Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4657-2022
© Author(s) 2022. 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-26-4657-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2022
Research article |
| 22 Sep 2022
| 22 Sep 2022
Future snow changes and their impact on the upstream runoff in Salween
Chenhao Chai
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
The University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
The University of Chinese Academy of Sciences, Beijing 100049, China
Deliang Chen
Department of Earth Sciences, University of Gothenburg, Gothenburg
40530, Sweden
Jing Zhou
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
Hu Liu
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
The University of Chinese Academy of Sciences, Beijing 100049, China
Jingtian Zhang
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
The University of Chinese Academy of Sciences, Beijing 100049, China
Yuanwei Wang
School of Geographical Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
School of Geography and Planning, Sun Yat-sen University, Guangzhou
510275, China
Ruishun Liu
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy
of Sciences, Beijing 100101, China
The University of Chinese Academy of Sciences, Beijing 100049, China
Related authors
No articles found.
Cheng Shen, Hui-Shuang Yuan, Zhi-Bo Li, Jinling Piao, Youli Chang, and Deliang Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1156, https://doi.org/10.5194/egusphere-2025-1156, 2025
Short summary
Short summary
Near-surface wind speed affects air quality, water cycles, and wind energy, but its future changes in South Asia remain uncertain. This study explores how internal climate variability, particularly the Interdecadal Pacific Oscillation, affects wind speed trends in the region. Using advanced climate simulations, we show that accounting for this variability reduces uncertainty in future projections. Our findings can improve climate adaptation strategies and wind energy planning.
Zhi-Bo Li, Chao Liu, Cesar Azorin-Molina, Soon-Il An, Yang Zhao, Yang Xu, Jongsoo Shin, Deliang Chen, and Cheng Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1377, https://doi.org/10.5194/egusphere-2025-1377, 2025
Short summary
Short summary
Our research explores how European wind speeds respond to the removal of carbon dioxide from the atmosphere, focusing on their importance for wind energy. Using advanced climate models, we discovered that wind speeds react differently during periods of increased and decreased carbon dioxide levels. This study not only advances our understanding of climate dynamics but also aids in optimizing strategies for wind energy, crucial for future planning and policy-making in response to climate change.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
Short summary
Short summary
Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Zengyun Hu, Xi Chen, Deliang Chen, Zhuo Zhang, Qiming Zhou, and Qingxiang Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-82, https://doi.org/10.5194/gmd-2024-82, 2024
Preprint withdrawn
Short summary
Short summary
ERC firstly unified the evaluating, ranking, and clustering by a simple mathematic equation based on Euclidean Distance. It provides new system to solve the evaluating, ranking, and clustering tasks in SDGs. In fact, ERC system can be applied in any scientific domain.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences, 21, 2253–2272, https://doi.org/10.5194/bg-21-2253-2024, https://doi.org/10.5194/bg-21-2253-2024, 2024
Short summary
Short summary
Chinese subtropical forest ecosystems are an extremely important component of global forest ecosystems and hence crucial for the global carbon cycle and regional climate change. However, there is still great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. We provide first quantitative estimates of the individual and interactive effects of different drivers on the gross primary productivity changes of various subtropical forest types in China.
Qian Lin, Jie Chen, and Deliang Chen
EGUsphere, https://doi.org/10.5194/egusphere-2024-826, https://doi.org/10.5194/egusphere-2024-826, 2024
Preprint archived
Short summary
Short summary
Glaciers of the Tibetan Plateau (TP) have experienced widespread retreat in recent decades, but impacts of glacier changes that have occurred on regional climate, including precipitation, is still unknown. Thus, this study addressed this knowledge gap, and found that glacier changes exert a more pronounced impact on summer extreme precipitation events than mean precipitation over the TP. This provides a certain theoretical reference for the further improvement of long-term glacier projection.
Fangzhong Shi, Xiaoyan Li, Shaojie Zhao, Yujun Ma, Junqi Wei, Qiwen Liao, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 163–178, https://doi.org/10.5194/hess-28-163-2024, https://doi.org/10.5194/hess-28-163-2024, 2024
Short summary
Short summary
(1) Evaporation under ice-free and sublimation under ice-covered conditions and its influencing factors were first quantified based on 6 years of eddy covariance observations. (2) Night evaporation of Qinghai Lake accounts for more than 40 % of the daily evaporation. (3) Lake ice sublimation reaches 175.22 ± 45.98 mm, accounting for 23 % of the annual evaporation. (4) Wind speed weakening may have resulted in a 7.56 % decrease in lake evaporation during the ice-covered period from 2003 to 2017.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
Short summary
Short summary
Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
He Sun, Tandong Yao, Fengge Su, Wei Yang, Guifeng Huang, and Deliang Chen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-16, https://doi.org/10.5194/hess-2023-16, 2023
Manuscript not accepted for further review
Short summary
Short summary
Based on field research campaigns since 2017 in the Yarlung Zangbo (YZ) river basin and a well-validated model, our results reveal that large regional differences in runoff regimes and changes exist in the basin. Annual runoff shows decreasing trend in the downstream sub-basin but increasing trends in the upper and middle sub-basins, due to opposing precipitation changes. Glacier runoff plays more important role in annual total runoff in downstream basin.
