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In California’s Central Valley, the forecast from climate models is that future precipitation events will be less frequent, more extreme, and deliver a higher proportion of the precipitation as rain. The combination of these effects will challenge the state’s ability to capture and store this critical freshwater resource and will also threaten downstream communities with flooding. A water management strategy gaining popularity in California is managed aquifer recharge (MAR), where excess surface water is captured and directed to selected sites to recharge the underlying groundwater systems. AgMAR is a form of MAR where water is spread over agricultural land to recharge the underlying aquifer over short periods during the wet winter months. One concern with this strategy is the decades of intensive agriculture in the Central Valley. The intense usage of fertilizers such as nitrogen threatens to contaminate the groundwater systems on which municipalities and homeowners rely. Research into nitrate mobilization has shown that the stratigraphic characterization of a site is the dominant factor in determining the infiltration rate and mobilization of contaminants. Therefore, in order to develop a model of nitrate mobilization, detailed information is needed about surface stratigraphy. In this study, a towed-transient electromagnetic geophysical method (tTEM) was used to image the subsurface, in combination with sediment type logs, to characterize the subsurface sediments. tTEM data were acquired on a 56-ha commercial almond farm in the early spring of 2022. The tTEM data were inverted to recover a resistivity model of the site, exhibiting a high degree of spatial variability. Regions of high resistivity typically suggest coarser grained material that is more permeable and hydraulically conductive, whereas regions of lower resistivity tend to be composed of finer grained material and are less hydraulically conductive. We created a site-specific resistivity-to-sediment-type transform to extract sediment-type data from the tTEM data using data from the 1D resistivity models along with twenty co-located sediment type logs and water table measurements. Using the maximum likelihood model, we transformed the recovered resistivity model into a sediment-type model. The integration of tTEM data and well-derived information about sediment types to construct a sediment-type model can provide information about connected pathways for recharge and help inform nitrate mobilization models. This study is allowing us to develop a methodology that can be applied elsewhere for the assessment of a site for agMAR when there are concerns about nitrate mobilization. This work is in support of a larger project on groundwater sustainability in agricultural systems in the southwestern United States.

A novel Markov chain Monte Carlo (MCMC)-based methodology was developed for the transformation of resistivity, derived from airborne electromagnetic (AEM) data, into sediment type. This methodology was developed and tested using AEM data and well sediment type and resistivity logs from Butte and Glenn Counties in the California Central Valley. Our methodology accounts for the spatially varying sensitivity of the AEM method by constructing different transforms separated based on the sensitivity of the AEM method. The large spatial separation that typically exists between the AEM data and the wells with sediment type logs was avoided by planning the acquisition of AEM data so as to fly as close as possible to the well locations. We had 55 locations with sediment type logs and AEM data separated by 100 m, determined to be the maximum acceptable separation distance. Differences in vertical resolution between the AEM method and the sediment type logs were addressed by modeling the physics of the AEM measurement, allowing for a comparison of field and AEM data generated during the MCMC process. The influence of saturation state was captured by creating one set of transforms for the region above the top of the saturated zone and another for below. Using the set of transforms developed at the 55 locations, an inverse distance weighting scheme that included a well quality ranking was used to construct a set of 12 (six sensitivity bins, and two saturation states) resistivity-to-sediment-type transforms at every AEM data location. These represent a set of transforms that accommodate the variation in AEM sensitivity and are independent of the inversion used to retrieve the resistivity model. Thus, these transforms avoid two of the significant limitations common to resistivity-to-sediment-type transforms used to interpret AEM data.

