Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System
This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and doma...
| Published in: | Journal of Geophysical Research: Oceans |
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| Main Authors: | , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Wiley Periodicals, Inc.
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2027.42/167423 https://doi.org/10.1029/2020JD034131 |
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ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/167423 |
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openpolar |
| institution |
Open Polar |
| collection |
University of Michigan: Deep Blue |
| op_collection_id |
ftumdeepblue |
| language |
unknown |
| topic |
Noah‐MP downscaling High Mountain Asia hyper‐resolution modeling Atmospheric and Oceanic Sciences Science |
| spellingShingle |
Noah‐MP downscaling High Mountain Asia hyper‐resolution modeling Atmospheric and Oceanic Sciences Science Xue, Yuan Houser, Paul R. Maggioni, Viviana Mei, Yiwen Kumar, Sujay V. Yoon, Yeosang Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| topic_facet |
Noah‐MP downscaling High Mountain Asia hyper‐resolution modeling Atmospheric and Oceanic Sciences Science |
| description |
This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.Key PointsThe skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presentedThe study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrainThe study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulations Peer Reviewed ... |
| format |
Article in Journal/Newspaper |
| author |
Xue, Yuan Houser, Paul R. Maggioni, Viviana Mei, Yiwen Kumar, Sujay V. Yoon, Yeosang |
| author_facet |
Xue, Yuan Houser, Paul R. Maggioni, Viviana Mei, Yiwen Kumar, Sujay V. Yoon, Yeosang |
| author_sort |
Xue, Yuan |
| title |
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| title_short |
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| title_full |
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| title_fullStr |
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| title_full_unstemmed |
Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System |
| title_sort |
evaluation of high mountain asia‐land data assimilation system (version 1) from 2003 to 2016, part i: a hyper‐resolution terrestrial modeling system |
| publisher |
Wiley Periodicals, Inc. |
| publishDate |
2021 |
| url |
https://hdl.handle.net/2027.42/167423 https://doi.org/10.1029/2020JD034131 |
| genre |
Arctic The Cryosphere |
| genre_facet |
Arctic The Cryosphere |
| op_relation |
Xue, Yuan; Houser, Paul R.; Maggioni, Viviana; Mei, Yiwen; Kumar, Sujay V.; Yoon, Yeosang (2021). "Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System." Journal of Geophysical Research: Atmospheres 126(8): n/a-n/a. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/167423 doi:10.1029/2020JD034131 Journal of Geophysical Research: Atmospheres Ruiz‐Arias, J. A., Alsamamra, H., Tovar‐Pescador, J., & Pozo‐Vázquez, D. ( 2010 ). Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions. Energy Conversion and Management, 51 ( 5 ), 881 – 893. https://doi.org/10.1016/j.enconman.2009.11.024 Kumar, S. V., Peters‐Lidard, C. D., Mocko, D., & Tian, Y. ( 2013 ). Multiscale evaluation of the improvements in surface snow simulation through terrain adjustments to radiation. Journal of Hydrometeorology, 14 ( 1 ), 220 – 232. https://doi.org/10.1175/jhm-d-12-046.1 Kumar, S. V., Peters‐Lidard, C. D., Tian, Y., Houser, P., Geiger, J., Olden, S., et al. ( 2006 ). Land information system: An interoperable framework for high resolution land surface modeling. Environmental Modelling & Software, 21 ( 10 ), 1402 – 1415. https://doi.org/10.1016/j.envsoft.2005.07.004 Latt, Z. Z. ( 2015 ). Flood assessment and improving flood forecasting for a monsoon dominated river basin: With emphasis on black‐box models and GIS (Unpublished doctoral dissertation). Universitätsbibliothek der Leuphana Universität Lüneburg. Lawrence, M. G. ( 2005 ). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society, 86 ( 2 ), 225 – 234. https://doi.org/10.1175/bams-86-2-225 Marshall, J., & Plumb, R. A. ( 1989 ). Atmosphere, ocean and climate dynamics: An introductory text (Vol. 43 ). Academic Press. Mei, Y., Maggioni, V., Houser, P., Xue, Y., & Rouf, T. ( 2020 ). A nonparametric statistical technique for spatial downscaling of precipitation over high mountain Asia. Water Resources Research, 56 ( 11 ), e2020WR027472. https://doi.org/10.1029/2020wr027472 Mishra, S. K., Hayse, J., Veselka, T., Yan, E., Kayastha, R. B., LaGory, K., et al. ( 2018 ). An integrated assessment approach for estimating the economic impacts of climate change on river systems: An application to hydropower and fisheries in a Himalayan river, Trishuli. Environmental Science & Policy, 87, 102 – 111. https://doi.org/10.1016/j.envsci.2018.05.006 Molteni, F., Buizza, R., Palmer, T. N., & Petroliagis, T. ( 1996 ). The ECMWF ensemble prediction system: Methodology and validation. Quarterly Journal of the Royal Meteorological Society, 122 ( 529 ), 73 – 119. https://doi.org/10.1002/qj.49712252905 Nash, J. E., & Sutcliffe, J. V. ( 1970 ). River flow forecasting through conceptual models part I–A discussion of principles. Journal of Hydrology, 10 ( 3 ), 282 – 290. https://doi.org/10.1016/0022-1694(70)90255-6 Niu, G.‐Y., Yang, Z.‐L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., et al. ( 2011 ). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. Journal of Geophysical Research, 116 ( D12 ). https://doi.org/10.1029/2010jd015139 Pulliainen, J. ( 2006 ). Mapping of snow water equivalent and snow depth in boreal and sub‐arctic zones by assimilating space‐borne microwave radiometer data and ground‐based observations. Remote Sensing of Environment, 101 ( 2 ), 257 – 269. https://doi.org/10.1016/j.rse.2006.01.002 Rouf, T., Mei, Y., Maggioni, V., Houser, P., & Noonan, M. ( 2019 ). A physically‐based atmospheric variables downscaling technique. Journal of Hydrometeorology, 21, 93 – 108. Singh, R. S., Reager, J. T., Miller, N. L., & Famiglietti, J. S. ( 2015 ). Toward hyper‐resolution land‐surface modeling: The effects of fine‐scale topography and soil texture on CLM 4.0 simulations over the Southwestern U.S. Water Resources Research, 51 ( 4 ), 2648 – 2667. https://doi.org/10.1002/2014wr015686 Strehl, A., & Ghosh, J. ( 2002 ). Cluster ensembles—A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3 ( Dec ), 583 – 617. Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.‐P., et al. ( 2011 ). Estimating northern hemisphere snow water equivalent for climate research through assimilation of space‐borne radiometer data and ground‐based measurements. Remote Sensing of Environment, 115 ( 12 ), 3517 – 3529. https://doi.org/10.1016/j.rse.2011.08.014 Tao, J., & Barros, A. P. ( 2018 ). Multi‐year atmospheric forcing datasets for hydrologic modeling in regions of complex terrain–Methodology and evaluation over the integrated precipitation and hydrology experiment 2014 domain. Journal of Hydrology, 567, 824 – 842. https://doi.org/10.1016/j.jhydrol.2016.12.058 Wan, Z., Hook, S. J., & Hulley, G. C. ( 2015 ). Modis/terra land surface temperature/emissivity daily l3 global 1km grid, version 6. NASA EOSDIS LP DAAC. Xue, Y., Houser, P. R., Maggioni, V., Mei, Y., Kumar, S. V., & Yoon, Y. ( 2019 ). Assimilation of satellite‐based snow cover and freeze/thaw observations over high mountain Asia. Frontiers in Earth Science, 7, 115. https://doi.org/10.3389/feart.2019.00115 Yang, K., Qin, J., Zhao, L., Chen, Y., Tang, W., Han, M., et al. ( 2013 ). A multiscale soil moisture and freeze‐thaw monitoring network on the third pole. Bulletin of the American Meteorological Society, 94 ( 12 ), 1907 – 1916. https://doi.org/10.1175/bams-d-12-00203.1 Yang, Z.‐L., Niu, G.‐Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., et al. ( 2011 ). The community Noah land surface model with multiparameterization options (Noah‐MP): 2. Evaluation over global river basins. Journal of Geophysical Research, 116 ( D12 ). https://doi.org/10.1029/2010jd015140 Yoon, Y., Kumar, S. V., Forman, B. A., Zaitchik, B., Kwon, Y., Qian, Y., et al. ( 2019 ). Evaluating the uncertainty of terrestrial water budget components over high mountain Asia. Frontiers in Earth Science, 7, 120. https://doi.org/10.3389/feart.2019.00120 You, Q., Min, J., Zhang, W., Pepin, N., & Kang, S. ( 2015 ). Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau. Climate Dynamics, 45 ( 3–4 ), 791 – 806. https://doi.org/10.1007/s00382-014-2310-6 Yuan, F., Zhang, L., Win, K., Ren, L., Zhao, C., Zhu, Y., et al. ( 2017 ). Assessment of GPM and TRMM multi‐satellite precipitation products in streamflow simulations in a data‐sparse mountainous watershed in Myanmar. Remote Sensing, 9 ( 3 ), 302. https://doi.org/10.3390/rs9030302 Zhang, C., Tang, Q., Chen, D., van der Ent, R. J., Liu, X., Li, W., & Haile, G. G. ( 2019 ). Moisture source changes contributed to different precipitation changes over the northern and southern Tibetan Plateau. Journal of Hydrometeorology, 20 ( 2 ), 217 – 229. https://doi.org/10.1175/jhm-d-18-0094.1 Zhao, W., & Li, A. ( 2015 ). A review on land surface processes modelling over complex terrain. Advances in Meteorology, 2015, 607181. Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S.‐J. S., et al. ( 2019 ). Runoff from glacier ice and seasonal snow in High Asia: Separating melt water sources in river flow. Regional Environmental Change, 19 ( 5 ), 1249 – 1261. https://doi.org/10.1007/s10113-018-1429-0 Beck, H. E., Wood, E. F., McVicar, T. R., Zambrano‐Bigiarini, M., Alvarez‐Garreton, C., Baez‐Villanueva, O. M., et al. ( 2020 ). Bias correction of global high‐resolution precipitation climatologies using streamflow observations from 9372 catchments. Journal of Climate, 33 ( 4 ), 1299 – 1315. https://doi.org/10.1175/jcli-d-19-0332.1 Bohn, T. J., & Vivoni, E. R. ( 2019 ). MOD‐LSP, MODIS‐based parameters for hydrologic modeling of North American land cover change. Scientific Data, 6 ( 1 ), 1 – 13. https://doi.org/10.1038/s41597-019-0150-2 Bookhagen, B., & Burbank, D. W. ( 2010 ). Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. Journal of Geophysical Research, 115 ( F3 ). https://doi.org/10.1029/2009jf001426 Buck, A. L. ( 1981 ). New equations for computing vapor pressure and enhancement factor. Journal of Applied Meteorology, 20 ( 12 ), 1527 – 1532. https://doi.org/10.1175/1520-0450(1981)020<1527:nefcvp>2.0.co;2 Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., et al. ( 2003 ). Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research, 108 ( D22 ). https://doi.org/10.1029/2002jd003118 Cover, T. M., & Thomas, J. A. 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M., Kansakar, S. R., Gerrard, A., & Rees, G. ( 2005 ). Flow regimes of Himalayan rivers of Nepal: Nature and spatial patterns. Journal of Hydrology, 308 ( 1–4 ), 18 – 32. https://doi.org/10.1016/j.jhydrol.2004.10.018 Immerzeel, W. W., Droogers, P., De Jong, S. M., & Bierkens, M. F. P. ( 2009 ). Large‐scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. Remote Sensing of Environment, 113 ( 1 ), 40 – 49. https://doi.org/10.1016/j.rse.2008.08.010 Kollet, S. J., Maxwell, R. M., Woodward, C. S., Smith, S., Vanderborght, J., Vereecken, H., & Simmer, C. ( 2010 ). Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources. Water Resources Research, 46 ( 4 ). https://doi.org/10.1029/2009wr008730 Konzelmann, T., van de Wal, R. S., Greuell, W., Bintanja, R., Henneken, E. A., & Abe‐Ouchi, A. ( 1994 ). Parameterization of global and longwave incoming radiation for the Greenland ice sheet. Global and Planetary Change, 9 ( 1–2 ), 143 – 164. https://doi.org/10.1016/0921-8181(94)90013-2 |
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ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/167423 2023-08-20T04:03:12+02:00 Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System Xue, Yuan Houser, Paul R. Maggioni, Viviana Mei, Yiwen Kumar, Sujay V. Yoon, Yeosang 2021-04 application/pdf https://hdl.handle.net/2027.42/167423 https://doi.org/10.1029/2020JD034131 unknown Wiley Periodicals, Inc. Universitätsbibliothek der Leuphana Universität Lüneburg Xue, Yuan; Houser, Paul R.; Maggioni, Viviana; Mei, Yiwen; Kumar, Sujay V.; Yoon, Yeosang (2021). "Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System." Journal of Geophysical Research: Atmospheres 126(8): n/a-n/a. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/167423 doi:10.1029/2020JD034131 Journal of Geophysical Research: Atmospheres Ruiz‐Arias, J. A., Alsamamra, H., Tovar‐Pescador, J., & Pozo‐Vázquez, D. ( 2010 ). Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions. Energy Conversion and Management, 51 ( 5 ), 881 – 893. https://doi.org/10.1016/j.enconman.2009.11.024 Kumar, S. V., Peters‐Lidard, C. D., Mocko, D., & Tian, Y. ( 2013 ). Multiscale evaluation of the improvements in surface snow simulation through terrain adjustments to radiation. Journal of Hydrometeorology, 14 ( 1 ), 220 – 232. https://doi.org/10.1175/jhm-d-12-046.1 Kumar, S. V., Peters‐Lidard, C. D., Tian, Y., Houser, P., Geiger, J., Olden, S., et al. ( 2006 ). Land information system: An interoperable framework for high resolution land surface modeling. Environmental Modelling & Software, 21 ( 10 ), 1402 – 1415. https://doi.org/10.1016/j.envsoft.2005.07.004 Latt, Z. Z. ( 2015 ). Flood assessment and improving flood forecasting for a monsoon dominated river basin: With emphasis on black‐box models and GIS (Unpublished doctoral dissertation). Universitätsbibliothek der Leuphana Universität Lüneburg. Lawrence, M. G. ( 2005 ). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society, 86 ( 2 ), 225 – 234. https://doi.org/10.1175/bams-86-2-225 Marshall, J., & Plumb, R. A. ( 1989 ). Atmosphere, ocean and climate dynamics: An introductory text (Vol. 43 ). Academic Press. Mei, Y., Maggioni, V., Houser, P., Xue, Y., & Rouf, T. ( 2020 ). A nonparametric statistical technique for spatial downscaling of precipitation over high mountain Asia. Water Resources Research, 56 ( 11 ), e2020WR027472. https://doi.org/10.1029/2020wr027472 Mishra, S. K., Hayse, J., Veselka, T., Yan, E., Kayastha, R. B., LaGory, K., et al. ( 2018 ). An integrated assessment approach for estimating the economic impacts of climate change on river systems: An application to hydropower and fisheries in a Himalayan river, Trishuli. Environmental Science & Policy, 87, 102 – 111. https://doi.org/10.1016/j.envsci.2018.05.006 Molteni, F., Buizza, R., Palmer, T. N., & Petroliagis, T. ( 1996 ). The ECMWF ensemble prediction system: Methodology and validation. Quarterly Journal of the Royal Meteorological Society, 122 ( 529 ), 73 – 119. https://doi.org/10.1002/qj.49712252905 Nash, J. E., & Sutcliffe, J. V. ( 1970 ). River flow forecasting through conceptual models part I–A discussion of principles. Journal of Hydrology, 10 ( 3 ), 282 – 290. https://doi.org/10.1016/0022-1694(70)90255-6 Niu, G.‐Y., Yang, Z.‐L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., et al. ( 2011 ). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. Journal of Geophysical Research, 116 ( D12 ). https://doi.org/10.1029/2010jd015139 Pulliainen, J. ( 2006 ). Mapping of snow water equivalent and snow depth in boreal and sub‐arctic zones by assimilating space‐borne microwave radiometer data and ground‐based observations. Remote Sensing of Environment, 101 ( 2 ), 257 – 269. https://doi.org/10.1016/j.rse.2006.01.002 Rouf, T., Mei, Y., Maggioni, V., Houser, P., & Noonan, M. ( 2019 ). A physically‐based atmospheric variables downscaling technique. Journal of Hydrometeorology, 21, 93 – 108. Singh, R. S., Reager, J. T., Miller, N. L., & Famiglietti, J. S. ( 2015 ). Toward hyper‐resolution land‐surface modeling: The effects of fine‐scale topography and soil texture on CLM 4.0 simulations over the Southwestern U.S. Water Resources Research, 51 ( 4 ), 2648 – 2667. https://doi.org/10.1002/2014wr015686 Strehl, A., & Ghosh, J. ( 2002 ). Cluster ensembles—A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3 ( Dec ), 583 – 617. Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.