Evaluating Global Sensitivity Analysis Methods for Hydrologic Modeling over the Columbia River Basin (original) (raw)
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Global sensitivity analysis in hydrological modeling: Review of concepts, methods
2020
Sensitivity analysis (SA) aims to identify the key parameters affecting modeling performance. It plays an important role in model parameterization, calibration, optimization and uncertainty quantification. However, the increasing complexity of hydrological models results in a large number of parameters to be estimated. To better understand how these complex models work, efficient SA methods are required to select and implement before the application of hydrological modeling. This paper focused on the comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorizes research efforts on global SA methods and applications with an emphasis on the research accomplished in hydrological modeling field. Both advantages and disadvantages are also discussed and summarized. An application framework as well as typical practical steps of SA in hydrological modeling is outlined. Further discussion on the severe important and often overlooked topics is presented, including the relationship between parameter identification, uncertainty analysis and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying sensitivity analysis. Finally, some conclusions and guidance recommendations on sensitivity analysis in hydrological modeling are proposed along with a list of important future research directions to provide more robust analysis in assessing hydrological modeling performance.
Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.
An efficient integrated approach for global sensitivity analysis of hydrological model parameters
Environmental Modelling & Software, 2013
Efficient sensitivity analysis, particularly for the global sensitivity analysis (GSA) to identify the most important or sensitive parameters, is crucial for understanding complex hydrological models, e.g., distributed hydrological models. In this paper, we propose an efficient integrated approach that integrates a qualitative screening method (the Morris method) with a quantitative analysis method based on the statistical emulator (variance-based method with the response surface method, named the RSMSobol' method) to reduce the computational burden of GSA for time-consuming models. Using the Huaihe River Basin of China as a case study, the proposed approach is used to analyze the parameter sensitivity of distributed time-variant gain model (DTVGM). First, the Morris screening method is used to qualitatively identify the parameter sensitivity. Subsequently, the statistical emulator using the multivariate adaptive regression spline (MARS) method is chosen as an appropriate surrogate model to quantify the sensitivity indices of the DTVGM. The results reveal that the soil moisture parameter WM is the most sensitive of all the responses of interest. The parameters Kaw and g 1 are relatively important for the water balance coefficient (WB) and NasheSutcliffe coefficient (NS), while the routing parameter RoughRss is very sensitive for the NasheSutcliffe coefficient (NS) and correlation coefficient (RC) response of interest. The results also demonstrate that the proposed approach is much faster than the brute-force approach and is an effective and efficient method due to its low CPU cost and adequate degree of accuracy.
Sensitivity analysis of hydrological models: review and way forward
Journal of Water and Climate Change
Different hydrological models provide diverse perspectives of the system being modeled, and inevitably, are imperfect representations of reality. Irrespective of the choice of models, the major source of error in any hydrological modeling is the uncertainty in the determination of model parameters, owing to the mismatch between model complexity and available data. Sensitivity analysis (SA) methods help to identify the parameters that have a strong impact on the model outputs and hence influence the model response. In addition, SA assists in analyzing the interaction between parameters, its preferable range and its spatial variability, which in turn influence the model outcomes. Various methods are available to perform SA and the perturbation technique varies widely. This study attempts to categorize the SA methods depending on the assumptions and methodologies involved in various methods. The pros and cons associated with each SA method are discussed. The sensitivity pertaining to t...
Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes
2007
Abstract When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi-method global sensitivity analysis (MMGSA).
On the Sensitivity Analysis of Two Hydrologic Models
2004
Hydrology plays a fundamental role in environmental planning, management and restoration (Singh 1995). The rainfall-runoff model typically modeling continuous flow with a long time step is one of the fundamental models used in hydrology. One of its applications ...
Environmental Modelling & Software, 2014
Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400e600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LP s (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.
A design of experiment aided sensitivity analysis and parameterization for hydrological modeling
Canadian Journal of Civil Engineering, 2012
To provide a better understanding of the water balance in the Deer River watershed of the Hudson Bay lowlands, the Semi-distributed Land Use-based Runoff Process hydrological model was applied to simulate the runoff over a 20 year period. The purpose of this study is to develop an approach to examine the sensitivity of the ten parameters and their interactions via statistical design of experiment methodology. Using the proposed approach, the contribution of each parameter and how they interact with one another were evaluated. The results indicated that the interaction between “retention constant for fast storage” and “precipitation factor” had the greatest positive impact on the Nash–Sutcliffe efficiency (NSE) and the quadratic factor term of “precipitation factor” had the greatest negative effect on the NSE. The proposed approach provided an effective tool for evaluating the contribution of the input parameters and could also be applied for calibration of other hydrological models.
2005, On the Sensitivity Analysis of Two Hydrologic Models
2016
The development of new hydrological models and the enhancement of the existing models are needed for water management. For instance, increasing rainfall may accelerate water erosion in watersheds and raise the probability of flooding events in the urban areas. The impacts include environmental and social-economic conditions. The hydrologic models SEADS, a physical process based model and GURUH, an algebraic model based on statistical relationships, are being used to predict the hydrologic impacts of urbanization. These models need to be fully tested so that they can be used with confidence. The implementation of sensitivity analysis to these models is a useful tool in the calibration of the models, in their applications to
AM Multi-method global sensitivity analyses – results for a rainfall-runoff model
2018
A sensitivity study was carried out for the discharge of a small Austrian catchment using three global sensitivity methods implemented in a conceptual rainfall-runoff model: Sobol’s method, the Mutual Entropy and the Regional Sensitivity Analysis (RSA). Since RSA is a graphical method, the KolmogorovSmirnoff statistic was used for obtaining a quantitative measure of the sensitivities. It was observed that the parameter rankings as well as the temporal sensitivity dynamics agreed in general between the methods. However, the agreement was best between Sobol’s method and the Mutual Entropy. The graphical RSA method, gave some additional insights about the relationship of parameter values and discharge levels, which were not supplied by the other two methods. Finally, the implications these findings have for model calibration are discussed.