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 The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

2015 ◽  
Vol 19 (1) ◽  
pp. 275-291 ◽  
Author(s):  
M. C. Demirel ◽  
M. J. Booij ◽  
A. Y. Hoekstra
Keyword(s):  
Lead Time ◽  
Potential Evapotranspiration ◽  
Data Driven ◽  
Low Flow ◽  
Lead Times ◽  
Model Output ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Initial Model ◽  
Meteorological Forcing

Abstract. This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for 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 models. 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 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 runoff during low-flow periods 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. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. 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 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 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 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 The effects of climatic anomalies on low flows in Switzerland

2019 ◽  
Author(s):  
Marius G. Floriancic ◽  
Wouter R. Berghuijs ◽  
James W. Kirchner ◽  
Peter Molnar
Keyword(s):  
River Flow ◽  
Potential Evapotranspiration ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Low Flow ◽  
Weather Forecasts ◽  
Environmental Consequences ◽  
Low Flows ◽  
Insight Into ◽  
Seasonal Weather

Abstract. Large parts of Europe have faced extreme low river flows in recent summers (2003, 2011, 2015, 2018) with major economic and environmental consequences. Understanding the origins of extremes like these is important for water resources management. To reveal how weather drives low flows, we explore how deviations from mean seasonal climatic conditions (i.e. climatic anomalies) of precipitation and potential evapotranspiration shaped the occurrence and magnitude of the annual 7-day lowest flows (Qmin) across 380 Swiss catchments from 2000 through 2018. Most annual low flows followed periods of below average precipitation and above average potential evapotranspiration, and the most extreme low flows resulted from both of these drivers acting together. Extremely dry years saw simultaneous drought conditions across large parts of Europe, but low flow timing during these years was still spatially variable across Switzerland. Longer climatic anomalies led to lower low flows. Most low flows were typically preceded by climatic anomalies lasting up to two months, whereas low flows in the extreme years (2003, 2011, 2015, 2018) were associated with much longer-lasting climatic anomalies. Weather conditions on even longer time scales have been reported to sometimes affect river flow. However, across Switzerland, we found that precipitation totals in winter only slightly influenced the magnitude and timing of summer and autumn low flows. Our results provide insight into how precipitation and potential evapotranspiration jointly shape summer and winter low flows across Switzerland, and could potentially aid in assessing low-flow risks in similar mountain regions using seasonal weather forecasts.

scholarly journals The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling

2018 ◽  
Vol 19 (5) ◽  
pp. 1295-1304
Author(s):  
C. Sezen ◽  
T. Partal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Potential Evapotranspiration ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Ann Model ◽  
The Usa ◽  
Streamflow Data ◽  
Artificial Neural ◽  
Better Than

Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.

scholarly journals Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

2018 ◽  
Vol 10 (8) ◽  
pp. 2837 ◽  
Author(s):  
Dereje Birhanu ◽  
Hyeonjun Kim ◽  
Cheolhee Jang ◽  
Sanghyun Park
Keyword(s):  
Potential Evapotranspiration ◽  
Estimation Method ◽  
Model Performance ◽  
Low Flow ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
University Of Arizona ◽  
Optimizing Model

In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

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 Exploration of sub-annual calibration schemes of hydrological models

2016 ◽  
Vol 48 (4) ◽  
pp. 1014-1031 ◽  
Author(s):  
Kue Bum Kim ◽  
Dawei Han
Keyword(s):  
Conceptual Models ◽  
Model Parameters ◽  
Hydrological Models ◽  
Calibration Period ◽  
Calibration Scheme ◽  
Annual Variations ◽  
Pros And Cons ◽  
Time Invariant ◽  
Annual Period ◽  
Better Than

This study compared hydrological model performances under different sub-annual calibration schemes using two conceptual models, IHACRES and HYMOD. In several publications regarding sub-annual calibration, the authors showed that such an approach generally performed better than the conventional whole period method. Hence, there are advantages in dividing the data into sub-annual periods for calibration. However, little attention has been paid to the issue of how to calibrate the non-continuous sub-annual period. Unlike the conventional calibration which assumes time-invariant parameters for the calibration period, the model parameters vary in sub-annual calibration. We have explored two sub-annual calibration schemes, serial calibration scheme (SCS) and parallel calibration scheme (PCS). We assume that the relationships between the rainfall and runoff could be different for each sub-annual period and consider intra-annual variations of the system. The models are then evaluated for a different validation period to avoid over-fitting and the optimal sub-annual calibration period is explored. Overall, we have found that PCS performed slightly better than SCS and the optimal calibration periods are seasonal and bimonthly for IHACRES and biannual for HYMOD. Since there are pros and cons in both SCS and PCS, we recommend choosing the method depending on the purpose of the model usage.

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