Advancing the Understanding and Representation of Ecohydrological Processes using Tracer-enabled Modeling Approaches

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

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top30cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP+EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20-25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP+EOF methods. Here, five CRNP surveys and 6-8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective

Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth’s terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE=5.45 wt %, R2=0.68), soil bulk density (RMSE=0.173 g cm-3, R2=0.203), and soil organic carbon (RMSE=1.47 wt %, R2=0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE~0.035cm3 cm-3 at a SWC=0.40 cm3 cm-3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSEm-2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments

A Statistical Method for Generating Temporally Downscaled Geochemical Tracers in Precipitation

Sampling intervals of precipitation geochemistry measurements are often coarser than those required by fine-scale hydrometeorological models. This study presents a statistical method to temporally downscale geochemical tracer signals in precipitation so that they can be used in high-resolution, tracer-enabled applications. In this method, we separated the deterministic component of the time series and the remaining daily stochastic component, which was approximated by a conditional multivariate Gaussian distribution. Specifically, statistics of the stochastic component could be explained from coarser data using a newly identified power-law decay function, which relates data aggregation intervals to changes in tracer concentration variance and correlations with precipitation amounts. These statistics were used within a copula framework to generate synthetic tracer values from the deterministic and stochastic time series components based on daily precipitation amounts. The method was evaluated at 27 sites located worldwide using daily precipitation isotope ratios, which were aggregated in time to provide low resolution testing datasets with known daily values. At each site, the downscaling method was applied on weekly, biweekly and monthly aggregated series to yield an ensemble of daily tracer realizations. Daily tracer concentrations downscaled from a biweekly series had average (+/- standard deviation) absolute errors of 1.69‰ (1.61‰) for δ2H and 0.23‰ (0.24‰) for δ18O relative to observations. The results suggest coarsely sampled precipitation tracers can be accurately downscaled to daily values. This method may be extended to other geochemical tracers in order to generate downscaled datasets needed to drive complex, fine-scale models of hydrometeorological processes

Integration of Hydrogeophysical Datasets for Improved Water Resource Management in Irrigated Systems

Water scarcity is predicted to be the major limitation to increasing agronomic outputs to meet future food and fiber demands. With the agricultural sector accounting for 80 – 90% of all consumptive water use and an average water use efficiency (WUE) of less than 45%, major advances must be made in irrigation water management. Precision agriculture, specifically variable-rate irrigation (VRI) and variable-speed irrigation (VSI) systems, offers the technologies to address and manage for infield variability and incorporate that into management decisions. The major limitation to implementing this technology often lies in the management of spatial datasets and the development of irrigation prescription maps that address variables impacting yield and soil moisture. While certain datasets and mapping technologies exist in practice, this study explored the utility of the recently developed cosmic-ray neutron probe (CRNP) which measures soil water content (SWC) in the top ~30cm of the soil profile. The key advantages of CRNP are that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: 1) improve the delineation of management zones within a field and 2) estimate spatial soil hydraulic properties (i.e. field capacity and wilting point) to make effective irrigation prescription maps. To accomplish this, a series of CRNP SWC surveys were collected in a 53-ha field near Sutherland, Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOF) to isolate the underlying spatial structure. Results indicated the measured SWC at field capacity and wilting point were better correlated to CRNP EOF as compared to other commonly used datasets. Based on this work, a soil sampling strategy and CRNP EOF analysis was proposed for better quantifying soil hydraulic properties. While the proposed strategy will increase overall effort as compared to traditional techniques, rising scrutiny for agricultural water-use may increase the adoption of this technology. Advisor: Trenton Fran

Combined analysis of soil moisture measurements from roving and fixed cosmic ray neutron probes for multiscale real-time monitoring

Soil moisture partly controls land-atmosphere mass and energy exchanges and ecohydrological processes in natural and agricultural systems. Thus, many models and remote sensing products continue to improve their spatiotemporal resolution of soil moisture, with some land surface models reaching 1 km resolution. However, the reliability and accuracy of both modeled and remotely sensed soil moisture require comparison with ground measurements at the appropriate spatiotemporal scales. One promising technique is the cosmic ray neutron probe. Here we further assess the suitability of this technique for real-time monitoring across a large area by combining data from three fixed probes and roving surveys over a 12 km× 12km area in eastern Nebraska. Regression analyses indicated linear relationships between the fixed probe averages and roving estimates of soil moisture for each grid cell, allowing us to derive an 8 h product at spatial resolutions of 1, 3, and 12km, with root-mean-square error of 3%, 1.8%, and 0.9%

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective

Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth’s terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE=5.45 wt %, R2=0.68), soil bulk density (RMSE=0.173 g cm-3, R2=0.203), and soil organic carbon (RMSE=1.47 wt %, R2=0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE~0.035cm3 cm-3 at a SWC=0.40 cm3 cm-3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSEm-2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments