Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4369-2020
© 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-24-4369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2020
Research article |
| 09 Sep 2020
| 09 Sep 2020
The influence of a prolonged meteorological drought on catchment water storage capacity: a hydrological-model perspective
Zhengke Pan
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Changjiang Institute of Survey, Planning, Design and Research, Wuhan,
430010, China
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern,
Oslo, 0316, Norway
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Chong-Yu Xu
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern,
Oslo, 0316, Norway
Lei Cheng
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Jing Tian
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Shujie Cheng
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Kang Xie
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan 430072, China
Hubei Provincial Key Lab of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
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Cited
13 citations as recorded by crossref.
- Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
- Detecting and attributing drought‐induced changes in catchment hydrological behaviours in a southeastern Australia catchment using a data assimilation method Y. Liu et al. 10.1002/hyp.14289
- An Analytical Baseflow Coefficient Curve for Depicting the Spatial Variability of Mean Annual Catchment Baseflow S. Cheng et al. 10.1029/2020WR029529
- Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks K. Xie et al. 10.1088/1748-9326/ad404b
- Recent development on drought propagation: A comprehensive review Z. Zhou et al. 10.1016/j.jhydrol.2024.132196
- Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation J. Tian et al. 10.5194/hess-26-4853-2022
- Drought decreases annual streamflow response to precipitation, especially in arid regions A. Matanó et al. 10.5194/hess-29-2749-2025
- Temporal variation scale of the catchment water storage capacity of 91 MOPEX catchments J. Tian et al. 10.1016/j.ejrh.2022.101236
- Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method Z. Gui et al. 10.3390/w16081126
- The Spread of Multiple Droughts in Different Seasons and Its Dynamic Changes S. Zhu et al. 10.3390/rs15153848
- Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China J. Tian et al. 10.1038/s41598-021-86961-1
- Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
- Probabilistic runoff forecasting by integrating improved conceptual hydrological model with interpretable deep learning approach in a typical karst basin, Southwest China S. Lai et al. 10.1016/j.jhydrol.2025.132950
13 citations as recorded by crossref.
- Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
- Detecting and attributing drought‐induced changes in catchment hydrological behaviours in a southeastern Australia catchment using a data assimilation method Y. Liu et al. 10.1002/hyp.14289
- An Analytical Baseflow Coefficient Curve for Depicting the Spatial Variability of Mean Annual Catchment Baseflow S. Cheng et al. 10.1029/2020WR029529
- Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks K. Xie et al. 10.1088/1748-9326/ad404b
- Recent development on drought propagation: A comprehensive review Z. Zhou et al. 10.1016/j.jhydrol.2024.132196
- Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation J. Tian et al. 10.5194/hess-26-4853-2022
- Drought decreases annual streamflow response to precipitation, especially in arid regions A. Matanó et al. 10.5194/hess-29-2749-2025
- Temporal variation scale of the catchment water storage capacity of 91 MOPEX catchments J. Tian et al. 10.1016/j.ejrh.2022.101236
- Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method Z. Gui et al. 10.3390/w16081126
- The Spread of Multiple Droughts in Different Seasons and Its Dynamic Changes S. Zhu et al. 10.3390/rs15153848
- Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China J. Tian et al. 10.1038/s41598-021-86961-1
- Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
- Probabilistic runoff forecasting by integrating improved conceptual hydrological model with interpretable deep learning approach in a typical karst basin, Southwest China S. Lai et al. 10.1016/j.jhydrol.2025.132950
Latest update: 16 Jul 2025
Short summary
This study aims to identify the response of catchment water storage capacity (CWSC) to meteorological drought by examining the changes of hydrological-model parameters after drought events. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.
This study aims to identify the response of catchment water storage capacity (CWSC) to...
