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1. Erosion mechanisms of claypan soils in southeastern Kansas

   Mathis II, M.A. (March 2019, Eighth International Conference on Case Histories in Geotechnical Engineering)

   Claypan soils cover approximately 40,469 km2 in the United States and are characterized by a highly impermeable layer within 0.5 m from the ground surface. This impermeable layer acts as a barrier for infiltrating water, which may increase erosion rates and sediment transport. Two of the main problems associated with these processes are abutment scour and reservoir sedimentation. This study focuses on the undermining of surficial soils due to an impermeable claypan layer in Southeastern Kansas. The potential areas of critical soil loss and hydrologic flow patterns were determined using LiDAR-derived digital elevation maps across two 0.45 km2 sites. These sites were located in areas of both high and low elevation. Electrical resistivity tomography (ERT) was used in areas identified with LiDAR to measure the depth to claypan, which was originally believed to be uniform across the region. The results indicated that the claypan layer was located from 0.5 to 0.75 m and dissipated moving across the site from an area of high elevation to an area of low elevation. Undisturbed soil samples were collected based on the ERT analysis, in areas with and without the claypan. An erosion function apparatus (EFA) was used to directly measure erosion due to sheet flow and to identify the controlling mechanism causing surficial soil loss. The knowledge gained on claypan erosion mechanisms will improve the prediction of near surface soil erodibility to support aging infrastructure. 

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2. Electrical Resistivity Tomography of Claypan Soils in Southeastern Kansas

   https://doi.org/https://doi.org/10.4148/ 2378-5977.7574  

   Mathis, M. A.; Tucker-Kulesza, S. E.; and Sassenrath, G. F. (March 2018, Kansas Agricultural Experiment Station Research Reports)

   Claypan soils cover approximately 10 million acres across several states in the central United States. The soils are characterized by a highly impermeable clay layer within the profile that impedes water flow and root growth. While some claypan soils can be productive, they must be carefully managed to avoid reductions to crop productivity due to root restrictions, water, and nutrient limitations. Clay soils are usually resistant to erosion but may exacerbate erosion of the silt-loam topsoil. Soil production potential is the capacity of soil to produce at a given level (yield per acre). The productive capacity is tied to soil characteristics, which can be highly variable within a field. In this project, we have used imagery analysis to study the aerial images and terrain of fields during different productive times of the year to identify where soil samples should be collected for more discrete analysis. Soil samples provide valuable information; however, the amount of data obtained from a relatively small area within a field does not provide sufficient information to delineate the subsurface characteristics. To address the limitations of sampling, we have also employed the use of yield maps collected from commercial yield monitors on production-scale combines and surface electrical conductivity measurements (Sassenrath and Kulesza, 2017). Soil conductivity is a measurement of how well a representative volume of soil conducts electricity. Soil conductivity is a function of the soil clay content, moisture content, and other measurable soil properties (Kitchen et al., 2003); as such, it has become a valuable tool for mapping in-field variability. The main advantage of a soil conductivity measurement is that the entire surface of a field can be imaged. The disadvantage of a soil conductivity measurement is that data are only collected near the surface (10 – 30 inches) and the measurements are relative measurements. This means that the conductivity mappers can identify changes in soil properties, but they cannot directly tell researchers what caused these changes. Electrical resistivity tomography (ERT) is a popular near-surface geophysical measurement for geophysical and engineering applications. The term “near-surface” generally means down to around 30 feet in the subsurface. Electrical resistivity is the reciprocal measurement of electrical conductivity; therefore, both systems measure differences in the same soil properties. ERT measurements are different than surface electrical conductivity measurements because ERT collects a “slice” of data into the subsurface, as opposed to only changes at the surface area. Relative measurements, similar to those collected in an electrical conductivity survey, are collected; however, in ERT studies the data are mathematically inverted to yield the true electrical resistivity of the soil with depth. This allows an interpretation of the changing soil properties with depth to reduce the required amount of sampling. A disadvantage of an ERT survey is that the data acquisition is stationary so mapping an entire field is not feasible. We have used a coupled process of imagery and terrain analysis, yield maps, and electrical conductivity measurements to guide the locations of ERT surveys in this project (Tucker-Kulesza et al. 2017). 

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3. Quantifying the Natural Climate Solution Potential of Agricultural Systems by Combining Eddy Covariance and Remote Sensing

   https://doi.org/10.1029/2022JG006895  

   Wiesner, Susanne; Desai, Ankur R.; Duff, Alison J.; Metzger, Stefan; Stoy, Paul C. (September 2022, Journal of Geophysical Research: Biogeosciences)

   Abstract Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems. 

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4. The extent of soil loss across the US Corn Belt

   https://doi.org/10.1073/pnas.1922375118  

   Thaler, Evan A.; Larsen, Isaac J.; Yu, Qian (February 2021, Proceedings of the National Academy of Sciences)

   Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 10⁵km²of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields. 

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5. Sensitivity Analysis and Uncertainty Quantification of PFAS Fate and Transport in Heterogeneous Riparian Sediments

   https://doi.org/10.1021/acsearthspacechem.4c00037  

   Li, Pei; McGarr, Jeffery T; Moeini, Farzad; Dai, Zhenxue; Soltanian, Mohamad Reza (August 2024, ACS Earth and Space Chemistry)

   Per- and polyfluoroalkyl substances (PFAS) are surface-active contaminants, which are detected in groundwater globally, presenting serious health concerns. The vadose zone and surface water are recognized as primary sources of PFAS contamination. Previous studies have explored PFAS transport and retention mechanisms in the vadose zone, revealing that adsorption at interfaces and soil/sediment heterogeneity significantly influences PFAS retention. However, our understanding of how surface water−groundwater interactions along river corridors impact PFAS transport remains limited. To analyze PFAS transport during surface water−groundwater interactions, we performed saturated−unsaturated flow and reactive transport simulations in heterogeneous riparian sediments. Incorporating uncertainty quantification and sensitivity analysis, we identified key physical and geochemical sediment properties influencing PFAS transport. Our models considered aqueous-phase transport and adsorption both at the air−water interface (AWI) and the solid-phase surface. We tested different cases of heterogeneous sediments with varying volume proportions of higher permeability sediments, conducting 2000 simulations for each case, followed by global sensitivity and response surface analyses. Results indicate that sediment porosities, which are correlated to permeabilities, are crucial for PFAS transport in riparian sediments during river stage fluctuations. High-permeable sediment (e.g., sandy gravel, sand) is the preferential path for the PFAS transport, and low-permeable sediment (e.g., silt, clay) is where PFAS is retained. Additionally, the results show that adsorption at interfaces (AWI and solid phase) has a small impact on PFAS retention in riparian environments. This study offers insights into factors influencing PFAS transport in riparian sediments, potentially aiding the development of strategies to reduce the risk of PFAS contamination in groundwater from surface water. 

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