Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5439-2020
https://doi.org/10.5194/hess-24-5439-2020
Research article
 | 
20 Nov 2020
Research article |  | 20 Nov 2020

Accelerated hydrological cycle over the Sanjiangyuan region induces more streamflow extremes at different global warming levels

Peng Ji, Xing Yuan, Feng Ma, and Ming Pan

Related authors

Improving Streamflow Simulation through Machine Learning-Powered Data Integration and Its Implications for Forecasting in the Western U.S.
Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2025-1708,https://doi.org/10.5194/egusphere-2025-1708, 2025
Short summary
Ensembling Differentiable Process-based and Data-driven Models with Diverse Meteorological Forcing Datasets to Advance Streamflow Simulation
Peijun Li, Yalan Song, Ming Pan, Kathryn Lawson, and Chaopeng Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-483,https://doi.org/10.5194/egusphere-2025-483, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Comprehensive Global Assessment of 23 Gridded Precipitation Datasets Across 16,295 Catchments Using Hydrological Modeling
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194,https://doi.org/10.5194/egusphere-2024-4194, 2025
Short summary
Impacts of tile drainage on hydrology, soil biogeochemistry, and crop yield in the U.S. Midwestern agroecosystems
Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-340,https://doi.org/10.5194/hess-2024-340, 2024
Preprint under review for HESS
Short summary
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024,https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025,https://doi.org/10.5194/hess-29-2551-2025, 2025
Short summary
Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025,https://doi.org/10.5194/hess-29-2393-2025, 2025
Short summary
Towards a robust hydrologic data assimilation system for hurricane-induced river flow forecasting
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025,https://doi.org/10.5194/hess-29-2407-2025, 2025
Short summary
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025,https://doi.org/10.5194/hess-29-2133-2025, 2025
Short summary
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025,https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary

Cited articles

Bibi, S., Wang, L., Li, X., Zhou, J., Chen, D., and Yao, T.: Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: a review, Int. J. Climatol., 38, e1–e17, https://doi.org/10.1002/joc.5411, 2018. 
Chen, J., Gao, C., Zeng, X., Xiong, M., Wang, Y., Jing, C. Krysanova, V., Huang, J., Zhao, N., and Su, B.: Assessing changes of river discharge under global warming of 1.5 C and 2 C in the upper reaches of the Yangtze River Basin: Approach by using multiple-GCMs and hydrological models, Quatern. Int., 453, 1–11, https://doi.org/10.1016/j.quaint.2017.01.017, 2017. 
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F., Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell, G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe S., Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Future Gener. Comp. Sy., 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014 (data available at: https://esgf-node.llnl.gov/search/cmip6/, last access: 5 March 2020). 
Cuo, L., Zhang, Y., Zhu, F., and Liang, L.: Characteristics and changes of streamflow on the Tibetan Plateau: A review, J. Hydrol.-Reg. Stud., 2, 49–68, https://doi.org/10.1016/j.ejrh.2014.08.004, 2014. 
Download
Short summary
By performing high-resolution land surface modeling driven by the latest CMIP6 climate models, we find both the dry streamflow extreme over the drought-prone Yellow River headwater and the wet streamflow extreme over the flood-prone Yangtze River headwater will increase under 1.5, 2.0 and 3.0 °C global warming levels and emphasize the importance of considering ecological changes (i.e., vegetation greening and CO2 physiological forcing) in the hydrological projection.
Share