Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5439-2020
© 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-24-5439-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Accelerated hydrological cycle over the Sanjiangyuan region induces more streamflow extremes at different global warming levels
Key Laboratory of Regional Climate-Environment for Temperate East
Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Feng Ma
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Department of Civil and Environmental Engineering, Princeton
University, Princeton, New Jersey, USA
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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
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
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
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This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.
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
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Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
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
Revised manuscript accepted for HESS
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We explore tile drainage’ impacts on the integrated hydrology-biogeochemistry-plant system, using ecosys with soil oxygen and microbe dynamics. We found that tile drainage lowers soil water content and improves soil oxygen levels, which helps crops grow better, especially during wet springs, and the developed root system also helps mitigate drought stress on dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.
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
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023, https://doi.org/10.5194/hess-27-1077-2023, 2023
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Flash drought is referred to the rapid development of drought events with a fast decline of soil moisture, which has serious impacts on agriculture, the ecosystem, human health, and society. While flash droughts have received much research attention, there is no consensus on its definition. Here we used a stochastic water balance framework to quantify the timing of soil moisture crossing different thresholds, providing an efficient tool for diagnosing and monitoring flash droughts.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Junjiang Liu, Xing Yuan, Junhan Zeng, Yang Jiao, Yong Li, Lihua Zhong, and Ling Yao
Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, https://doi.org/10.5194/hess-26-265-2022, 2022
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Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Miao Zhang and Xing Yuan
Hydrol. Earth Syst. Sci., 24, 5579–5593, https://doi.org/10.5194/hess-24-5579-2020, https://doi.org/10.5194/hess-24-5579-2020, 2020
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We identify flash drought events by considering the decline rate of soil moisture and the drought persistency, and we detect the response of ecosystem carbon and water fluxes to flash droughts based on FLUXNET observations. We find rapid declines in carbon assimilation within 16–24 d of flash drought onset, where savannas show the highest sensitivity. Water use efficiency increases for forests but decreases for herbaceous ecosystems during the recovery stage of flash droughts.
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.
Dai, Y. J., Zeng, X. B., Dickinson, R. E., Baker, I., Bonan, G. B.,
Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G.
Y., Oleson, K. W., Schlosser, C. A., and Yang, Z. L.: The Common Land Model,
B. Am. Meteorol. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Döll, P., Trautmann, T., Gerten, D., Schmied, H. M., Ostberg, S., Saaed,
F., and Schleussner, C.: Risks for the global freshwater system at
1.5 ∘C and 2 ∘C global warming, Environ. Res. Lett., 13,
044038, https://doi.org/10.1088/1748-9326/aab792, 2018.
Dosio, A. and Fischer, E. M.: Will half a degree make a difference? Robust
projections of indices of mean and extreme climate in Europe under
1.5 ∘C, 2 ∘C, and 3 ∘C global warming,
Geophys. Res. Lett., 45, 935–944, https://doi.org/10.1002/2017GL076222,
2018.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fowler, M. D., Kooperman, G. J., Randerson, J. T., and Pritchard, M. S.: The
effect of plant physiological responses to rising CO2 on global streamflow,
Nat. Clim. Change, 9, 873–879, https://doi.org/10.1038/s41558-019-0602-x,
2019.
He, J. and Yang, K.: China meteorological forcing dataset (1979–2018), National Tibetan Plateau Data Center, https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.fle, 2018.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Hoegh-Guldberg, O., Jacob, D., Taylor, M., Bindi, M., Brown, S., Camilloni,
I., Diedhiou, A., Djalante, R., Ebi, K. L., Engelbrecht, F., Guiot, J.,
Hijioka, Y., Mehrotra, S., Payne, A., Seneviratne, S. I., Thomas, A.,
Warren, R., and Zhou, G.: Impacts of 1.5 ∘C Global Warming
on Natural and Human Systems, in: Global Warming of 1.5 ∘C. An
IPCC Special Report on the impacts of global warming of 1.5 ∘C
above pre-industrial levels and related global greenhouse gas emission
pathways, in the context of strengthening the global response to the threat
of climate change, sustainable development, and efforts to eradicate
poverty, edited by: Masson Delmotte, V., Zhai, P., Pörtner, H.-O.,
Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W.,
Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou,
X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterfield, T., 186–203, available at: https://www.ipcc.ch/sr15/ (last access: 25 June 2020), 2018.
