A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

Liu, Y.R.; Li, Y.P.; Huang, Guohe; Zhang, J.L.; Fan, Yurui

doi:10.1016/j.jhydrol.2017.08.048

Cited by 44 publications

(17 citation statements)

References 58 publications

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“…For parameter sensitivity, the results showed that the parameter CN2 was the most sensitive ( Figure 2) using any of the three methods and CN2 played a key role in peak flow simulation (Figure 3). CN2 is a function of soil's permeability, land use, and initial soil water condition, which suggests the potential of surface runoff from rainfall in a watershed [47]. CN2 had positive effects on the peak flow (Figure 3), and this may be because of concentrated distribution of rainfall in the wet season, causing infiltration excessed surface flow and a significant increase in peak flow.…”

Section: Parameter Sensitivity and Uncertaintymentioning

confidence: 99%

Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau

Zhao

Qiu

et al. 2018

Water

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Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed-Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R 2) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community.

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Section: Parameter Sensitivity and Uncertaintymentioning

confidence: 99%

Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau

Zhao

Qiu

et al. 2018

Water

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“…More specifically, the factorial analysis of variance method is used to diagnose the curve relationship between the parameters and the response [126,127]. In other words, FAV technique is used for measuring the specific variations of hydrological responses in terms of posterior distributions to investigate the individual and interactive effects of parameters on model outputs [128]. To complete this description, FAV comprises a powerful statistical tool to facilitate the exploration of the main effects.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…This is called the Bayesian-based multilevel factorial analysis (BMFAV), and it is used to assess parameter uncertainties and their effects on hydrological model outputs. In this sense, there are several other applications aimed to approximate the posterior distributions of model parameters with Bayesian inference [128,132]. Another important application of FAV was to develop a BMFAV method to address the dynamic influence of hydrological model parameters on runoff simulation [133].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods

et al. 2020

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The concept of sustainability is assumed for this research from a temporal perspective. Rivers represent natural systems with an inherent internal memory on their runoff and, by extension, to their hydrological behavior, that should be identified, characterized and quantified. This memory is formally called temporal dependence and allows quantifying it for each river system. The ability to capture that temporal signature has been analyzed through different methods and techniques. However, there is a high heterogeneity on those methods’ analytical capacities. It is found in this research that the most advanced ones are those whose output provides a dynamic and quantitative assessment of the temporal dependence for each river system runoff. Since the runoff can be split into temporal conditioned runoff fractions, advanced methods provide an important improvement over classic or alternative ones. Being able to characterize the basin by calculating those fractions is a very important progress for water managers that need predictive tools for orienting their water policies to a certain manner. For instance, rivers with large temporal dependence will need to be controlled and gauged by larger hydraulic infrastructures. The application of this approach may produce huge investment savings on hydraulic infrastructures and an environmental impact minimization due to the achieved optimization of the binomial cost-benefit.

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“…The mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (R), coefficient of determination (R 2 ), and Nash-Sutcliffe efficiency coefficient (NSE) are used to assess the predictive power of the integrated model in simulating snow albedo [48]. These terms are defined as follows:…”

Section: Model Accuracy Validationmentioning

confidence: 99%

Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Shao

et al. 2020

Remote Sensing

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Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

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A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

Cited by 44 publications

References 58 publications

Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau

Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau

Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods

Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

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