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 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 The effect of background hydrometeorological conditions on the sensitivity of evapotranspiration to model parameters: analysis with measurements from an Italian alpine catchment

2003 ◽  
Vol 7 (6) ◽  
pp. 848-861 ◽  
Author(s):  
N. Montaldo ◽  
V. Toninelli ◽  
J. D. Albertson ◽  
M. Mancini ◽  
P. A. Troch
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Sampling Strategy ◽  
Bowen Ratio ◽  
Eddy Correlation ◽  
Actual Evapotranspiration ◽  
Soil Parameters ◽  
Surface Model ◽  
Model Parameters ◽  
Short Period

Abstract. Recent developments have made land-surface models (LSMs) more complex through the inclusion of more processes and controlling variables, increasing numbers of parameters and uncertainty in their estimates. To overcome these uncertainties, prior to applying a distributed LSM over the whole Toce basin (Italian Alps), a field campaign was carried out at an experimental plot within the basin before exploring the skill and parameter importance (sensitivity) using the TOPLATS model, an existing LSM. In the summer and autumn of 1999, which included both wet (atmosphere controlled) and dry (soil controlled) periods, actual evapotranspiration estimates were performed using Bowen ratio and, for a short period, eddy correlation methods. Measurements performed with the two methods are in good agreement. The calibrated LSM predicts actual evapotranspiration quite well over the whole observation period. A sensitivity analysis of the evapotranspiration to model parameters was performed through the global multivariate technique during both wet and dry periods of the campaign. This approach studies the influence of each parameter without conditioning on certain values of the other variables. Hence, all parameters are varied simultaneously using, for instance, a uniform sampling strategy through a Monte Carlo simulation framework. The evapotranspiration is highly sensitive to the soil parameters, especially during wet periods. However, the evapotranspiration is also sensitive to some vegetation parameters and, during dry periods, wilting point is the most critical for evapotranspiration predictions. This result confirms the importance of correct representation of vegetation properties which, in water-limited conditions, control evapotranspiration. Keywords: evapotranspiration, sensitivity analysis, land surface model, eddy correlation, Alpine basin

scholarly journals Discovery of spatial pattern of prickles on stem of Rosa hybrida ‘Red Queen’ and mathematical model of the pattern

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazuaki Amikura ◽  
Hiroshi Ito ◽  
Miho S. Kitazawa
Keyword(s):  
Mathematical Model ◽  
Spatial Pattern ◽  
Plant Species ◽  
Spatial Patterns ◽  
Comprehensive Analysis ◽  
Leaf Primordia ◽  
Rosa Hybrida ◽  
Model Parameters ◽  
Red Queen ◽  
Developmental Patterns

AbstractThe developmental patterns of many organisms are orchestrated by the diffusion of factors. Here, we report a novel pattern on plant stems that appears to be controlled by inhibitor diffusion. Prickles on rose stems appear to be randomly distributed, but we deciphered spatial patterns of prickles on Rosa hybrida cv. ‘Red Queen’ stem. The prickles primarily emerged at 90 to 135 degrees from the spiral phyllotaxis that connected leaf primordia. We proposed a simple mathematical model that explained the emergence of the spatial patterns and reproduced the prickle density distribution on rose stems. We confirmed the model can reproduce the observed prickle patterning on stems of other plant species using other model parameters. These results indicated that the spatial patterns of prickles on stems of different plant species are organized by similar systems. Rose cultivation by humans has a long history. However, prickle development is still unclear and this is the first report of prickle spatial pattern with a mathematical model. Comprehensive analysis of the spatial pattern, genome, and metabolomics of other plant species may lead to novel insights for prickle development.

Multi-site multi-variable hydrologic model development for spatially heterogeneous river basins to achieve realistic basin modelling

2020 ◽  
Author(s):  
Saumya Srivastava ◽  
Nagesh Kumar Dasika
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Uncertainty Analysis ◽  
River Basin ◽  
Performance Measures ◽  
Model Calibration ◽  
Sediment Load ◽  
Hydrologic Model ◽  
Performance Measure ◽  
Hydrologic Models

<p>Hydrologic modelling is an indispensable tool for simulation of river basin processes in water resources planning and management. Hydrologic models are used to understand dynamic interactions between climate and river basin hydrology. Model calibration, validation, parameter sensitivity and uncertainty analysis are essential prior to the application of hydrologic models. A large catchment with high spatial variability and heterogeneity can be modeled realistically when calibration is done at multiple locations, for multiple hydrologic variables like streamflow, soil moisture, sediment flow, evapotranspiration, etc. This ensures maximum utilization of field measurements of the hydrological variables, reduces the uncertainty in parameter identification and highlights the areas that need greater calibration effort. In the present study, hydrologic model simulations are run for the Mahanadi river basin in India using SWAT (Soil and Water Assessment Tool) and model calibration, uncertainty analysis, sensitivity analysis and validation are performed using SUFI-2 optimization algorithm in SWAT-CUP (SWAT Calibration and Uncertainty Programs). Entire Mahanadi basin is calibrated for several variables like streamflow, soil moisture, sediment load and evapotranspiration at various locations. The spatial heterogeneity of the catchment is taken into account in model calibration by choosing appropriate ranges of different parameters for each sub basin based on the soil types, slope classes and land use land cover present in the sub basins. When multi-site multi-variable calibration is carried out, serial calibration for individual variables and locations gives different result when compared with the simultaneous calibration for all variables and locations. In this study, a comparison of serial calibration for individual hydrologic variables and calibration sites versus simultaneous calibration for all hydrologic variables and calibration sites is made. Various performance measures like Nash-Sutcliffe efficiency (NSE), percent bias, coefficient of determination, modified NSE, etc. are used to quantify the model fit between the observed and the simulated values of various variables. The choice of performance measure affects the calibration solution, and depends on the calibration variables for which observed data is available. The performances of the fitted parameters are conditional with respect to the calibration variables and the choice of the performance measure. The present study talks about the suitability of the performance measure to different hydrologic variables like streamflow, sediment load, soil moisture, etc. The model simulation results for the Mahanadi river basin are compared with the observed values of hydrologic variables using different performance measures for calibration and validation of the model. The results show that model performance is enhanced when it is calibrated at multiple locations, for multiple variables, by taking the spatial variability of parameters across various sub-basins into account. This study explores the suitability of different performance measures for different hydrologic variables and compares the serial and simultaneous calibration for multiple hydrologic variables at multiple locations.</p>

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 Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics

2017 ◽  
Vol 18 (4) ◽  
pp. 1121-1142 ◽  
Author(s):  
Julian Koch ◽  
Gorka Mendiguren ◽  
Gregoire Mariethoz ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Patterns ◽  
Land Surface ◽  
Performance Metrics ◽  
Surface Model ◽  
Model Parameters ◽  
Spatial Sensitivity ◽  
Spatial Performance ◽  
Surface Patterns ◽  
Spatial Sensitivity Analysis

Abstract Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.

scholarly journals Simultaneously determining global sensitivities of model parameters and model structure

2020 ◽  
Vol 24 (12) ◽  
pp. 5835-5858
Author(s):  
Juliane Mai ◽  
James R. Craig ◽  
Bryan A. Tolson
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Structural Model ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Framework ◽  
Model Structure Uncertainty ◽  
Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

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