Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion-Reference-Cited by-同舟云学术

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

Published:2017-05 Issue: Volume:548 Page:484-497
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Wang S.,Huang G.H.,Baetz B.W.,Ancell B.C.

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference67 articles.

1. Sequential streamflow assimilation for short-term hydrological ensemble forecasting;Abaza;J. Hydrol.,2014

2. An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction;Ajami;Water Resour. Res.,2007

3. An improved particle filter algorithm based on ensemble Kalman filter and Markov chain Monte Carlo method;Bi;IEEE J. Selected Top. Appl. Earth Observations Remote Sensing,2015

4. Correcting the mathematical structure of a hydrological model via Bayesian data assimilation;Bulygina;Water Resour. Res.,2011

5. State and parameter update in a coupled energy/hydrologic balance model using ensemble Kalman filtering;Cammalleri;J. Hydrol.,2012

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrated Multisource Data Assimilation and NSGA-II Multiobjective Optimization Framework for Streamflow Simulations;Journal of Hydrologic Engineering;2024-12

2. Nonlinear and periodic dynamics of chaotic hydro-thermal process of Skokomish river;Stochastic Environmental Research and Risk Assessment;2023-06-27

3. Coupling duo-assimilation to hydrological model to enhance flood forecasting;Journal of Applied Water Engineering and Research;2023-04-24

4. An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges;2023-04-13

5. A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation;Hydrological Sciences Journal;2023-03-12