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Evaluating water stress controls on primary production in biogeochemical and remote sensing based models


Metadata FieldValueLanguage
dc.contributorQiaozhen Mu, qiaozhen@ntsg.umt.eduen_US
dc.creatorRunning, Steven
dc.creatorHanqin, Tian
dc.creatorMingliang, Liu
dc.creatorHeinsch, Faith
dc.creatorMaosheng, Zhao
dc.creatorMu, Qiaozhen
dc.date.accessioned2022-12-02T14:55:10Z
dc.date.available2022-12-02T14:55:10Z
dc.date.created2007
dc.identifier10.1029/2006JG000179en_US
dc.identifier.urihttps://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2006JG000179en_US
dc.identifier.urihttps://aurora.auburn.edu/handle/11200/50469
dc.identifier.urihttp://dx.doi.org/10.35099/aurora-537
dc.description.abstractWater stress is one of the most important limiting factors controlling terrestrial primary production, and the performance of a primary production model is largely determined by its capacity to capture environmental water stress. The algorithm that generates the global near-real-time MODIS GPP/NPP products (MOD17) uses VPD ( vapor pressure deficit) alone to estimate the environmental water stress. This paper compares the water stress calculation in the MOD17 algorithm with results simulated using a process-based biogeochemical model (Biome-BGC) to evaluate the performance of the water stress determined using the MOD17 algorithm. The investigation study areas include China and the conterminous United States because of the availability of daily meteorological observation data. Our study shows that VPD alone can capture interannual variability of the full water stress nearly over all the study areas. In wet regions, where annual precipitation is greater than 400 mm/yr, the VPD-based water stress estimate in MOD17 is adequate to explain the magnitude and variability of water stress determined from atmospheric VPD and soil water in Biome-BGC. In some dry regions, where soil water is severely limiting, MOD17 underestimates water stress, overestimates GPP, and fails to capture the intraannual variability of water stress. The MOD17 algorithm should add soil water stress to its calculations in these dry regions, thereby improving GPP estimates. Interannual variability in water stress is simpler to capture than the seasonality, but it is more difficult to capture this interannual variability in GPP. The MOD17 algorithm captures interannual and intraannual variability of both the Biome-BGC-calculated water stress and GPP better in the conterminous United States than in the strongly monsoon-controlled China.en_US
dc.formatPDFen_US
dc.relation.ispartofJournal of Geophysical Research: Biogeosciencesen_US
dc.relation.ispartofseries2169-8953en_US
dc.rights©American Geophysical Union YEAR. This is this the version of record co-published by the American Geophysical Union and John Wiley & Sons, Inc. It is made available under the CC-BY-NC-ND 4.0 license. Item should be cited as: Mu, Qiaozhen, et al. "Evaluating water stress controls on primary production in biogeochemical and remote sensing based models." Journal of Geophysical Research: Biogeosciences 112.G1 (2007).en_US
dc.titleEvaluating water stress controls on primary production in biogeochemical and remote sensing based modelsen_US
dc.typeTexten_US
dc.type.genreJournal Article, Academic Journalen_US
dc.citation.volume112en_US
dc.citation.issueG1en_US
dc.description.statusPublisheden_US
dc.description.peerreviewYesen_US

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