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At least 829 records · Page 46Linked to original sources

USGS2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. Finally, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

Nevada↗

USGS2005

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2026

Sedimentological and geochemical characterization of lacustrine deposits of the Babouri-Figuil basin, northern Cameroon: Implications for source rocks distribution and petroleum exploration

The West and Central African Rift System (WCARS) refers to the series of Cretaceous rift basins where commercial hydrocarbon accumulations have been discovered. Some of the WCARS frontier basins are currently being investigated to increase our understanding of these basins in light of new commercial discoveries. The present study was performed in the Babouri-Figuil Basin (BFB), which is genetically related to the WCARS and constitutes an area of interest in terms of petroleum prospecting, where the distribution of petroleum source rocks and potential targets for petroleum exploration across the entire basin is poorly understood. For the current study, an integrated facies analysis along with organic and inorganic geochemical techniques were applied to the basin's Cretaceous deposits with the aim of reconstructing the paleodepositional environment, assessing factors that triggered the input of organic matter, and providing a spatial overview of the organic matter accumulation in the basin based on outcrop samples. An alluvial fan-lacustrine-braided river system is inferred from the facies analysis of the stratigraphic sequence consisting of conglomerate, sandstone, siltstone, limestone, marlstone, and claystone. Bulk analysis of organic matter reveals that black shale and massive claystone are the main prospective petroleum source rocks in the basin. Inorganic geochemical analyses reveal the influence of anoxic conditions, moderate to high primary productivity, and low terrigenous inputs in organic matter enrichment. The formations rich in organic matter are predominantly concentrated in the western and eastern parts of the basin which may represent areas with depressions, characterized by high accommodation space. In terms of the regional context of the WCARS rift basins, typical hydrocarbon exploration in the BFB may target basal-conglomerate, sandstone beds situated directly above or/and interbedded with the Lower Cretaceous source rocks, and the Upper Cretaceous sandstone beds. Basement rocks (granite, granodiorite, and gneisses) and oil shale deposits may represent potential unconventional hydrocarbon exploration. The current integrated study provides an insight that should guide future hydrocarbon exploration campaigns in the basin.

Babouri-Figuil basin↗

USGS2025

An automated compositing method for producing annual clear images from Landsat Collection 2 for annual NLCD production

Quality image input is fundamental to the quality of derived land cover products. Substantial time and effort are usually required to prepare images. Here, we present a novel and streamlined compositing algorithm that ingests Landsat Collection 2 Analysis Ready Data (ARD) and outputs cloud-free and gap-free composite imagery, which can be directly used for classification. This method leverages and improves the previous National Land Cover Database (NLCD) Virtual Median Value Point (VMVP) compositing method, the first part of the image preparation for NLCD 2019 operational production. The NLCD 2019 image preparation approach includes a second part, a residual cloud and cloud shadow detection and gap-filling method, to produce final cloud-free and gap-free composite imagery. The second part requires one clear reference image for each target year. Additional reference images are needed for producing reasonable observations for perennial ice/snow areas because Pixel QA (Quality Assessment) from ARD has difficulties differentiating ice/snow areas from clouds. Unlike the NLCD 2019 image preparation approach, our new compositing method, which is referred to as Automated VMVP (AVMVP), uses Landsat ARD as the only input and does not require reference images and extra steps. In this method, we developed new spectral filter criteria coupled with counts of clear observations using Pixel QA to identify potential cloud and cloud shadow observations on initially selected observations from the NLCD VMVP compositing algorithm. We also automate “gap-filling” using clear observations retrieved from a maximum of ±2 years around the target year when needed. Finally, a percentile-filtered compositing method was developed for the perennial ice/snow areas. All these steps are streamlined, pixel-based, and directly run on Landsat Collection 2 ARD. We have run successful tests on the conterminous United States (CONUS). Composite images derived from our innovative method were used to produce the CONUS Annual NLCD Collection 1 product suite that covers the period from 1985 to 2023.

conterminous United States↗

USGS2026

Satellite embeddings for crop type classification: A comparative examination

Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.

