The Use of Radar Methods to Determine Moisture Content in the Vadose Zone

Water content is a critical parameter affecting both liquid-phase and vapor-phase contaminant transport in the vadose zone. This means that accurate estimate of in situ water content must be obtained in order to design for the appropriate handling or remediation of a contaminated region of the vadose zone. Traditional methods of sampling the subsurface by drilling and/or direct sampling are very time consuming, limited in terms of spatial coverage, and have the associated risk of contacting and increasing the size of the contaminated area. One solution is to use geophysical methods which can provide a high-resolution, non-invasive means of sampling or imagin the subsurface

Overpumping Leads to California Groundwater Arsenic Threat

Water resources are being challenged to meet domestic, agricultural, and industrial needs. To complement finite surface water supplies that are being stressed by changes in precipitation and increased demand, groundwater is increasingly being used. Sustaining groundwater use requires considering both water quantity and quality. A unique challenge for groundwater use, as compared with surface water, is the presence of naturally occurring contaminants within aquifer sediments, which can enter the water supply. Here we find that recent groundwater pumping, observed through land subsidence, results in an increase in aquifer arsenic concentrations in the San Joaquin Valley of California. By comparison, historic groundwater pumping shows no link to current groundwater arsenic concentrations. Our results support the premise that arsenic can reside within pore water of clay strata within aquifers and is released due to overpumping. We provide a quantitative model for using subsidence as an indicator of arsenic concentrations correlated with groundwater pumping

Development and Application of a 1D Compaction Model to Understand 65 Years of Subsidence in the San Joaquin Valley

High rates of land subsidence, caused by groundwater overdraft, are resulting in millions of dollars of infrastructure damage in California\u27s San Joaquin Valley (SJV). In recent years, the use of interferometric synthetic aperture radar (InSAR) has enabled us to substantially improve our understanding of this subsidence. However, only very occasionally have the InSAR data been integrated with a physical model of subsurface compaction. Here, we have used InSAR and other data to parameterize and calibrate a 1D compaction model. We applied our model to a study area in the SJV where we had access to the necessary information on hydraulic head to develop model inputs. Our model simulated subsidence in the three aquifer system layers over the period 1952–2017, and is the first 1D compaction model in the SJV to simulate multiple aquifer system layers from the 1950s to 2017. The results from our model suggest that previous studies have significantly underestimated the time constants governing the slow, residual compaction of subsurface clays. We suggest that residual compaction of clays is a process that continues for decades-to-centuries, indicating that to significantly reduce subsidence requires some recovery of head, not just a stabilization. We also show how compaction in the lower, confined aquifer has accounted for over 90% of subsidence in the past 20 years. Although our study area is small, our findings are likely representative of the subsiding regions of the SJV, and our methodology can be applied to unconsolidated aquifer systems exhibiting subsidence worldwide

High-resolution Monthly Satellite Precipitation Product over the Conterminous United States

We present a data set that enhanced the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) monthly product 3B43 in its accuracy and spatial resolution. For this, we developed a correction function to improve the accuracy of TRMM 3B43, spatial resolution of ~25 km, by estimating and removing the bias in the satellite data using a ground-based precipitation data set. We observed a strong relationship between the bias and land surface elevation; TRMM 3B43 tends to underestimate the ground-based product at elevations above 1500 m above mean sea level (m.amsl) over the conterminous United States. A relationship was developed between satellite bias and elevation. We then resampled TRMM 3B43 to the Digital Elevation Model (DEM) data set at a spatial resolution of 30 arc second (~1 km on the ground). The produced high-resolution satellite-based data set was corrected using the developed correction function based on the bias-elevation relationship. Assuming that each rain gauge represents an area of ~1 km2, we verified our product against 9,200 rain gauges across the conterminous United States. The new product was compared with the gauges, which have 50, 60, 70, 80, 90, and 100% temporal coverage within the TRMM period of 1998 to 2015. Comparisons between the high-resolution corrected satellite-based data and gauges showed an excellent agreement. The new product captured more detail in the changes in precipitation over the mountainous region than the original TRMM 3B43