Ji, P. and Yuan, X.: High-resolution land surface modeling of hydrological
changes over the Sanjiangyuan region in the eastern Tibetan Plateau: 2.
Impact of climate and land cover change, J. Adv. Model. Earth. Sy., 10,
2829–2843, https://doi.org/10.1029/2018MS001413, 2018.
Jia, B., Cai, X., Zhao, F., Liu, J., Chen, S., Luo, X., Xie, Z., and Xu, J.:
Potential future changes of terrestrial water storage based on climate
projections by ensemble model simulations, Adv. Water Resour., 142, 103635,
https://doi.org/10.1016/j.advwatres.2020.103635, 2020.
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009.
Kuang, X. and Jiao, J.: Review on climate change on the Tibetan Plateau
during the last half century, J. Geophys. Res.-Atmos., 121, 3979–4007,
https://doi.org/10.1002/2015JD024728, 2016.
Li, J., Liu, D., Li, Y., Wang, S., Yang, Y., Wang, X., Guo, H., Peng, S.,
Ding, J., Shen, M., and Wang, L.: Grassland restoration reduces water yield
in the headstream region of Yangtze River, Sci. Rep.-UK, 7, 2162, https://doi.org/10.1038/s41598-017-02413-9, 2017.
Li, W., Jiang, Z., Zhang, X., Li, L., and Sun, Y.: Additional risk in extreme
precipitation in China from 1.5 ∘C to 2.0 ∘C global
warming levels, Sci. Bull., 63, 228, https://doi.org/10.1016/j.scib.2017.12.021, 2018.
Liang, L., Li, L., Liu, C., and Cuo, L.: Climate change in the Tibetan
Plateau Three Rivers Source Region: 1960–2009, Int. J. Climatol., 33,
2900–2916, https://doi.org/10.1002/joc.3642, 2013.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Marcott, S. A., Shakun, J. D., Clark, P. U., and Mix, A. C.: A
Reconstruction of Regional and Global Temperature for the Past 11,300 Years,
Science, 339, 1198–1201, https://doi.org/10.1126/science.1228026, 2013.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Marx, A., Kumar, R., and Thober, S.: Climate change alters low flows in
Europe under global warming of 1.5, 2, and 3∘C, Hydrol. Earth.
Syst. Sc., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, 2018.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020.
Mohammed, K., Islam, A. S., Islam, G. M. T., Alfieri, L., Bala, S. K., and
Khan, M. J. U.: Extreme flows and water availability of the Brahmaputra
River under 1.5 and 2 ∘C global warming scenarios, Clim.
Change, 145, 159–175, https://doi.org/10.1007/s10584-017-2073-2, 2017.
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 dataset, J. Geophys. Res., 117,
D08101, https://doi.org/10.1029/2011JD017187, 2012.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M.,
Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E.,
Lamarque, J. F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z. L.: Technical
description of version 4.5 of the Community Land Model (CLM) (Rep.
NCAR/TN-503 + STR, 420), 420 pp., https://doi.org/10.5065/D6RR1W7M, 2013.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Samaniego, L., Kumar, R., Breuer, L., Chamorro, A., Flörke, M.,
Pechlivanidis, I. G., Schäfer, D., Shah, H., Vetter, T., Wortmann, M.,
and Zeng, X.: Propagation of forcing and model uncertainties on to
hydrological drought characteristics in a multi-model century-long
experiment in large river basins, Clim. Change, 141, 435–449, https://doi.org/10.1007/s10584-016-1778-y, 2017.
Thober, T., Kumar, R., and Waders, N.: Multi-model ensemble projections of
European river floods and high flows at 1.5, 2, and 3 degrees global
warming, Environ. Res. Lett., 13, 014003, https://doi.org/10.1088/1748-9326/aa9e35, 2018.
Vicente-Serrano, S. M., Lopez-Moreno, J. I., Begueria, S., Lorenzo-Lacruz,
J., Azorin-Molina, C., and Moran-Tejeda, E.: Accurate computation of a
streamflow drought index, J. Hydrol. Eng., 17, 318–332,
https://doi.org/10.1061/(Asce)He.1943-5584.0000433, 2012.
Watkins, M. M., Wiese, D. N., Yuan, D. N., Boening, C., and Landerer, F. W.:
Improved methods for observing Earth's time variable mass distribution with
GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120,
2648–2671, https://doi.org/10.1002/2014JB011547, 2015.