The ability to relate geophysical measurements to the material properties of the subsurface is fundamental for the successful application of geophysical methods. Estimating the electrical resistivity from material properties can be challenging at many hydrogeologic field sites, which typically lack the spatial density and resolution of the measurements needed to develop an accurate rock-physics relationship. We have developed rock-physics transforms using the machine-learning method of gradient-boosted decision trees (GBDTs). We adopt as our study area the coastal Salinas Valley, in which saltwater intrusion results in changes in resistivity. We use measurements available in boreholes, including salinity and sediment type, to predict the resistivity. In some transforms, we include as predictors in the GBDT algorithm the location of each measurement and the aquifer corresponding to each measurement. We also explore incorporating the predictions of a baseline rock-physics transform as a prior term within the objective function of the GBDT algorithm to guide the predictions made by the machine-learning algorithm. The use of location and aquifer information improves the predictions of the GBDT transform by 28% compared with when location and aquifer information are not included. After the salinity, the easting of each measurement is the most important predictor, due to the spatial pattern of salinity changes in the area. The next most important predictor is the aquifer corresponding to each measurement. The benefit of including the baseline transform in the objective function is greatest for small data sets and when the accuracy of the baseline transform is already high. Finally, using the resistivity predicted by the GBDT, we generate 1D resistivity models, which we use to simulate the acquisition of airborne electromagnetic (AEM) data. In most cases, the 1D resistivity models and the corresponding AEM data match well with the models and data corresponding to the resistivity measured in boreholes.

Land surface subsidence due to excessive groundwater pumping is an increasing concern in California, USA. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique for measuring centimeter-to-millimeter surface deformation at 10–100 m spatial resolution. Here, a data-driven approach that attributes deformation to individual depth intervals within an aquifer system by integrating head data acquired from each of three screened intervals in a monitoring well with InSAR surface deformation measurements was developed. The study area was the Colusa Basin in northern Central Valley. To reconstruct the surface deformation history over the study area, 13 ALOS-PALSAR scenes acquired between 2006 and 2010 were processed. Up to ~3-cm year−1 long-term subsidence and up to ~6 cm seasonal subsidence were observed using the InSAR technique. The technique developed in this paper integrates the InSAR-observed seasonal deformation rate and the co-located head measurements in multiple depth intervals to estimate the elastic skeletal storage coefficient, the time delay between the head change and the observed deformation, and subsequently the deformation of each depth interval. This technique can be implemented when hydraulic head measurements within each depth interval are not correlated with each other. Using this approach, the depth interval that contributed the most to the total subsidence, as well as storage parameters for all intervals, are estimated. The technique can be used for identification of the depth interval within the aquifer system responsible for deformation.

Mapping and monitoring saltwater intrusion are critical for the sustainable management of groundwater in coastal aquifers around the world. Increasingly, geophysical methods, such as electrical resistivity tomography (ERT), have been used to address these needs. We identify methods for the inversion of ERT data that would most accurately map the location and geometry of an intrusion wedge. This is accomplished using laboratory and synthetic experiments, with the classic representation of an intrusion wedge perpendicular to the coast. The laboratory experiments allow us to collect ERT data on a saltwater intrusion wedge in an environment in which we have supporting data that provided (1) the distribution of salinity within the tank with which to verify our inversion results, (2) the resistivity, porosity, and permeability of the porous medium, and (3) the transform between resistivity and salinity. The synthetic experiments allow exploration of issues of specific interest related to the presence of lithologic heterogeneity at a field site: the role of lithologic heterogeneity in introducing complexity into the resistivity-salinity relationship and the geometry of the saltwater intrusion wedge. We find that using a reference model with a good approximation of the wedge to inform the inversion greatly improves the ability of the resulting resistivity profile to map the wedge. Where there is no, or limited, lithologic heterogeneity, a parametric approach, which constrains the range of possible solutions by solving for a sharp interface between the saltwater and freshwater regions, is very effective at capturing the wedge location and geometry. Where there is lithologic heterogeneity, a hybrid between the parametric and informed inversion approaches is most effective, resolving the wedge with a high level of accuracy with little a priori information.

