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At least 361 records · Page 20Linked to original sources
USGS2020

2019 Status of the Lake Ontario Lower Trophic Levels

Spring total phosphorus (TP) in 2019 was 3.2 µg/L (offshore) and 4.7 µg/L (nearshore), both all-time lows; however, there is no significant time trend in our data series (1995-2019 for nearshore; 2002-2019 for offshore). Apr/May – Oct mean TP concentrations were low at both nearshore and offshore locations (range, 3.7 – 6.5 µg/L). TP and SRP concentrations were not significantly different between nearshore and offshore habitats. Chlorophyll- a and Secchi depth values are indicative of oligotrophic conditions in nearshore and offshore habitats. Offshore summer chlorophyll-a declined significantly 1995 – 2019. Nearshore chlorophyll- a increased 1995 – 2004 and then stabilized 2005 – 2019. In 2019, epilimnetic chlorophyll-a averaged between 1.3 and 2.9 μg/L across sites, and Apr/May – Oct concentrations were not significantly different between nearshore and offshore sites. Summer Secchi depth increased significantly in the offshore 1995 – 2019 from ~6 m to ~8 m. In the nearshore Secchi depth increased 1995 – 2004 but has remained around 6 m since 1999. Apr/May – Oct Secchi depth ranged from 3.8 m to 9.1 m (12 ft to 30 ft) at individual sites and was significantly higher offshore (7.6 m; 25 ft) than nearshore (5.7 m; 19 ft). In 2019, nearshore summer zooplankton biomass increased to 16.7 mg/m 3 after an all-time low (10.3 mg/m 3 ) in 2017. Offshore biomass (12.0 mg/m 3 ) was near the all-time low (8.1 mg/m 3 , 2006). Apr/May – Oct epilimnetic zooplankton density and biomass were not different between nearshore and offshore sites. However, zooplankton average size was significantly higher in the offshore (0.72 mm) than the nearshore (0.61 mm). Peak (July) epilimnetic biomass of Cercopagis was 2.4 mg/m 3 in the nearshore and 1.4 mg/m 3 in the offshore. Peak (September) epilimnetic biomass of Bythotrephes was 2.0 mg/m 3 in the nearshore and 2.9 mg/m 3 in the offshore. Summer nearshore zooplankton density and biomass declined significantly 1995 – 2004 and then remained stable 2005 – 2019. The decline was due mainly to reductions in cyclopoids copepods. Summer epilimnetic daytime offshore zooplankton density decreased significantly 1995 – 2004, but biomass did not. Density and biomass declined significantly 1995 – 2019. Density was 3885/m 3 in 2019, about one-fourth the level observed the previous year. Offshore summer epilimnetic zooplankton biomass in 2019 was 12 mg/m 3 —well below the mean from 2005 – 2018 (20 mg/m 3 ). Most offshore zooplankton biomass was found in the metalimnion in July and early-October, and in the hypolimnion in September. Limnocalanus dominated the metalimnion in July while daphnids comprised most of the biomass in October. In September, Limnocalanus dominated the hypolimnion.

New York, Ontario↗
USGS2024

Climate vulnerability assessment of Oregon hatchery programs

The goal of this project was to assess the vulnerability to climate change impacts for a sample set of hatchery programs representing different geographic areas and primary anadromous species raised in state-managed Oregon hatcheries (Summary Figure 1). Freshwater and marine ecosystem processes can significantly influence salmon and steelhead survival, and understanding how these factors have affected historical returns can help managers evaluate the climate vulnerability of hatchery stocks. We examined stock-specific trends in smolt-to-adult returns (SARs), which represent the proportion of smolts released from the hatchery that are recovered in fisheries or as returning adult spawners. SARs are among the most consistent long term estimators of survival for hatchery-origin stocks. Depending on the stock, adult recoveries could occur in marine fisheries, freshwater fisheries, returns to the hatchery or another collection facility, and spawning ground surveys. We collected time series data on relevant ecological indicators and used generalized additive models (GAMs) to explore both univariate and multivariate relationships with SARs for each hatchery stock. An additional aspect of this assessment was to evaluate the climate vulnerability of resident trout stocking programs in the Department's East and West regions, incorporating insights from Oregon Department of Fish and Wildlife (ODFW) staff interviews and published data on the thermal tolerance of hatchery trout stocks. This assessment is provided in the ‘Climate vulnerability of trout stocking programs’ subsection below.

