期刊
WATER RESOURCES RESEARCH
卷 55, 期 8, 页码 6896-6910出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019WR025181
关键词
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资金
- U.S. National Science Foundation [EF-1241286, DBI-1565128]
- University of Utah Graduate Research Fellowship program
- China Scholarship Council
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Local meteoric water lines (LMWLs) represent the site-specific long-term covariation of hydrogen and oxygen stable isotope ratios. LMWLs have practical utility as a hydrologic framework and as benchmarks for evaluating hydroclimatic processes in isotope-enabled climate models. In this manuscript, we characterize the global distribution of LMWLs and compare them to LMWLs from model data. To evaluate the sensitivity of the covariance of stable isotope ratios to data set length, we paired time series rarifaction with Bayesian ellipse estimation. We then applied a threshold of 48 months and estimated LMWLs at 398 sites in 25 Koppen climate classes using orthogonal distance regression. Slopes ranged from 4.8 to 10.9, with an average of 7.64 +/- 0.64. Intercepts ranged from -24 parts per thousand to 27 parts per thousand, with an average of 6.85 +/- 6.2 parts per thousand. We identified three processes: (1) subcloud evaporation of rain, (2) atmospheric remoistening by rainfall evaporation, and (3) conditions of snow formation as important controls on slopes and intercepts in arid, humid, and seasonally snowy regions, respectively. We compared observational LMWLs with those from a suite of isotope-enabled climate models. At arid and snowy sites, model data produced higher slopes and intercepts than observational data. At humid sites, model data exhibited dampened variability in slopes and intercepts relative to observational data. These results indicate potential for improvement in the precipitation and/or isotope parameterizations of raindrop evaporation, advection of reevaporated water, evapotranspiration fractionation, and supersaturation in mixed-phase clouds. This meta-analysis demonstrates LMWLs utility for identifying specific hydroclimatic and isotopic processes in observations and models.
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