The acquisition of airborne electromagnetic (AEM) data colocated with water well data provides an essential data set for constructing an accurate rock-physics relationship between resistivity from AEM and lithology from wells. Results from an AEM survey in Butte and Glenn Counties, California, USA, where 41 water wells are located within 100 m of the AEM flight lines, provide a unique opportunity to investigate the effect of water wells on AEM data. Based on systematic numerical analyses and examination of the field data, we find that the only noise source related to the wells that affects the AEM signals in our study area is the power lines; the effect of the steel casings in the study area is negligible. Our numerical simulations yield two important findings: (1) the effect of the power line is mainly dependent upon the resistivity of the ground pathway between the base of the power-line poles, and (2) for the resistivity structure in the study area, when the ground contact resistance is sufficiently low, the effect of the power line will be observed in the AEM data until there is a separation distance of 174 m from the power line; the total power-line percentage effect less than 3% has been considered to be negligible. However, we find that, in the field data, an average separation distance between the power line and high-quality AEM data (i.e., no impact from the power lines) is only 60% of this predicted distance. We attribute this to the high ground contact resistance caused by degradation of the grounded electrodes located at the bottom of the power-line poles. For our 41 wells, after removing any soundings contaminated by power-line noise, we are left with high-quality soundings located 0–160 m from the water wells, with an average distance of 77 m.

Working with airborne electromagnetic (AEM) data acquired in the Kaweah Subbasin in the Central Valley of California, U.S.A., we developed a new approach for imaging the top of the bedrock and the ...

In an airborne electromagnetic (AEM) data set acquired in unsaturated and saturated zones, the depth of the top of the saturated zone (TSZ) at the time of data acquisition should be accounted for in the resistivity-to-lithology transform. We have developed and tested a methodology for estimating the TSZ from AEM data, using data collected in three survey areas in the Central Valley of California and water-table elevation (WTE) measurements from nearby wells. The methodology is based on the difference in the distribution of resistivity values above and below the TSZ, using the WTE measurements to optimize two components of the general workflow. From the AEM data acquired in Tulare County, in the southern portion of the Central Valley, where the WTE measurements were acquired two to four weeks before the AEM data acquisition, we have found estimates of the TSZ with a root-mean-square (rms) error of 10.6 m when compared to the WTE measurements. From the two survey areas in Butte and Glenn counties, in the northern portion of the Central Valley, where WTE measurements were available at the time of, and closer to the locations of, AEM data acquisition, we have found estimates of the TSZ with an rms error ranging from 3.8 to 5.3 m, depending on the form of inversion. The level of error found in the three survey areas is comparable to the thickness of the layers in the resistivity models at the depths of the TSZ. Because the intended use of these estimates is to locate the TSZ for use in developing and applying the resistivity-to-lithology transform, the level of error associated with this new methodology is acceptable.

Saltwater intrusion can pose a serious threat to groundwater quality in coastal regions. Estimating the extent of saltwater intrusion is vital for groundwater managers to plan appropriate mitigation strategies. The airborne electromagnetic (AEM) method is commonly used to evaluate groundwater resources, but it is challenging to apply in coastal environments because the low resistivity of saltwater-saturated aquifers attenuates the electromagnetic signal quickly and the relationship between electrical resistivity and pore water salinity is complex. However, if successful, the AEM method can supply information to address questions of critical importance in coastal regions. We investigated the extent of, and controls on, saltwater intrusion using the AEM method in the northern Salinas Valley, CA, USA. We collected 635 line-km of AEM data in the study area, the inversion results of which produced estimates of the electrical resistivity of the subsurface, reaching depths of between 50 and approximately 200 m below the ground surface. We have developed a relationship between the AEM electrical resistivity model and groundwater salinity, calibrated from borehole geophysical and water quality measurements, which allowed us to generate images revealing the distribution of saltwater and fresher groundwater in the study area. This fresher groundwater (defined as “a source of drinking water”) was successfully mapped out in the unconfined aquifer (the Dune Sand Aquifer) and the uppermost confined aquifer (the 180-Foot Aquifer) in the study area, illustrating a groundwater recharge process that helps mitigate saltwater intrusion in the 180-Foot Aquifer. Deep, low-resistivity bodies also were mapped, indicating regions where saltwater likely is migrating vertically from the 180-Foot Aquifer into the lower confined aquifer (the 400-Foot Aquifer). The findings from this case study demonstrate the value of acquiring AEM data for investigating the distribution of salinity in coastal aquifers impacted by saltwater intrusion.

