scholarly journals Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

2020 ◽  
Vol 24 (10) ◽  
pp. 4971-4996
Author(s):  
Haifan Liu ◽  
Heng Dai ◽  
Jie Niu ◽  
Bill X. Hu ◽  
Dongwei Gui ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Global Sensitivity Analysis ◽  
Hydrological Models ◽  
Climate Scenarios ◽  
Analysis Method ◽  
Global Sensitivity ◽  
Uncertainty Sources ◽  
Sensitivity Indices ◽  
Sensitivity Analysis Method

Abstract. Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic outputs of interest: evapotranspiration (ET) and groundwater contribution to streamflow (QG). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for QG predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.

scholarly journals Hierarchical Sensitivity Analysis for Large Scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

2019 ◽  
Author(s):  
Haifan Liu ◽  
Heng Dai ◽  
Jie Niu ◽  
Bill X. Hu ◽  
Han Qiu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Overland Flow ◽  
Global Sensitivity Analysis ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Hydrological Models ◽  
Global Sensitivity ◽  
Uncertainty Sources ◽  
Sensitivity Indices

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex processes coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates a hierarchical uncertainty framework and a variance-based global sensitivity analysis, was implemented to quantitatively analyze several uncertainties of a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters, model structures (with/without overland flow module), and climate forcing. We apply the approach in a ~ 9000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on three important hydrologic outputs: evapotranspiration (ET), ground evaporation (EG), and groundwater contribution to streamflow (QG). It is found that, in general, model parameters (especially those within the streamside model grid cells) are the most important uncertainty contributor for all sensitivity indices. In addition, the overland flow module significantly contributes to model predictive uncertainty. These results can assist model calibration and provide modelers a better understanding of the general sources of uncertainty in predictions of complex hydrological systems in Amazonia. We demonstrated a pilot example for comprehensive global sensitivity analysis of large-scale complex hydrological models in this research. The hierarchical sensitivity analysis methodology used is mathematically rigorous and can be applied to a wide range of large-scale hydrological models with various sources of uncertainty.

scholarly journals Hierarchical Sensitivity Analysis for Large-scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

2020 ◽  
Author(s):  
Haifan Liu ◽  
Heng Dai ◽  
Jie Niu ◽  
Bill X. Hu ◽  
Han Qiu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Vadose Zone ◽  
Model Calibration ◽  
Large Scale ◽  
Global Sensitivity Analysis ◽  
Hydrologic Model ◽  
Global Sensitivity ◽  
Uncertainty Sources ◽  
Sensitivity Indices ◽  
Zone Parameters

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex process coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates the concept of variance-based global sensitivity analysis with a hierarchical uncertainty framework, was implemented to quantitatively analyse several uncertainties associated with a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters (three vadose zone parameters, two groundwater parameters, and one overland flow parameter), model structure (different thicknesses to represent unconfined and confined aquifer layers) and climate scenarios. We apply the approach to an ~ 9,000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on two important hydrologic outputs: evapotranspiration (ET) and groundwater contribution to streamflow (QG). It was found that, in general, parameters, especially the vadose zone parameters, are the most important uncertainty contributors for all sensitivity indices. In addition, the influence of climate scenarios on ET predictions is also nonignorable. Furthermore, the thickness of the aquifers along the river grid cells is important for both ET and QG. These results can assist in model calibration and provide modelers with a better understanding of the general sources of uncertainty in predictions associated with complex hydrological systems in Amazonia. We demonstrated a pilot example of comprehensive global sensitivity analysis of large-scale, complex hydrological and environmental models in this study. The hierarchical sensitivity analysis methodology used is mathematically rigorous and capable of being implemented in a variety of large-scale hydrological models with various sources of uncertainty.

Investigation of the uncertainty of the in-plane mechanical properties of composite laminates

2011 ◽  
Vol 226 (7) ◽  
pp. 1739-1750 ◽  
Author(s):  
Tian Longfei ◽  
Lu Zhenzhou ◽  
Hao Wenrui
Keyword(s):  
Mechanical Properties ◽  
Sensitivity Analysis ◽  
Composite Laminates ◽  
Global Sensitivity Analysis ◽  
Nonlinear Models ◽  
Analysis Method ◽  
Global Sensitivity ◽  
Response Variance ◽  
Sensitivity Analysis Method ◽  
Input Variables

The uncertainty of the in-plane mechanical properties of the laminate used in an aircraft wing structure is investigated. Global sensitivity analysis is used to identify the source of the uncertainties of the response performance. Due to the limitations of the existing global sensitivity analysis method for nonlinear models with correlated input variables, a new one using nonlinear regression is proposed. Furthermore, a contribution matrix is defined for engineering convenience. Two nonlinear numerical examples are employed in this article to demonstrate the ability of the proposed global sensitivity analysis method. After applying the proposed global sensitivity analysis method to the laminate model, the contribution matrices are obtained; from these matrices, researchers can identify the dominant variance contributions that contribute the most to the response variance. Factor analysis is then employed to analyze the global sensitivity analysis results and determine the most efficient methods to decrease the variances of the in-plane elastic constants. Monte Carlo simulation is used to demonstrate the efficiency of the methods in decreasing the variances.

A Global Sensitivity Analysis Method for Multi-Input Multi-Output System and its Application in Structural Design

2021 ◽  
pp. 2141005
Author(s):  
Qiming Liu ◽  
Nichen Tong ◽  
Xu Han
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Sensitivity Index ◽  
Response Functions ◽  
Analysis Method ◽  
Order Function ◽  
Global Sensitivity ◽  
Sensitivity Analysis Method ◽  
Output System ◽  
Input Variables

Commonly, variance-based global sensitivity analysis methods are popular and applicable to quantify the impact of a set of input variables on output response. However, for many engineering practical problems, the output response is not single but multiple, which makes some traditional sensitivity analysis methods difficult or unsuitable. Therefore, a novel global sensitivity analysis method is presented to evaluate the importance of multi-input variables to multi-output responses. First, assume that a multi-input multi-output system (MIMOS) includes [Formula: see text] variables and [Formula: see text] responses. A set of summatory functions [Formula: see text] and [Formula: see text] are constructed by the addition and subtraction of any two response functions. Naturally, each response function is represented using a set of summatory function. Subsequently, the summatory functions [Formula: see text] and [Formula: see text] are further decomposed based on the high dimensional model representation (HDMR), respectively. Due to the orthogonality of all the decomposed function sub-terms, the variance and covariance of each response function can be represented using the partial variances of all the decomposed function sub-terms on the corresponding summatory functions, respectively. The total fluctuation of MIMOS is calculated by the sum of the variances and covariances on all the response functions. Further, the fluctuation is represented as the sum of the total partial variances for all the [Formula: see text]-order function sub-terms, and the total partial variance is the sum of [Formula: see text] partial variances for the corresponding [Formula: see text]-order function sub-terms. Then, the function sensitivity index (FSI) [Formula: see text] for s-order function sub-terms is defined by the ratio of the total partial variance and total fluctuation, which includes first-order, second-order, and high-order FSI. The variable sensitivity index [Formula: see text] of variable [Formula: see text] is calculated by the sum of all the FSIs including the contribution of variable [Formula: see text]. Finally, numerical example and engineering application are employed to demonstrate the accuracy and practicality of the presented global sensitivity analysis method for MIMOS.

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