Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System
dc.contributor.author | Xue, Yuan | |
dc.contributor.author | Houser, Paul R. | |
dc.contributor.author | Maggioni, Viviana | |
dc.contributor.author | Mei, Yiwen | |
dc.contributor.author | Kumar, Sujay V. | |
dc.contributor.author | Yoon, Yeosang | |
dc.date.accessioned | 2021-05-12T17:21:52Z | |
dc.date.available | 2022-05-12 13:21:50 | en |
dc.date.available | 2021-05-12T17:21:52Z | |
dc.date.issued | 2021-04 | |
dc.identifier.citation | 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. | |
dc.identifier.issn | 2169-897X | |
dc.identifier.issn | 2169-8996 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/167423 | |
dc.description.abstract | 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 | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.publisher | Universitätsbibliothek der Leuphana Universität Lüneburg | |
dc.subject.other | Noah‐MP | |
dc.subject.other | downscaling | |
dc.subject.other | High Mountain Asia | |
dc.subject.other | hyper‐resolution modeling | |
dc.title | Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016, Part I: A Hyper‐Resolution Terrestrial Modeling System | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Atmospheric and Oceanic Sciences | |
dc.subject.hlbtoplevel | Science | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/167423/1/jgrd56955_am.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/167423/2/jgrd56955.pdf | |
dc.identifier.doi | 10.1029/2020JD034131 | |
dc.identifier.source | Journal of Geophysical Research: Atmospheres | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Marshall, J., & Plumb, R. A. ( 1989 ). Atmosphere, ocean and climate dynamics: An introductory text (Vol. 43 ). Academic Press. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Rouf, T., Mei, Y., Maggioni, V., Houser, P., & Noonan, M. ( 2019 ). A physically‐based atmospheric variables downscaling technique. Journal of Hydrometeorology, 21, 93 – 108. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Strehl, A., & Ghosh, J. ( 2002 ). Cluster ensembles—A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3 ( Dec ), 583 – 617. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Zhao, W., & Li, A. ( 2015 ). A review on land surface processes modelling over complex terrain. Advances in Meteorology, 2015, 607181. | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Cover, T. M., & Thomas, J. A. ( 1991 ). Entropy, relative entropy and mutual information. Elements of Information Theory, 2, 1 – 55. | |
dc.identifier.citedreference | Dandekhya, S., England, M., Ghate, R., Goodrich, C., Nepal, S., Prakash, A., et al. ( 2017 ). The Gandaki basin: Maintaining livelihoods in the face of landslides, floods, and drought. HI‐AWARE Working Paper ( 9 ). | |
dc.identifier.citedreference | Fiddes, J., & Gruber, S. ( 2014 ). Toposcale v.1.0: Downscaling gridded climate data in complex terrain. Geoscientific Model Development, 7 ( 1 ), 387 – 405. https://doi.org/10.5194/gmd-7-387-2014 | |
dc.identifier.citedreference | Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., et al. ( 2015 ). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66 | |
dc.identifier.citedreference | Gafurov, A., Vorogushyn, S., Farinotti, D., Duethmann, D., Merkushkin, A., & Merz, B. ( 2015 ). Snow‐cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data. The Cryosphere, 9 ( 2 ), 451 – 463. https://doi.org/10.5194/tc-9-451-2015 | |
dc.identifier.citedreference | Ghatak, D., Zaitchik, B., Kumar, S., Matin, M. A., Bajracharya, B., Hain, C., & Anderson, M. ( 2018 ). Influence of precipitation forcing uncertainty on hydrological simulations with the NASA South Asia land data assimilation system. Hydrology, 5 ( 4 ), 57. https://doi.org/10.3390/hydrology5040057 | |
dc.identifier.citedreference | Grin, E., Schaller, M., & Ehlers, T. A. ( 2018 ). Spatial distribution of cosmogenic 10be derived denudation rates between the western Tian Shan and northern Pamir, Tajikistan. Geomorphology, 321, 1 – 15. https://doi.org/10.1016/j.geomorph.2018.08.007 | |
dc.identifier.citedreference | Gupta, A. S., & Tarboton, D. G. ( 2016 ). A tool for downscaling weather data from large‐grid reanalysis products to finer spatial scales for distributed hydrological applications. Environmental Modelling & Software, 84, 50 – 69. | |
dc.identifier.citedreference | Hannah, D. 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | 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 | |
dc.identifier.citedreference | Kulmatov, R., Opp, C., Groll, M., & Kulmatova, D. ( 2013 ). Assessment of water quality of the trans‐boundary Zarafshan river in the territory of Uzbekistan. Journal of Water Resource and Protection, 5 ( 01 ), 17. https://doi.org/10.4236/jwarp.2013.51003 | |
dc.working.doi | NO | en |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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