‐P., et al. ( 2011 ). Estimating northern hemisphere snow water equivalent for climate research through assimilation of space‐borne radiometer data and ground‐based measurements. Remote Sensing of Environment, 115 ( 12 ), 3517 – 3529. https://doi.org/10.1016/j.rse.2011.08.014 Tao, J., & Barros, A. P. ( 2018 ). Multi‐year atmospheric forcing datasets for hydrologic modeling in regions of complex terrain–Methodology and evaluation over the integrated precipitation and hydrology experiment 2014 domain. Journal of Hydrology, 567, 824 – 842. https://doi.org/10.1016/j.jhydrol.2016.12.058 Wan, Z., Hook, S. J., & Hulley, G. C. ( 2015 ). Modis/terra land surface temperature/emissivity daily l3 global 1km grid, version 6. NASA EOSDIS LP DAAC. Xue, Y., Houser, P. R., Maggioni, V., Mei, Y., Kumar, S. V., & Yoon, Y. ( 2019 ). Assimilation of satellite‐based snow cover and freeze/thaw observations over high mountain Asia. Frontiers in Earth Science, 7, 115. https://doi.org/10.3389/feart.2019.00115 Yang, K., Qin, J., Zhao, L., Chen, Y., Tang, W., Han, M., et al. ( 2013 ). A multiscale soil moisture and freeze‐thaw monitoring network on the third pole. Bulletin of the American Meteorological Society, 94 ( 12 ), 1907 – 1916. https://doi.org/10.1175/bams-d-12-00203.1 Yang, Z.‐L., Niu, G.‐Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., et al. ( 2011 ). The community Noah land surface model with multiparameterization options (Noah‐MP): 2. Evaluation over global river basins. Journal of Geophysical Research, 116 ( D12 ). https://doi.org/10.1029/2010jd015140 Yoon, Y., Kumar, S. V., Forman, B. A., Zaitchik, B., Kwon, Y., Qian, Y., et al. ( 2019 ). Evaluating the uncertainty of terrestrial water budget components over high mountain Asia. Frontiers in Earth Science, 7, 120. https://doi.org/10.3389/feart.2019.00120 You, Q., Min, J., Zhang, W., Pepin, N., & Kang, S. ( 2015 ). Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau. Climate Dynamics, 45 ( 3–4 ), 791 – 806. https://doi.org/10.1007/s00382-014-2310-6 Yuan, F., Zhang, L., Win, K., Ren, L., Zhao, C., Zhu, Y., et al. ( 2017 ). Assessment of GPM and TRMM multi‐satellite precipitation products in streamflow simulations in a data‐sparse mountainous watershed in Myanmar. Remote Sensing, 9 ( 3 ), 302. https://doi.org/10.3390/rs9030302 Zhang, C., Tang, Q., Chen, D., van der Ent, R. J., Liu, X., Li, W., & Haile, G. G. ( 2019 ). Moisture source changes contributed to different precipitation changes over the northern and southern Tibetan Plateau. Journal of Hydrometeorology, 20 ( 2 ), 217 – 229. https://doi.org/10.1175/jhm-d-18-0094.1 Zhao, W., & Li, A. ( 2015 ). A review on land surface processes modelling over complex terrain. Advances in Meteorology, 2015, 607181. Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P., Khalsa, S.‐J. S., et al. ( 2019 ). Runoff from glacier ice and seasonal snow in High Asia: Separating melt water sources in river flow. Regional Environmental Change, 19 ( 5 ), 1249 – 1261. https://doi.org/10.1007/s10113-018-1429-0 Beck, H. E., Wood, E. F., McVicar, T. R., Zambrano‐Bigiarini, M., Alvarez‐Garreton, C., Baez‐Villanueva, O. M., et al. ( 2020 ). Bias correction of global high‐resolution precipitation climatologies using streamflow observations from 9372 catchments. Journal of Climate, 33 ( 4 ), 1299 – 1315. https://doi.org/10.1175/jcli-d-19-0332.1 Bohn, T. J., & Vivoni, E. R. ( 2019 ). MOD‐LSP, MODIS‐based parameters for hydrologic modeling of North American land cover change. Scientific Data, 6 ( 1 ), 1 – 13. https://doi.org/10.1038/s41597-019-0150-2 Bookhagen, B., & Burbank, D. W. ( 2010 ). 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Global and Planetary Change, 9 ( 1–2 ), 143 – 164. https://doi.org/10.1016/0921-8181(94)90013-2 IndexNoFollow Noah‐MP downscaling High Mountain Asia hyper‐resolution modeling Atmospheric and Oceanic Sciences Science Article 2021 ftumdeepblue https://doi.org/10.1029/2020JD03413110.1175/bams-86-2-22510.1029/2010jd01513910.1016/j.rse.2006.01.00210.1016/j.rse.2011.08.01410.1175/bams-d-12-00203.110.1029/2010jd01514010.3389/feart.2019.0012010.3390/rs903030210.1007/s10113-018-1429-010.1175/jcli-d-19 2023-07-31T21:20:31Z This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations.Key PointsThe skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presentedThe study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrainThe study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulations Peer Reviewed ... Article in Journal/Newspaper Arctic The Cryosphere University of Michigan: Deep Blue Journal of Geophysical Research: Oceans 127 4 |