• Adhikari, A., M. R. Ehsani, Y. Song, and A. Behrangi, 2020: Comparative assessment of snowfall retrieval from microwave humidity sounders using machine learning methods. Earth Space Sci., 7, e2020EA001357, https://doi.org/10.1029/2020EA001357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bookhagen, B., and D. W. Burbank, 2010: Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res., 115, F03019, https://doi.org/10.1029/2009JF001426.

    • Search Google Scholar
    • Export Citation
  • Chen, X., D. Long, Y. Hong, C. Zeng, and D. Yan, 2017: Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin? Water Resour. Res., 53, 24312466, https://doi.org/10.1002/2016WR019656.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuo, L., and Y. Zhang, 2017: Spatial patterns of wet season precipitation vertical gradients on the Tibetan Plateau and the surroundings. Sci. Rep., 7, 5057, https://doi.org/10.1038/s41598-017-05345-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahri, Z. H., and Coauthors, 2021: Spatio‐temporal evaluation of gridded precipitation products for the high‐altitude Indus basin. Int. J. Climatol., 41, 42834306, https://doi.org/10.1002/joc.7073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ehsani, M. R., and Coauthors, 2020: 2019–2020 Australia fire and its relationship to hydroclimatological and vegetation variabilities. Water, 12, 3067, https://doi.org/10.3390/w12113067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z., D. Duethmann, and F. Tian, 2021: A meta-analysis based review of quantifying the contributions of runoff components to streamflow in glacierized basins. J. Hydrol., 603, 126890, https://doi.org/10.1016/j.jhydrol.2021.126890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hock, R., 2003: Temperature index melt modelling in mountain areas. J. Hydrol., 282, 104115, https://doi.org/10.1016/S0022-1694(03)00257-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janitza, S., and R. Hornung, 2018: On the overestimation of random forest’s out-of-bag error. PLOS ONE, 13, e0201904, https://doi.org/10.1371/journal.pone.0201904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, Y., K. Yang, C. Shao, X. Zhou, L. Zhao, Y. Chen, and H. Wu, 2021: A downscaling approach for constructing a high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis. Atmos. Res., 256, 105574, https://doi.org/10.1016/j.atmosres.2021.105574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kan, B., F. Su, B. Xu, Y. Xie, J. Li, and H. Zhang, 2018: Generation of high mountain precipitation and temperature data for a quantitative assessment of flow regime in the upper Yarkant basin in the Karakoram. J. Geophys. Res. Atmos., 123, 84628486, https://doi.org/10.1029/2017JD028055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khanal, S., A. F. Lutz, P. D. A. Kraaijenbrink, B. van den Hurk, T. Yao, and W. W. Immerzeel, 2021: 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lai, H.-W., H. W. Chen, J. Kukulies, T. Ou, and D. Chen, 2021: Regionalization of seasonal precipitation over the Tibetan Plateau and associated large-scale atmospheric systems. J. Climate, 34, 26352651, https://doi.org/10.1175/JCLI-D-20-0521.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., 2019: Study on discharge simulation and projection over the watersheds of Upper Indus Basin. Ph.D. thesis, University of Chinese Academy of Sciences.

  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land-surface water and energy fluxes. J. Geophys. Res., 99, 14 41514 428, https://doi.org/10.1029/94JD00483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, and E. F. Wood, 1996: One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer Variable Infiltration Capacity model. J. Geophys. Res., 101, 21 40321 422, https://doi.org/10.1029/96JD01448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Y., and Coauthors, 2018: Contrasting streamflow regimes induced by melting glaciers across the Tien Shan-Pamir-North Karakoram. Sci. Rep., 8, 16470, https://doi.org/10.1038/s41598-018-34829-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lutz, A. F., W. W. Immerzeel, A. B. Shrestha, and M. F. P. Bierkens, 2014: Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Climate Change, 4, 587592, https://doi.org/10.1038/nclimate2237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lutz, A. F., W. W. Immerzeel, P. D. A. Kraaijenbrink, A. B. Shrestha, and M. F. P. Bierkens, 2016: Climate change impacts on the upper Indus hydrology: Sources, shifts and extremes. PLOS ONE, 11, e0165630, https://doi.org/10.1371/journal.pone.0165630.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mölg, T., F. Maussion, and D. Scherer, 2014: Mid-latitude westerlies as a driver of glacier variability in monsoonal High Asia. Nat. Climate Change, 4, 6873, https://doi.org/10.1038/nclimate2055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mukhopadhyay, B., and A. Khan, 2014: A quantitative assessment of the genetic sources of the hydrologic flow regimes in Upper Indus Basin and its significance in a changing climate. J. Hydrol., 509, 549572, https://doi.org/10.1016/j.jhydrol.2013.11.059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mukhopadhyay, B., and A. Khan, 2015: A reevaluation of the snowmelt and glacial melt in river flows within Upper Indus Basin and its significance in a changing climate. J. Hydrol., 527, 119132, https://doi.org/10.1016/j.jhydrol.2015.04.045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nan, Y., Z. He, F. Tian, Z. Wei, and L. Tian, 2021: Can we use precipitation isotope outputs of isotopic general circulation models to improve hydrological modeling in large mountainous catchments on the Tibetan Plateau? Hydrol. Earth Syst. Sci., 25, 61516172, https://doi.org/10.5194/hess-25-6151-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oppel, H., and S. Fischer, 2020: A new unsupervised learning method to assess clusters of temporal distribution of rainfall and their coherence with flood types. Water Resour. Res., 56, e2019WR026511, https://doi.org/10.1029/2019WR026511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ou, T., D. Chen, X. Chen, C. Lin, K. Yang, H. -W. Lai, and F. Zhang, 2020: Simulation of summer precipitation diurnal cycles over the Tibetan Plateau at the gray-zone grid spacing for cumulus parameterization. Climate Dyn., 54, 35253539, https://doi.org/10.1007/s00382-020-05181-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, F., Y. Hong, and D. P. Lettenmaier, 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata basin. J. Hydrometeor., 9, 622640, https://doi.org/10.1175/2007JHM944.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, F., L. Zhang, T. Ou, D. Chen, T. Yao, K. Tong, and Y. Qi, 2016: Hydrological response to future climate changes for the major upstream river basins in the Tibetan Plateau. Global Planet. Change, 136, 8295, https://doi.org/10.1016/j.gloplacha.2015.10.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, F., and Coauthors, 2022: Contrasting fate of western Third Pole’s water resources under 21st century climate change. Earth’s Future, 10, e2022EF002776, https://doi.org/10.1029/2022EF002776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, H., and F. Su, 2020: Precipitation correction and reconstruction for streamflow simulation based on 262 rain gauges in the upper Brahmaputra of southern Tibetan Plateau. J. Hydrol., 590, 125484, https://doi.org/10.1016/j.jhydrol.2020.125484.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, H., F. Su, J. Huang, T. Yao, Y. Luo, and D. Chen, 2020: Contrasting precipitation gradient characteristics between westerlies and monsoon dominated upstream river basins in the Third Pole. Chin. Sci. Bull., 65, 91104, https://doi.org/10.1360/TB-2019-0491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, H., F. Su, Z. He, T. Ou, D. Chen, Z. Li, and Y. Li, 2021a: Hydrological evaluation of high-resolution precipitation estimates from the WRF Model in the Third Pole river basins. J. Hydrometeor., 22, 20552071, https://doi.org/10.1175/JHM-D-20-0272.1.

