Multi-variable sensitivity and identifiability analysis for a complex environmental model in view of integrated water quantity and water quality modeling

2012 ◽  
Vol 65 (3) ◽  
pp. 539-549 ◽  
Author(s):  
Jiri Nossent ◽  
Willy Bauwens
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Process Model ◽  
Swat Model ◽  
Screening Method ◽  
Water Quality Modeling ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Environmental Models

Environmental models are often over-parameterized. A sensitivity analysis can identify influential model parameters for, e.g. the parameter estimation process, model development, research prioritization and so on. This paper presents the results of an extensive study of the Latin-Hypercube–One-factor-At-a-Time (LH-OAT) procedure applied to the Soil and Water Assessment Tool (SWAT). The LH-OAT is a sensitivity analysis method that can be categorized as a screening method. The results of the sensitivity analyses for all output variables indicate that the SWAT model of the river Kleine Nete is mainly sensitive to flow related parameters. Rarely, water quality parameters get a high priority ranking. It is observed that the number of intervals used for the Latin-Hypercube sampling should be sufficiently high to achieve converged parameter rankings. Additionally, it is noted that the LH-OAT method can enhance the understanding of the model, e.g. on the use of water quality input data.

Water Quality Modeling of the Lower Han River, Korea

1985 ◽  
Vol 17 (6-7) ◽  
pp. 979-990 ◽  
Author(s):  
L. F. Tischler ◽  
R. M. Bradley ◽  
S. J. Park ◽  
D. G. Rhee
Keyword(s):  
Water Quality ◽  
Urban Areas ◽  
Water Quality Modeling ◽  
Industrial Wastes ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Army Corps ◽  
Han River ◽  
Calibration And Verification ◽  
Treatment Alternatives

The Han River Basin in the Republic of Korea covers an area of about 27,000 sq km south of the Demilitarized Zone, almost one quarter of the total area of the country. The total population in the basin is of the order of 13 million, of which more than 80 percent are concentrated in the urban contrbation of Greater Seoul alongside the Lower Han River. Within the vicinity of Seoul the river is used extensively for water supply, irrigation and both contact and non-water contact recreation. Treated nightsoil, untreated sullage, partially treated sewage and industrial wastes generated in the urban areas are also discharged to the river which is heavily polluted in the downstream reaches as a result. The finite difference water quality model QUAL-II was adapted to the Lower Han River using data obtained from extensive field water quality surveys especially designed to provide the necessary calibration and verification data base. Hydraulic data for the model were obtained from operation of the U. S. Army Corps of Engineers HEC-II computer model. Model calibration and verification was confirmed by statistical comparison of model simulations with the field data. The calibrated model was used to evaluate a number of different treatment alternatives for two different future years. Extensive model sensitivity analyses were run on both the key model parameters and the treatment alternatives to quantify the reliability of the prediction.

scholarly journals Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces

2020 ◽  
Vol 34 (11) ◽  
pp. 1813-1830
Author(s):  
Daniel Erdal ◽  
Sinan Xiao ◽  
Wolfgang Nowak ◽  
Olaf A. Cirpka
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Parameter Space ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Large Sample Size ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Additional Costs ◽  
Active Subspace

Abstract Ensemble-based uncertainty quantification and global sensitivity analysis of environmental models requires generating large ensembles of parameter-sets. This can already be difficult when analyzing moderately complex models based on partial differential equations because many parameter combinations cause an implausible model behavior even though the individual parameters are within plausible ranges. In this work, we apply Gaussian Process Emulators (GPE) as surrogate models in a sampling scheme. In an active-training phase of the surrogate model, we target the behavioral boundary of the parameter space before sampling this behavioral part of the parameter space more evenly by passive sampling. Active learning increases the subsequent sampling efficiency, but its additional costs pay off only for a sufficiently large sample size. We exemplify our idea with a catchment-scale subsurface flow model with uncertain material properties, boundary conditions, and geometric descriptors of the geological structure. We then perform a global-sensitivity analysis of the resulting behavioral dataset using the active-subspace method, which requires approximating the local sensitivities of the target quantity with respect to all parameters at all sampled locations in parameter space. The Gaussian Process Emulator implicitly provides an analytical expression for this gradient, thus improving the accuracy of the active-subspace construction. When applying the GPE-based preselection, 70–90% of the samples were confirmed to be behavioral by running the full model, whereas only 0.5% of the samples were behavioral in standard Monte-Carlo sampling without preselection. The GPE method also provided local sensitivities at minimal additional costs.

