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

2018 ◽  
Vol 22 (2) ◽  
pp. 1299-1315 ◽  
Author(s):  
Mehmet C. Demirel ◽  
Juliane Mai ◽  
Gorka Mendiguren ◽  
Julian Koch ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spatial Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Parameter Distribution ◽  
Objective Functions ◽  
Performance Metric ◽  
Spatial Performance

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 satellite-based 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.

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

2017 ◽  
Author(s):  
Mehmet C. Demirel ◽  
Juliane Mai ◽  
Gorka Mendiguren ◽  
Julian Koch ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spatial Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Parameter Distribution ◽  
Objective Functions ◽  
Performance Metric ◽  
Spatial Performance

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 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 pedotransfer 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 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 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 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 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.

scholarly journals Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting 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). 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 definition 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.

scholarly journals Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1188 ◽  
Author(s):  
Mehmet Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

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.

scholarly journals The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

2018 ◽  
Vol 11 (5) ◽  
pp. 1873-1886 ◽  
Author(s):  
Julian Koch ◽  
Mehmet Cüneyd Demirel ◽  
Simon Stisen
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Model Calibration ◽  
Performance Metrics ◽  
Model Performance ◽  
Hydrologic Model ◽  
System Modelling ◽  
Spatial Efficiency ◽  
Spatial Performance ◽  
Multiple Component

Abstract. The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

scholarly journals On the importance of multiple-component evaluation of spatial patterns for optimization of earth system models – A case study using mHM v5.6 at catchment scale

2017 ◽  
Author(s):  
Julian Koch ◽  
Mehmet C. Demirel ◽  
Simon Stisen
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Model Calibration ◽  
Performance Metrics ◽  
Hydrologic Model ◽  
System Modelling ◽  
Earth System ◽  
Spatial Efficiency ◽  
Spatial Performance ◽  
Multiple Component

Abstract. The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well tested toolbox of metrics to evaluate temporal model performance. On the contrary, spatial performance evaluation is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex earth system processes. This study makes a contribution towards advancing spatial pattern oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial pattern oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics, because stand-alone metrics tend to fail to provide holistic pattern information to the optimizer. The three SPAEF components are found to be independent which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms this study suggests applying bias insensitive metrics which further allow comparing variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.6), but we see great potential across disciplines related to spatial distributed earth system modelling.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Multiobjective sensitivity analysis and optimization of a distributed hydrologic model MOBIDIC

2014 ◽  
Vol 11 (3) ◽  
pp. 3505-3539 ◽  
Author(s):  
J. Yang ◽  
F. Castelli ◽  
Y. Chen
Keyword(s):  
Sensitivity Analysis ◽  
Water Balance ◽  
Surface Runoff ◽  
Hydrologic Model ◽  
Objective Functions ◽  
Analysis Techniques ◽  
Distributed Hydrologic Model ◽  
Conflicting Objectives ◽  
State Dependent Parameter Method ◽  
Initial Starting

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives which arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for a distributed hydrologic model MOBIDIC, which combines two sensitivity analysis techniques (Morris method and State Dependent Parameter method) with a multiobjective optimization (MOO) approach &amp;varepsilon;-NSGAII. This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina with three objective functions, i.e., standardized root mean square error of logarithmic transformed discharge, water balance index, and mean absolute error of logarithmic transformed flow duration curve, and its results were compared with those with a single objective optimization (SOO) with the traditional Nelder–Mead Simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show: (1) the two sensitivity analysis techniques are effective and efficient to determine the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization; (2) both MOO and SOO lead to acceptable simulations, e.g., for MOO, average Nash–Sutcliffe is 0.75 in the calibration period and 0.70 in the validation period; (3) evaporation and surface runoff shows similar importance to watershed water balance while the contribution of baseflow can be ignored; (4) compared to SOO which was dependent of initial starting location, MOO provides more insight on parameter sensitivity and conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provides an alternative way for future MOBIDIC modelling.

scholarly journals Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

2014 ◽  
Vol 18 (10) ◽  
pp. 4101-4112 ◽  
Author(s):  
J. Yang ◽  
F. Castelli ◽  
Y. Chen
Keyword(s):  
Sensitivity Analysis ◽  
Water Balance ◽  
Surface Runoff ◽  
Hydrologic Model ◽  
Optimization Approach ◽  
Objective Functions ◽  
Analysis Techniques ◽  
Distributed Hydrologic Model ◽  
Conflicting Objectives ◽  
Initial Starting

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach ε-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more insight into parameter sensitivity and the conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provide an alternative way for future MOBIDIC modeling.

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