Behind the scenes of runoff performance

2020 ◽  
Author(s):  
Tanja de Boer-Euser ◽  
Laurène Bouaziz ◽  
Guillaume Thirel ◽  
Lieke Melsen ◽  
Joost Buitink ◽  
...  
Keyword(s):  
Model Comparison ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Process Representation ◽  
Complex Interactions ◽  
International Model ◽  
Internal States ◽  
Short Term Forecasting ◽  
Intercomparison Study

<p>Hydrological models are valuable tools for short-term forecasting of river flows, long-term predictions for water resources management and to increase our understanding of the complex interactions of water storage and release processes at the catchment scale. Hydrological models provide relatively robust estimates of streamflow dynamics, as shown by the countless applications in many regions across the world. However, various model structures can lead to similar aggregated outputs, i.e. model equifinality. To provide reliable estimates, it is of critical importance that not only the aggregated response but also the internal behaviors are consistent with their real-world equivalents. In a previous international comparison study (de Boer-Euser et al., 2017), eight research groups followed the same protocol to calibrate their twelve models on streamflow for several catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar runoff performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of actual evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar runoff performance represent internal states and fluxes differently. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, the plausibility of process representation could only be evaluated to some extent as many variables remain unknown, highlighting the need for more experimental research. The study further emphasizes the value of multi-model, multi-parameter studies to reveal to decision-makers the uncertainty inherent to the lack of evaluation data and the heterogeneous hydrological landscape.</p><p>References:<br>de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., and Willems, P.: Looking beyond general metrics for model comparison – lessons from an international model intercomparison study, Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, 2017.</p>

scholarly journals Behind the scenes of streamflow model performance

2020 ◽  
Author(s):  
Laurène J. E. Bouaziz ◽  
Guillaume Thirel ◽  
Tanja de Boer-Euser ◽  
Lieke A. Melsen ◽  
Joost Buitink ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Model Performance ◽  
Remotely Sensed ◽  
Decision Makers ◽  
Internal Variables ◽  
Hydrological Models ◽  
Process Representation ◽  
Snow Storage ◽  
Internal States

Abstract. Streamflow is often the only variable used to constrain hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate a total of twelve hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar streamflow performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar streamflow performance represent internal states and fluxes differently. Substantial dissimilarities between models are found for annual and seasonal evaporation and interception rates, the number of days per year with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. Relatively small root-zone storage capacities for several models lead to drying-out of the root-zone storage and significant reduction of evaporative fluxes each summer, which is not suggested by remotely-sensed estimates of evaporation and root-zone soil moisture. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, we could evaluate the plausibility of model representations only to some extent, as many of these internal variables remain unknown, highlighting the need for experimental research. We also encourage modelers to rely on multi-model and multi-parameter studies to reveal to decision-makers the uncertainties inherent to the heterogeneity of catchments and the lack of evaluation data.

scholarly journals Looking beyond general metrics for model comparison – lessons from an international model intercomparison study

2016 ◽  
Author(s):  
Tanja de Boer-Euser ◽  
Laurène Bouaziz ◽  
Jan De Niel ◽  
Claudia Brauer ◽  
Benjamin Dewals ◽  
...  
Keyword(s):  
Model Comparison ◽  
Hydrological Model ◽  
Root Zone ◽  
Model Development ◽  
Model Complexity ◽  
Model Intercomparison ◽  
Research Groups ◽  
International Model ◽  
Overall Performance ◽  
Intercomparison Study

Abstract. International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although comparative studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. Nash–Sutcliffe Efficiency), clear differences could be observed for specific events. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the Ourthe catchment. This intercomparison enhanced the understanding of the hydrological functioning of the catchment and, above all, helped to evaluate each model against a set of alternative models.

scholarly journals Looking beyond general metrics for model comparison – lessons from an international model intercomparison study

2017 ◽  
Vol 21 (1) ◽  
pp. 423-440 ◽  
Author(s):  
Tanja de Boer-Euser ◽  
Laurène Bouaziz ◽  
Jan De Niel ◽  
Claudia Brauer ◽  
Benjamin Dewals ◽  
...  
Keyword(s):  
Model Comparison ◽  
Root Zone ◽  
Model Development ◽  
Extreme Value Distribution ◽  
Model Complexity ◽  
Low Flows ◽  
International Model ◽  
Overall Performance ◽  
Flow Duration Curves ◽  
Intercomparison Study

Abstract. International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on a variety of specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best-performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events. We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models.

Evaluation of three semi-distributed hydrological models in simulating discharge from a small forest and arable dominated catchment

Biologia ◽  
2017 ◽  
Vol 72 (9) ◽  
Author(s):  
Ilona Kása ◽  
Györgyi Gelybó ◽  
Ágota Horel ◽  
Zsófia Bakacsi ◽  
Eszter Tóth ◽  
...  
Keyword(s):  
Model Performance ◽  
Coefficient Of Determination ◽  
Lake Balaton ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Discharge Data ◽  
Flow Duration Curves ◽  
Distributed Hydrological Models ◽  
Intercomparison Study ◽  
Light Precipitation

AbstractCatchment scale hydrological models are promising tools for simulating the effect of catchment-specific processes and management on soil and water resources. Here, we present a model intercomparison study of runoff simulations using three different semi-distributed rainfall-runoff catchment models. The objective of this study was to demonstrate the applicability of the Hydrologiska Byrans Vattenavdelning (HBV-Light); Precipitation, Evapotranspiration and Runoff Simulator for Solute Transport (PERSiST); and INtegrated CAtchment (INCA) models on Somogybabod Catchment, near Lake Balaton, Hungary.The models were calibrated and validated against observed discharge data at the outlet of the catchment for the period of January 1, 2006 –July 12, 2015. Model performance was evaluated using graphical representations, e.g. daily and monthly hydrographs and Flow Duration Curves (FDC) and model evaluation statistic; Nash–Sutcliffe efficiency (NSE) and coefficient of determination (

scholarly journals SuperflexPy 1.2.0: an open source Python framework for building, testing and improving conceptual hydrological models

2020 ◽  
Author(s):  
Marco Dal Molin ◽  
Dmitri Kavetski ◽  
Fabrizio Fenicia
Keyword(s):  
Open Source ◽  
Model Comparison ◽  
Conceptual Models ◽  
Predictive Accuracy ◽  
Model Specification ◽  
Full Potential ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Wide Range ◽  
Lumped Models

Abstract. Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes, and to support operational water resources management. Conceptual models, where catchment dynamics are approximated using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g. a reservoir) to spatially distributed configurations (e.g. nested sub-catchments), with the ability to customize all individual model elements. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations, and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. SuperflexPy is available as open-source code, and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work. A comprehensive documentation is available online and provided as supplementary material to this paper.

scholarly journals SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models

2021 ◽  
Vol 14 (11) ◽  
pp. 7047-7072
Author(s):  
Marco Dal Molin ◽  
Dmitri Kavetski ◽  
Fabrizio Fenicia
Keyword(s):  
Open Source ◽  
Water Resource Management ◽  
Model Comparison ◽  
Conceptual Models ◽  
Predictive Accuracy ◽  
Model Specification ◽  
Full Potential ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Wide Range

Abstract. Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes and to support operational water resource management. Conceptual models, which approximate catchment dynamics using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands, and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g., a reservoir) to spatially distributed configurations (e.g., nested sub-catchments), with the ability to customize all individual model components. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, discusses the software design and implementation choices, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. Comprehensive documentation is available online and provided as a Supplement to this paper. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations for model comparison and for operational work.

Reducing complexity in multivariate electricity price forecasting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendrik Kohrs ◽  
Benjamin Rainer Auer ◽  
Frank Schuhmacher
Keyword(s):  
Electricity Market ◽  
Model Comparison ◽  
Variable Importance ◽  
Support Vector ◽  
Dynamic Factor ◽  
Forecast Combination ◽  
Content Type ◽  
Electricity Price Forecasting ◽  
Bayesian Shrinkage ◽  
Short Term Forecasting

Purpose In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue. Design/methodology/approach In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems. Findings This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study. Originality/value With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

scholarly journals Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

2008 ◽  
Vol 12 (3) ◽  
pp. 751-767 ◽  
Author(s):  
T. Vischel ◽  
G. G. S. Pegram ◽  
S. Sinclair ◽  
W. Wagner ◽  
A. Bartsch
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Good Correspondence ◽  
Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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