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 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.

A downward structural sensitivity analysis of hydrological models to improve low-flow simulation

2011 ◽  
Vol 411 (1-2) ◽  
pp. 66-76 ◽  
Author(s):  
Raji Pushpalatha ◽  
Charles Perrin ◽  
Nicolas Le Moine ◽  
Thibault Mathevet ◽  
Vazken Andréassian
Keyword(s):  
Sensitivity Analysis ◽  
Flow Simulation ◽  
Low Flow ◽  
Hydrological Models ◽  
Structural Sensitivity ◽  
Structural Sensitivity Analysis

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 Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain

2019 ◽  
Vol 23 (10) ◽  
pp. 4011-4032 ◽  
Author(s):  
Rosanna A. Lane ◽  
Gemma Coxon ◽  
Jim E. Freer ◽  
Thorsten Wagener ◽  
Penny J. Johnes ◽  
...  
Keyword(s):  
Great Britain ◽  
Water Balance ◽  
River Flow ◽  
Performance Metrics ◽  
Model Performance ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Peak Flows ◽  
Flood Peak ◽  
Model Structures

Abstract. Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

scholarly journals The importance of topography controlled sub-grid process heterogeneity in distributed hydrological models

2015 ◽  
Vol 12 (12) ◽  
pp. 13301-13358 ◽  
Author(s):  
R. C. Nijzink ◽  
L. Samaniego ◽  
J. Mai ◽  
R. Kumar ◽  
S. Thober ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Model Performance ◽  
Hydrologic Model ◽  
Low Flow ◽  
Evaluation Procedure ◽  
Base Case ◽  
Hydrological Process ◽  
Hydrological Models ◽  
Knowledge Based ◽  
Distributed Hydrological Models

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affect the partitioning of water and energy. However, it remains unclear to which extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated in the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge based model constraints reduces model uncertainty; and (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both, the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as overall measure for model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 % respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. Besides, it was shown that suitable semi-quantitative prior constraints in combination with the transfer function based regularization approach of mHM, can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

scholarly journals The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

2016 ◽  
Vol 20 (3) ◽  
pp. 1151-1176 ◽  
Author(s):  
Remko C. Nijzink ◽  
Luis Samaniego ◽  
Juliane Mai ◽  
Rohini Kumar ◽  
Stephan Thober ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Model Performance ◽  
Hydrologic Model ◽  
Low Flow ◽  
Evaluation Procedure ◽  
Base Case ◽  
Hydrological Process ◽  
Hydrological Models ◽  
Knowledge Based ◽  
Distributed Hydrological Models

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

scholarly journals Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions

2012 ◽  
Vol 16 (4) ◽  
pp. 1171-1189 ◽  
Author(s):  
G. Seiller ◽  
F. Anctil ◽  
C. Perrin
Keyword(s):  
Test Procedure ◽  
Model Performance ◽  
Flow Simulation ◽  
Added Value ◽  
Hydrological Models ◽  
Model Ensemble ◽  
Climate Conditions ◽  
Sample Test ◽  
Improved Performance ◽  
The Individual

Abstract. 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).

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 Is the groundwater reservoir linear? Learning from data in hydrological modelling

2005 ◽  
Vol 2 (4) ◽  
pp. 1717-1755 ◽  
Author(s):  
F. Fenicia ◽  
H. H. G. Savenije ◽  
P. Matgen ◽  
L. Pfister
Keyword(s):  
A Priori ◽  
Model Performance ◽  
Flow Simulation ◽  
Calibration Procedure ◽  
Low Flow ◽  
Low Flows ◽  
Learning From Data ◽  
Recession Curve ◽  
Master Recession Curve ◽  
High Flows

Abstract. Although catchment behaviour during recession periods appears to be better identifiable than in other periods, the representation of hydrograph recession is often weak in hydrological simulations. Reason lies in the various sources of uncertainty that affect hydrological simulations, and in particular in the inherent uncertainty concerning model conceptualizations, when they are based on an a-priori representation of the natural system. When flawed conceptualizations combine with calibration strategies that favour an accurate representation of peak flows, model structural inadequacies manifest themselves in a biased representation of other aspects of the simulation, such as flow recession and low flows. In this paper we try to reach good model performance in low flow simulation and make use of a flexible model structure that can adapt to match the observed discharge behaviour during recession periods. Moreover, we adopt a step-wise calibration procedure where we try to avoid that the simulation of low flows is neglected in favour of other hydrograph characteristics. The model used is designed to reproduce specific hydrograph characteristics and is composed of four reservoirs: an interception reservoir, an unsaturated soil reservoir, a fast reacting reservoir, and a slow reacting reservoir. The slow reacting reservoir conceptualises the processes that lead to the generation of the slow hydrograph component, and is characterized by a storage-discharge relation that is not determined a-priori, but is derived from the observations following a ``top-down'' approach. The procedure used to determine this relation starts by calculating a synthetic master recession curve that represents the long-term recession of the catchment. Next, a calibration procedure follows to force the outflow from the slow reacting reservoir to match the master recession curve. Low flows and high flows related parameters are calibrated in separate stages because we consider them to be related to different processes, which can be identified separately. This way we avoid that the simulation of low discharges is neglected in favour of a higher performance in simulating peak discharges. We have applied this analysis to several catchments in Luxembourg, and in each case we have determined which form (linear or non linear) of the storage-discharge relationship best describes the slow reacting reservoir. We conclude that in all catchments except one (where human interference is high) a linear relation applies.

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