scholarly journals Low-flow forecasting in France: update on the latest developments of the PREMHYCE operational forecast platform

2021 ◽  
Author(s):  
François Bourgin ◽  
François Tilmant ◽  
Anne-Lise Véron ◽  
François Besson ◽  
Didier François ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Low Flow ◽  
Climatic Data ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
National Scale ◽  
Ensemble Forecasts ◽  
Operational Forecast ◽  
Set Up ◽  
Model Approach

<p>Low-flow forecasting can help to improve water management at places where a number of uses can be affected by diminishing water supply from rivers. Several French institutes (INRAE, BRGM, EDF, Lorraine University and Météo-France) have been collaborating to set up an operational platform, called PREMHYCE, for low-flow forecasting at the national scale, in cooperation with operational services. PREMHYCE includes five hydrological models and low-flow forecasts can be issued up to 90 days ahead for more than 800 basins. Several input scenarios are considered: ECMWF 14-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data, and a no precipitation scenario. Outputs from the different hydrological models are combined into a multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, as well as key low-flow indicators, such as the probabilities of being under low-flow thresholds provided by operational services. The presentation will show the main characteristics of this operational forecast platform, its latest developments and the results on the recent low-flow periods.</p>

scholarly journals PREMHYCE: An operational tool for low-flow forecasting

2020 ◽  
Author(s):  
Pierre Nicolle ◽  
François Besson ◽  
François Bourgin ◽  
Didier François ◽  
Matthieu Le Lay ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Evaluation Framework ◽  
Low Flow ◽  
Climatic Data ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Water Shortages ◽  
Low Flows ◽  
Wide Range ◽  
Operational Tool

<p>In many countries, rivers are the primary supply of water. A number of uses are concerned (drinking water, irrigation, hydropower…) and they can be strongly affected by water shortages. Therefore, there is a need of 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 institutes (Irstea, BRGM, Météo-France, EDF and Lorraine University) have been collaborating to develop an operational tool for low-flow forecasting, called PREMHYCE. It is tested in real time since 2017, and implemented on 259 catchments in metropolitan France, in cooperation with operational services which provide streamflow observations and use low-flow forecasts from the tool. PREMHYCE includes five hydrological models which can be calibrated on gauged catchments and which assimilate flow observations. Low-flow forecasts can be issued up to 90 days ahead, based on several inputs scenarios: ECMWF 10-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data as ensembles of future input scenarios, and a no precipitation scenario. Climatic data (precipitation, evapotranspiration and temperature) are provided by Météo-France with the daily gridded SAFRAN reanalysis on the 1959-2019 period, which includes a wide range of conditions. The tool provides text files and graphical representation of forecasted low-flows, and probability to be under low-flow thresholds provided by users. Outputs from the different hydrological models can be combined within a multi-model approach to improve robustness of the forecastsThe presentation will show the main characteristics of this operational tool, the probabilistic evaluation framework, results on the recent low-flow periods, and how feedbacks from end-users can help improving the tool.</p>

scholarly journals PREMHYCE: An operational tool for low-flow forecasting

2020 ◽  
Vol 383 ◽  
pp. 381-389
Author(s):  
Pierre Nicolle ◽  
François Besson ◽  
Olivier Delaigue ◽  
Pierre Etchevers ◽  
Didier François ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Low Flow ◽  
Climatic Data ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Water Shortages ◽  
Low Flows ◽  
Wide Range ◽  
Physically Based ◽  
Operational Tool

Abstract. 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.

scholarly journals On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

scholarly journals A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting

2013 ◽  
Vol 141 (10) ◽  
pp. 3462-3476 ◽  
Author(s):  
Mabrouk Abaza ◽  
François Anctil ◽  
Vincent Fortin ◽  
Richard Turcotte
Keyword(s):  
Absolute Error ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Hydrological Models ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Study Results ◽  
Prediction Systems ◽  
Set Up ◽  
Meteorological Models

Abstract Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty.

scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2017 ◽  
Vol 21 (8) ◽  
pp. 4103-4114 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Warning System ◽  
Skill Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Global Water

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

scholarly journals Skill of a global forecasting system in seasonal ensemble streamflow prediction

2016 ◽  
Author(s):  
Naze Candogan Yossef ◽  
Rens van Beek ◽  
Albrecht Weerts ◽  
Hessel Winsemius ◽  
Marc F. P. Bierkens
Keyword(s):  
Information Service ◽  
Brier Score ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Set Up ◽  
Framework Programme Project ◽  
Seasonal Streamflow

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system FEWS-World which has been set up within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the global hydrological model PCR-GLOBWB. We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the ECMWF and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the skill from the ESP ensembles, which contain no actual information on weather, serves as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier Score to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual to theoretical skill in order to quantify the percentage of the theoretical skill that is achieved. The results suggest that the skill of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

scholarly journals How can expert knowledge increase the realism of conceptual hydrological models? A case study based on the concept of dominant runoff process in the Swiss Pre-Alps

2018 ◽  
Vol 22 (8) ◽  
pp. 4425-4447 ◽  
Author(s):  
Manuel Antonetti ◽  
Massimiliano Zappa
Keyword(s):  
Spatial Distribution ◽  
Expert Knowledge ◽  
Hydrological Models ◽  
Top Down ◽  
Bottom Up ◽  
Process Maps ◽  
Uncertainty Sources ◽  
Simulation Results ◽  
Set Up ◽  
Better Than

Abstract. Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed.The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.

scholarly journals Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 110
Author(s):  
Raphael Schneider ◽  
Simon Stisen ◽  
Anker Lajer Højberg
Keyword(s):  
Large Scale ◽  
Agricultural Land ◽  
Hydrological Cycle ◽  
Drainage System ◽  
Water Model ◽  
Generation Process ◽  
Hydrological Models ◽  
Drainage Patterns ◽  
Drain Flow ◽  
Set Up

About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km2) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.

scholarly journals Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

2017 ◽  
Author(s):  
Diana Lucatero ◽  
Henrik Madsen ◽  
Jens C. Refsgaard ◽  
Jacob Kidmose ◽  
Karsten H. Jensen
Keyword(s):  
Hydrological Model ◽  
Predictive Distribution ◽  
Low Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Initial State ◽  
Lower Accuracy ◽  
Spatially Distributed ◽  
Streamflow Forecasts ◽  
Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

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