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At least 595 records · Page 33Linked to original sources
USGS2023

The 3D Elevation Program—Supporting Montana’s economy

Introduction Montana, America’s fourth largest State with an area of 147,040 square miles, is defined by its diverse terrain. The western two-fifths of the State falls within the Rocky Mountains and the eastern three-fifths is in the Great Plains. Because of its location along the Continental Divide, the rivers in Montana drain into either the Pacific Ocean or the Gulf of Mexico. Montana is often called the Treasure State due to its mineral wealth, which includes oil, gas, and coal, but the State’s primary economic activity is agriculture. Other economic activities include natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Montana↗
USGS2016

Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Nature Climate Change↗
USGS1994

The space-time structure of oil and gas field growth in a complex depositional system

Shortly after the discovery of an oil and gas field, an initial estimate is usually made of the ultimate recovery of the field. With the passage of time, this initial estimate is almost always revised upward. The phenomenon of the growth of the expected ultimate recovery of a field, which is known as "field growth," is important to resource assessment analysts for several reasons. First, field growth is the source of a large part of future additions to the inventory of proved reserves of crude oil and natural gas in most petroliferous areas of the world. Second, field growth introduces a large negative bias in the forecast of the future rates of discovery of oil and gas fields made by discovery process models. In this study, the growth in estimated ultimate recovery of oil and gas in fields made up of sandstone reservoirs formed in a complex depositional environment (Frio strand plain exploration play) is examined. The results presented here show how the growth of oil and gas fields is tied directly to the architectural element of the shoreline processes and tectonics that caused the deposition of the individual sand bodies hosting the producible hydrocarbon. ?? 1994 Oxford University Press.

Nonrenewable Resources↗
USGS2005

CO2 dynamics in the Amargosa Desert: Fluxes and isotopic speciation in a deep unsaturated zone

Natural unsaturated-zone gas profiles at the U.S. Geological Survey's Amargosa Desert Research Site, near Beatty, Nevada, reveal the presence of two physically and isotopically distinct CO 2 sources, one shallow and one deep. The shallow source derives from seasonally variable autotrophic and heterotrophic respiration in the root zone. Scanning electron micrograph results indicate that at least part of the deep CO 2 source is associated with calcite precipitation at the 110-m-deep water table. We use a geochemical gas-diffusion model to explore processes of CO 2 production and behavior in the unsaturated zone. The individual isotopic species 12 CO 2 , 13 CO 2 , and 14 CO 2 are treated as separate chemical components that diffuse and react independently. Steady state model solutions, constrained by the measured δ 13 C (in CO 2 ), and δ 14 C (in CO 2 ) profiles, indicate that the shallow CO 2 source from root and microbial respiration composes ∼97% of the annual average total CO 2 production at this arid site. Despite the small contribution from deep CO 2 production amounting to ∼0.1 mol m −2 yr −1 , upward diffusion from depth strongly influences the distribution of CO 2 and carbon isotopes in the deep unsaturated zone. In addition to diffusion from deep CO 2 production, 14 C exchange with a sorbed CO 2 phase is indicated by the modeled δ 14 C profiles, confirming previous work. The new model of carbon-isotopic profiles provides a quantitative approach for evaluating fluxes of carbon under natural conditions in deep unsaturated zones.

Water Resources Research↗
USGS2019

Perspective: Developing flow policies to balance the water needs of humans and wetlands requires a landscape scale approach inclusive of future scenarios and multiple timescales

Maintenance of the natural flow regime is essential for continued wetland integrity; however, the flow regime is greatly influenced by both natural and anthropogenic forces. Wetlands may be particularly susceptible to altered flow regimes as they are directly impacted by water flows at a variety of time scales. In Puerto Rico, contemporary water management is decreasing freshwater recharge to wetlands and contributes to the salinization of important coastal wetlands as sea levels rise. Further, downscaled climate models predict an increase in drought frequency, intensity, and duration by mid-century. Conflicts over water allocation seem imminent between human and ecological needs. Current minimum flow policies are insufficient given the complexities of ecosystem processes and the changes in precipitation patterns and sea level rise that are expected in the future. Improved flow policies need to be established that reflect the functional relationships between specific representative ecological resources and components of the natural flow regime across all relevant time scales. Similarly, flow policies need to be developed within a landscape scale to implicitly address the socio-ecological trade-offs as well as the complexities of water management. Multi-disciplinary collaborations will be essential for increasing our resiliency to anticipated future changes.