Hao Li, Baoying Shan, Liu Liu, Lei Wang, Akash Koppa, Feng Zhong, Dongfeng Li, Xuanxuan Wang, Wenfeng Liu, Xiuping Li, and Zongxue Xu
Hydrol. Earth Syst. Sci., 26, 6399–6412, https://doi.org/10.5194/hess-26-6399-2022, https://doi.org/10.5194/hess-26-6399-2022, 2022
Short summary
Short summary
This study examines changes in water yield by determining turning points in the direction of yield changes and highlights that regime shifts in historical water yield occurred in the Upper Brahmaputra River basin, both the climate and cryosphere affect the magnitude of water yield increases, climate determined the declining trends in water yield, and meltwater has the potential to alleviate the water shortage. A repository for all source files is made available.
Jianting Zhao, Lin Zhao, Zhe Sun, Fujun Niu, Guojie Hu, Defu Zou, Guangyue Liu, Erji Du, Chong Wang, Lingxiao Wang, Yongping Qiao, Jianzong Shi, Yuxin Zhang, Junqiang Gao, Yuanwei Wang, Yan Li, Wenjun Yu, Huayun Zhou, Zanpin Xing, Minxuan Xiao, Luhui Yin, and Shengfeng Wang
The Cryosphere, 16, 4823–4846, https://doi.org/10.5194/tc-16-4823-2022, https://doi.org/10.5194/tc-16-4823-2022, 2022
Short summary
Short summary
Permafrost has been warming and thawing globally; this is especially true in boundary regions. We focus on the changes and variability in permafrost distribution and thermal dynamics in the northern limit of permafrost on the Qinghai–Tibet Plateau (QTP) by applying a new permafrost model. Unlike previous papers on this topic, our findings highlight a slow, decaying process in the response of permafrost in the QTP to a warming climate, especially regarding areal extent.
Changgui Lin, Erik Kjellström, Renate Anna Irma Wilcke, and Deliang Chen
Earth Syst. Dynam., 13, 1197–1214, https://doi.org/10.5194/esd-13-1197-2022, https://doi.org/10.5194/esd-13-1197-2022, 2022
Short summary
Short summary
This study endorses RCMs' added value on the driving GCMs in representing observed heat wave magnitudes. The future increase of heat wave magnitudes projected by GCMs is attenuated when downscaled by RCMs. Within the downscaling, uncertainties can be attributed almost equally to choice of RCMs and to the driving data associated with different GCMs. Uncertainties of GCMs in simulating heat wave magnitudes are transformed by RCMs in a complex manner rather than simply inherited.
Chunlüe Zhou, Cesar Azorin-Molina, Erik Engström, Lorenzo Minola, Lennart Wern, Sverker Hellström, Jessika Lönn, and Deliang Chen
Earth Syst. Sci. Data, 14, 2167–2177, https://doi.org/10.5194/essd-14-2167-2022, https://doi.org/10.5194/essd-14-2167-2022, 2022
Short summary
Short summary
To fill the key gap of short availability and inhomogeneity of wind speed (WS) in Sweden, we rescued the early paper records of WS since 1925 and built the first 10-member centennial homogenized WS dataset (HomogWS-se) for community use. An initial WS stilling and recovery before the 1990s was observed, and a strong link with North Atlantic Oscillation was found. HomogWS-se improves our knowledge of uncertainty and causes of historical WS changes.
Xiangde Xu, Chan Sun, Deliang Chen, Tianliang Zhao, Jianjun Xu, Shengjun Zhang, Juan Li, Bin Chen, Yang Zhao, Hongxiong Xu, Lili Dong, Xiaoyun Sun, and Yan Zhu
Atmos. Chem. Phys., 22, 1149–1157, https://doi.org/10.5194/acp-22-1149-2022, https://doi.org/10.5194/acp-22-1149-2022, 2022
Short summary
Short summary
A vertical transport window of tropospheric vapor exists on the Tibetan Plateau (TP). The TP's thermal forcing drives the vertical transport
windowof vapor in the troposphere. The effects of the TP's vertical transport window of vapor are of importance in global climate change.
