scholarly journals The impact of hydrological model structure on the simulation of extreme runoff events

2021 ◽  
Vol 21 (3) ◽  
pp. 961-976
Author(s):  
Gijs van Kempen ◽  
Karin van der Wiel ◽  
Lieke Anna Melsen
Keyword(s):  
Hydrological Model ◽  
Low Flow ◽  
Temperate Climate ◽  
Hydrological Process ◽  
Model Structure ◽  
Hydrological Models ◽  
Climate Zones ◽  
The Impact ◽  
Model Structures ◽  
Runoff Events

Abstract. Hydrological extremes affect societies and ecosystems around the world in many ways, stressing the need to make reliable predictions using hydrological models. However, several different hydrological models can be selected to simulate extreme events. A difference in hydrological model structure results in a spread in the simulation of extreme runoff events. We investigated the impact of different model structures on the magnitude and timing of simulated extreme high- and low-flow events by combining two state-of-the-art approaches: a modular modelling framework (FUSE) and large ensemble meteorological simulations. This combination of methods created the opportunity to isolate the impact of specific hydrological process formulations at long return periods without relying on statistical models. We showed that the impact of hydrological model structure was larger for the simulation of low-flow compared to high-flow events and varied between the four evaluated climate zones. In cold and temperate climate zones, the magnitude and timing of extreme runoff events were significantly affected by different parameter sets and hydrological process formulations, such as evaporation. In the arid and tropical climate zones, the impact of hydrological model structures on extreme runoff events was smaller. This novel combination of approaches provided insights into the importance of specific hydrological process formulations in different climate zones, which can support adequate model selection for the simulation of extreme runoff events.

scholarly journals The impact of hydrological model structure on the simulation of extreme runoff events

2020 ◽  
Author(s):  
Gijs van Kempen ◽  
Karin van der Wiel ◽  
Lieke Anna Melsen
Keyword(s):  
Hydrological Model ◽  
Low Flow ◽  
Temperate Climate ◽  
Hydrological Process ◽  
Model Structure ◽  
Hydrological Models ◽  
Climate Zones ◽  
The Impact ◽  
Model Structures ◽  
Runoff Events

Abstract. Hydrological extremes affect societies and ecosystems around the world in many ways, stressing the need to make reliable predictions using hydrological models. However, several hydrological models can be selected to simulate extreme events. A difference in hydrological model structure results in a spread in the simulation of extreme runoff events. We investigated the impact of different model structures on the magnitude and timing of simulated extreme high- and low-flow events, by combining two state-of-the-art approaches; a modular modelling framework (FUSE) and large ensemble meteorological simulations. This combination of methods created the opportunity to isolate the impact of specific hydrological process formulations at long return periods without relying on statistical models. We showed that the impact of hydrological model structure was larger for the simulation of low-flow compared to high-flow events and varied between the four evaluated climate zones. In cold and temperate climate zones, the magnitude and timing of extreme runoff events were significantly affected by different parameter sets and hydrological process formulations, such as evaporation. The impact of hydrological model structures on extreme runoff events was smaller in the arid and tropical climate zones. This novel combination of approaches provided insights into the importance of specific hydrological processes formulations in different climate zones, which can support adequate model selection for the simulation of extreme runoff events.

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 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.

Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

2021 ◽  
Author(s):  
Markus Hrachowitz ◽  
Petra Hulsman ◽  
Hubert Savenije
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Model Development ◽  
Water Storage ◽  
Spatiotemporal Variability ◽  
Model Structure ◽  
Hydrological Models ◽  
Total Water ◽  
Spatiotemporal Model ◽  
Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

scholarly journals Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

2017 ◽  
Vol 21 (8) ◽  
pp. 3937-3952 ◽  
Author(s):  
Federico Garavaglia ◽  
Matthieu Le Lay ◽  
Fréderic Gottardi ◽  
Rémy Garçon ◽  
Joël Gailhard ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Flow Simulation ◽  
Evaluation Framework ◽  
Model Structure ◽  
Runoff Simulation ◽  
Snow Cover Area ◽  
Mountainous Regions ◽  
Distributed Approach ◽  
The Impact

Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

scholarly journals The influence of conceptual model structure on model performance: a comparative study for 237 French catchments

2013 ◽  
Vol 17 (10) ◽  
pp. 4227-4239 ◽  
Author(s):  
W. R. van Esse ◽  
C. Perrin ◽  
M. J. Booij ◽  
D. C. M. Augustijn ◽  
F. Fenicia ◽  
...  
Keyword(s):  
Conceptual Model ◽  
Performance Metrics ◽  
Low Flow ◽  
Multiple Time ◽  
Large Set ◽  
Model Structure ◽  
Flow Measurements ◽  
Flexible Approach ◽  
Fixed Model ◽  
Model Structures

Abstract. Models with a fixed structure are widely used in hydrological studies and operational applications. For various reasons, these models do not always perform well. As an alternative, flexible modelling approaches allow the identification and refinement of the model structure as part of the modelling process. In this study, twelve different conceptual model structures from the SUPERFLEX framework are compared with the fixed model structure GR4H, using a large set of 237 French catchments and discharge-based performance metrics. The results show that, in general, the flexible approach performs better than the fixed approach. However, the flexible approach has a higher chance of inconsistent results when calibrated on two different periods. When analysing the subset of 116 catchments where the two approaches produce consistent performance over multiple time periods, their average performance relative to each other is almost equivalent. From the point of view of developing a well-performing fixed model structure, the findings favour models with parallel reservoirs and a power function to describe the reservoir outflow. In general, conceptual hydrological models perform better on larger and/or wetter catchments than on smaller and/or drier catchments. The model structures performed poorly when there were large climatic differences between the calibration and validation periods, in catchments with flashy flows, and in catchments with unexplained variations in low flow measurements.

scholarly journals Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017)

2018 ◽  
Vol 22 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Daniele P. Viero
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Uncertainty ◽  
Model Structure ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Irregular Frequency ◽  
Crowdsourced Data ◽  
Internal States ◽  
The Impact

Abstract. Citizen science and crowdsourcing are gaining increasing attention among hydrologists. In a recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) into hydrological models to improve the accuracy of real-time flood forecasts. The authors used synthetic CSD (i.e. not actually measured), because real CSD were not available at the time of the study. In their work, which is a proof-of-concept study, Mazzoleni et al. (2017) showed that assimilation of CSD improves the overall model performance; the impact of irregular frequency of available CSD, and that of data uncertainty, were also deeply assessed. However, the use of synthetic CSD in conjunction with (semi-)distributed hydrological models deserves further discussion. As a result of equifinality, poor model identifiability, and deficiencies in model structure, internal states of (semi-)distributed models can hardly mimic the actual states of complex systems away from calibration points. Accordingly, the use of synthetic CSD that are drawn from model internal states under best-fit conditions can lead to overestimation of the effectiveness of CSD assimilation in improving flood prediction. Operational flood forecasting, which results in decisions of high societal value, requires robust knowledge of the model behaviour and an in-depth assessment of both model structure and forcing data. Additional guidelines are given that are useful for the a priori evaluation of CSD for real-time flood forecasting and, hopefully, for planning apt design strategies for both model calibration and collection of CSD.

scholarly journals Generalization of parameters in the storage–discharge relation for a low flow based on the hydrological analysis of sensitivity

2015 ◽  
Vol 371 ◽  
pp. 69-73 ◽  
Author(s):  
K. Fujimura ◽  
Y. Iseri ◽  
S. Kanae ◽  
M. Murakami
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Water Resource Management ◽  
Hydrological Model ◽  
Low Flow ◽  
Optimum Parameters ◽  
Proportional Relationship ◽  
Two Parameters ◽  
The Impact ◽  
Different Climates

