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Incorporating water isoscapes in hydrological and water resource investigations

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Water cycle science is confronted with the critical challenge of understanding sources, pathways, and processes that govern the availability of water and its interaction with biogeochemical cycles across a range of Earth systems. These problems are inherently spatial in nature, and require observational tools that can establish connectivity within the water cycle, quantify processes affecting water cycling across scales, and provide robust tests of hydrological models. Spatial analysis of water H and O isotope data has recently emerged as a valuable approach to such problems. Isotope distributions in hydrological systems are associated with variation in water sources, upstream processes that fractionate isotopes along transport trajectories, and local conditions that govern partitioning of water among pools and fluxes. Data products and models that represent spatiotemporal isotope distributions (isoscapes) provide a basis for using isotopes in quantitative spatial hydrology research. The potential of this work, as well as current limitations, is reflected in recent case studies focused on atmospheric, land surface, groundwater, and managed systems. Many of limitations, stemming from challenges associated with data availability and spatiotemporal resolution, should be reduced in the future as new sample networks, improved instrumentation, more sophisticated data analysis approaches, and enhanced data sharing emerge. These advances will increase the accessibility of isoscape applications and their relevance to a wide range of hydrological and water resource problems. WIREs Water 2015, 2:107–119. doi: 10.1002/wat2.1069 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality
Characteristic time and length scales for coherent modes of H and O isotope variability in atmospheric (light blue), surface (purple), soil (brown), ground (red), and human‐managed (green) water systems. Examples of phenomena that have or can be studied at each scale using isoscapes are shown.
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Bayesian analysis and model of the likelihood of local water use based on tap water samples from the western United States. (a) Likelihood that city tap water sample was derived from surface water within the local HUC8 watershed, based on comparison of observed tap water isotope ratios with values from a modeled surface water isoscape. Black outlines indicate locations where the likelihood of local surface water use is less than 0.05. (b) Average likelihood that cities within a HUC8 watershed use either local surface water or recently recharged, local groundwater, based on comparison of observed values with surface and precipitation water isoscapes (circles, for all watersheds represented by five or more samples) and a hydrogeomorphic and socioeconomic model (background color) optimized to the isotopic information. (Reprinted with permission from Ref . Copyright 2014 American Geophysical Union)
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Simulated effect of specification of the runoff ratio (discharge/precipitation) as a fixed or seasonally varying (monthly) parameter in a simple water isotope mass‐balance model for streams and rivers. The pattern suggests that surface water isotope ratios are sensitive indicators of the seasonality of runoff generation throughout much of the continental interior. (Reprinted with permission from Ref . Copyright 2011 American Geophysical Union)
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Modeled oxygen isotope ratios of precipitation across the experimental watersheds of H. J. Andrews experimental forest (black outlines) for a storm event on October 3–5, 2002. Isoscape values are based on geostatistical interpolation (ordinary kriging, stable isotropic semivariogram model with nugget) of residuals from an elevation‐based model (δ18O = −0.022 x E − 6.1; E = elevation in meters). Isoscape model root mean squared error = 0.26‰. (Isotope data, Reprinted with permission from Ref . Copyright 2005 American Geophysical Union. Digital elevation and watershed boundaries, Reprinted with permission from Refs and . Copyright 2005 The Authors)
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Estimated rainout fraction (fraction of vapor lost since last saturation of the air parcel) for the Superstorm Sandy rainfall area as the storm center approached and made landfall on the U.S. coast. Values were estimated from interpolated precipitation water isoscapes and estimated source‐region vapor isotope ratios using a Rayleigh distillation model. (Reprinted with permission from Ref . Copyright 2014 Academic Press)
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Isoscape analysis of shallow groundwaters from the lower peninsula of Michigan. (a) Measured and interpolated (universal kriging) pattern of water deuterium excess (D‐excess). (b) Estimated annual precipitation derived from lake water recycling, calculated from the interpolated D‐excess surface, an isotope mixing model, and mean annual precipitation data assuming that the groundwater represents an unbiased sample of incident precipitation. (Reprinted with permission from Ref . Copyright 2012 The Authors)
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Nested scales of spatial and temporal variability in precipitation water isoscapes. (a) Global, climatological, mean annual δ2H isoscape generated through geostatistical interpolation of long‐term monitoring data (data from http://waterisotopes.org, Reprinted with permission from Ref . Copyright 2003 American Geophysical Union). (b) Summer‐season δ2H isoscape for the eastern USA‐based on statistical modeling of monitoring station data (data from http://isomap.org, job 27016, Reprinted with permission from Ref . Copyright 2014 British Ecological Society). (c) 12‐h average δ2H isoscape for the morning of October 30, 2012, based on geostatistical interpolation of rainwater data collected during Superstorm Sandy (Reprinted with permission from Ref . Copyright 2014 The Authors). Note the difference in range and pattern of values between examples, including the inversion of the N‐S isotopic gradient in (c), suggesting contrasting water cycle controls on the isoscapes at each temporal and spatial scale. Boxes in (a) and (b) show the extent of the subsequent map panel, in all panels dots show the location of sampling sites.
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