Cited articles
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature,
438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F.,
and Schneider, D.: Snowmelt rate dictates streamflow, Geophys. Res. Lett.,
43, 8006–8016, https://doi.org/10.1002/2016GL069690, 2016.
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25∘ V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), [data set], https://doi.org/10.5067/E7TYRXPJKWOQ (last access: 9 September 2022), 2020.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. M., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 global 3hourly 0.1∘ precipitation: methodology and quantitative assessment, GloH2O, [data set], http://www.gloh2o.org/mswx/ (last access: 9 September 2022), 2019.
Bian, Q., Xu, Z., Zheng, H., Li, K.,
Liang, J., Fei, W., Shi, C., Zhang, S.,
and Yang, Z.: Multiscale Changes in Snow Over the Tibetan Plateau
During 1980–2018 Represented by Reanalysis Data Sets and Satellite
Observations, J. Geophys. Res.-Atmos., 125, e2019JD031914,
https://doi.org/10.1029/2019JD031914, 2020.
Bibi, S., Wang, L., Li, X. P., Zhou, J., Chen, D. L., and Yao, T. D.: Climatic
and associated cryospheric, biospheric, and hydrological changes on the
Tibetan Plateau: a review, Int. J. Climatol., 38, 1–17,
https://doi.org/10.1002/joc.5411, 2018.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T.,
von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., and Immerzeel
W. W.: Importance of snow and glacier meltwater for agriculture on the
Indo-Gangetic Plain, Nat. Sustain., 2, 594–601,
https://doi.org/10.1038/s41893-019-0305-3, 2019.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Chen, D. L., Xu, B. Q. Yao, T. D., Guo, Z. T., Cui, P., Chen, F. H., Zhang,
R. H., Zhang, X. Z., Zhang, Y. L., Fan, J. Hou, Z. Q., and Zhang, T. H.:
Assessment of past, present and future environmental changes on the Tibetan
Plateau, Chin. Sci. Bull., 60, 3025–3035,
https://doi.org/10.1360/N972014-01370, 2015.
China Meteorological Administration: Hourly observations from ground weather stations in China, National Meteorological Science Data Center, [data set], http://data.cma.cn/data/cdcdetail/dataCode/A.0012.0001.html, last access: 9 September 2022.
Cuo, L., Zhang, Y. X., Zhu, F., and Liang, L. Q.: 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.
Chen, Y. P., Gagen, M. H., Chen, F., Zhang, H. L., Shang, H. M., and Xu, H.
F.: Precipitation variations recorded in tree rings from the upper Salween
and Brahmaputra River valleys, China. Ecol. Indi., 113, 106189, https://doi.org/10.1016/j.ecolind.2020.106189, 2020.
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020.
Cuo, L., Beyene, T. K., Voisin, N., Su, F. G., Lettenmaier, D. P., Alberti,
M., and Richey, J. E.: Effects of mid-twenty-first century climate and land
cover change on the hydrology of the Puget Sound basin, Washington, Hydrol. Proc., 25,
1729–1753, https://doi.org/10.1002/hyp.7932, 2 011.
Ding, Y. J., Zhang, S. Q., Wu, J. K., Zhao, Q. D., Li, X. Y., and Qin, J.:
Recent progress on studies on cryospheric hydrological processes changes in
China, Adv. Water Sci.,
31, 690–702, https://doi.org/10.14042/j.cnki.32.1309.2020.05.006,
2020.
Ding, J., Yan, H., Xue, S., Feng, J., and Chen, Z.: Study on cooperative
development of water resources for international rivers in Southeast Asia,
J. Water Resour,
26, 97–102, https://doi.org/10.11705/j.issn.1672-643X.2015.02.018, 2015.
Earth Resources Observation and Science Center: A global land cover database primarily derived from 1992 to 1993 1-km AVHRR data, The U.S. Geological Survey's (USGS), [data set], https://doi.org/10.5066/F7GB230D (last access: 9 September 2022), 1997.
Etchevers, P., Durand, Y., Habets, F., Martin, E., and Noilhan, J.: Impact
of spatial resolution on the hydrological simulation of the Durance
high-Alpine catchment, France, Ann. Glaciol., 32, 87–92, https://doi.org/10.3189/172756401781819337, 2001.
Fan, H. and He, D. M.: Regional climate and its change in the Nujiang River
basin, Acta Geogr. Sin., 67, 621–630,
https://doi.org/10.11821/xb201205005, 2012.
FAO: Digital soil map of the world and derived soil properties, Land and
Water Digital Media Series Rev. 1, United Nations Food and Agriculture
Organization CD-ROM, 2003.
