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Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water
Open accessAbstract
Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10 9 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.
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“we use distributed precipitation amounts as a proxy for river discharge and water levels at virtual stations, which integrate upstream watershed processes77.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [77]
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Cites
A Description of the Advanced Research WRF Version 2
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., & Wang, W. (2005) · NSF National Center for Atmospheric Research · doi:10.5065/d6dz069t · Cited by 2,504 · cited 1×
“This approach would follow the development path for the Weather and Research Forecast (WRF) model [Michalakes et al., 2001; Skamarock et al., 2005].”
New Global Hydrography Derived from Spaceborne Elevation Data
Lehner, B., Verdin, K., & Jarvis, A. (2008) · Eos, Transactions American Geophysical Union · doi:10.1029/2008eo100001 · Cited by 2,411
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Research works printed but not linked: 37
- 10.1016/S0022‐1694(00)00278‐X · DOI · Paper page
- 10.1029/2006GL028396 · DOI
- 10.1175/JAM2322.1 · doi:10.1175/jam2322.1
- 10.1175/BAMS‐84‐8‐1013 · DOI · Paper page
- 10.1088/1748‐9326/4/3/035006 · DOI · Paper page
- 10.5194/hess‐13‐2413‐2009 · doi:10.5194/hess-13-2413-2009
- 10.1109/JPROC.2010.2043031 · DOI · Paper page
- Eagleson P. S., 1991, Opportunities in the Hydrologic Sciences
- 10.1016/0309‐1708(94)90019‐1 · doi:10.1016/0309-1708(94)90019-1
- 10.1109/JPROC.2010.2043918 · DOI · Paper page
- Famiglietti J. S. L.Murdoch V.Lakshmi andR. P.Hooper(2009) Towards a framework for community modeling in hydrologic science: Blueprint for a community hydrologic modeling platform paper presented at2nd Workshop on a Community Hydrologic Modeling Platform Univ. of Memphis Memphis Tenn. 31 March to 1 April.
- 10.1029/2007WR006386 · DOI · Paper page
- 10.1109/RADAR.2009.4977083 · doi:10.1109/radar.2009.4977083
- 10.1016/j.rse.2003.04.001 · DOI · Paper page
- 10.1029/2002WR001334 · doi:10.1029/2002wr001334
- 10.1007/s10546‐009‐9356‐8 · doi:10.1007/s10546-009-9356-8
- International Commission of Large Dams, 2003, World Register of Dams
- 10.1029/2007WR006004 · DOI · Paper page
- 10.1029/2009WR008730 · DOI · Paper page
- Lehning M., 2009, The Fourth Paradigm, Data‐Intensive Scientific Discovery, 45
- Lovejoy T. E. andL.Hannah(Eds.) (2005) Climate Change and Biodiversity 401 pp. Yale Univ. Press New Haven Conn.
- 10.1111/j.1752‐1688.2010.00465.x · DOI
- 10.1142/9789812799685_0024 · DOI · Paper page
- 10.1007/BF01205969 · DOI · Paper page
- 10.1038/416617a · DOI · Paper page
- 10.1029/2008WR007071 · doi:10.1029/2008wr007071
- 10.1016/S0140-6736(05)70201-4 · DOI · Paper page
- 10.1016/S0309-1708(02)00065-9 · doi:10.1016/s0309-1708(02)00065-9
- 10.1002/hyp.5155 · DOI · Paper page
- 10.1890/1051-0761(1997)007[0737:HAOTGN]2.0.CO;2 · DOI · Paper page
- 10.1007/978-3-642-18948-7_33 · doi:10.1007/978-3-642-18948-7_33
- 10.1016/S0921‐8181(03)00023‐7 · DOI · Paper page
- 10.5194/hess‐6‐583‐2002 · DOI · Paper page
- 10.5194/hess‐6‐559‐2002 · DOI · Paper page
- 10.1016/S0048‐9697(98)00037‐0 · DOI · Paper page
- 10.1016/S0048‐9697(98)00038‐2 · DOI · Paper page
- 10.1016/S0309‐1708(01)00021‐5 · DOI · Paper page
Cited by
Lehner, B., & Grill, G. (2013) · Hydrological Processes · doi:10.1002/hyp.9740 · Cited by 2,193
Self-citationRainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., & Adler, R. F. (2019) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-17-0138.1 · Cited by 1,411
Self-citationSeasonal Drought Prediction: Advances, Challenges, and Future Prospects
Hao, Z., Singh, V. P., & Xia, Y. (2018) · Reviews of Geophysics · doi:10.1002/2016rg000549 · Cited by 607
Global hydrology 2015: State, trends, and directions
Bierkens, M. F. P. (2015) · Water Resources Research · doi:10.1002/2015wr017173 · Cited by 493
Self-citation
Metadata
- Authors
- Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B., Lettenmaier, D. P., Peters‐Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., & Whitehead, P.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2011-05
- Volume
- 47
- Issue
- 5
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W2045944858
- Cited by
- 957
- References
- 2
- FWCI
- 29.01
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B., Lettenmaier, D. P., Peters‐Lidard, C., Sivapalan, M., . . . Whitehead, P. (2011). Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. Water Resources Research. https://doi.org/10.1029/2010wr010090
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.