California↗

USGS2015

Application of the FluEgg model to predict transport of Asian carp eggs in the Saint Joseph River (Great Lakes tributary)

The Fluvial Egg Drift Simulator (FluEgg) is a three-dimensional Lagrangian model that simulates the movement and development of Asian carp eggs until hatching based on the physical characteristics of the flow field and the physical and biological characteristics of the eggs. This tool provides information concerning egg development and spawning habitat suitability including: egg plume location, egg vertical and travel time distribution, and egg-hatching risk. A case study of the simulation of Asian carp eggs in the Lower Saint Joseph River, a tributary of Lake Michigan, is presented. The river hydrodynamic input for FluEgg was generated in two ways — using hydroacoustic data and using HEC-RAS model data. The HEC-RAS model hydrodynamic input data were used to simulate 52 scenarios covering a broad range of flows and water temperatures with the eggs at risk of hatching ranging from 0 to 93% depending on river conditions. FluEgg simulations depict the highest percentage of eggs at risk of hatching occurs at the lowest discharge and at peak water temperatures. Analysis of these scenarios illustrates how the interactive relation among river length, hydrodynamics, and water temperature influence egg transport and hatching risk. An improved version of FluEgg, which more realistically simulates dispersion and egg development, is presented. Also presented is a graphical user interface that facilitates the use of FluEgg and provides a set of post-processing analysis tools to support management decision-making regarding the prevention and control of Asian carp reproduction in rivers with or without Asian carp populations.

Michigan↗

USGS2009

Directed site exploration for permeable reactive barrier design

Permeable reactive barriers (PRBs) are being employed for in situ site remediation of groundwater that is typically flowing under natural gradients. Site characterization is of critical importance to the success of a PRB. A design-specific site exploration approach called quantitatively directed exploration (QDE) is presented. The QDE approach employs three spatially related matrices: (1) covariance of input parameters, (2) sensitivity of model outputs, and (3) covariance of model outputs to identify the most important location to explore based on a specific design. Sampling at the location that most reduces overall site uncertainty produces a higher probability of success of a particular design. The QDE approach is demonstrated on the Kansas City Plant, Kansas City, MO, a case study where a PRB was installed and failed. It is shown that additional quantitatively directed site exploration during the design phase could have prevented the remedial failure that was caused by missing a geologic body having high hydraulic conductivity at the south end of the barrier. The most contributing input parameter approach using head uncertainty clearly indicated where the next sampling should be made toward the high hydraulic conductivity zone. This case study demonstrates the need to include the specific design as well as site characterization uncertainty when choosing the sampling locations. ?? 2008 Elsevier B.V.

Journal of Hazardous Materials↗

USGS2011

The influence of irrigation water on the hydrology and lake water budgets of two small arid-climate lakes in Khorezm, Uzbekistan

Little is known regarding the origins and hydrology of hundreds of small lakes located in the western Uzbekistan province of Khorezm, Central Asia. Situated in the Aral Sea Basin, Khorezm is a productive agricultural region, growing mainly cotton, wheat, and rice. Irrigation is provided by an extensive canal network that conveys water from the Amu Darya River (AD) throughout the province. The region receives on average 10 cm/year of precipitation, yet potential evapotranspiration exceeds this amount by about 15 times. It was hypothesized that the perennial existence of the lakes of interest depends on periodic input of excess irrigation water. This hypothesis was investigated by studying two small lakes in the region, Tuyrek and Khodjababa. In June and July 2008, surface water and shallow groundwater samples were collected at these lake systems and surrounding communities and analyzed for δ 2 H, δ 18 O, and major ion hydrochemistry to determine water sources. Water table and lake surface elevations were monitored, and the local aquifer characteristics were determined through aquifer tests. These data and climate data from a Class A evaporation pan and meteorological stations were used to estimate water budgets for both lakes. Lake evaporation was found to be about 0.7 cm/day during the study period. Results confirm that the waters sampled at both lake systems and throughout central Khorezm were evaporated from AD water to varying degrees. Together, the water budgets and stable isotope and major ion hydrochemistry data suggest that without surface water input from some source (i.e. excess irrigation water), these and other Khorezm lakes with similar hydrology may decrease in volume dramatically, potentially to the point of complete desiccation.

