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Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

Open access
Wood, Eric F., Roundy, Joshua K., Troy, Tara J., van Beek, L. P. H., Bierkens, Marc F. P., Blyth, Eleanor, de Roo, Ad, Döll, Petra, Ek, Mike, Famiglietti, James, Gochis, David, van de Giesen, Nick, Houser, Paul, Jaffé, Peter R., Kollet, Stefan, Lehner, Bernhard, Lettenmaier, Dennis P., Peters‐Lidard, Christa, Sivapalan, Murugesu, Sheffield, Justin, Wade, Andrew, Whitehead, Paul·2011·Water Resources Research

Abstract

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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Crossref · 10.1029/2010wr010090

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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
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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CoChat. (2026). Citation check: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. https://app.cochat.ai/papers/10.1029/2010wr010090
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.