Wetlands↗
USGS2020

Observations of an extreme atmospheric river storm with a diverse sensor network

Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

California↗
USGS2024

Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020

Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging. In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions. Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p -value cutoff point. A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected. A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State. Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.

South Dakota↗
USGS2014

Brood surveys and hunter observations used to predict gobbling activity wild turkeys in Mississippi

The Mississippi Department of Wildlife, Fisheries, and Parks utilize data from turkey hunter observations and brood surveys from across the state to manage wild turkey Meleagris gallopavo populations. Since 1995, hunters have collected gobbling and jake observation data, while the Mississippi Department of Wildlife, Fisheries, and Parks' personnel and cooperating wildlife managers of several natural resource agencies throughout the state have collected brood survey data. Both sources of data serve to forecast poult recruitment and gobbling activity. The objective of this study was to evaluate if these data can serve as a viable predictor of gobbling activity. We used three mixed models to investigate the relationship between the number of jakes observed per hour of hunting 1 y prior and the total number of poults per hens 2 y prior (model 1), number of gobblers heard per hour of hunting and the number of jakes observed per hour of hunting 1 y prior (model 2), the number of gobblers heard per hour of hunting and the total number poults per total hens observed 2 y prior (model 3) using data from 1995 to 2008 among five wild turkey management regions encompassing the state. We incorporated region as a random effect to account for spatial variation. We found the number of jakes observed per hour of hunting 1 y prior correlated with the total number of poults per total hens observed 2 y prior. We also found the number of gobblers heard per hour of hunting correlated with the number of jakes observed per hour of hunting 1 y prior. Additionally, we found that the total poults per total hens observed 2 y prior was correlated to the number of gobblers heard per hour of hunting. Our results show promise for using indices of gobbling activity, jake observations, and brood surveys to estimate gobbling activity.

Journal of Fish and Wildlife Management↗
USGS2015

Hydrologic record extension of water-level data in the Everglades Depth Estimation Network (EDEN), 1991-99

The real-time Everglades Depth Estimation Network (EDEN) has been established to support a variety of scientific and water management purposes. The expansiveness of the Everglades, limited number of gaging stations, and extreme sensitivity of the ecosystem to small changes in water depth have created a need for accurate water-level and water-depth maps. The EDEN water-surface elevation model uses data from approximately 240 gages in the Everglades to create daily continuous interpolations of the water-surface elevation and water depth for the freshwater portion of the Everglades from 2000 to the present (2014). These maps provide hydrologic data previously unavailable for assessing biological and ecological studies. Ecologists working in the Everglades expressed a need to the EDEN project team for daily EDEN water-level surfaces from 1990 to 1999. The additional 10 years of surfaces will provide ecologists and resource managers with two decades (1991–2011) of surfaces to analyze hydrologic dynamics. Before 2000, many of the EDEN gages used to generate water surfaces were not in operation. These datasets were extended to provide estimations of hydrologic time-series histories. The general approach to the record extension (hindcasts) was to (1) create a database of available data from 1990 to the present; (2) use dynamic cluster analysis to group stations with similar hydrologic behaviors for subareas of the Everglades with a large number of stations; (3) use results from the cluster analysis to select candidate explanatory variables; (4) develop linear regression or artificial neural network models to extend water-level records; and (5) evaluate record extensions by using model performance statistics and comparison of water-surface maps for similar hydrologic conditions for the hindcasted period (1991–99) and measured period (2000–11). To hindcast and fill data records, 214 empirical models were developed—189 are linear regression models and 25 are artificial neural network models. The coefficient of determination (R 2 ) for 163 of the models is greater than 0.80 and the median percent model error (root mean square error divided by the range of the measured data) is 5 percent. To evaluate the performance of the hindcast models as a group, contour maps of modeled water-level surfaces at 2-centimeter (cm) intervals were generated using the hindcasted data. The 2-cm contour maps were examined for selected days to verify that water surfaces from the EDEN model are consistent with the input data. The biweekly 2-cm contour maps did show a higher number of issues during days in 1990 as compared to days after 1990. May 1990 had the lowest water levels in the Everglades of the 21-year dataset used for the hindcasting study. To hindcast these record low conditions in 1990, many of the hindcast models would require large extrapolations beyond the range of the predictive quality of the models. For these reasons, it was decided to limit the hindcasted data to the period January 1, 1991, to December 31, 1999. Overall, the hindcasted and gap-filled data are assumed to provide reasonable estimates of station-specific water-level data for an extended historical period to inform research and natural resource management in the Everglades.

