scholarly journals Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1641 ◽  
Author(s):  
Phuong Cu Thi ◽  
James Ball ◽  
Ngoc Dao
Keyword(s):  
Threshold Value ◽  
Uncertainty Estimation ◽  
Design Flow ◽  
Design Flood ◽  
Bayesian Techniques ◽  
Flood Estimation ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Values ◽  
The Impact

In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.

scholarly journals Sampling uncertainty of UK design flood estimation

2021 ◽  
Author(s):  
Anthony Hammond
Keyword(s):  
Sampling Error ◽  
Uncertainty Estimation ◽  
Design Flow ◽  
Single Site ◽  
Design Flood ◽  
Sampling Uncertainty ◽  
Flood Estimation ◽  
The Uk ◽  
Analytical Expressions ◽  
Quantifying Uncertainty

Abstract The UK standard for estimating flood frequencies is outlined by the flood estimation handbook (FEH) and associated updates. Estimates inevitably come with uncertainty due to sampling error as well as model and measurement error. Using resampling approaches adapted to the FEH methods, this paper quantifies the sampling uncertainty for single site, pooled (ungauged), enhanced single site (gauged pooling) and across catchment types. This study builds upon previous progress regarding easily applicable quantifications of FEH-based uncertainty estimation (Kjeldsen 2015, 2021; Dixon 2017). Where these previous studies have provided simple analytical expressions for quantifying uncertainty for single site and ungauged design flow estimates, this study provides an easy-to-use method for quantifying uncertainty for enhanced single site estimates.

scholarly journals Appraisal of the generalized likelihood uncertainty estimation (GLUE) method

2008 ◽  
Vol 44 (12) ◽  
Author(s):  
Jery R. Stedinger ◽  
Richard M. Vogel ◽  
Seung Uk Lee ◽  
Rebecca Batchelder
Keyword(s):  
Uncertainty Estimation ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

scholarly journals Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kairong Lin ◽  
Yanqing Lian ◽  
Yanhu He
Keyword(s):  
Hydrological Modeling ◽  
Threshold Value ◽  
Efficiency Index ◽  
Uncertainty Interval ◽  
Xinanjiang Model ◽  
Shuffled Complex Evolution ◽  
Interval Width ◽  
Behavioral Parameter ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM). The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE) method with the shuffled complex evolution Metropolis (SCEM-UA) sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash-Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.

scholarly journals A Successful Approach of the First Ecological Compensation Demonstration for Crossing Provinces of Downstream and Upstream in China

2020 ◽  
Vol 12 (15) ◽  
pp. 6021
Author(s):  
Guoguang Li ◽  
Qingxiu Wang ◽  
Guihuan Liu ◽  
Yue Zhao ◽  
Yuqiu Wang ◽  
...  
Keyword(s):  
Point Source ◽  
Environmental Performance ◽  
National Level ◽  
Ecological Compensation ◽  
Investment Optimization ◽  
Glue Method ◽  
New Perspective ◽  
Set Up ◽  
Generalized Likelihood Uncertainty Estimation ◽  
The Impact

As the first pilot provincial water environmental compensation set up at the national level, the Xin’anjiang River Basin plays a very important exemplary and guiding role in the ecological compensation of transboundary basins in China. There is no paper evaluating the environmental performance in watershed scale after getting rid of the natural factor’s effect. Here we issue a new approach to evaluate it, combing the SPAtially Referenced Regression On Watershed attributes (SPARROW) models and data envelopment analysis (DEA) method, based on counterfactual scenarios. After ecological compensation, the results show that the decrease of total nitrogen (TN) non-point source export coefficient was stable (17.16–17.78% in different sources), while that of total phosphorus (TP; 8.51–17.75%) and permanganate index (CODMn; 13.10–21.41%) was not. The projects of fertilizer application’s effects were relatively obvious; on average, the decreases of the export coefficients were 17.16%, 17.75%, and 21.41% in TN, TP, and CODMn models, respectively, showing the importance of eco-compensation regulation, not only in non-point source pollution reduction but also resulting in high levels of eco-compensation efficiencies, especially in scale efficiencies. By assessing parameter and modeling uncertainty with the use of the generalized likelihood uncertainty estimation (GLUE) method, the models’ structure well represents the hydrological behavior. This study also provides policymakers with a new perspective in accurately measuring the impact of environmental performance, to guide the next step of environmental investment optimization.

Sign in / Sign up

Export Citation Format

Share Document