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

Phuong Cu Thi,James Ball,Ngoc Dao

doi:10.3390/w10111641

Abstract

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.

Highlights

Design flood estimation is an essential component in engineering planning and management of catchments
The parameter uncertainty and model fitness were tested against 2 criteria: (1) Are these parameter set values normally distributed? (2) If yes, is there significant difference between two sets of these data? These criteria were tested using the Shapiro-Wilk test [46] and Welch Two Sample t-test [47]
The parameter uncertainty and model fitness were tested against 2 criteria: (1) Are these parameter set values normally distributed? (2) If yes, is there significant difference between two sets of these data? These criteria were tested using the Shapiro-Wilk test [46] and Welch Two Sample ttest [47]

Summary

Introduction

Design flood estimation is an essential component in engineering planning and management of catchments. The design flood flows can be estimated from observed flows or by implementing the catchment modelling approach. In both cases, the uncertainty in flood quantiles is an important issue which has been addressed in a number of analyses. In the catchment modelling approach, this uncertainty may arise from different errors, which have been classified as three main sources of errors: catchment input error, catchment response error (output error), and model error (parameter uncertainty) (e.g., [1,2,3]). A framework for catchment modelling system sources of uncertainty was developed by Kuczera [1], called The Bayesian Total Error Analysis (BATEA) framework. The system consists of three main errors: input, model, and response errors.

Results

Discussion

Conclusion