Florida↗
USGS2013

Mapping, monitoring, and modeling Western Gateway Community landscape dynamics

Federal public lands in the western United States are becoming increasingly surrounded by Gateway Communities. These communities are undergoing landscape change due to population growth, economic growth, and the resulting land-use development. Socioeconomic, demographic, and land-use changes in Gateway Communities are often perceived as threats to Federal land resources, natural amenities, cultural resources, and recreational opportunities. However, land-surface disturbances on Federal public lands, such as conventional and alternative energy development (which impact surrounding Gateway Communities), are also environmental and societal issues that Federal land and adjacent regional community planners need to consider in their long-range land-use planning.

Fact Sheet↗
USGS1996

Reactive solute transport in streams: 2. Simulation of a pH modification experiment

We present an application of an equilibrium-based solute transport model to a p H-modification experiment conducted on the Snake River, an acidic, metal-rich stream located in the Rocky Mountains of Colorado. During the experiment, instream p H decreased from 4.2 to 3.2, causing a marked increase in dissolved iron concentrations. Model application requires specification of several parameters that are estimated using tracer techniques, mass balance calculations, and geochemical data. Two basic questions are addressed through model application: (1) What are the processes responsible for the observed increase in dissolved iron concentrations? (2) Can the identified processes be represented within the equilibrium-based transport model? Simulation results indicate that the increase in iron was due to the dissolution of hydrous iron oxides and the photoreduction of ferric iron. Dissolution from the streambed is represented by considering a trace compartment consisting of freshly precipitated hydrous iron oxide and an abundant compartment consisting of aged precipitates that are less soluble. Spatial variability in the solubility of hydrous iron oxide is attributed to heterogeneity in the streambed sediments, temperature effects, and/or variability in the effects of photoreduction. Solubility products estimated via simulation fall within a narrow range ( pK sp from 40.2 to 40.8) relative to the 6 order of magnitude variation reported for laboratory experiments ( pK sp from 37.3 to 43.3). Results also support the use of an equilibrium-based transport model as the predominate features of the iron and p H profiles are reproduced. The model provides a valuable tool for quantifying the nature and extent of p H-dependent processes within the context of hydrologic transport.

Water Resources Research↗
USGS2014

Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs

As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially different results and/or computational instability. However, when only fixed effects are of interest, the survey package (svyglm and svyolr) may be suitable for a model-assisted analysis for trend. We provide possible directions for future research into combined analysis for ordinal and continuous vital sign indictors.

Natural Resource Report↗
USGS2017

Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana

Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican ( Pelecanus occidentalis ) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to assess island response to restoration and natural processes. This report, along with the accompanying USGS data release, serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the accompanying data release.

Louisiana↗
USGS2025

A joint Gaussian process model of geochemistry, geophysics, and temperature for groundwater TDS in the San Ardo Oil Field, California, USA