Food and Agriculture Organization of United Nations, Land and Water Develepment Division: Digital soil map of the world and derived soil properties, FAO, [data set], https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8, (last access: 9 September 2022), 2003.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), [data set], https://doi.org/10.5067/7MCPBJ41Y0K6, (last access: 9 September 2022), 2015.
Guo, J. H. (Eds.): Hydrogeography of western Sichuan and northern Yunnan, Scientific Publishing (China), BeiJing, China, 1985.
Guo, W. Q., Liu, S. Y., Xu, L., Wu, L. Z., Shangguan, D. H., Yao, X. J.,
Wei, J. F., Bao, W. J., Yu, P. C., Liu, Q., and Jiang, Z. L.: The second
Chinese glacier inventory: data, methods and results, J. Glaciol., 61,
357–372, https://doi.org/10.3189/2015JoG14J209, 2015.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
impr oving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
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.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), [data set], https://doi.org/10.24381/cds.adbb2d47 (last access: 9 September 2022), 2019.
He, D., Zhao, W., and Feng, Y.: Research progress of international rivers
in China, J. Geogr. Sci. 14, 21–28,
https://doi.org/10.1007/BF02841103, 2004.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow-atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change, 8,
954–963, https://doi.org/10.1029/2011GL048049, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., et al.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803,
2020.
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y.,
Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin,
S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report
on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C.,
Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K.,
Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., Weyer, N. M.,
https://www.ipcc.ch/srocc/chapter/chapter-2/ (last access: 9 September 2022), 2019.
Hong, M. and He S.: Spatial and Temporal Change of Rainfall in Nujiang Basin
in Recent 50 Years, Res. Soil Water Conser., 26, 248–252,
https://doi.org/10.13869/j.cnki.rswc.2019.03.036, 2019.
Huning, L. S. and AghaKouchak, A.: Global snow drought hot spots and
characteristics, P. Natl. Acad. Sci. USA, 117, 19753–19759,
https://doi.org/10.1073/pnas.1915921117, 2020.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T.,
Hyde, S., Brumby, S., Davies, B.J., Elmore, A. C., Emmer, A., Feng, M.,
Fernandez, A., Haritashya, U., Kargel, J. S., Koppes, M.,
Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S.,
Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S.,
Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T.,
and Baillie, J. E. M.: Importance and vulnerability of the world's water towers,
Nature, 577, 364–369,
https://doi.org/10.1038/s41586-019-1822-y, 2020.
Immerzeel, W. W., Van Beek, L. P., and Bierkens, M. F. P.: Climate change
will affect the Asian water towers, Science, 328, 1382–1385,
https://doi.org/10.1126/science.1183188, 2010.
IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing
Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V.,
Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegrie, A.,
Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., IPCC, https://www.ipcc.ch/report/srocc/ (last access: 9 September 2022), 2019.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database, [data set], http://srtm.csi.cgiar.org, (last access: 9 September 2022), 2008.
Jia, X., Zhang, C., Wu, R. G., and Qian, Q. F.: Influence of Tibetan Plateau
autumn snow cover on interannual variations in spring precipitation over
southern China, Clim. Dynam., 56, 767–782,
https://doi.org/10.1007/s00382-020-05497-8, 2021.
Kraaijenbrink, P. D. A., Stigter, E. E., Yao, T. D., and Immerzeel, W. W.:
Climate change decisive for Asia's snow meltwater supply, Nat. Clim. Chang.,
1, 591–597, https://doi.org/10.1038/s41558-021-01074-x, 2021.
Khanal, S., Lutz, A. F., Kraaijenbrink, P. D. A., van den Hurk, B., Yao, T.,
and Immerzeel, W. W.: Variable 21st century climate change response for
rivers in High Mountain Asia at seasonal to decadal time scales, Water
Resour. Res., 57, e2020WR029266,
https://doi.org/10.1029/2020WR029266, 2021.
Kapnick, S., Delworth, T., Ashfaq, M., Malyshev, S., and Milly, P. C. D.:
Snowfall less sensitive to warming in Karakoram than in Himalayas due to a
unique seasonal cycle, Nat. Geosci., 7, 834–840,
https://doi.org/10.1038/ngeo2269, 2014.
Lange, S. and Büchner, M.: ISIMIP3b bias-adjusted atmospheric climate input data (v1.1). ISIMIP Repository, [data set], https://doi.org/10.48364/ISIMIP.842396.1 (last access: 9 September 2022), 2021.
Li, H., Li, X., Yang, D., Wang, J., Gao, B., Pan, X., Zhang, Y., and Hao, X.:
Tracing snowmelt paths in an integrated hydrological model for understanding
seasonal snowmelt contribution at basin scale, J. Geophys. Res.-Atmos., 124,
8874–8895, https://doi.org/10.1029/2019JD030760, 2019.
