Climate change impacts everyone’s food and water security. Increasing global temperatures accelerate the hydrologic cycle and consequently impact the water resources for billions of people worldwide. Countless models have been developed to represent various components of the hydrologic cycle at various spatial and temporal scales. These are often validated against bulk fluxes and are widely used to predict the response of hydrologic systems to changing stressors. Natural tracers, such as stable water isotopes, can be applied within modeling frameworks to provide additional points of comparison between observed and modeled environmental pools and fluxes. A tracer-enabled modeling approach allows for process-level inferences based not only on the size of fluxes, but also on the spatial and temporal transport and mixing of the geochemical signatures associated with bulk fluxes. These process-level inferences can facilitate improved understanding and a multi-response evaluation of a model’s performance.
In this dissertation, I show how natural tracer datasets can be applied to improve our understanding and representation of ecohydrologic processes ranging from fine-scale subsurface flow dynamics to ecosystem scale evapotranspiration (ET) flux partitions. First, I developed a statistical downscaling method which can be applied on coarse resolution time series of geochemical tracers in precipitation. The statistical downscaling method had low absolute error across the 27 datasets from sites located worldwide. The results suggest coarsely sampled precipitation tracers can be accurately downscaled to daily values.
Next, I tested if isotopic separations occurred within 650 distinct configurations of soil properties, climatologies, and mobile/immobile soil-water domains using an advanced soil physics model. The model simulations showed separations in isotope ratios between storage and drainage waters during periods of high precipitation, soil water content, and drainage. Across soil types and climates, lower saturated hydraulic conductivity and higher rainfall rates amplified isotopic differences, illustrating how mobile and immobile domains interact with local conditions to physically result in subsurface separations. These results exposed how different critical-zone solute fluxes can be generated by representing contrasting transport dynamics in distinct domains across a range of soils and climate conditions.
Lastly, I investigated the uncertainty in total ET for three land surface models (LSMs) in the North American Land Data Assimilation System (NLDAS) configuration using observation datasets of precipitation and ET at 14 sites across the United States from the National Ecological Observatory Network (NEON). The biweekly precipitation collections of stable water isotope ratios were statistically downscaled to correspond with daily NLDAS forcings and used as conservative tracers within a mass balance model built from LSM outputs. The mass balance simulated stable water isotope concentrations (δ) for each ET partition, subsurface drainage, surface runoff, and storage. Simulated δET was directly compared to daily δET observations, which were calibrated from NEON tower measurements of atmospheric water vapor. An inter-model comparison suggested distinct differences exist amongst simulated δET and this can be associated with disparities in the relative contributions of interception, plant transpiration, and soil evaporation to the total ET. These findings can improve the general understanding of land-surface processes influencing the water and carbon cycle from regional to global scales
