Abstract

Stable isotope ratio measurements (isotope values) of surface water provide information on hydrological processes and can be used to determine provenance of hydrogen and oxygen stored in animal and plant tissues. Development of maps of the distribution of isotope values (isoscapes) for river networks is limited by methods to interpolate point measures to values for the entire network. Isotope values of precipitation and environmental characteristics that drive fractionation processes within the catchment also affect downstream reaches via flow. Many environmental characteristics, such as man-made dams, are no more likely to affect nearby unconnected reaches than distant ones. Hence, distance-based geospatial and statistical interpolation methods used to develop isoscapes for precipitation and terrestrial systems are less appropriate for river networks. We used a water balance-based method, which represents patterns of surface flow and mixing, and added a regression-based correction step using catchment environmental predictors. We applied this method across the river network of New Zealand, comprising over 600,000 reaches and over 400,000 kilometres of rivers. Inputs to the model are national rainfall precipitation isoscapes, a digital elevation layer, a national river water isotope monitoring dataset (3 years of monthly sampling at 58 sites) and reach scale river environmental databases across the New Zealand river network. δ2H and δ18O isoscapes produced using this regression-based kriging method showed improved fit to validation data, compared to a model for which residuals were applied as a correction factor across the river network using ordinary kriging. The resulting river water isoscapes have potential applications in ecology, hydrology and provenance studies for which understanding of spatial variation between precipitation and surface water isotope values are required.

Highlights

  • Stable hydrogen and oxygen isotope measurements in surface water provide powerful tools for hydrology, ecology and food science; uses include identifying runoff sources to rivers, quantifying land-atmosphere water fluxes, determining natal origins 30 of migratory fish, and tracing the geographical origins of food (Kelly et al, 2005; Cable et al, 2011; Gao and Beamish, 1999; Gibson et al, 1996)

  • Variations in the isotope 35 values of precipitation reflect the temperature at which condensation occurs and prior condensation as air masses are transported over land, with the general effect that: 1. Precipitation becomes more depleted in heavy isotopes at higher latitudes, and higher elevations (Dansgaard, 1964)

  • Understanding of the processes controlling precipitation isotope values has led to the development of predictive 45 statistical models for global-scale mapping of precipitation isotopes values that serve to inform on spatial patterns of surface water isotope values (Bowen and Revenaugh, 2003; Bowen et al, 2011)

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Summary

Introduction

Stable hydrogen and oxygen isotope measurements in surface water provide powerful tools for hydrology, ecology and food science; uses include identifying runoff sources to rivers, quantifying land-atmosphere water fluxes, determining natal origins 30 of migratory fish, and tracing the geographical origins of food (Kelly et al, 2005; Cable et al, 2011; Gao and Beamish, 1999; Gibson et al, 1996). When spatial variability within the catchment is low and sampling density is high, ordinary kriging methods may be 70 appropriate, New Zealand is an example of a country with relatively large distances between long-term river monitoring sites but high variation in relief and climate over relatively short distances Under these conditions, using regression based statistical methods with databases of explanatory environmental, rather than distance-based methods, to explain hydrological variation across the reaches of river networks provides superior prediction of spatial variability (Snelder and Biggs, 2002; Hicks et al, 2011). We calculate residuals between modelled surface water isotope values and values measured at 58 long-term river water sampling sites across New Zealand We extrapolate these residuals across New Zealand using a regression method that incorporates explanatory environmental variables from the River 80 Environment Classification database (Snelder and Biggs, 2002), Freshwater Ecosystems of New Zealand (FENZ) geodatabase (Leathwick et al, 2010), as well as GIS lake data as predictors.

Water balance model for estimation of river water stable isotope values
Model input data
Calculation and kriging of model residuals
Data for final model validation
Precipitation isotope values
River water stable isotopes modelled using the water balance method
Kriging of isotope residuals
Spatial distributions of model residuals
Final model validation
Patterns and drivers of final model performance
Final conclusions and implications 415
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