Khorezm↗

USGS2014

Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.

Brahmaputra;Ganges↗

USGS2019

Evidence for conservative transport of dissolved organic carbon in major river basins in the Gulf of Maine Watershed

Transport and fate of dissolved organic carbon (DOC) in rivers are important aspects of the carbon cycle and the critical linkage between terrestrial, aquatic, and marine systems. Recent studies have quantified fluvial export to the marine environment in many systems, but in-stream losses of DOC are poorly constrained. This study compares DOC yields (kg C/ha) between the area-weighted averages of several tributaries within larger watersheds with the DOC yields of the larger watersheds to gain insight on in-stream losses in larger river systems. Four large watersheds, 22 tributaries to those watersheds, and 5 additional main stem locations in Maine were studied during 1 April to 15 November in 2011 through 2013. There were no significant differences in the area-weighted average DOC yield of the tributaries and the larger watersheds indicating little net in-stream loss in the main stems of the larger rivers. It is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries based on similarity in DOC yield longitudinally along the main stems of two of the rivers. In addition, wetland abundance, which is associated with higher DOC yield in this environment, did not consistently increase from tributaries to the larger watershed or longitudinally along the main stems. This geographic distribution of wetlands therefore also indicates that it is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries. These findings suggest that in-stream losses of DOC in these larger river systems are minimal and that the vast majority of DOC in major rivers in Maine is transported conservatively to the coastal ocean.

Maine↗

USGS2024

Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams

Headwater stream temperature often exhibits spatial variation at the kilometer-scale, but the relative importance of the underlying hydrogeological processes and riverine perturbations remains poorly understood. In this study, we investigated the relative importance of groundwater (GW) and other processes on downstream annual stream temperature signal characteristics using deterministic heat budget model (HFLUX) scenarios within an idealized stream reach representative of mountainous forested conditions. We summarized annual stream thermal regimes from the relationship of paired sinusoidal air and water temperature signals (amplitude ratio, phase lag, and mean ratio). Results showed that downstream changes in annual temperature depended on the thermal gradient between water and the hypothetical equilibrium temperature (where all heat fluxes sum to zero). GW inflow, riparian shading, and the boundary input signal were the most significant factors affecting downstream annual water temperature signals, while flow volume and channel dimensions impacted how quickly annual temperature signals changed. Effects of GW were dominated by advective rather than conductive heat exchange processes, but conduction played a larger role when GW input was more spatially diffuse. Our results indicated several mechanisms by which local processes may affect stream thermal resilience to disturbances and can help guide management of wildfire and climate change.

Journal of Hydrology↗

USGS2024

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Sardon catchment↗

USGS2024

Indications of preferential groundwater seepage feeding northern peatland pools

Groundwater seepage from underlying permeable glacial sedimentary structures, such as eskers, has been hypothesized to directly feed pools in northern peat bogs. These hypotheses directly contradict classical peat bog models for ombrogenous systems, wherein meteoric water is the sole water input to these systems. Variations in the underlying mineral sediment in contact with the peat imply that unrecognized hydrogeologic connectivity may exist with pools in northern peat bogs, particularly where high permeability materials are in contact with the peat. Seepage dynamics originating from these structural variations were investigated using a suite of thermal and hydrogeophysical methods deployed around pools in a peat bog of northeastern Maine, USA. Thermal characterization methods mapped anomalies that were confirmed as matrix seepage or preferential flow pathways (PFPs). Geochemical methods were employed at identified thermal anomalies to confirm upwelling of solute-rich groundwater. Conduits around pools were associated with surficial terminations of suspected peat pipes, based on the inference of pathways extending down into the peat, that focus flow through PFPs in the peat matrix. Discharge also occurred through the peat matrix adjacent to suspected pipe structures and matrix seepage rates were quantified using analysis of diurnal temperature signals recorded at multiple depths. Seepage rates, with a maximum of nearly 0.4 m/d, were measured at localized points around pools. Periods of synchronized temperatures paired with highly muted diurnal temperature signals, recorded in diurnal temperature with depth data, were interpreted qualitatively as activation of strong upward discharge rates through suspected peat pipes. These time periods correlated strongly with local precipitation events around the peatland. Ground-penetrating radar surveys revealed discontinuities in the low permeability glacio-marine clay at the mineral sediment-peat interface, interpreted to be regional glacial esker deposits, which were located beneath and around pools. Heat tracing, specific conductance contrasts, seepage rates, and trace metal concentrations all imply groundwater seepage originating from underlying permeable glacial esker deposits and directly sourcing pools. Preferential groundwater inputs into northern peat bogs may play a key role in developing and maintaining pool systems, with enhanced solute transport impacting peatland ecology, water resources, and carbon cycling.

