Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model-Reference-Cited by-同舟云学术

Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model

Published:2022-11 Issue:11 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Wu Haijiang¹²^ORCID,Su Xiaoling¹²^ORCID,Singh Vijay P.³⁴⁵^ORCID,Zhang Te²

Affiliation:

1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas Ministry of Education Northwest A & F University Yangling China

2. College of Water Resources and Architectural Engineering Northwest A & F University Yangling China

3. Department of Biological and Agricultural Engineering Texas A&M University College Station TX USA

4. Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA

5. National Water and Energy Center UAE University Al Ain UAE

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022WR033146

Reference69 articles.

1. Pair-copula constructions of multiple dependence

2. Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction

3. A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

4. A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought

5. Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

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