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At least 343 records · Page 19Linked to original sources
USGS1999

The oil and gas resource potential of the Arctic National Wildlife Refuge 1002 area, Alaska

In anticipation of the need for scientific support for policy decisions and in light of the decade-old perspective of a previous assessment, the USGS has completed a reassessment of the petroleum potential of the ANWR 1002 area. This was a comprehensive study by a team of USGS scientists in collaboration on technical issues (but not the assessment) with colleagues in other agencies and universities. The study incorporated all available public data and included new field and analytic work as well as the reevaluation of all previous work. Using a methodology similar to that used in previous USGS assessments in the ANWR and the NPRA, this study estimates that the total quantity of technically recoverable oil in the 1002 area is 7.7 BBO (mean value), which is distributed among 10 plays. Using a conservative estimate of 512 million barrels as a minimum commercially developable field size, then about 2.6 BBO of oil distributed in about three fields is expected to be economically recoverable in the undeformed part of the 1002 area. Using a similar estimated minimum field size, which may not be conservative considering the increased distance from infrastructure, the deformed area would be expected to have about 600 MMBO in one field. The amounts of in-place oil estimated for the 1002 area are larger than previous USGS estimates. The increase results in large part from improved resolution of reprocessed seismic data and geologic analogs provided by recent nearby oil discoveries.

Alaska↗
USGS2010

Analysis and simulation of water-level, specific conductance, and total phosphorus dynamics of the Loxahatchee National Wildlife Refuge, Florida, 1995-2006

The Arthur R. Marshall Loxahatchee Wildlife Refuge (Refuge) was established in 1951 through a license agreement between the South Florida Water Management District and the U.S. Fish and Wildlife Service (USFWS) as part of the Migratory Bird Conservation Act. Under the license agreement, the State of Florida owns the land of the Refuge and the USFWS manages the land. Fifty-seven miles of levees and borrow canals surround the Refuge. Water in the canals surrounding the marsh is controlled by inflows and outflows through control structures. The transport of canal water with higher specific conductance and nutrient concentrations to the interior marsh has the potential to alter critical ecosystem functions of the marsh. Data-mining techniques were applied to 12 years (1995-2006) of historical data to systematically synthesize and analyze the dataset to enhance the understanding of the hydrology and water quality of the Refuge. From the analysis, empirical models, including artificial neural network (ANN) models, were developed to answer critical questions related to the relative effects of controlled releases, precipitation, and meteorological forcing on water levels, specific conductance, and phosphorous concentrations of the interior marsh. Data mining is a powerful tool for converting large databases into information to solve complex problems resulting from large numbers of explanatory variables or poorly understood process physics. For the application of the linear regression and ANN models to the Refuge, data-mining methods were applied to maximize the information content in the raw data. Signal processing techniques used in the data analysis and model development included signal decomposition, digital filtering, time derivatives, time delays, and running averages. Inputs to the empirical models included time series, or signals, of inflows and outflows from the control structures, precipitation, and evapotranspiration. For a complex hydrologic system like the Refuge, the statistical accuracy of the models and predictive capability were good. The water-level models have coefficient of determination (R 2 values ranging from 0.90 to 0.98. The R 2 for the specific conductance model is 0.82, and the R2 for the total phosphorus model is 0.51. The accuracy of the models was attributable to the quantity and quality of the available data. To make the models directly available to all stakeholders, an easy-to-use decision support system (DSS) called the Loxahatchee Artificial Neural Network Model (LOXANN) DSS was developed as a spreadsheet application that integrates the historical database, linear regression and ANN models, model controls, streaming graphics, and model output. The LOXANN DSS allows Refuge managers and other users to easily execute the water level, specific conductance, and phosphorous models to evaluate various water-resource management scenarios. The user is able to choose from three options in setting the control-structure flows: as a percentage of historical flow, as a constant flow, or as a user-defined hydrograph. Output from the LOXANN DSS includes tabular time series of predictions of the measured data and predictions of the user-specified conditions. A three-dimensional visualization routine also was developed that displays longitudinal specific conductance conditions. Two scenarios were simulated with the LOXANN DSS. One scenario increased the historical flows at four control structures by 40 percent. The second scenario used a user-defined hydrograph to set the outflow from the Refuge to the weekly average inflow to the Refuge delayed by 2 days. Both scenarios decreased the potential of canal water intruding into the marsh by decreasing the slope of the water level between the canals and the marsh.

Scientific Investigations Report↗
USGS2026

Flood-inundation extents and hydraulic characteristics of Carson Slough, Ash Meadows National Wildlife Refuge, Nevada

Mud Lake Dam, a rock and earth embankment structure, crosses an ephemeral stream that once flowed naturally into Carson Slough, the largest wetland complex in southern Nevada and a critical habitat for several threatened and endangered species. The dam was breached in the 1990s, rerouting stormwater into a new channel. The Bureau of Land Management and U.S. Fish and Wildlife Service are considering possible dam modifications to establish streamflow back to the natural channel. The U.S. Geological Survey, in collaboration with the Bureau of Land Management and U.S. Fish and Wildlife Service, developed hydrologic and hydraulic models to assess how peak-flow breach expansion and possible dam modifications could affect flooding in Carson Slough. A precipitation-runoff model was developed to estimate 6-hour and 24-hour flood streamflows for 10-, 25-, 50-, and 100-year floods, which were used as inputs to a hydraulic model simulating three scenarios: 2023 conditions, peak-flow breach expansion, and possible dam modifications. Model outputs included flood-inundation extents and hydraulic characteristics such as water depth and velocity. Under 2023 conditions, streamflow enters the dam breach and follows the breach channel to the west into Carson Slough, inundating Soda Spring. Only under the highest flood scenario (24-hour duration, 100-year rainfall recurrence interval) did streamflow also enter the natural channel directly downstream from the dam. Under possible dam modification conditions, streamflow remained solely in the natural channel and flowed southwest into Carson Slough. Under peak-flow breach expansion, streamflow entered the breach and natural channels and into Carson Slough. Compared to 2023 conditions, the total inundated extent for peak-flow breach expansion increased by 7–11 percent, whereas possible dam modification conditions decreased by 4–7 percent. Inundation extents remained largely similar throughout most of the study area, and the primary exception was in the 1-mile segment directly downstream from the dam.

Nevada↗