scholarly journals Global sensitivity analysis of parameter uncertainty in landscape evolution models

2018 ◽  
Vol 11 (12) ◽  
pp. 4873-4888 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Landscape Evolution ◽  
Relative Sensitivity ◽  
Function Approach ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Function ◽  
Sediment Yields ◽  
Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

Discharge Assessment by Using Integrated Hydrologic Model for Environmental Technology Development

2014 ◽  
Vol 911 ◽  
pp. 378-382 ◽  
Author(s):  
Mohd Fozi Ali ◽  
Nor Faiza A. Rahman ◽  
Khairi Khalid
Keyword(s):  
Water Quality ◽  
River Water ◽  
River Discharge ◽  
Technology Development ◽  
Swat Model ◽  
Hydrologic Model ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Environmental Technology ◽  
Current State

River water quality degradation is one of the most significant environmental challenges. Over the years, many models have been used to investigate the current state of Malaysian rivers and its effects to the environment. River discharge is an important factor in water quality investigation. An integrative computational model, GIS coupled with SWAT model was being used to predict river discharge of this research. The simulation results in the period 1999 to 2010 represented fluctuation of discharge relatively well with both R2and NSI values were above 0.6. The results proved that the development of integrative GIS technology coupled with SWAT model is a good tool for environmental technology development in terms of investigating the current state of Langat river water quality as well as the capability of simulating the river discharge in the river basin. This shows that GIS-SWAT interface can be a reliable tool for water quality modeling in Malaysia in the future and further development on the software technology is a benefit for the water resources and environmental studies.

scholarly journals Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

2017 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Global Sensitivity Analysis ◽  
Landscape Evolution ◽  
Sensitivity Analyses ◽  
Large Set ◽  
Sediment Yields ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Morris Method

Abstract. Landscape Evolution Models have a long history of use as exploratory models, providing greater understanding of the role large scale processes have on the long-term development of the Earth’s surface. As computational power has advanced so has the development and sophistication of these models. This has seen them applied at increasingly smaller scale and shorter-term simulations at greater detail. However, this has not gone hand-in-hand with more rigorous verifications that are commonplace in the applications of other types of environmental models- for example Sensitivity Analyses. This can be attributed to a paucity of data and methods available in order to calibrate, validate and verify the models, and also to the extra complexity Landscape Evolution Models represent – without these it is not possible to produce a reliable Objective Function against which model performance can be judged. To overcome this deficiency, we present a set of Model Functions – each representing an aspect of model behaviour – and use these to assess the relative sensitivity of a Landscape Evolution Model (CAESAR-Lisflood) to a large set of parameters via a global Sensitivity Analysis using the Morris Method. This novel combination of behavioural Model Functions and the Morris Method provides insight into which parameters are the greatest source of uncertainty in the model, and which have the greatest influence over different model behaviours. The method was repeated over two different catchments, showing that across both catchments and across most model behaviours the choice of Sediment Transport formula was the dominate source of uncertainty in the CAESAR-Lisflood model, although there were some differences between the two catchments. Crucially, different parameters influenced the model behaviours in different ways, with Model Functions related to internal geomorphic changes responding in different ways to those related to sediment yields from the catchment outlet. This method of behavioural sensitivity analysis provides a useful method of assessing the performance of Landscape Evolution Models in the absence of data and methods for an Objective Function approach.

scholarly journals Sensitivity Analysis of Model Parameters for Water Quality Simulation

2016 ◽  
Vol 25 (4) ◽  
pp. 1655-1660
Author(s):  
Nireshni Naidoo ◽  
Muthukrishnavellaisamy Kumarasamy
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Model Parameters ◽  
Water Quality Simulation

scholarly journals Groundwater pollution risk using a modified Latin hypercube sampling

2006 ◽  
Vol 8 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Husam Baalousha
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Pollution ◽  
Probabilistic Models ◽  
Latin Hypercube Sampling ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Deterministic Models ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

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