Abstract. The storage-discharge relations have been widely used for water resource management and have led to reliable estimation of the impact of climate change on water resources. In a previous study, we carried out a sensitivity analysis of the parameters in a discharge-storage relation in the form of a power function and found that the optimum parameters can be characterized by an exponential function (Fujimura et al., 2014). The aim of this study is to extend the previous study to clarify the properties of the parameters in the storage–discharge relations by carrying out a sensitivity analysis of efficiency using a hydrological model. The study basins are four mountainous basins in Japan with different climates and geologies. The results confirm that the two parameters in the storage–discharge relations can be expressed in an inversely proportional relationship. In addition, we can conveniently assume a practical function for the storage–discharge relations where only one parameter is used on the basis of the new relationship between the two parameters.

scholarly journals An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

2013 ◽  
Vol 17 (2) ◽  
pp. 565-578 ◽  
Author(s):  
J. A. Velázquez ◽  
J. Schmid ◽  
S. Ricard ◽  
M. J. Muerth ◽  
B. Gauvin St-Denis ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Hydrological Model ◽  
Global Climate ◽  
Regional Climate ◽  
Structural Complexity ◽  
Global Climate Models ◽  
Hydrological Models ◽  
The Impact

Abstract. Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.

scholarly journals Uncertainty in rainfall input data in a conceptual water balance model: effects on outputs and implications for predictability

2014 ◽  
Vol 18 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Enrique Muñoz ◽  
Pedro Tume ◽  
Gabriel Ortíz
Keyword(s):  
Input Data ◽  
Hydrological Model ◽  
Balance Model ◽  
Model Structure ◽  
Hydrological Models ◽  
Water Resources Planning ◽  
Levels Of Detail ◽  
Output Uncertainty ◽  
Main Input ◽  
Resources Planning

<p>As hydrological models become more prevalent in water resources planning and management, increasing levels of detail and precision are needed. Currently, reliable models that simulate the hydrological behavior of a basin are indispensable; however, it is also necessary to know the limits of the predictability and reliability of the model outputs. The present study evaluates the influence of uncertainty in the main input variable of the model, rainfall, on the output uncertainty of a hydrological model. Using concepts of identifiability and sensitivity, the uncertainty in the model structure and parameters was estimated. Then, the output uncertainty caused by uncertainties in i) the rainfall amounts and ii) the periods of the rainfall was determined. The main conclusion is that uncertainty in rainfall estimation during rainy periods produces greater output uncertainty. However, in non-rainy periods, the output uncertainty is not very sensitive to the uncertainty in rainfall. Finally, uncertainties in rainfall during the basin filling and emptying periods (Apr. – Jun. and Sep. – Nov., respectively) alter the uncertainty in subsequent periods. Therefore, uncertainties in these periods could result in limited ranges of model predictability.</p><p> </p><p><strong>Resumen</strong></p><p>Los modelos hidrológicos se han vuelto cada vez más necesarios en la planificación y gestión de recursos hídricos, donde un aumento en los niveles de detalle y precisión es necesario. Actualmente disponer de modelos para simular el comportamiento hidrológico de una cuenca resulta indispensable, sin embargo, también es necesario conocer los límites de predictibilidad y de confiabilidad de las salidas de un modelo. En este estudio se evalúan la influencia de la incertidumbre en la principal entrada de un modelo, la precipitación, sobre la incertidumbre de las salidas de un modelo hidrológico. Utilizando conceptos de identificabilidad y sensibilidad se estima la incertidumbre de los parámetros y estructura de un modelo. Luego, la incertidumbre en las salidas causadas por incertidumbre en i) los montos de precipitación, ii) los períodos de precipitación fue calculada. Como conclusiones se obtuvo que la incertidumbre en la estimación de la precipitación en períodos de lluvia produce mayor incertidumbre sobre las salidas. En períodos no lluviosos, la incertidumbre de las salidas es poco sensible a incertidumbre sobre las precipitaciones. Finalmente, incertidumbres en periodos de llenado y vaciado (Abril-Junio y Septiembre-Noviembre respectivamente) afectan la incertidumbre en las salidas en los períodos subsecuentes. Por lo tanto incertidumbres en aquellos períodos pueden resultar en rangos limitados de predictibilidad de un modelo.</p>

Sign in / Sign up

Export Citation Format

Share Document