Articles | Volume 24, issue 1
https://doi.org/10.5194/hess-24-381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jan 2020
Research article |
| 24 Jan 2020
| 24 Jan 2020
Inter-annual variability of the global terrestrial water cycle
Dongqin Yin
CORRESPONDING AUTHOR
Research School of Earth Sciences, Australian National University,
Canberra, ACT, 2601, Australia
Australian Research Council Centre of Excellence for Climate System
Science, Canberra, ACT, 2601, Australia
Michael L. Roderick
Research School of Earth Sciences, Australian National University,
Canberra, ACT, 2601, Australia
Australian Research Council Centre of Excellence for Climate Extremes, Canberra, ACT, 2601, Australia
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- Estimating the annual runoff frequency distribution based on climatic conditions and catchment characteristics: A case study across China Z. Liu et al. 10.1016/j.iswcr.2022.06.001
- Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions Z. Feng & W. Niu 10.1016/j.knosys.2020.106580
- Dynamic Changes of Terrestrial Water Cycle Components over Central Asia in the Last Two Decades from 2003 to 2020 M. Odinaev et al. 10.3390/rs15133318
- Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem W. Niu et al. 10.1088/1748-9326/abf60c
- Divergent determinants on interannual variability of terrestrial water cycle across the globe J. Zhu et al. 10.1016/j.scitotenv.2024.174046
- Assessing the Steady‐State Assumption in Water Balance Calculation Across Global Catchments J. Han et al. 10.1029/2020WR027392
- Response of drylands' water‐cycle to the global warming M. Luo et al. 10.1002/joc.7088
- Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs W. Niu et al. 10.1016/j.energy.2024.130449
- Lake Evaporation and Its Effects on Basin Evapotranspiration and Lake Water Storage on the Inner Tibetan Plateau L. Wang et al. 10.1029/2022WR034030
- An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015 H. Zheng et al. 10.5194/essd-15-2755-2023
- Parallel computing and swarm intelligence based artificial intelligence model for multi-step-ahead hydrological time series prediction W. Niu et al. 10.1016/j.scs.2020.102686
- Experimental investigation into the permeability of water vapor in shales T. Liu & Q. Yu 10.1016/j.jhydrol.2022.127697
- Anthropogenic Impacts on the Water Cycle over Drylands in the Northern Hemisphere M. Luo et al. 10.1175/JCLI-D-22-0037.1
- Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation W. Niu et al. 10.1007/s11269-020-02737-8
- Annual water deficit in response to climate variabilities across the globe D. Yin et al. 10.1088/1748-9326/ac5d9b
- Streamflow seasonality in a snow-dwindling world J. Han et al. 10.1038/s41586-024-07299-y
- Deriving hydropower reservoir operation policy using data-driven artificial intelligence model based on pattern recognition and metaheuristic optimizer Z. Feng et al. 10.1016/j.jhydrol.2023.129916
- Evolution of Sustainable Water Resource Utilization in Hunan Province, China N. Liu et al. 10.3390/w14162477
Latest update: 06 Mar 2025
Short summary
We focus on the initial analysis of inter-annual variability in the global terrestrial water cycle, which is key to understanding hydro-climate extremes. We find that (1) the partitioning of inter-annual variability is totally different with the mean state partitioning; (2) the magnitude of covariances can be large and negative, indicating the variability in the sinks can exceed variability in the source; and (3) the partitioning is relevant to the water storage capacity and snow/ice presence.
We focus on the initial analysis of inter-annual variability in the global terrestrial water...