Author(s): Kang, S; Knight, R; Greene, T; Buck, C; Fogg, G | Abstract: Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, USA to improve the understanding of the aquifer system. Around 800 line-kilometers of high-quality data were acquired, imaging to a depth of ∼300nm. We developed a workflow designed to obtain, from the AEM data, information about the large-scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different inversions incorporating various forms of available information and posterior sampling of the recovered resistivity models, we produced 6,006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large-scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large-scale and small-scale interfaces between sediment types. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area.

[copied directly from first paragraph of paper] Land subsidence, caused by groundwater extraction and subsequent subsurface compaction, is an issue of global concern. Since the 1920s, there have been numerous periods of subsidence in California’s San Joaquin Valley leading to widespread sinking of the land surface which has locally exceeded 9 m. The most recent period of severe subsidence, which was triggered by the 2012-15 drought, is now causing damage which threatens the long-term viability of critical water distribution infrastructure in the Valley. However, there is neither a continuous monitoring record of the subsidence nor high-quality records of the hydrologic head changes in the subsurface which have caused the subsidence, making it impossible to understand, and thus mitigate, the subsidence. Here, we leverage subsidence and hydraulic head data from a variety of sources to create and validate a one-dimensional model of subsurface compaction and subsidence over the 65 years between 1952-2017. This model, which simulated up to 7.5 m of subsidence since 1952, provides a complete record of subsidence in our study region by filling crucial gaps in the observed record. Our model reveals the long-term processes causing subsidence, which operated over decades-to-centuries and caused exceptionally high rates of baseline subsidence in 2017, resulting in a critical risk of future subsidence. This risk is exacerbated as the Valley moves into drought conditions again in Spring 2021. We demonstrated an approach which provided the understanding of subsidence in the Valley needed to directly inform sustainable groundwater management, and which is applicable in subsiding regions around the World.

The ability to identify, at potential managed aquifer recharge sites, the presence of connected pathways of hydraulically conductive sediments from the ground surface to the water table could help minimize costs and risks associated with recharge operations. A spatially dense dataset had previously been acquired in an almond [Prunus dulcis (Mill.) D.A. Webb] grove in Tulare, CA, using tTEM, a towed transient electromagnetic (tTEM) geophysical method. In order to interpret reliable information about sediment type from the tTEM data, a transform from the tTEM‐derived property, electrical resistivity, to sediment type is required. The uncertainty associated with derived models of sediment type can be significantly reduced if a site‐ and dataset‐specific transform is used. Cone penetrometer testing (CPT) was conducted at five locations, strategically selected based on a review of the tTEM data. Co‐located measurements of sediment type, derived from the CPT, and electrical resistivity, derived from the tTEM data, were used to create a resistivity‐to‐sediment‐type transform, with sediment type classified as either coarse‐grain‐dominated (sand and gravel) or fine‐grain‐dominated (silt and clay) material. The transform captured the uncertainty associated with variable water salinity and content, the resolution of the tTEM data, and other components of the tTEM measurement workflow. Using this transform, models of sediment type were generated for the unsaturated zone at the site. Within these models are features, which we interpret as potential recharge pathways, corresponding to high fractions of coarse‐grain‐dominated material amongst regions of fine‐grain‐dominated material. The workflow developed at this site can provide a framework for using tTEM and CPT for recharge site assessment.

