An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models

Qi, Wei1,2; Liu, Junguo1; Yang, Hong3,4; Sweetapple, Chris5

2018-03

10.1016/j.jhydrol.2018.01.026

JOURNAL OF HYDROLOGY   影响因子和分区

0022-1694

1879-2707

Global precipitation products are very important datasets in flow simulations, especially in poorly gauged regions. Uncertainties resulting from precipitation products, hydrological models and their combinations vary with time and data magnitude, and undermine their application to flow simulations. However, previous studies have not quantified these uncertainties individually and explicitly. This study developed an ensemble-based dynamic Bayesian averaging approach (e-Bay) for deterministic discharge simulations using multiple global precipitation products and hydrological models. In this approach, the joint probability of precipitation products and hydrological models being correct is quantified based on uncertainties in maximum and mean estimation, posterior probability is quantified as functions of the magnitude and timing of discharges, and the law of total probability is implemented to calculate expected discharges. Six global fine-resolution precipitation products and two hydrological models of different complexities are included in an illustrative application. e-Bay can effectively quantify uncertainties and therefore generate better deterministic discharges than traditional approaches (weighted average methods with equal and varying weights and maximum likelihood approach). The mean Nash-Sutcliffe Efficiency values of e-Bay are up to 0.97 and 0.85 in training and validation periods respectively, which are at least 0.06 and 0.13 higher than traditional approaches. In addition, with increased training data, assessment criteria values of e-Bay show smaller fluctuations than traditional approaches and its performance becomes outstanding. The proposed e-Bay approach bridges the gap between global precipitation products and their pragmatic applications to discharge simulations, and is beneficial to water resources management in ungauged or poorly gauged regions across the world. (C) 2018 Published by Elsevier B.V.

Ensemble simulation Hydrological modelling Satellite precipitation products Uncertainty Water resources

SCI ; EI

英语

第一 ; 通讯

China Postdoctoral Science Foundation[2017M622516]

Engineering ; Geology ; Water Resources

Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources

WOS:000427338900033

ELSEVIER SCIENCE BV

20180604776605

Flow simulation ; Hydrology ; Maximum likelihood estimation ; Precipitation (meteorology) ; Uncertainty analysis

Precipitation:443.3 ; Water Resources:444 ; Fluid Flow, General:631.1 ; Statistical Methods:922 ; Probability Theory:922.1

ENGINEERING

Web of Science

1.South Univ Sci & Technol China, Sch Environm Sci & Engn, Xueyuan Rd 1088, Shenzhen 518055, Shenzhen, Peoples R China
2.Wuhan Univ, State Key Lab Water Resource & Hydropower Engn Sc, Wuhan 430072, Hubei, Peoples R China
3.Swiss Fed Inst Aquat Sci & Technol, Eawag, Dubendorf, Switzerland
4.Univ Basel, Dept Environm Sci, Basel, Switzerland
5.Univ Exeter, Coll Engn Math & Phys Sci, Ctr Water Syst, North Pk Rd, Exeter EX4 4QF, Devon, England

Qi, Wei,Liu, Junguo,Yang, Hong,et al. An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models[J]. JOURNAL OF HYDROLOGY,2018,558:405-420.

Qi, Wei,Liu, Junguo,Yang, Hong,&Sweetapple, Chris.(2018).An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models.JOURNAL OF HYDROLOGY,558,405-420.

Qi, Wei,et al."An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models".JOURNAL OF HYDROLOGY 558(2018):405-420.