Li, W., Guo, W., Qiu, B., Xue, Y., Hu, P., and Wei, J.: Influence of
Tibetan Plateau snow cover on East Asian atmospheric circulation at
medium-range time scales, Nat. Commun., 9, 4243,
https://doi.org/10.1038/s41467-018-06762-5, 2018.
Liang, S. L.: The Global Land Surface Satellite (GLASS) Product Suite, National Earth System Science Data Center, National Science & Technology Infrastructure of China, [data set], https://doi.org/10.12041/geodata.GLASS_LAI_MODIS(0.05D).ver1.db (last access: 9 September 2022), 2015.
Lutz, A., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.:
Consistent increase in High Asia's runoff due to increasing glacier melt and
precipitation, Nat. Clim. Change, 4, 587–592,
https://doi.org/10.1038/nclimate2237, 2014.
Liu, S., Ding W., Mo X. G., Wang S., Liu C., Luo X., He D., Bajracharya, S.
R., Shrestha, A., and Agrawal, N. K.: Climate Change and Its Impact on
Runoff in Lancang and Nujiang River Basins, Adv. Clim. Change Res., 13,
356–365, 2017.
Luo, X., He, D. M., Ji, X. Lu, Y., and Li, Y.: Low Flow Variations in the
Middle and Upper Nujiang River Basin and Possible Responds to Climate Change
in Recent 50 Years, Acta Geogr. Sin., 36, 107–113,
https//doi.org/10.13249/j.cnki.sgs.2016.01.013, 2016.
Liu, C., Bai, P., Wang, Z., Liu S., and Liu, X. M.: Study on prediction of
ungaged basins case study on the Tibetan Platea, J. Hydra. Eng., 47,
272–282, https://doi.org/10.13243/j.cnki.slxb.20150925, 2016.
Liu, S., Yan, D., Wang, H., Qin, T., Wen, B., and Lu, Y.: Separation of
snowfall from precipitation and its evolution trend and reasons analysis in
upper reaches of Nujiang River Basin, J. Hydra. Eng., 49, 254–262,
https//doi.org/10.13243/j.cnki.slxb.20160789, 2018.
Liu, W., Wang, L., Sun, F., Li, Z., Wang, H., Liu, J., Yang, T., Zhou, J.,
and Qi, J.: Snow hydrology in the upper Yellow River basin under climate
change: A land surface modeling perspective. J. Geophys. Res-Atmos., 123,
12676–12691, https://doi.org/10.1029/2018JD028984, 2018.
Li, C., Su, F., Yang, D., Tong, K., Meng, F., and Kan, B.: Spatiotemporal
variation of snow cover over the Tibetan Plateau based on MODIS snow
product, 2001–2014, Int. J. Climatol., 38, 708–728,
https://doi.org/10.1002/joc.5204, 2017.
Lange, S. and Büchner, M.: ISIMIP3b bias-adjusted atmospheric climate
input data (v1.1), ISIMIP Repository,
https://doi.org/10.48364/ISIMIP.842396.1, 2021a.
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019.
Lange, S.: ISIMIP3BASD v2.5.0, Zenodo,
https://doi.org/10.5281/zenodo.4686991, 2021b.
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A.,
Bellouin, N., Schmied, H. M., Hersbach, H., Buontempo, C., and Cagnazzo, C.:
WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0),
https://doi.org/10.48364/ISIMIP.342217, 2021c.
Muhammad, S.: Improved daily MODIS TERRA/AQUA Snow and Randolph Glacier Inventory (RGI6.0) data for High Mountain Asia (2002–2019), PANGAEA [data set], https://doi.org/10.1594/PANGAEA, (last access: 9 September 2022), 2020.
Musselman, K. N., Addor, N., Vano, J. A., and Molotch, N. P.: Winter melt
trends portend widespread declines in snow water resources, Nat. Clim.
Change, 11, 418–424,
https://doi.org/10.1038/s41558-021-01014-9, 2021.
Mao, R. J., Wang, L., Zhou, J., Liu, X. P., Qi, J., and Zhong, X. Y.:
Evaluation of Various Precipitation Products Using Ground-Based Discharge
Observation at the Nujiang River Basin, China, Water, 11, 2308,
https://doi.org/10.3390/w11112308, 2019.
Muhammad, S. and Thapa, A.: An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06∗) for high-mountain Asia between 2002 and 2018, Earth Syst. Sci. Data, 12, 345–356, https://doi.org/10.5194/essd-12-345-2020, 2020.