Summary In the Central Valley of California, U.S.A., we used satellite data to map the precipitation in the valley and snowpack thickness in the adjacent mountains, and the subsidence from groundwater pumping. Geophysical methods provided the subsurface data needed to explain the pattern of subsidence and to search for recharge pathways.

Land subsidence due to groundwater extraction can cause a permanent loss of groundwater storage, and thus mitigating this is crucial for sustainable groundwater management. One challenge in effective mitigation is accurately predicting the effect of groundwater pumping on land deformation due to uncertainty in subsurface hydrostratigraphy. In this study, we demonstrate how a coupled hydrogeophysical model, combining geophysical and InSAR data, can improve estimates of subsidence by providing information about the subsurface layers that are deforming. Using this model, we estimate future scenarios of subsidence, and the potential of managed aquifer recharge for reducing subsidence. We find that if no management changes are made, roughly ~2 m of subsidence will occur from 2019 to 2038, but that active managed aquifer recharge could reduce land subsidence by as much as ~4 m. This model can enable water managers to mitigate subsidence by prioritizing areas for managed aquifer recharge.

This paper presents a meta-review of digital technology applications for dynamic environmental management, which provide contemporaneous signals and incentives to influence resource users' behaviours, thereby generating more spatially and temporally flexible responses to variable ecosystem conditions.

The precise quantification of peat deposits at local to global scale is of key importance for the implementation of adequate conservation policies of peatlands. To this end, new remote sensing applications are needed, that provide high resolution data sets at regional scale. In this presentation, we present the results obtained using Airborne Electromagnetics (AEM) to estimate peat thickness and carbon content of a large peatland site located in Indonesia. The effectiveness of the AEM method for assessing peat thickness and volumes, and in turn carbon stocks, is tested by comparing the results to ground-truth measurements. Our results show that the AEM method can detect both the top and the bottom of a peatland profile over a clay substrate at high spatial resolution, allowing for an accurate three-dimensional morphological description of the peat body. The AEM method performs extremely well along the flight lines, where the instrument clearly detects the peat layer and differentiates it from the underlying mineral substrate. Moving away from the flight lines, the accuracy slightly decreases because the interpolation of the AEM data does not fully capture the highly variable morphology of the peat bottom. We conclude that in varying dome conditions, a high flight line density is preferable to describe the spatial distribution of the peat layer. Once the volume of the peatland is determined, the average organic carbon content by soil volume retrieved from field campaigns and laboratory analyses is used to estimate the total organic carbon stored in the peatland.The results obtained with the AEM method are compared to those obtained with an empirical method that uses the soil topography to predict the thickness of the peatland. This empirical approach is based on the analyses of several previous studies available from the literature that show how it is common for some dome-shaped peatlands to present a linear correlation between peat thickness and soil topography. In this study, we show that the linear correlation is site-specific, and when used for prediction purposes, it provides incorrect peat volume estimates when it is extended to other sites or over large territories. When compared to the AEM method, our results show that the AEM method is superior in detecting the peat morphology and volume.

IntroductionYoung people aged 16 to 24 have the highest prevalence of genital chlamydia and gonorrhoea compared with other age groups and re-infection rates following treatment are high. Long-term adverse health effects include subfertility and ectopic pregnancy, particularly among those with repeated infections. We developed the safetxt intervention delivered by text message to reduce sexually transmitted infection (STI) by increasing partner notification, condom use and (STI) testing among young people in the UK.Methods and analysisA single-blind randomised trial to reliably establish the effect of the safetxt intervention on chlamydia and gonorrhoea infection at 1 year. We will recruit 6250 people aged 16 to 24 years who have recently been diagnosed with chlamydia, gonorrhoea or non-specific urethritis from health services in the UK. Participants will be allocated to receive the safetxt intervention (text messages designed to promote safer sexual health behaviours) or to receive the control text messages (monthly messages asking participants about changes in contact details) by an automated remote online randomisation system. The primary outcome will be the cumulative incidence of chlamydia and gonorrhoea infection at 1 year assessed by nucleic acid amplification tests. Secondary outcomes include partner notification, correct treatment of infection, condom use and STI testing prior to sex with new partners.Ethics and disseminationEthics approval was obtained from NHS Health Research Authority - London – Riverside Research Ethics Committee (REC reference: 15/LO/1665) and the London School of Hygiene & Tropical Medicine. We will submit the results of the trial for publication in peer-reviewed journals.Trial registration numberInternational Standard Randomised Controlled Trials Number:ISRCTN64390461. Registered on 17thMarch 2016.WHO trial registration data setavailable at:http://apps.who.int/trialsearch/Trial2.aspx?TrialID=ISRCTN64390461.Trial protocol version12, 19thJuly 2018.

Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K. Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1 H, the time-varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct-push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger-Doll Research (SDR) NMR-K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower-K (K

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's 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.

Background Irbesartan, a long acting selective angiotensin-1 receptor inhibitor, in Marfan syndrome might reduce aortic dilatation, which is associated with dissection and rupture. We aimed to determine the effects of irbesartan on the rate of aortic dilatation in children and adults with Marfan syndrome. Methods We did a placebo-controlled, double-blind randomised trial at 22 centres in the UK. Individuals aged 6–40 years with clinically confirmed Marfan syndrome were eligible for inclusion. Study participants were all given 75 mg open label irbesartan once daily, then randomly assigned to 150 mg of irbesartan (increased to 300 mg as tolerated) or matching placebo. Aortic diameter was measured by echocardiography at baseline and then annually. All images were analysed by a core laboratory blinded to treatment allocation. The primary endpoint was the rate of aortic root dilatation. This trial is registered with ISRCTN, number ISRCTN90011794. Findings Between March 14, 2012, and May 1, 2015, 192 participants were recruited and randomly assigned to irbesartan (n=104) or placebo (n=88), and all were followed for up to 5 years. Median age at recruitment was 18 years (IQR 12–28), 99 (52%) were female, mean blood pressure was 110/65 mm Hg (SDs 16 and 12), and 108 (56%) were taking β blockers. Mean baseline aortic root diameter was 34·4 mm in the irbesartan group (SD 5·8) and placebo group (5·5). The mean rate of aortic root dilatation was 0·53 mm per year (95% CI 0·39 to 0·67) in the irbesartan group compared with 0·74 mm per year (0·60 to 0·89) in the placebo group, with a difference in means of −0·22 mm per year (−0·41 to −0·02, p=0·030). The rate of change in aortic Z score was also reduced by irbesartan (difference in means −0·10 per year, 95% CI −0·19 to −0·01, p=0·035). Irbesartan was well tolerated with no observed differences in rates of serious adverse events. Interpretation Irbesartan is associated with a reduction in the rate of aortic dilatation in children and young adults with Marfan syndrome and could reduce the incidence of aortic complications. Funding British Heart Foundation, the UK Marfan Trust, the UK Marfan Association.

Geophysical data have proven to be very useful for lithological characterization. However, quantitatively integrating the information gained from acquiring geophysical data generally requires colocated lithological and geophysical data for constructing a rock-physics relationship. In this contribution, the issue of integrating noncolocated geophysical and lithological data is addressed, and the results are applied to simulate groundwater flow in a heterogeneous aquifer in the Prairie Waters Project North Campus aquifer recharge site, Colorado. Two methods of constructing a rock-physics transform between electrical resistivity tomography (ERT) data and lithology measurements are assessed. In the first approach, a maximum likelihood estimation (MLE) is used to fit a bimodal lognormal distribution to horizontal crosssections of the ERT resistivity histogram. In the second approach, a spatial bootstrap is applied to approximate the rock-physics relationship. The rock-physics transforms provide soft data for multiple point statistics (MPS) simulations. Subsurface models are used to run groundwater flow and tracer test simulations. Each model's uncalibrated, predicted breakthrough time is evaluated based on its agreement with measured subsurface travel time values from infiltration basins to selected groundwater recovery wells. We find that incorporating geophysical information into uncalibrated flow models reduces the difference with observed values, as compared to flow models without geophysical information incorporated. The integration of geophysical data also narrows the variance of predicted tracer breakthrough times substantially. Accuracy is highest and variance is lowest in breakthrough predictions generated by the MLE-based rock-physics transform. Calibrating the ensemble of geophysically constrained models would help produce a suite of realistic flow models for predictive purposes at the site. We find that the success of breakthrough predictions is highly sensitive to the definition of the rock-physics transform; it is therefore important to model this transfer function accurately.

