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dc.contributor.authorNg, Gene-Hua Crystal
dc.contributor.authorMcLaughlin, Dennis
dc.contributor.authorEntekhabi, Dara
dc.contributor.authorScanlon, Bridget
dc.date.accessioned2013-03-13T19:00:57Z
dc.date.available2013-03-13T19:00:57Z
dc.date.issued2009-09
dc.date.submitted2009-05
dc.identifier.issn0043-1397
dc.identifier.urihttp://hdl.handle.net/1721.1/77891
dc.description.abstract1] It is difficult to estimate groundwater recharge in semiarid environments, where precipitation and evapotranspiration nearly balance. In such environments, groundwater supplies are sensitive to small changes in the processes that control recharge. Numerical modeling provides the temporal resolution needed to analyze these processes but is highly sensitive to model errors. Natural chloride tracer measurements in the unsaturated zone provide more robust indicators of low recharge rates but yield estimates at coarse time scales that mask most control mechanisms. This study presents a new probabilistic approach for analyzing diffuse recharge in semiarid environments, with an application to study sites in the U.S. southern High Plains. The approach uses data assimilation to combine model predictions and chloride-based recharge estimates. It has the advantage of providing probability distributions rather than point values for uncertain soil and vegetation properties. These can then be used to quantify recharge uncertainty. Estimates of moisture flux time series indicate that percolation (or potential recharge) at the data sites is episodic and exhibits interannual variability. Most percolation occurs during intense rains when crop roots are not fully developed and there is ample antecedent soil moisture. El Niño events can contribute to interannual variability of recharge if they bring rainy winters that provide wet antecedent conditions for spring precipitation. Data assimilation methods that combine modeling and chloride observations provide the high temporal resolution information needed to identify mechanisms controlling diffuse recharge and offer a way to examine the effects of land use change and climatic variability on groundwater resources.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (grant 0003361)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (grant 0121182)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (grant 0530851)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant 0540259)en_US
dc.language.isoen_US
dc.publisherAmerican Geophysical Unionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1029/2009wr007831en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMIT web domainen_US
dc.titleUsing data assimilation to identify diffuse recharge mechanisms from chemical and physical data in the unsaturated zoneen_US
dc.typeArticleen_US
dc.identifier.citationNg, Gene-Hua Crystal et al. “Using Data Assimilation to Identify Diffuse Recharge Mechanisms from Chemical and Physical Data in the Unsaturated Zone.” Water Resources Research 45.9 (2009): 1-18. CrossRef. Web. Copyright 2009 by the American Geophysical Union.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.contributor.mitauthorNg, Gene-Hua Crystal
dc.contributor.mitauthorMcLaughlin, Dennis
dc.contributor.mitauthorEntekhabi, Dara
dc.relation.journalWater Resources Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsNg, Gene-Hua Crystal; McLaughlin, Dennis; Entekhabi, Dara; Scanlon, Bridgeten
dc.identifier.orcidhttps://orcid.org/0000-0002-8362-4761
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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