Maine↗

USGS2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Colorado, Utah↗

USGS2025

A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed

Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water signal ratios as proxies for groundwater influence but were limited to specific sites and periods, without dynamic forecasting. This study is the first to forecast daily A r as a spatiotemporal signal using a Graph Convolutional Network–Long Short-Term Memory (GCN-LSTM) model. The model was trained using hydroclimate data (air temperature, precipitation, shortwave radiation, streamflow) and watershed physical features (e.g., sand content, slope). Results showed high predictive skill, achieving R 2 (NSE, RMSE) of 0.86 (0.73, 0.0004) for one-day-ahead to 0.52 (0.50, 0.0009) for seven-days ahead forecasts. Prior studies often have not explicitly incorporated spatial hydrogeologic drivers, but this model explicitly incorporates them to assess their impact on A r forecasting and stream-groundwater connectivity. Feature analysis identified mean sand, elevation, slope, clay, and TWI as key predictors of A r . Stronger groundwater signals appeared in hillslopes, elevations, and tributaries, highlighting watershed influence on streamflow. However, limitations include reliance on historical air–water temperature patterns for training and limited representation of extreme climate conditions. Despite these limitations, unlike previous studies relying on measured in-situ stream and air temperature, this study forecasts A r directly from climate and physiographic features after training, avoiding in-situ data requirements. Findings aiding predictions of stream ecosystem resilience.

New York↗

USGS2007

Comparative lahar hazard mapping at Volcan Citlaltépetl, Mexico using SRTM, ASTER and DTED-1 digital topographic data

In this study, we evaluated and compared the utility of spaceborne SRTM and ASTER DEMs with baseline DTED-1 “bald-earth” topography for mapping lahar inundation hazards from volcan Citlaltépetl, Mexico, a volcano which has had a history of producing debris flows of various extents. In particular, we tested the utility of these topographic datasets for resolving ancient valley-filling deposits exposed around the flanks of the volcano, for determining their magnitude using paleohydrologic methods and for forecasting their inundation limits in the future. We also use the three datasets as inputs to a GIS stream inundation flow model, LAHARZ, and compare the results. In general all three datasets, with spatial resolution of 90 m or better, were capable of resolving debris flow and lahar deposits at least 3 × 10 6 m 3 in volume or larger. Canopy- and slope-related height errors in the ASTER and SRTM DEMs limit their utility for measuring valley-filling cross-sectional area and deriving flow magnitude for the smallest deposits using a cross-sectional area to volume scaling equation. Height errors in the ASTER and SRTM DEMs also causes problems in resolving stream valley hydrography which controls lahar flow paths and stream valley morphology which controls lahar filling capacity. However, both of the two spaceborne DEM datasets are better than DTED-1 at resolving fine details in stream hydrography and erosional morphologies of volcaniclastics preserved in the valleys around the more humid, eastern flanks of the volcanic range. The results of LAHARZ flow inundation modeling using all three DEMs as inputs are remarkably similar and co-validate one another. For example, at Citlaltépetl all lahar simulations show that the city of Orizaba is the most vulnerable to flows similar in magnitude to, or larger than, one that occurred in 1920. Many of the other cities and towns illustrated are built higher up on terrace deposits of older debris flows, and are safe from all but the largest flows, which occur less frequently. Finally, ASTERs 60 km swath width and 8% duty cycle presents a challenge for mapping lahar inundation hazards at E–W oriented stream valleys in low-latitude areas with persistent cloud cover. However, its continued operations enhances its utility as a means for updating the continuous but one-time coverage of SRTM, and for filling voids in the SRTM dataset such as those that occur along steep-sided valleys prone to hazards from future lahars.

