Experimental Estimation of the GPR Groundwave Sampling Depth

Monitoring near-surface soil water content is essential for efficient water management and for understanding hydrologic processes in soils. Ground-penetrating radar (GPR) groundwaves are an approach that can be used to monitor the near-surface soil water content, but the efficacy of this technique is currently limited by the uncertainty surrounding the groundwave sampling depth. This research experimentally determines the sampling depth of GPR groundwaves under dry and saturated conditions in a sandy soil. Data were acquired using 250, 500, and 1000 MHz antennas within an experimental tank containing soil layers of contrasting electromagnetic velocities. Results show that the groundwave sampling depth is a function of frequency in both dry and saturated soils, and sampling depth is inversely related to frequency. A comparison of data acquired under dry and saturated conditions indicates that the groundwave sampling depth is slightly less in saturated soil than in dry soil, but the dependence of sampling depth on soil water content may be less than has been predicted using numerical modeling. The minimum sampling depth observed in this experiment was 12 cm for the 1000 MHz antennas in saturated sand, and the maximum sampling depth was 30 cm for the 250 MHz antennas in dry sand

Analyzing Spatio-Temporal Mechanisms of Land Subsidence in the Parowan Valley, Utah, USA

Parowan Valley, Utah (USA), is an agricultural region experiencing rapid subsidence due to extensive groundwater extraction from aquifers with a significant portion of fine-grained sediments. To analyze the subsidence spatio-temporally, time-series Interferometric Synthetic Aperture Radar (InSAR) of 155 Sentinel-1 C-band scenes were processed. These data showed approximately 30 cm of ground subsidence in Parowan Valley from 2014 to 2020. Because of the high temporal sampling rate of the Sentinel-1 satellite (12-day cycle), it is possible to determine the seasonal changes of ground deformation and relate this to groundwater extraction. To better understand the relationship between ground deformation and groundwater extraction in the Parowan Valley, temporal changes in hydraulic head data from US Geological Survey observation wells were monitored. Additionally, well logs were analyzed and used to construct a map that showed the percentage of fine-grained material in the subsurface. The investigation of hydraulic head and geology, together with InSAR-derived ground displacement data, indicates that the most subsidence occurs where there is a co-occurrence of high groundwater demand and a high percentage of fine-grained sediments, but recharge likely plays a role in mitigating subsidence in some areas. The subsidence developed in Parowan Valley shows a long-term trend as well as seasonal variation and appears to be influenced by both agricultural activity and annual precipitation

Phase Control of Squeezed Vacuum States of Light in Gravitational Wave Detectors

Quantum noise will be the dominant noise source for the advanced laser interferometric gravitational wave detectors currently under construction. Squeezing-enhanced laser interferometers have been recently demonstrated as a viable technique to reduce quantum noise. We propose two new methods of generating an error signal for matching the longitudinal phase of squeezed vacuum states of light to the phase of the laser interferometer output field. Both provide a superior signal to the one used in previous demonstrations of squeezing applied to a gravitational-wave detector. We demonstrate that the new signals are less sensitive to misalignments and higher order modes, and result in an improved stability of the squeezing level. The new signals also offer the potential of reducing the overall rms phase noise and optical losses, each of which would contribute to achieving a higher level of squeezing. The new error signals are a pivotal development towards realizing the goal of 6 dB and more of squeezing in advanced detectors and beyond

Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Finite Line Relief Well System Design for Dams and Levees

An Analytical Method is Commonly Used to Design Relief Well Systems by Assuming the Well Line Extends an Infinite Distance Parallel to the Dam or Levee. This Assumption May Be Met in Some Cases, But When a Well Line is of Finite Length, This Can Severely Underestimate Excess Heads. Although These Consequences Have Been Historically Recognized, a Practical Graph-Based Analytical Approach for Finite Well Line Design Has Not Been Developed. Finite Well Lines Exist and Continue to Be Installed at Many Locations, So This Study Developed a Practical Design Method for Such Systems. Analytical Solutions and Numerical Models Were Used to Improve Understanding of the Performance of Partial and Full Penetration Finite Well Systems. Performance Was Found to Be Dependent on Well System Geometry, the Ratios of Effective Seepage Entry and Exit Distances to Well Spacing, and the Number of Wells. Model Results Were Used to Develop New Uplift Factors that More Accurately Define Excess Heads Along and Landward of Finite Well Systems that Fully or Partially Penetrate the Aquifer

