4.3 Article

Water balance model for mean annual hydrogen and oxygen isotope distributions in surface waters of the contiguous United States

Journal

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2010JG001581

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Funding

  1. U.S. federal government
  2. U.S. National Science Foundation [DBI-0743543]
  3. Direct For Biological Sciences
  4. Div Of Biological Infrastructure [0743543] Funding Source: National Science Foundation

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The stable H and O isotope composition of river and stream water records information on runoff sources and land-atmosphere water fluxes within the catchment and is a potentially powerful tool for network-based monitoring of ecohydrological systems. Process-based hydrological models, however, have thus far shown limited power to replicate observed large-scale variation in U. S. surface water isotope ratios. Here we develop a geographic information system-based model to predict long-term annual average surface water isotope ratios across the contiguous United States. We use elevation-explicit, gridded precipitation isotope maps as model input and data from a U. S. Geological Survey monitoring program for validation. We find that models incorporating monthly variation in precipitation-evapotranspiration (P-E) amounts account for the majority (> 89%) of isotopic variation and have reduced regional bias relative to models that do not consider intra-annual P-E effects on catchment water balance. Residuals from the water balance model exhibit strong spatial patterning and correlations that suggest model residuals isolate additional hydrological signal. We use interpolated model residuals to generate optimized prediction maps for U. S. surface water delta H-2 and delta O-18 values. We show that the modeled surface water values represent a relatively accurate and unbiased proxy for drinking water isotope ratios across the United States, making these data products useful in ecological and criminal forensics applications that require estimates of the local environmental water isotope variation across large geographic regions.

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