Interferometric Synthetic Aperture Radar (InSAR) data from multiple satellite missions were combined to study the temporal and spatial variability of head and storage properties in a confined aquifer system on a decadal time scale. The area of study was a 4,500 km2 agricultural basin in the San Luis Valley (SLV), Colorado. We had available previous analyses of C-band ERS-1/2 data from June 1992 to November 2000, and L-band ALOS PALSAR data from October 2009 to March 2011. We used C-band Envisat data to fill in the time period from November 2006 to July 2010. In processing the Envisat data, we successfully employed a phase interpolation between persistent scatterer pixels to reduce the impact of vegetation decorrelation, which can significantly reduce the quality of C-band InSAR data over agricultural basins. In comparing the results from the L-band ALOS data and C-band Envisat data in a 10-month overlapping time period, we found that the shorter wavelength of C-band InSAR allowed us to preserve small deformation signals that were not detectable using L-band ALOS data. A significant result was the finding that the elastic storage properties of the SLV confined aquifer system remained stable over the 20 year time period and vary slowly in space, allowing us to combine InSAR data acquired from multiple missions to fill the temporal and spatial gaps in well data. The InSAR estimated head levels were validated with well measurements, which indicate little permanent water-storage loss over the study time period in the SLV.

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998–2015). However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias between the satellite and ground-based estimates is compared with mean surface temperature and elevation, respectively. A weak linear relationship is observed between the bias and temperature but a moderate inverse linear relationship is observed between the bias and elevation. Based on these observations, a linear model is developed for the TRMM 3B43–PRISM bias and elevation. The developed model is calibrated and validated using Monte Carlo cross validation with 25% of the available data as a calibration set and the remaining 75% of the data as a validation set. The estimated model parameters are then used in a correction formula for the TRMM 3B43 dataset for elevations above 1500 m above mean sea level. The corrected TRMM 3B43 product is verified for the high-elevation regions over the entire United States as well as in two high-elevation local regions in the western United States. The results show a significant improvement in the accuracy of the monthly satellite product in the high elevations of the United States.

Electrical Resistivity Tomography data were acquired along 40 km of the Monterey Bay coast in central California. These data resulted in electrical resistivity images to depths of approximately 280 m.b.s.l., which were used to understand the distribution of freshwater and saltwater in the subsurface, and factors controlling this distribution. The resulting resistivity sections were interpreted in conjunction with existing data sets, including well logs, seismic reflection data, geologic reports, hydrologic reports, and land use maps from the region. Interpretation of these data shows a complex pattern of saltwater intrusion resulting from geology, pumping, and recharge. The resistivity profiles were used to identify geological flow conduits and barriers such as palaeo-channels and faults, localized saltwater intrusion from individual pumping wells, infiltration zones of surface fresh and brackish water, and regions showing improvements in water quality due to management actions. The use of ERT data for characterizing the subsurface in this region has led to an understanding of the spatial distribution of freshwater and saltwater at a level of detail unattainable with the previously deployed traditional well based salinity mapping and monitoring techniques alone. Significant spatial variability in the extent and geometry of intrusion observed in the acquired data highlights the importance of adopting continuous subsurface characterization methods such as this one.