Marty, C., Tilg, A., and Jonas, T.: Recent Evidence of Large-Scale Receding
Snow Water Equivalents in the European Alps, J. Hydrometeorol., 18,
1021–1031, https://doi.org/10.1175/JHM-D-16-0188.1, 2017.
Nepal, S., Flügel, W. A., and Shrestha, A. B.: Upstream-downstream
linkages of hydrological processes in the Himalayan region, Ecol. Proc.,
3, 1–16, https://doi.org/10.1186/s13717-014-0019-4, 2014.
Nepal, S., Khatiwada, K. R., Pradhananga, S., Kralisch, S., Samyn, D.,
Bromand, M. T., Jamal, N., Dildar, M., Durrani, F., Rassouly F., Azizi, F.,
Salehi, W., Malikzooi, R., Krause, P., Koirala, S., and Chevallier, P.:
Future snow projecteds in a small basin of the Western Himalaya, Sci. Total
Environ., 795,
148587, https://doi.org/10.1016/j.scitotenv.2021.148587, 2021.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J.,
Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg,
J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018,
Nature, 581, 294–298,
https://doi.org/10.1038/s41586-020-2258-0, 2020.
Panday, P. K., Thibeault, J., and Frey, K. E.: Changing temperature and
precipitation extremes in the Hindu Kush-Himalayan region: an analysis of
CMIP3 and CMIP5 simulations and projecteds, Int. J. Climatol., 35,
3058–3077, https://doi.org/10.1002/joc.4192, 2015.
Qin, Y., Abatzoglou, J. T., Siebert, S., Huning, L. S., AghaKouchak, A.,
Mankin, J. S., Hong, C., Tong, D., Davis, S. J., and Mueller, N. D.:
Agricultural risks from changing snowmelt, Nat. Clim. Change, 10, 459–465,
https://doi.org/10.1038/s41558-020-0746-8, 2020.
Qi, J., Wang, L., Zhou, J., Song, L., Li, X. P., and Zeng, T.: Coupled Snow
and Frozen Ground Physics Improves Cold Region Hydrological Simulations: An
Evaluation at the upper Yangtze River Basin (Tibetan Plateau), J. Geophys.
Res.-Atmos., 124, 12985–13004,
https://doi.org/10.1029/2019JD031622, 2019.
Qi, W., Feng, L., Yang, H., and Liu, J.: Warming winter, drying spring and
shifting hydrological regimes in Northeast China under climate change, J.
Hydrol., 606, 127390,
https://doi.org/10.1016/j.jhydrol.2021.127390, 2022.
Qi, W., Feng, L., Liu, J., and Yang, H.: Snow as an important natural
reservoir for runoff and soil moisture in Northeast China. J. Geophys.
Res.-Atmos., 125, e2020JD033086,
https://doi.org/10.1029/2020JD033086, 2020.
Shrestha, M., Wang, L., Koike, T., Xue, Y., and Hirabayashi, Y.: Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites, Hydrol. Earth Syst. Sci., 14, 2577–2594, https://doi.org/10.5194/hess-14-2577-2010, 2010.
Shrestha, M., Wang, L., Koike, T., Tsutsui, H., Xue, Y., and Hirabayashi, Y.: Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data, Hydrol. Earth Syst. Sci., 18, 747–761, https://doi.org/10.5194/hess-18-747-2014, 2014.
Sanjay, J., Krishnan, R., Shrestha, A. B., Rajbhandari, R., and Ren, G. Y.:
Downscaled climate change projecteds for the Hindu Kush Himalayan region
using CORDEX South Asia regional climate models, Adv. Clim. Change Res., 8,
185–198, https://doi.org/10.1016/j.accre.2017.08.003, 2017.
Su, F., Zhang L., Ou, T., Chen, D., Yao, T., and Tong, K.: Hydrological
response to future climate changes for the major upstream river basins in
the Tibetan Plateau, Global Planet. Change, 136, 82–95,
https://doi.org/10.1016/j.gloplacha.2015.10.012, 2016.
Song, L., Wang, L., Li, X. P., Zhou, J., Luo, D. L., Jin, H. J., Qi, J.,
Zeng, T., and Yin, Y. Y.: Improving permafrost physics in a distributed
cryosphere-hydrology model and its evaluations at the upper Yellow River
Basin, J. Geophys. Res-Atmos., 125, e2020JD032916,
https://doi.org/10.1029/2020JD032916, 2020.
Scherrer, S. C., Ceppi, P., Croci-Maspoli, M., and Appenzeller, C.:
Snow-albedo feedback and Swiss spring temperature trends, Theor. Appl.
Climatol., 110, 509–516,
https://doi.org/10.1007/s00704-012-0712-0, 2012.
Sohrabi, M. M., Tonina, D., Benjankar, R.,Kumar, M., Kormos, P., Marks, D.
and Luce, C.: On the role of spatial resolution on snow estimates using a
process-based snow model across a range of climatology and elevation,
Hydrol. Proc., 33, 1260–1275, https://doi.org/10.1002/hyp.13397, 2019.
Tang, Q. H., Cuo, L., Su, F. G., Liu, X. C., Sun, H., Ding, J., Wang, L.,
Leng, G. Y., Zhang, Y. Q., Sang, Y. F., Fang, H. Y., Zhang, S. F., Han, D.
M., Liu, X. M., He, L., Xu, X. M., Tang, Y., and Chen, D. L.: Streamflow
change on the Qinghai-Tibet Plateau and its impacts, Chin. Sci. Bull., 64,
2807–2821, https://doi.org/10.1360/TB-2019-0141, 2019.
Tang, Q. H., Liu, X. C., Zhou, Y. Y., Wang, J., and Yun, X. B.: Cascading
Impacts of Asian Water Tower Change on Downstream Water Systems, Bull. Chin.
Acad. Sci., 34, 1306–1312,
https://doi.org/10.16418/j.issn.1000-3045.2019.11.013, 2019.
Viviroli, D., Kummu, M., Meybeck, M., Kallio, M., and Wada, Y.: Increasing
dependence of lowland populations on mountain water resources, Nat. Sustain.,
3, 917–928, https://doi.org/10.1038/s41893-020-0559-9, 2020.
Wan, Z.: New refinements and validation of the collection-6 MODIS
land-surface temperature/emissivity product, Remote Sens. Environ., 140,
36–45, https://doi.org/10.1016/j.rse.2013.08.027, 2014.
Wan, Z., Hook, S., and Hulley, G.: MYD11A2 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD11A2.006 (last access: 9 September 2022), 2015.
Wang, L., Sun, L. T., Shrestha, M., Li, X. P., Liu, W. B., Zhou, J., Yang,
K., Lu, H., and Chen, D. L.: Improving snow process modeling with
satellitebased estimation of near-surface-air-temperature lapse rate, J.
Geophys. Res.-Atmos., 121, 12005–12030,
https://doi.org/10.1002/2016JD025506, 2016.
Wang, L., Yao, T. D., Chai, C. H., Cuo, L., Su, F. G., Zhang, F., Yao, Z.,
Zhang, Y. S., Li, X. P., Qi, J., Hu, Z. D., Liu, J. S., and Wang, Y. W.:
TP-River: Monitoring and Quantifying Total River Runoff from the Third Pole,
B. Am. Meteorol. Soc., 102, 948–965,
https://doi.org/10.1175/BAMS-D-20-0207.1, 2020.
Wang, L., Koike, T., Yang, K., and Yeh, J. F.: Assessment of a distributed
biosphere hydrological model against streamflow and MODIS land surface
temperature in the upper Tone River Basin, J. Hydrol., 377, 21–34,
https://doi.org/10.1016/j.jhydrol.2009.08.005, 2009b.
Wang, L., Koike, T., Yang, K., Jin, R., and Li, H.: Frozen soil parameterization in a distributed biosphere hydrological model, Hydrol. Earth Syst. Sci., 14, 557–571, https://doi.org/10.5194/hess-14-557-2010, 2010.
Wang, L., Zhou, J., Qi, J., Sun, L., Yang, K., Tian, L., Lin, Y., Liu, W.,
Shrestha, M., Xue, Y., Koike, T., Ma, Y., Li, X., Chen, Y., Chen, D., Piao,
S., and Lu, H.: Development of a land surface model with coupled snow and
frozen soil physics, Water Resour. Res., 53, 5085–5103,
https://doi.org/10.1002/2017WR020451, 2017.
Winstral, A., Marks, D., and Gurney, R: Assessing the Sensitivities of a
Distributed Snow Model to Forcing Data Resolution, J. Hydrometeorol., 15,
1366–1383, https://doi.org/10.1175/JHM-D-13-0169.1, 2014.
Xiao, L., Che, T., Chen, L., Xie, H., and Dai, L.: Quantifying Snow Albedo
Radiative Forcing and Its Feedback during 2003–2016, Remote Sens., 9, 883,
https://doi.org/10.3390/rs9090883, 2017.
Xu, W., Ma, L., Ma, M., Zhang, H., and Yuan, W.: Spatial-temporal
variability of snow cover and depth in the Qinghai-Tibetan Plateau, J.
Climate, 30, 1521–1533, https://doi.org/10.1175/JCLI-D-15-0732.1, 2017.
