Supporting data - Recycling of nitrate and organic matter by plants in the vadose zone of a saturated riparian buffer

Data from the analysis of nitrate as nitrogen in the soil and soil pore water within the vadose zone of a saturated riparian buffer (SRB). Additional properties measured include: Organic matter (as %), bulk density, moisture content, and porosity. Soil samples were collected pre-growing season (n=57) and post-growing season from two plots (n=29): vegetated plots and barren plots. Statistical comparison of among the treatments, Pre-growing season, plot with plants, and barren plot, and among the different depths, 30 cm, 60 cm, and 90 cm identified significantly different soil NO3--N concentrations. Plots with plants experienced a reduction in nitrate from the soil and vadose waters. Plants withdrew nitrate from the vadose zone, generating organic matter. Nitrate concentrations in the soils underlying the barren plot were high because there was no uptake and the residual plants materials decomposed, returning nitrogen to the vadose. Soil pore water samples were collected using a lysimeter from the barren plots (n=64) and the vegetated plots (n=35)

High-Resolution Mapping of Tile Drainage in Agricultural Fields Using Unmanned Aerial System (UAS)-Based Radiometric Thermal and Optical Sensors

With the growing concerns of water quality related to tile drainage in agricultural lands, developing an efficient and cost-effective method of mapping tile drainage is essential. This research aimed to establish mapping of tile drainage systems in agricultural fields using optical and radiometric thermal sensors mounted on Unmanned Aerial System (UAS). The overarching hypothesis is that in a tile-drained land, spatial distribution of soil water content is affected by tile lines, therefore, contrasting soil temperature signals exist between areas along the tile lines and between the tile lines. Designated flights were conducted to assess the effectiveness of the UAS under various conditions such as rainfall, crop cover, crop maturity and time of the day. Image correction, mosaicking, image enhancements and map production were conducted using Agisoft and ENVI image analysis software. The results showed intermediate growth stage of soybean plants and rainfall helped delineating tile lines. In-situ soil temperature measurements revealed appropriate time of the day (14:00 to 18:00 h) for thermal image detection of the tile lines. The role of soil moisture and plant cover is not resolved, thus, further refinement of the approach considering these factors is necessary to develop efficient mapping techniques of tile drainage using UAS thermal and optical sensors

Downscaling GRACE TWSA Data into High-Resolution Groundwater Level Anomaly Using Machine Learning-Based Models in a Glacial Aquifer System

With continued threat from climate change and human impacts, high-resolution and continuous hydrologic data accessibility has a paramount importance for predicting trends and availability of water resources. This study presents a novel machine learning (ML)-based downscaling algorithm that produces a high spatial resolution groundwater level anomaly (GWLA) from the Gravity Recovery and Climate Experiment (GRACE) data by utilizing the relationship between Terrestrial Water Storage Anomaly (TWSA) from GRACE and other land surface and hydro-climatic variables (e.g., vegetation coverage, land surface temperature, precipitation, streamflow, and in-situ groundwater level data). The predicted downscaled GWLA data were tested using monthly in-situ groundwater level observations. Of the 32 groundwater monitoring wells available in the study site, 21 wells were used to develop the ML-based downscaling model, while the remaining 11 wells were used to assess the performance of the ML-based downscaling model. The test results showed that the model satisfactorily reproduces the spatial and temporal variation of the GWLA in the area, with acceptable correlation coefficient and Nash-Sutcliffe Efficiency values of ~0.76 and ~0.45, respectively. GRACE TWSA was the most influential predictor variable in the models, followed by stream discharge and soil moisture storage. Though model limitations and uncertainty could exist due to high spatial heterogeneity of the geologic materials and omission of human impact (e.g., abstraction), the significance of the result is undeniable, particularly in areas where in-situ well measurements are sparse

Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1°) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002–2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems

Investigating Thermal Controls on the Hyporheic Flux as Evaluated Using Numerical Modeling of Flume-Derived Data

The flux of water through the hyporheic zone (HZ) is controlled by stream bedforms, sinuosity, surface water velocity, local water table, seasonality, and hydraulic conductivity (K) of the bed material. Dependent on both the kinematic viscosity and density of water, K values are a function of temperature. In most studies, changes in temperature have been neglected because of the limited effect either density or viscosity has on K values. However, these variations are important given the role of K in HZ flux, which lead to the hypothesis that flow into the HZ would be more efficient (faster rate and greater depth) under warmer conditions than under cool conditions. To discern how water temperature affects flow depth in the HZ, VS2DHI simulations were created to map flow under both warm and cool thermal conditions. The models employed data collected from a series of varying temperature hydrologic flume tests in which the effects of hyporheic flow altering variables such as sinuosity, surface water velocity and volume, and bed-forms were controlled. Results verify that K values in the HZ were larger under warm conditions generating deeper HZ pathways, while the smaller K values under cool conditions produced shallower pathways. The simulations confirmed a faster speed of frontal movement under warm conditions than cool. Péclet numbers revealed a shallower advective extinction depth under cool conditions as opposed to warm

Investigating Thermal Controls on the Hyporheic Flux as Evaluated Using Numerical Modeling of Flume-Derived Data

The flux of water through the hyporheic zone (HZ) is controlled by stream bedforms, sinuosity, surface water velocity, local water table, seasonality, and hydraulic conductivity (K) of the bed material. Dependent on both the kinematic viscosity and density of water, K values are a function of temperature. In most studies, changes in temperature have been neglected because of the limited effect either density or viscosity has on K values. However, these variations are important given the role of K in HZ flux, which lead to the hypothesis that flow into the HZ would be more efficient (faster rate and greater depth) under warmer conditions than under cool conditions. To discern how water temperature affects flow depth in the HZ, VS2DHI simulations were created to map flow under both warm and cool thermal conditions. The models employed data collected from a series of varying temperature hydrologic flume tests in which the effects of hyporheic flow altering variables such as sinuosity, surface water velocity and volume, and bed-forms were controlled. Results verify that K values in the HZ were larger under warm conditions generating deeper HZ pathways, while the smaller K values under cool conditions produced shallower pathways. The simulations confirmed a faster speed of frontal movement under warm conditions than cool. Péclet numbers revealed a shallower advective extinction depth under cool conditions as opposed to warm