Decline in availability of fresh groundwater has expanded interest in brackish groundwater resources; however, the distribution of brackish groundwater is poorly understood. Water resources in sedimentary basins across the United States often overlie oil and gas development. Mapping of groundwater total dissolved solids (TDS) using data from oil well geophysical logs has become an important technique for identifying fresh and brackish groundwater. Existing geophysical log analysis methods use porosity and temperature to relate formation resistivity to TDS. Typically, natural geothermal gradients are used to estimate temperature at the location of collected resistivity. However, in thermally enhanced oil fields, steam is injected into the subsurface to mobilize high viscosity oil, creating variable temperature distributions. Furthermore, TDS derived from resistivity also depends on the fractions of dominant ions. Typically, chloride and bicarbonate fractions must be determined. It is also necessary to model TDS across many geologic units with heterogenous porosity distributions. Collectively, each quantity used to estimate TDS (resistivity, porosity, temperature, bicarbonate fraction) varies in space and time, and available data points are rarely collocated. Here, we present a new method of mapping groundwater TDS that continuously models each quantity together with a joint Gaussian process. This method enables mapping fresh and brackish water with practically available data. We apply this method to the San Ardo Oil Field in Monterey County, California, where steam injection occurs. In some areas of the aquifer system overlying the oil zone, the temperature is ∼75 °C, roughly twice the natural background value. Groundwater TDS is typically <1,500 mg/L in the aquifer and increases with depth to ∼9,000 mg/L in the oil-producing zone. A low-permeability clay layer delineates the fresh and brackish water, likely by inhibiting surface recharge from penetrating the deeper zones, allowing higher-TDS connate water to remain in place. Weaker lateral TDS trends may be controlled by recharge patterns associated with the Salinas River. Our model reveals with high certainty that groundwater has freshened in one localized part of the oil-producing zone and suggests with less certainty that more widespread freshening has also occurred. The lowering of TDS was possibly from decades of low-TDS steam injection and the associated fluid production and disposal operations.

California↗
USGS2003

Quaternary vegetation and climate change in the western United States: Developments, perspectives, and prospects

This chapter explores the strengths and shortcomings of the major sources of data on Quaternary vegetation and climate change and discusses the use of models as a means to explore past and potential future environmental changes. The flora and major vegetation types of the western United States are present for several million years. Ongoing changes in atmospheric chemistry , climate, and human activities may lead to major vegetation changes over the coming decades to centuries. The combination of observations from the paleoenvironmental record, modern ecological studies, and modeling now permit assessments of the magnitude of potential future changes in the context of natural variability. They also provide opportunities for hypothesis testing and identification of the processes driving past changes in vegetation and climate. Understanding the dynamics of paleoenvironmental change can contribute to current conservation and natural resource management efforts and will help conservation and natural resource managers anticipate the potential rate, magnitude, and complexity of future vegetation change.

western United States↗
USGS2023

The 3D Elevation Program—Supporting Minnesota's economy

Introduction In Minnesota, high-quality elevation data are essential for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, coastal zone management, and many other business uses. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Minnesota↗
USGS2006

Effects of Proposed Additional Ground-Water Withdrawals from the Mississippi River Valley Alluvial Aquifer on Water Levels in Lonoke County, Arkansas

The Grand Prairie Water Users Association, located in Lonoke County, Arkansas, plans to increase ground-water withdrawals from the Mississippi River Valley alluvial aquifer from their current (2005) rate of about 400 gallons per minute to 1,400 gallons per minute (2,016,000 gallons per day). The effect of pumping from a proposed well was simulated using a digital model of ground-water flow. The proposed additional withdrawals were added to an existing pumping cell specified in the model, with increased pumping beginning in 2005, and specified to pump at a total combined rate of 2,016,000 gallons per day for a period of 46 years. In addition, pumping from wells owned by Cabot Water Works, located about 2 miles from the proposed pumping, was added to the model beginning in 2001 and continuing through to the end of 2049. Simulated pumping causes a cone of depression to occur in the alluvial aquifer with a maximum decline in water level of about 8.5 feet in 46 years in the model cell of the proposed well compared to 1998 withdrawals. However, three new dry model cells occur south of the withdrawal well after 46 years. This total water-level decline takes into account the cumulative effect of all wells pumping in the vicinity, although the specified pumping rate from all other model cells in 2005 is less than for actual conditions in 2005. After 46 years with the additional pumping, the water-level altitude in the pumped model cell was about 177.4 feet, which is 41.7 feet higher than 135.7 feet, the altitude corresponding to half of the original saturated thickness of the alluvial aquifer (a metric used to determine if the aquifer should be designated as a Critical Ground-Water Area (Arkansas Natural Resources Commission, 2006)).

Scientific Investigations Report↗
USGS2021

UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence

Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches ( R 2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.

Arizona↗