Journal of Volcanology and Geothermal Research↗

USGS2017

Evidence for degassing of fresh magma during the 2004-2008 eruption of Mount St. Helens: Subtle signals from the hydrothermal system

Results from chemical and isotopic analyses of water and gas collected between 2002 and 2016 from sites on and around Mount St. Helens are used to assess magmatic degassing related to the 2004-2008 eruption. During 2005 the chemistry of hot springs in The Breach of Mount St. Helens showed no obvious response to the eruption, and over the next few years, changes were subtle, giving only slight indications of perturbations in the system. By 2010 however, water chemistry, temperatures, and isotope compositions (δD and δ 18 O) clearly indicated some inputs of volatiles and heat associated with the eruption, but the changes were such that they could be attributed to a pre-existing, gas depleted magma. An increase of ~ 1.5‰ in the δ 13 C values of dissolved carbon in the springs was noted in 2006 and continued through 2009, a change that was mirrored by a similar shift in δ 13 C-CO 2 in bubble gas emissions. These changes require input of a new source of carbon to the hydrothermal system and provide clear evidence of CO 2 from an undegassed body of magma. Rising trends in 3 He/ 4 He ratios in gas also accompanied the increases in δ 13 C. Since 2011 maximum R C /R A values are ≥ 6.4 and are distinctly higher than 5 samples collected between 1986 and 2002, and provide additional evidence for some involvement of new magma as early as 2006, and possibly earlier, given the unknown time needed for CO 2 and He to traverse the system and arrive at the springs.

Washington↗

USGS2007

Ra and Rn isotopes as natural tracers of submarine groundwater discharge in Tampa Bay, Florida

A suite of naturally occurring radionuclides in the U/Th decay series ( 222 Rn, 223,224,226,228 Ra) were studied during wet and dry conditions in Tampa Bay, Florida, to evaluate their utility as groundwater discharge tracers, both within the bay proper and within the Alafia River/estuary — a prominent free-flowing river that empties into the bay. In Tampa Bay, almost 30% of the combined riverine inputs still remain ungauged. Consequently, groundwater/surface water (hyporheic) exchange in the discharging coastal rivers, as well as submarine groundwater discharge (SGD) within the bay, are still unresolved components of this system's water and material budgets. Based on known inputs and sinks, there exists an excess of 226 Ra in the water column of Tampa Bay, which can be evaluated in terms of a submarine groundwater contribution to the bay proper. Submarine groundwater discharge rates calculated using a mass balance of excess 226 Ra ranged from 2.2 to 14.5 L m − 2 day − 1 , depending on whether the estuarine residence time was calculated using 224 Ra/xs 228 Ra isotope ratios, or whether a long term, averaged model-derived estuarine residence time was used. When extrapolated to the total shoreline length of the bay, such SGD rates ranged from 1.6 to 10.3 m 3 m − 1 day − 1 . Activities of 222 Rn were also elevated in surface water and shallow groundwater of the bay, as well as in the Alafia River estuary, where upstream activities as high as 250 dpm L − 1 indicate enhanced groundwater/surface water exchange, facilitated by an active spring vent. From average nutrient concentrations of 39 shallow, brackish, groundwater samples, rates of nutrient loading into Tampa Bay by SGD rates were estimated, and these ranged from 0.2 to 1.4 × 10 5 mol day − 1 (PO 4 3− ), 0.9–6.2 × 10 5 mol day − 1 (SiO 4 − ), 0.7–5.0 × 10 5 mol day − 1 (dissolved organic nitrogen, DON), and 0.2–1.4 × 10 6 mol day − 1 (total dissolved nitrogen, TDN). Such nutrient loading estimates, when compared to average river discharge estimates (e.g., TDN = 6.9 × 10 5 mol day − 1 ), suggest that SGD-derived nutrient fluxes to Tampa Bay are indeed important components to the overall nutrient economy of these coastal waters.

Florida↗