    • Search Google Scholar
    • Export Citation
  • Sun, H., and Coauthors, 2021b: General overestimation of ERA5 precipitation in flow simulations for high mountain Asia basins. Environ. Res. Commun., 3, 121003, https://doi.org/10.1088/2515-7620/ac40f0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., M. P. Clark, S. M. Papalexiou, Z. Ma, and Y. Hong, 2020: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, K., F. Su, D. Yang, and Z. Hao, 2014a: Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. J. Hydrol., 519, 423437, https://doi.org/10.1016/j.jhydrol.2014.07.044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, K., F. Su, D. Yang, L. Zhang, and Z. Hao, 2014b: Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. Int. J. Climatol., 34, 265285, https://doi.org/10.1002/joc.3682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, K., F. Su, and B. Xu, 2016: Quantifying the contribution of glacier meltwater in the expansion of the largest lake in Tibet. J. Geophys. Res. Atmos., 121, 11 15811 173, https://doi.org/10.1002/2016JD025424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viviroli, D., and Coauthors, 2011: Climate change and mountain water resources: Overview and recommendations for research, management and policy. Hydrol. Earth Syst. Sci., 15, 471504, https://doi.org/10.5194/hess-15-471-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q., and Coauthors, 2020: Sequence-based statistical downscaling and its application to hydrologic simulations based on machine learning and big data. J. Hydrol., 586, 124875, https://doi.org/10.1016/j.jhydrol.2020.124875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., Y. Luo, L. Sun, C. He, Y. Zhang, and S. Liu, 2016: Attribution of runoff decline in the Amu Darya River in central Asia during 1951–2007. J. Hydrometeor., 17, 15431560, https://doi.org/10.1175/JHM-D-15-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., Y. Luo, L. Sun, and M. Shafeeque, 2021: Different climate factors contributing for runoff increases in the high glacierized tributaries of Tarim River Basin, China. J. Hydrol. Reg. Stud., 36, 100845, https://doi.org/10.1016/j.ejrh.2021.100845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., D. Kane, Z. Zhang, D. Legates, and B. Goodison, 2005: Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, https://doi.org/10.1029/2005GL024057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, T., 2014: TPE international program: A program for coping with major future environmental challenges of the third pole region. Prog. Geogr., 33, 884892, https://doi.org/10.11820/dlkxjz.2014.07.003.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., F. Su, D. Yang, Z. Hao, and K. Tong, 2013: Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau. J. Geophys. Res. Atmos., 118, 85008518, https://doi.org/10.1002/jgrd.50665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and A. Ye, 2021: Machine learning for precipitation forecasts post-processing: Multimodel comparison and experimental investigation. J. Hydrometeor., 22, 30653085, https://doi.org/10.1175/JHM-D-21-0096.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., C.-Y. Xu, Z. Hao, L. Zhang, Q. Ju, and X. Lai, 2020: Variation of melt water and rainfall runoff and their impacts on streamflow changes during recent decades in two Tibetan Plateau basins. Water, 12, 3112, https://doi.org/10.3390/w12113112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and Coauthors, 2019: 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, 6081, https://doi.org/10.1016/j.jhydrol.2019.03.043.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins

He SunaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China

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Tandong YaoaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China
bCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Fengge SuaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China
bCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Zhihua HedCentre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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Guoqiang TangeCentre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada

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Ning LiaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Bowen ZhengaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Jingheng HuangaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research (TPESER), Chinese Academy of Sciences, Beijing, China
cUniversity of Chinese Academy of Sciences, Beijing, China

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Fanchong MengfCollege of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China

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Tinghai OugRegional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

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Deliang ChengRegional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

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Abstract

Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.

Significance Statement

The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: He Sun, sunhe@itpcas.ac.cn

Abstract

Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.

Significance Statement

The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: He Sun, sunhe@itpcas.ac.cn

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