High quality InSAR data linked to seasonal change in hydraulic head for an agricultural area in the San Luis Valley, Colorado

In the San Luis Valley (SLV), Colorado legislation passed in 2004 requires that hydraulic head levels in the confined aquifer system stay within the range experienced in the years 1978–2000. While some measurements of hydraulic head exist, greater spatial and temporal sampling would be very valuable in understanding the behavior of the system. Interferometric synthetic aperture radar (InSAR) data provide fine spatial resolution measurements of Earth surface deformation, which can be related to hydraulic head change in the confined aquifer system. However, change in cm-scale crop structure with time leads to signal decorrelation, resulting in low quality data. Here we apply small baseline subset (SBAS) analysis to InSAR data collected from 1992 to 2001. We are able to show high levels of correlation, denoting high quality data, in areas between the center pivot irrigation circles, where the lack of water results in little surface vegetation. At three well locations we see a seasonal variation in the InSAR data that mimics the hydraulic head data. We use measured values of the elastic skeletal storage coefficient to estimate hydraulic head from the InSAR data. In general the magnitude of estimated and measured head agree to within the calculated error. However, the errors are unacceptably large due to both errors in the InSAR data and uncertainty in the measured value of the elastic skeletal storage coefficient. We conclude that InSAR is capturing the seasonal head variation, but that further research is required to obtain accurate hydraulic head estimates from the InSAR deformation measurements

The effect of surface roughness on Nuclear Magnetic Resonance relaxation

Most theoretical treatments of Nuclear Magnetic Resonance (NMR) measurements of porous media assume ideal pore geometries for the pores (i.e. slabs, spheres or cylinders) with welldefined surface-to-volume ratios (S/V). This same assumption is commonly adopted for naturally occurring materials, where the pore geometry can differ substantially from these ideal shapes. In this paper the effect of the roughness of the pore surface on the T2 relaxation spectrum is studied. By homogenization of the problem using an electrostatic approach it is found that the effective surface relaxivity can increase dramatically in the presence of rough surfaces. This leads to a situation where the system responds as a pore with a smooth surface, but with significantly increased surface relaxivity. As a result the standard approach of assuming an idealized geometry with known surface to-volume and inverting the T2 relaxation spectrum to a pore size distribution is no longer valid. The effective relaxivity is found to be fairly insensitive to the shape of the roughness but strongly dependent on the width and depth of the surface geometry

Nonexponential decay of the surface-NMR signal and implications for water content estimation

ABSTRACT Noninvasive surface nuclear magnetic resonance (SNMR) measurements can yield direct and quantitative estimates of water content in the near surface. A fundamental assumption that is always made in the analysis of SNMR data is that the measured signal exhibits an exponential decay. Although the assumption of exponential decay is frequently valid, it can be shown that in the presence of an inhomogeneous magnetic field, the decay may be nonexponential in form. Simulated SNMR data were used to explore how the decay shape will vary with certain environmental and measurement conditions and to assess how nonexponential decay will affect SNMR-based estimates of water content. Results derived from analytical and pore-scale modeling demonstrated that the shape of the decay depends strongly on both pore geometry and the statistics of the regional or pore-scale magnetic field. In particular, the decay is most likely to be nonexponential when pores are large and when a strongly inhomogeneous magnetic field is present. For conditions in which the SNMR signal cannot be accurately modeled as exponential, standard processing approaches were found to result in significant errors in estimated water contentspecifically, water content tends to be overestimated. Analysis of data misfits suggests that, in practice, it will be difficult to directly identify errors associated with nonexponential decay based only on the measured signal. Therefore, a description of the conditions leading to nonexponential decay and the implications for water content estimates is useful to support improved interpretation of SNMR measurements

Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California\u27s Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts