Articles | Volume 22, issue 7
https://doi.org/10.5194/hess-22-4061-2018
© Author(s) 2018. 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-22-4061-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jul 2018
Research article |
| 27 Jul 2018
| 27 Jul 2018
Understanding terrestrial water storage variations in northern latitudes across scales
Department of Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
International Max Planck Research School for Global Biogeochemical Cycles, 07745 Jena, Germany
Sujan Koirala
Department of Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
Nuno Carvalhais
Department of Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
CENSE, Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal
Annette Eicker
HafenCity University, 20457 Hamburg, Germany
Manfred Fink
Department of Geography, Friedrich-Schiller University, 07743 Jena, Germany
Christoph Niemann
Department of Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
Department of Geography, Friedrich-Schiller University, 07743 Jena, Germany
Martin Jung
Department of Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany
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Cited
21 citations as recorded by crossref.
- Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations Y. Su & S. Zhang 10.3390/land13040508
- Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada A. Bahrami et al. 10.1002/hyp.13625
- The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model R. Tong et al. 10.5194/hess-25-1389-2021
- Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences F. Papa & F. Frappart 10.3390/rs13204162
- The importance of vegetation in understanding terrestrial water storage variations T. Trautmann et al. 10.5194/hess-26-1089-2022
- Diagnosing modeling errors in global terrestrial water storage interannual variability H. Lee et al. 10.5194/hess-27-1531-2023
- The benefits and trade-offs of multi-variable calibration of the WaterGAP global hydrological model (WGHM) in the Ganges and Brahmaputra basins H. Hasan et al. 10.5194/hess-29-567-2025
- Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan, southwest China Z. Jiang et al. 10.1016/j.rse.2020.112249
- Contributions of GRACE to understanding climate change B. Tapley et al. 10.1038/s41558-019-0456-2
- Using geodetic measurements derived terrestrial water storage to investigate the characteristics of drought in Yunnan, China C. Chen et al. 10.1007/s10291-023-01591-6
- Future terrestrial water reserves are projected to undergo stronger interannual variability J. Zhu et al. 10.1016/j.jhydrol.2024.131690
- Environment-sensitivity functions for gross primary productivity in light use efficiency models S. Bao et al. 10.1016/j.agrformet.2021.108708
- Towards hybrid modeling of the global hydrological cycle B. Kraft et al. 10.5194/hess-26-1579-2022
- Basin Mass Changes in Finland From GRACE: Validation and Explanation J. Jiao et al. 10.1029/2021JB023489
- Calibrating global hydrological models with GRACE TWS: does river storage matter? T. Trautmann et al. 10.1088/2515-7620/acece5
- Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland China Z. Jiang et al. 10.1016/j.jhydrol.2021.126349
- Differences in Response of Terrestrial Water Storage Components to Precipitation over 168 Global River Basins Y. Zhang et al. 10.1175/JHM-D-18-0253.1
- Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018 C. Notarnicola 10.3390/rs12233913
- Advances in GRACE satellite studies on terrestrial water storage: a comprehensive review J. Karki et al. 10.1080/10106049.2025.2482706
- Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. 10.1029/2022MS003464
- Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins S. Bibi et al. 10.5194/hess-28-1725-2024
21 citations as recorded by crossref.
- Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations Y. Su & S. Zhang 10.3390/land13040508
- Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada A. Bahrami et al. 10.1002/hyp.13625
- The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model R. Tong et al. 10.5194/hess-25-1389-2021
- Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences F. Papa & F. Frappart 10.3390/rs13204162
- The importance of vegetation in understanding terrestrial water storage variations T. Trautmann et al. 10.5194/hess-26-1089-2022
- Diagnosing modeling errors in global terrestrial water storage interannual variability H. Lee et al. 10.5194/hess-27-1531-2023
- The benefits and trade-offs of multi-variable calibration of the WaterGAP global hydrological model (WGHM) in the Ganges and Brahmaputra basins H. Hasan et al. 10.5194/hess-29-567-2025
- Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan, southwest China Z. Jiang et al. 10.1016/j.rse.2020.112249
- Contributions of GRACE to understanding climate change B. Tapley et al. 10.1038/s41558-019-0456-2
- Using geodetic measurements derived terrestrial water storage to investigate the characteristics of drought in Yunnan, China C. Chen et al. 10.1007/s10291-023-01591-6
- Future terrestrial water reserves are projected to undergo stronger interannual variability J. Zhu et al. 10.1016/j.jhydrol.2024.131690
- Environment-sensitivity functions for gross primary productivity in light use efficiency models S. Bao et al. 10.1016/j.agrformet.2021.108708
- Towards hybrid modeling of the global hydrological cycle B. Kraft et al. 10.5194/hess-26-1579-2022
- Basin Mass Changes in Finland From GRACE: Validation and Explanation J. Jiao et al. 10.1029/2021JB023489
- Calibrating global hydrological models with GRACE TWS: does river storage matter? T. Trautmann et al. 10.1088/2515-7620/acece5
- Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland China Z. Jiang et al. 10.1016/j.jhydrol.2021.126349
- Differences in Response of Terrestrial Water Storage Components to Precipitation over 168 Global River Basins Y. Zhang et al. 10.1175/JHM-D-18-0253.1
- Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018 C. Notarnicola 10.3390/rs12233913
- Advances in GRACE satellite studies on terrestrial water storage: a comprehensive review J. Karki et al. 10.1080/10106049.2025.2482706
- Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models S. Bao et al. 10.1029/2022MS003464
- Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins S. Bibi et al. 10.5194/hess-28-1725-2024
Discussed (final revised paper)
Latest update: 02 Apr 2025
Short summary
In this study, we adjust a simple hydrological model to several observational datasets, including satellite observations of the land's total water storage. We apply the model to northern latitudes and find that the dominating factor of changes in the total water storage depends on both the spatial and temporal scale of analysis. While snow dominates seasonal variations, liquid water determines year-to-year variations, yet with increasing contribution of snow when averaging over larger regions.
In this study, we adjust a simple hydrological model to several observational datasets,...