Field-Scale Estimation of Volumetric Water Content Using Ground-Penetrating Radar Ground Wave Techniques

Ground-penetrating radar (GPR) ground wave techniques were applied to estimate soil water content in the uppermost ∼10 cm of a 3 acre California vineyard several times over 1 year. We collected densely spaced GPR travel time measurements using 900 and 450 MHz antennas and analyzed these data to estimate water content. The spatial distribution of water content across the vineyard did not change significantly with time, although the absolute water content values varied seasonally and with irrigation. The GPR estimates of water content were compared to gravimetric water content, time domain reflectometry, and soil texture measurements. The comparisons of GPR-derived estimates of water content to gravimetric water content measurements showed that the GPR estimates had a root mean square error of volumetric water content of the order of 0.01. The results from this study indicate that GPR ground waves can be used to provide noninvasive, spatially dense estimates of shallow water content over large areas and in a rapid manner

Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Mapping the Volumetric Soil Water Content of a California Vineyard Using High-Frequency GPR Ground Wave Data

An attempt was made to establish the utility of ground-penetrating radar (GPR) as a quick and noninvasive field tool for shallow soil water content estimates as a function of space and time. Initially, detailed studies of collocated data, with electromagnetic velocity estimates from GPR data compared to gravimetric measurements of water content and to soil testure were carried out. Using the procedures developed during the detailed studies, full grids of GPR data were collected over the entire site several times. Data obtained indicate that incorporation of multiple frequency GPR grids can provide high-resolution estimates of soil water content variations as a function of depth as well as space and time

GPR Monitoring of Volumetric Water Content in Soils Applied to Highway Construction and Maintenance

An overview is given on two experiments, a controlled pit study and a transportation application in subasphalt soils. Both experiments show that common-offset ground-penetrating radar (GPR) reflection data can be used to estimate θv to a high degree of accuracy. The methodology developed in these two experiments provides a technique for obtaining quick, noninvasive, accurate, and high-resolution estimates of θv

Estimating Aquifer System Storage Loss with Water Levels, Pumping and InSAR Data in the Parowan Valley, Utah

In the Parowan Valley of Utah, Groundwater Levels Have Declined by as Much as 30 M over the Past 50 Years with Accompanying Subsidence Rates of Up to 5 Cm/year. Traditional Methods to Estimate Groundwater Storage Change Use a Combination of Groundwater Level and Storativity Estimates, But There is Often Considerable Uncertainty in These. in This Study, We Demonstrate a New Method that Relies on a Combination of Geodetic Data from InSAR, as Well as Groundwater Level and Pumping Data, to Estimate Both the Total Groundwater Storage Loss and the Percentages of Storage Loss in Fine- and Coarse-Grained Layers within an Aquifer System. We Find that When Aggregated over All of Parowan Valley, Fine- and Coarse-Grained Layers Account for Roughly Equal Portions of the Total Groundwater Storage Loss. However, in Confined Aquifers, Fine-Grained Layers Account for Most of the Storage Loss. This Has Important Implications on the Source of Groundwater in Depleting Aquifer Systems, as Many Models Do Not Account for Fine-Grained Layers as a Source of Water. We Find that in the Parowan Valley, the Aquifer Depletion is Roughly 12.5% of the Volume of Pumped Groundwater, Meaning that the Remainder of Pumped Groundwater is Sourced from Net Inflow. This Study Presents the First Method that Combines Geodetic and in Situ Groundwater Data to Provide Estimates of Groundwater Storage Change that Account for Both Coarse- and Fine-Grained Intervals, Which Are Typically Present in Significant Amounts in the Major Unconsolidated Aquifer Systems of the World