Xiao, Z., Liang, S., Wang, J., Chen, P., Yin, X., Zhang, L., and Song, J.: Use
of general regression neural networks for generating the GLASS Leaf Area
Index Product from Time Series MODIS Surface Reflectance, IEEE T. Geosci.
Remote, 52, 209–223,
https://doi.org/10.1109/TGRS.2013.2237780, 2014.
Xue, B., Wang, L., Yang, K., Tian, L., Qin, J., Chen, Y., Zhao, L., Ma, Y.,
Koike, T., Hu, Z., and Li, X.: Modeling the land surface water and energy
cycles of a mesoscale watershed in the central Tibetan Plateau during summer
with a distributed hydrological model, J. Geophys. Res.-Atmos, 118,
8857–8868, https://doi.org/10.1002/jgrd.50696, 2013.
Yan, D., Ma, N., and Zhang, Y.: Development of a fine-resolution snow depth
product based on the snow cover probability for the Tibetan Plateau:
Validation and spatial-temporal analyses, J. Hydrol., 604, 127027,
https://doi.org/10.1016/j.jhydrol.2021.127027, 2022.
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang,
S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan Plateau
and its influence on the broad climate system, Earth-Sci. Rev., 201, 103043,
https://doi.org/10.1016/j.earscirev.2019.103043, 2020.
Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau,
W. K., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J.,
Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L.,
Wang, W., Zhang, F., Sheng, Y., Guo, W., Yang, X., Ma, Y., Shen, S. S. P.,
Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V. P., Yang, K., Yang, D.,
Zhao, X., Qian, Y., Zhang, Y., and Li, Q.: Recent Third Pole's rapid warming
accompanies cryospheric melt and water cycle intensification and
interactions between monsoon and environment: Multidisciplinary approach
with observations, modeling, and analysis, B. Am. Meteorol. Soc., 100,
423–444, https://doi.org/10.1175/BAMS-D-17-0057.1, 2019.
Yao, T., Wu, G., Xu, B., Wang, W., Gao, J., and An, B.: Asian Water Tower
Change and Its Impacts, Bull. Chin. Acad. Sci., 34, 1203–1209,
https://doi.org/10.16418/j.issn.1000-3045.2019.11.003, 2019.
Yao, Z., Duan, R., and Liu, Z.: Changes in Precipitation and Air Temperature
and Its Impacts on Runoff in the Nujiang River basin, Resour. Sci., 34,
202–210, 2012.
You, W., Wu, X., and Guo, Z.: Transboundary flow change features of the
Nujiang river in the longitudinal range gorge region, Mount. Res., 26,
22–28, https://doi.org/10.3969/j.issn.1008-2786.2008.01.005, 2008.
Yang, K. and He, J.: China meteorological forcing dataset (1979–2018), National Tibetan Plateau Data Center, [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file, (last access: 9 September 2022), 2019.
Yang, F., Lu, H., Yang, K., Huang, G., Li, Y., Wang, W., Lu, P., Tian, F.,
and Huang, Y.: Hydrological characteristics and changes in the Nu-Salween
River basin revealed with model-based reconstructed data, J. Mt. Sci., 18,
2982–3002, https://doi.org/10.1007/s11629-021-6727-1, 2021.
Yang, Y., Wen, B., Yan, Y., Niu, Y., Dai, Y., Li, M., and Gong, X.:
Partitioning the contributions of cryospheric change to the increase of
streamflow on the Nu river, J. Hydrol., 598, 126330,
https://doi.org/10.1016/j.jhydrol.2021.126330, 2021.
Zhao, Q., 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.
Zhang, W., Xiao, Z., Zheng, J., and Ren, J.: Long-term variation
characteristics of Nujiang River discharge and its response to climate
change, Chin. Sci. Bull., 52, 135–141,
https://doi.org/10.1007/s11434-007-7019-z, 2007.
Zhong, X., Wang, L., Zhou, J., Li, X., and Wang, Y.: Precipitation Dominates
Long-Term Water Storage Changes in Nam Co Lake (Tibetan Plateau) Accompanied
by Intensified Cryosphere Melts Revealed by a Basin-Wide Hydrological
Modelling, Remote Sens-Base, 12, 1926,
https://doi.org/10.3390/rs12121926, 2020.
Zhou, J., Wang, L., Zhong, X., Yao, T., Qi, J., Wang, Y., and Xue, Y.:
Quantifying the major drivers for the expanding lakes in the interior
Tibetan Plateau, Sci. Bull., 67, 474–478,
https://doi.org/10.1016/j.scib.2021.11.010, 2021.
Short summary
This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.
This work quantifies future snow changes and their impacts on hydrology in the upper Salween...
