Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski, Tomasz; Nossent, Jiri; Chormański, Jarosław; Batelaan, Okke

doi:10.5194/hess-19-1887-2015

Cited by 20 publications

(17 citation statements)

References 54 publications

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“…This made the combined approach simple and robust when used with appropriate multiple metrics. It should be noted that LHS-OAT has been validated in different study areas [5,19]. Although the parameter interactions are not evaluated explicitly, the identified parameters seem to be the most important and relevant parameters for calibration.…”

Section: Utility Of the Multicriteria Spatial Sensitivity Analysismentioning

confidence: 99%

“…The basic idea behind any sensitivity analysis (SA) method is to relate the response of the model output to variations in the parameter values [4]. The SA methods can, therefore, enhance our control on spatiotemporal model behavior [5]. There are local (LSA) and global sensitivity analysis (GSA) methods that evaluate distinct and joint effects between different model parameters, respectively [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Demirel

Koch

Mendiguren

et al. 2018

Water

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Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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Section: Utility Of the Multicriteria Spatial Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Demirel

Koch

Mendiguren

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…This paper utilizes monthly patterns of AET first to understand and organize ET related model spatial parameterisations then to pursue a calibration. This is from the fact that adding only temporal aspect of the spatial observations to the objective function is not sufficient for achieving significant 20 (Berezowski et al, 2015). In the context of spatial model calibration, the sensitivity analysis should not only identify the parameters that affect the water balance and hydrograph dynamics but also the parameters that shape the spatial patterns of the simulated states and fluxes.…”

Section: Introductionmentioning

confidence: 99%

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Demirel¹,

Mai²,

Mendiguren³

et al. 2017

Preprint

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Abstract. Satellite based earth observations offer great opportunities to improve spatial model predictions by means of spatial pattern oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilized for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible 15 spatial parameterisation scheme. The mesoscale Hydrologic Model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multiscale parameter regionalization. In addition two new domain specific spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parametrisations are utilized as they are most relevant for 20 simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric i.e. comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data.The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite based 25 estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimizer. The calibration results reveal a limited trade-offs between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved 30 when including an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow only calibration. Since the overall water balance is usually a crucial goal in the hydrologic modelling, spatial pattern oriented optimization should always be accompanied by traditional discharge measurements. In such a multiobjective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge 35 observations.Hydrol. Earth Syst. Sci. Discuss., https://doi

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“…Prior to model calibration, sensitivity analysis is usually conducted to attribute response of the model outputs to the changes in model parameters (Shin et al, 2013), which can enhance our understanding of both temporal and spatial model behaviour (Berezowski et al, 2015). In the context of spatial model calibration, the sensitivity analysis should not only identify the parameters that affect the water balance and hydrograph dynamics but also the parameters that shape the spatial patterns of the simulated states and fluxes.…”

Section: Introductionmentioning

confidence: 99%

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Demirel

Mai

Mendiguren

et al. 2018

Hydrol. Earth Syst. Sci.

158

133

View full text Add to dashboard Cite

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data.The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellitebased estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

show abstract

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Cited by 20 publications

References 54 publications

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

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