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

2013 ◽  
Vol 10 (11) ◽  
pp. 13979-14040 ◽  
Author(s):  
P. Nicolle ◽  
R. Pushpalatha ◽  
C. Perrin ◽  
D. François ◽  
D. Thiéry ◽  
...  
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Flow Simulation ◽  
Evaluation Framework ◽  
Low Flow ◽  
Lead Times ◽  
Hydrological Models ◽  
Model Efficiency ◽  
Different Characteristics ◽  
Better Than

Abstract. Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons are needed to assess existing low-flow prediction models and to develop more efficient operational tools. This study 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 with different characteristics and conceptualizations were applied following a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks 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 in all circumstances, a few models appeared more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not contribute useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Preliminary attempts to implement simple multi-model approaches showed that additional efficiency gains can be expected from such approaches.

scholarly journals Comparison of hydrological model structures based on recession and low flow simulations

2011 ◽  
Vol 8 (4) ◽  
pp. 6833-6866 ◽  
Author(s):  
M. Staudinger ◽  
K. Stahl ◽  
J. Seibert ◽  
M. P. Clark ◽  
L. M. Tallaksen
Keyword(s):  
Hydrological Model ◽  
Lower Layer ◽  
Model Performance ◽  
Low Flow ◽  
Model Structure ◽  
Hydrological Models ◽  
Flow Simulations ◽  
Low Flows ◽  
The Impact ◽  
Model Structures

Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model, and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.

scholarly journals A virtual hydrological framework for evaluation of stochastic rainfall models

2019 ◽  
Vol 23 (11) ◽  
pp. 4783-4801
Author(s):  
Bree Bennett ◽  
Mark Thyer ◽  
Michael Leonard ◽  
Martin Lambert ◽  
Bryson Bates
Keyword(s):  
South Australia ◽  
Evaluation Framework ◽  
Statistical Characteristics ◽  
Surprising Result ◽  
Streamflow Prediction ◽  
Direct Evaluation ◽  
Wide Range ◽  
Rainfall Statistics ◽  
The Impact

Abstract. Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought, or climate change in a catchment. While considerable attention has been given to the development of stochastic rainfall models (SRMs), significantly less attention has been paid to developing methods to evaluate their performance. Typical evaluation methods employ a wide range of rainfall statistics. However, they give limited understanding about which rainfall statistical characteristics are most important for reliable streamflow prediction. To address this issue a formal evaluation framework is introduced, with three key features: (i) streamflow-based, to give a direct evaluation of modelled streamflow performance, (ii) virtual, to avoid the issue of confounding errors in hydrological models or data, and (iii) targeted, to isolate the source of errors according to specific sites and seasons. The virtual hydrological evaluation framework uses two types of tests, integrated tests and unit tests, to attribute deficiencies that impact on streamflow to their original source in the SRM according to site and season. The framework is applied to a case study of 22 sites in South Australia with a strong seasonal cycle. In this case study, the framework demonstrated the surprising result that apparently “good” modelled rainfall can produce “poor” streamflow predictions, whilst “poor” modelled rainfall may lead to “good” streamflow predictions. This is due to the representation of highly seasonal catchment processes within the hydrological model that can dampen or amplify rainfall errors when converted to streamflow. The framework identified the importance of rainfall in the “wetting-up” months (months where the rainfall is high but streamflow low) of the annual hydrologic cycle (May and June in this case study) for providing reliable predictions of streamflow over the entire year despite their low monthly flow volume. This insight would not have been found using existing methods and highlights the importance of the virtual hydrological evaluation framework for SRM evaluation.

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

2014 ◽  
Vol 18 (8) ◽  
pp. 2829-2857 ◽  
Author(s):  
P. Nicolle ◽  
R. Pushpalatha ◽  
C. Perrin ◽  
D. François ◽  
D. Thiéry ◽  
...  
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Flow Simulation ◽  
Evaluation Framework ◽  
Low Flow ◽  
Lead Times ◽  
Hydrological Models ◽  
Streamflow Variability ◽  
Model Efficiency ◽  
Better Than

Abstract. 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.

scholarly journals The skill of seasonal ensemble low flow forecasts for four different hydrological models

2014 ◽  
Vol 11 (5) ◽  
pp. 5377-5420 ◽  
Author(s):  
M. C. Demirel ◽  
M. J. Booij ◽  
A. Y. Hoekstra
Keyword(s):  
Conceptual Models ◽  
Potential Evapotranspiration ◽  
The Other ◽  
Data Driven ◽  
Low Flow ◽  
Lead Times ◽  
Hydrological Models ◽  
Meteorological Forcing ◽  
Low Flows ◽  
Better Than

Abstract. This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

scholarly journals Constraining a lumped rainfall-runoff model with piezometry to improve low-flow simulation

