Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics.

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees.

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 µg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

Characterizing the diverse hydrogeology underlying rivers and estuaries using new floating transient electromagnetic methodology.

The hydrogeology below large surface water features such as rivers and estuaries is universally under-informed at the long reach to basin scales (tens of km+). This challenge inhibits the accurate modeling of fresh/saline groundwater interfaces and groundwater/surface water exchange patterns at management-relevant spatial extents. Here we introduce a towed, floating transient electromagnetic (TEM) system (i.e. FloaTEM) for rapid (up to 15 km/h) high resolution electrical mapping of the subsurface below large water bodies to depths often a factor of 10 greater than other towed instruments. The novel FloaTEM system is demonstrated at a range of diverse 4th through 6th-order riverine settings across the United States including 1) the Farmington River, near Hartford, Connecticut; 2) the Upper Delaware River near Barryville, New York; 3) the Tallahatchie River near Shellmound, Mississippi; and, 4) the Eel River estuary, on Cape Cod, near Falmouth, Massachusetts. Airborne frequency-domain electromagnetic and land-based towed TEM data are also compared at the Tallahatchie River site, and streambed geologic scenarios are explored with forward modeling. A range of geologic structures and pore water salinity interfaces were identified. Process-based interpretation of the case study data indicated FloaTEM can resolve varied sediment-water interface materials, such as the accumulation of fines at the bottom of a reservoir and permeable sand/gravel riverbed sediments that focus groundwater discharge. Bedrock layers were mapped at several sites, and aquifer confining units were defined at comparable resolution to airborne methods. Terrestrial fresh groundwater discharge with flowpaths extending hundreds of meters from shore was also imaged below the Eel River estuary, improving on previous hydrogeological characterizations of that nutrient-rich coastal exchange zone. In summary, the novel FloaTEM system fills a critical gap in our ability to characterize the hydrogeology below surface water features and will support more accurate prediction of groundwater/surface water exchange dynamics and fresh-saline groundwater interfaces.

Spatiotemporal remote sensing of ecosystem change and causation across Alaska.

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio-environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high-latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time-series analysis of moderate-and high-resolution imagery was used to characterize land- and water-surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land-surface greening, browning, and wetness/moisture trend parameters derived from peak-growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface-water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high-latitude regions.

Hydrogeophysical methods for analyzing aquifer storage and recovery systems.

Hydrogeophysical methods are presented that support the siting and monitoring of aquifer storage and recovery (ASR) systems. These methods are presented as numerical simulations in the context of a proposed ASR experiment in Kuwait, although the techniques are applicable to numerous ASR projects. Bulk geophysical properties are calculated directly from ASR flow and solute transport simulations using standard petrophysical relationships and are used to simulate the dynamic geophysical response to ASR. This strategy provides a quantitative framework for determining site-specific geophysical methods and data acquisition geometries that can provide the most useful information about the ASR implementation. An axisymmetric, coupled fluid flow and solute transport model simulates injection, storage, and withdrawal of fresh water (salinity ∼500 ppm) into the Dammam aquifer, a tertiary carbonate formation with native salinity approximately 6000 ppm. Sensitivity of the flow simulations to the correlation length of aquifer heterogeneity, aquifer dispersivity, and hydraulic permeability of the confining layer are investigated. The geophysical response using electrical resistivity, time-domain electromagnetic (TEM), and seismic methods is computed at regular intervals during the ASR simulation to investigate the sensitivity of these different techniques to changes in subsurface properties. For the electrical and electromagnetic methods, fluid electric conductivity is derived from the modeled salinity and is combined with an assumed porosity model to compute a bulk electrical resistivity structure. The seismic response is computed from the porosity model and changes in effective stress due to fluid pressure variations during injection/recovery, while changes in fluid properties are introduced through Gassmann fluid substitution.