great challenge. The objective of this paper is to introducean effective technique for reconstructing missing daily discharge data when one has access to onlydaily streamflow data. The proposed procedure uses a combination of regression and autoregressive integratedmoving average models (ARIMA) called dynamic regression model. This model uses the linear relationshipbetween neighbor and correlated stations and then adjusts the residual term by fitting an ARIMAstructure. Application of the model to eight daily streamflow data for the Durance river watershed showedthat the model yields reliable estimates for the missing data in the time series. Simulation studies were alsoconducted to evaluate the performance of the procedure

Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds

International audienceWe introduce a new diagnosis tool that is well suited to analyzing simulation results over large samples of watersheds. It consists of a modification of the classical Taylor diagram to simultaneously visualize several error components (based on bias, standard deviation or squared errors) that are commonly used in efficiency criteria (such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE)) to evaluate hydrological model performance. We propose a methodological framework that explicitly links the graphical and numerical evaluation approaches, and show how they can be usefully combined to visually interpret numerical experiments conducted on large datasets. The approach is illustrated using results obtained by testing two rainfall-runoff models on a sample of 2050 watersheds from 8 countries and calibrated with two alternative objective functions (NSE and KGE). The assessment tool clearly highlights well-documented problems related to the use of the NSE for the calibration of rainfall-runoff models, which arise due to interactions between the ratio of simulated to observed standard deviations and the correlation coefficient. We also illustrate the negative impacts of classical mathematical transformations (square root) applied to streamflow when employing NSE and KGE as metrics for model calibration

Challenges of operational river forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors

Incertitudes sur les débits issus des courbes de tarage : Estimation de l’incertitude d’un débit sur le réseau hydrométrique d’EDF-DTG

Water resources management is a central concern for EDF, both in the fields of safety, regulation and energy production. To meet these needs, EDF manage an hydrometric network composed by more than 350 stations. Thus, ensuring the streamflow data quality has become a priority for EDF-DTG. EDF-DTG, like other data producers, faces some gauging stations whose hydraulic control sections are moderately to poorly stable. This is the reason why, a model of streamflow uncertainty, based on the use of historical gauging, was developed. Compared to classical approaches used to estimate streamflow uncertainty, this model is more suitable for our hydrometric pratices.La gestion de l’eau est au centre des préoccupations d’EDF, aussi bien dans les domaines de la sûreté, la réglementation, la production d’énergie, que du multi-usage de l’eau. Afin de répondre à ces besoins, EDF-DTG gère un réseau de mesure de débit de plus de 350 stations. Garantir la qualité des données de débit, tout en estimant leur incertitude est devenu un objectif majeur pour EDF-DTG. Comme tout gestionnaire de réseau, EDF-DTG est confronté à des stations de jaugeage dont les sections de contrôle sont moyennement à peu stables dans le temps. Ceci l’a amené à développer un modèle d’estimation des incertitudes des débits, basé sur la valorisation de l’historique des jaugeages, tout en prenant en compte la vie de la station. Par rapport aux approches d’estimation de l’incertitude classiquement utilisées, ce modèle est plus adapté à nos pratiques de l’hydrométrie et traduit de manière empirique le ressenti des hydromètres, confrontés à la réalité de la mesure.Mathevet Thibault, Carré Cécile, Garçon Rémy, Perret Christian. Incertitudes sur les débits issus des courbes de tarage : Estimation de l’incertitude d’un débit sur le réseau hydrométrique d’EDF-DTG. In: Mesures Hydrologiques et Incertitudes en Hydrométrie et Qualité de l’eau. Paris, 1 et 2 avril 2008. 2008

What type of floods are rainfall-runoff models able to simulate based on a long-term atmospheric reanalysis?

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Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds

International audienceTo assess the predictive performance, robustness and generality of watershed-scale hydrological models, we conducted a detailed multi-objective evaluation of two conceptual rainfall-runoff models (the GRX model, based on the GR4J model, and the MRX model, based on the MORDOR model), of differing complexity (with respectively, 5 and 11 free parameters in the rainfall-runoff module, and 4 and 11 free parameters in the snow module). These models were compared on a large sample of 2050 watersheds worldwide. Our results, based on the three components of the Kling-Gupta Efficiency metric (KGE), indicate that both models provide (on average) similar levels of performance in evaluation when calibrated with KGE, for water balance (mean bias lower than 2%), time-series variability (mean variability bias lower than 2%) and temporal correlation (mean correlation around 0.83). Further, both models clearly suffer from lack of robustness when simulating water balance, with a significant increase of the proportion of biased simulations over the evaluation periods (absolute bias lower than 2% in calibration and lower than 20% in evaluation for 80% of the watersheds). Simulation performance depend more on the hydro-meteorological conditions of a given period than on the complexity of the model structure. We also show that long-term aggregate statistics (computed on the overall period) can fail to reveal considerable sub-period variability in model performance, thereby providing inaccurate diagnostic assessment of the predictive model performance. Typically the median absolute bias is lower than 8% in evaluation, but the median maximum bias can be as high as 50% within a subperiod, for both models, when calibrated with KGE

Évaluation de la performance et de la robustesse de deux modèles conceptuels pluie-débit sur un échantillon mondial de bassins versants

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