Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling-Reference-Cited by-同舟云学术

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Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

Published:2022-06 Issue:6 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Cheng Guanhui¹²^ORCID,Huang Guohe (Gordon)³,Dong Cong²³^ORCID

Affiliation:

1. Institute for Energy, Environment and Sustainable Communities University of Regina Regina SK Canada

2. International Society for Environmental Information Sciences Beijing China

3. Faculty of Engineering and Applied Science University of Regina Regina SK Canada

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR031370

Reference98 articles.

1. Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

2. Accelerating advances in continental domain hydrologic modeling

3. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation

4. The hazards of split-sample validation in hydrological model calibration

5. Potential impacts of a warming climate on water availability in snow-dominated regions

Cited by 2 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. Macroscale composite principal-monotonicity distributed projection of climate-induced hydrologic changes: Tempo-spatial variabilities, and variation decompositions;Journal of Hydrology;2023-11

2. Development of a macroscale distributed hydro-modeling method: Bayesian principal-monotonicity inference;Journal of Hydrology;2023-01

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