2021 ◽  
Author(s):  
Antoine Pelletier ◽  
Vakzen Andréassian
Keyword(s):  
Flow Simulation ◽  
Daily Rainfall ◽  
Climatic Conditions ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Sample Test ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Most lumped hydrological models are focused on the rainfall-runoff relationship, since climatic conditions are the driving force of the hydrological behaviour of a catchment. Many hydrological models, like the ones used by the French national PREMHYCE platform, only take climatic variables as inputs – daily rainfall and potential evaporation – to simulate and forecast low-flows. Yet, a hydrological drought is generally a medium- to long-term phenomenon, which is the consequence of long records of dry climatic conditions. Daily lumped hydrological models often struggle to integrate these records to reproduce catchment memory.</p><p>In many French catchments, it was observed that this memory of past hydroclimatic conditions is well represented in piezometric signals that are broadly available over the national territory. Indeed, aquifers, especially the large ones, do store water on the long, feeding rivers during droughts: aquifers are not only <em>water carriers</em> – the etymology for the word <em>aquifer </em>– they are also <em>memory carriers</em>. A dataset of 108 catchments, each of them being associated with one or several piezometers, was used to investigate whether the GR6J daily lumped rainfall-runoff model could be constrained by piezometric time series to improve low-flow simulations. We found that a particular state of the model, the exponential store, is particularly well correlated with piezometry in most studied catchments.</p><p>In order to get a univocal relationship between the exponential store and piezometry, a multi-objective calibration approach was implemented, optimising both (i) flow simulation with a criterion focused on low-flows and (ii) affine correspondence between the exponential store level and piezometry. For that purpose, a new parameter was added to the model. The modified calibration was then evaluated through a split-sample test and the performance in simulating particular drought events. The calibrated store-piezometry relationship can now be used for data assimilation to improve low-flow forecasting.</p>

Future changes in snow and its influence on seasonal runoff and low flows in Czechia

2021 ◽  
Author(s):  
Michal Jenicek ◽  
Jan Hnilica ◽  
Ondrej Nedelcev ◽  
Vaclav Sipek
Keyword(s):  
Air Temperature ◽  
Future Climate ◽  
Low Flow ◽  
Large Set ◽  
Large Variability ◽  
Low Flows ◽  
Mitigation And Adaptation ◽  
Wide Range ◽  
The Future ◽  
Seasonal Runoff

<p>Mountains are often called as “water towers” because they substantially affect hydrology of downstream areas. However, snow storages are decreasing and snow melts earlier mainly due to air temperature increase. These changes largely affect seasonal runoff distribution, including summer low flows and thus influence the water availability. Therefore, it is important to investigate the future change in relation between snow and summer low flows, specifically to assess a wide range of hydrological responses to different climate predictions. Therefore, the main objectives of this study were 1) to simulate the future changes in snow storages for a large set of mountain catchments representing different elevations and to 2) analyse how the changes in snow storages will affect streamflow seasonality and low flows in the future reflecting a wide range of climate predictions. The predictions of the future climate from EURO-CORDEX experiment for 59 mountain catchments in Czechia were considered. These data were further used to drive a bucket-type catchment model, HBV-light, to simulate individual components of the rainfall-runoff process for the reference period and three future periods.</p><p>Future simulations showed a dramatic decrease in snow-related variables for all catchments at all elevations. For example, annual maximum SWE decreased by 30%-70% until the end of the 21<sup>st</sup> century compared to the current climate. Additionally, the snow will melt on average by 3-4 weeks earlier in the future. The results also showed the large variability between individual climate chains and indicated that the increase in air temperature causing the decrease in snowfall might be partly compensated by the increase in winter precipitation. Expected changes in snowpack will cause by a month earlier period with highest streamflow during melting season in addition to lower spring runoff volume due to lower snowmelt inputs. The future climate scenarios leading to overall dry conditions in summer are associated with both lowest summer precipitation and seasonal snowpack. The expected lower snow storages might therefore contribute to more extreme low flow periods. The results also showed considerably smaller changes for the RCP 2.6 scenario compared to the RCP 4.5 and RCP 8.5 both in terms snow storages and seasonal runoff. The results are therefore important for mitigation and adaptation strategy related to climate change impacts in mountain regions.</p>

scholarly journals Comparison of hydrological model structures based on recession and low flow simulations

2011 ◽  
Vol 15 (11) ◽  
pp. 3447-3459 ◽  
Author(s):  
M. Staudinger ◽  
K. Stahl ◽  
J. Seibert ◽  
M. P. Clark ◽  
L. M. Tallaksen
Keyword(s):  
Hydrological Model ◽  
Lower Layer ◽  
Model Performance ◽  
Low Flow ◽  
Model Structure ◽  
Hydrological Models ◽  
Flow Simulations ◽  
Low Flows ◽  
The Impact ◽  
Model Structures

Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.

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