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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24 citations as recorded by crossref.
- Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation Z. Feng et al. https://doi.org/10.1007/s11269-022-03176-3
- Estimating the annual runoff frequency distribution based on climatic conditions and catchment characteristics: A case study across China Z. Liu et al. https://doi.org/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 https://doi.org/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. https://doi.org/10.3390/rs15133318
- Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem W. Niu et al. https://doi.org/10.1088/1748-9326/abf60c
- Divergent determinants on interannual variability of terrestrial water cycle across the globe J. Zhu et al. https://doi.org/10.1016/j.scitotenv.2024.174046
- Assessing the Steady‐State Assumption in Water Balance Calculation Across Global Catchments J. Han et al. https://doi.org/10.1029/2020WR027392
- Response of drylands' water‐cycle to the global warming M. Luo et al. https://doi.org/10.1002/joc.7088
- Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs W. Niu et al. https://doi.org/10.1016/j.energy.2024.130449
- Unveiling remote and local drivers for the interdecadal increase in summer extreme high temperature days over southern China L. Xu et al. https://doi.org/10.1016/j.accre.2026.01.009
- Lake Evaporation and Its Effects on Basin Evapotranspiration and Lake Water Storage on the Inner Tibetan Plateau L. Wang et al. https://doi.org/10.1029/2022WR034030
- Technical note: Including hydrologic impact definition in climate projection uncertainty partitioning: a case study of the Central American mid-summer drought E. Maurer & I. Stewart https://doi.org/10.5194/hess-30-421-2026
- A novel hybrid model for actual evapotranspiration estimation in data-scarce arid regions: Integrating modified Budyko and machine learning models using deep learning M. Mohammadnezhad et al. https://doi.org/10.1016/j.scitotenv.2025.180438
- 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. https://doi.org/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. https://doi.org/10.1016/j.scs.2020.102686
- Experimental investigation into the permeability of water vapor in shales T. Liu & Q. Yu https://doi.org/10.1016/j.jhydrol.2022.127697
- Hydrological alterations driven by climate change and human activities: historical attribution, future projections, and process mechanisms in the Daqing River Basin, China X. Song et al. https://doi.org/10.2166/nh.2026.124
- Integrated statistical and feature based time series analysis of natural radionuclides and physicochemical parameters in surface water and groundwater of a hydrogeological system G. Luís et al. https://doi.org/10.1016/j.scitotenv.2025.181052
- Anthropogenic Impacts on the Water Cycle over Drylands in the Northern Hemisphere M. Luo et al. https://doi.org/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. https://doi.org/10.1007/s11269-020-02737-8
- Annual water deficit in response to climate variabilities across the globe D. Yin et al. https://doi.org/10.1088/1748-9326/ac5d9b
- Streamflow seasonality in a snow-dwindling world J. Han et al. https://doi.org/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. https://doi.org/10.1016/j.jhydrol.2023.129916
- Evolution of Sustainable Water Resource Utilization in Hunan Province, China N. Liu et al. https://doi.org/10.3390/w14162477
Saved (final revised paper)
Latest update: 20 Aug 2026
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...