Wiese, D. N., Yuan, D. N., Boening, C., Landerer, F. W., and Watkins., M. M.: JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height Release 06 Coastal Resolution Improvement (CRI) Filtered Version 1.0. Ver. 1.0. PO.DAAC, CA, USA, https://doi.org/10.5067/TEMSC-3MJC6, 2018 (data available at https://grace.jpl.nasa.gov/, last access: 7 August 2019).
Wiltshire, A., Gornall, J., Booth, B., Dennis, E., Falloon, P., Kay, G.,
McNeall, D., McSweeney, C., and Betts, R.: The importance of population,
climate change and CO2 plant physiological forcing in determining future
global water stress, Global Environ. Change, 23, 1083–1097,
https://doi.org/10.1016/j.gloenvcha.2013.06.005, 2013.
WMO: WMO Statement on the State of the Global Climate in 2019, https://library.wmo.int/index.php?lvl=notice_display&id=21700#.X7PmmjPm6jg (last access: 5 July 2020), 2020.
Xu, R., Hu, H., Tian, F., Li, C., and Khan, M. Y. A.,: Projected climate
change impacts on future streamflow of the Yarlung Tsangpo-Brahmaputra
River, Global Planet. Change, 175, 144–159, https://doi.org/10.1016/j.gloplacha.2019.01.012, 2019.
Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., and Chen, Y.: Recent climate
changes over the Tibetan plateau and their impacts on energy and water
cycle: A review, Global Planet. Change, 112, 79–91, https://doi.org/10.1016/j.gloplacha.2013.12.001, 2013.
Yang, Y., Rodericj, M. L., Zhang, S., McVicar, T. R., and Donohue, R. J.:
Hydrologic implications of vegetation response to elevated CO2 in climate
projections, Nat. Clim. Change, 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0, 2019.
Yuan, X., Zhang, M., Wang, L., and Zhou, T.: Understanding and seasonal
forecasting of hydrological drought in the Anthropocene, Hydrol. Earth.
Syst. Sc., 21, 5477–5492,
https://doi.org/10.5194/hess-21-5477-2017, 2017.
Yuan, X., Ji, P., Wang, L., Liang, X., Yang, K., Ye, A., Su, Z., and Wen,
J.: High resolution land surface modeling of hydrological changes over the
Sanjiangyuan region in the eastern Tibetan Plateau: 1. Model development and
evaluation, J. Adv. Model. Earth. Sy., 10, 2806–2828,
https://doi.org/10.1029/2018MS001413, 2018a.
Yuan, X., Jiao, Y., Yang, D., and Lei, H.: Reconciling the attribution of
changes in streamflow extremes from a hydroclimate perspective, Water
Resour. Res., 54, 3886–3895,
https://doi.org/10.1029/2018WR022714, 2018b.
Zhang, Y., You, Q., Chen, C., and Ge, J.: Impacts of climate change on
streamflows under RCP scenarios: A case study in Xin River Basin, China,
Atmos. Res., 178–179, 521–534,
https://doi.org/10.1016/j.atmosres.2016.04.018, 2016.
Zhao, Q., Ding, Y., Wang, J., Gao, H., Zhang, S., Zhao, C., Xu, J. Han, H., and
Shangguan, D.: Projecting climate change impacts on hydrological processes on
the Tibetan Plateau with model calibration against the glacier inventory
data and observed streamflow, J. Hydrol., 573, 60–81,
https://doi.org/10.1016/j.jhydrol.2019.03.043, 2019.
Zhu, Q., Jiang, H., Peng, C., Liu, J., Fang, X., Wei, X., Liu, S., and Zhou, G.:
Effects of future climate change, CO2 enrichment, and vegetation structure
variation on hydrological processes in China, Global Planet. Change, 80–81,
123–135, https://doi.org/10.1016/j.gloplacha.2011.10.010, 2012.
Zhu, Z. C., Piao, S. L., Myneni, R. B., Huang, M. T., Zeng, Z. Z., Canadell,
J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C. X.,
Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y. W., Liu, R. G.,
Mao, J. F., Pan, Y. Z., Peng, S. S., Penuelas, J., Poulter, B., Pugh, T. A.
M., Stocker, B. D., Viovy, N., Wang, X. H., Wang, Y. P., Xiao, Z. Q., Yang,
H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat.
Clim. Change, 6, 791–795, https://doi.org/10.1038/Nclimate3004, 2016.
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.
By performing high-resolution land surface modeling driven by the latest CMIP6 climate models,...