Oregon↗
USGS2025

Great Lakes lake trout (Salvelinus namaycush) thiamine monitoring program annual report

Thiamine deficiency in lake trout ( Salvelinus namaycush ) eggs has been linked to early life-stage mortality in the Great Lakes from the 1960s through the 1990s, potentially affecting lake trout recruitment. In response, the U.S. Geological Survey’s Great Lakes Science Center (GLSC), Eastern Ecological Science Center (EESC), and Columbia Environmental Research Center (CERC), and the State University of New York (SUNY) Brockport, in collaboration with partner agencies, initiated a cooperative monitoring program in the late 1990s to assess thiamine concentrations in lake trout eggs. In 2024, egg thiamine concentrations continued to show high variability across sampling sites. No samples with thiamine concentrations below the 4 nmol/g threshold recommended for successful reproduction were observed in Lakes Superior or Huron. In contrast, between 11% and 88% of lake trout sampled from sites in Lakes Michigan, Ontario, Champlain, and the Finger Lakes had egg thiamine concentrations below this critical threshold. Time series data revealed substantial temporal and spatial variation in mean lake trout egg thiamine concentrations across the Great Lakes region.

Finger Lakes, Great Lakes, Lake Champlain↗
USGS2025

Great Lakes lake trout (Salvelinus namaycush) thiamine monitoring program annual report

Thiamine deficiency in lake trout (Salvelinus namaycush) eggs has been linked to early life-stage mortality in the Great Lakes from the 1960s through the 1990s, potentially affecting lake trout recruitment. In response, the U.S. Geological Survey’s Great Lakes Science Center (GLSC), Eastern Ecological Science Center (EESC), and Columbia Environmental Research Center (CERC), and the State University of New York (SUNY) Brockport, in collaboration with partner agencies, initiated a cooperative monitoring program in the late 1990s to assess thiamine concentrations in lake trout eggs. In 2024, egg thiamine concentrations continued to show high variability across sampling sites. No samples with thiamine concentrations below the 4 nmol/g threshold recommended for successful reproduction were observed in Lakes Superior or Huron. In contrast, between 11% and 88% of lake trout sampled from sites in Lakes Michigan, Ontario, Champlain, and the Finger Lakes had egg thiamine concentrations below this critical threshold. Time series data revealed substantial temporal and spatial variation in mean lake trout egg thiamine concentrations across the Great Lakes region.

Lakes Superior, Michigan, Huron, Erie, Ontario, Ch↗
USGS2019

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing↗
USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗
USGS2007

Visualization and Time-Series Analysis of Ground-Water Data for C-Area, Savannah River Site, South Carolina, 1984-2004

In 2004, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, initiated a study of historical ground-water data of C-Area on the Savannah River Site in South Carolina. The soils and ground water at C-Area are contaminated with high concentrations of trichloroethylene and lesser amounts of tetrachloroethylene. The objectives of the investigation were (1) to analyze the historical data to determine if data-mining techniques could be applied to the historical database to ascertain whether natural attenuation of recalcitrant contaminants, such as volatile organic compounds, is occurring and (2) to determine whether inferential (surrogate) analytes could be used for more cost-effective monitoring. Twenty-one years of data (1984-2004) were collected from 396 wells in the study area and converted from record data to time-series data for analysis. A Ground-Water Data Viewer was developed to allow users to spatially and temporally visualize the analyte data. Overall, because the data were temporally and spatially sparse, data analysis was limited to only qualitative descriptions.

South Carolina↗
USGS1992

Monitoring rangeland dynamics in Senegal with advanced very high resolution radiometer data

Time‐series Normalized Difference Vegetation Index (NDVI) data, computed from Advanced Very High Resolution Radiometer data, are being used by regional and national programs in the African Sahel to monitor seasonal rangeland conditions. The data are often used as indicators of grazing conditions and drought. However, distinguishing rangelands from other vegetation cover types on NDVI images is difficult. A second complication is that rangeland types and their associated productivity vary geographically by soil type. To effectively assess rangeland conditions, seasonal fluctuations (due to climatic cycles) must be isolated from long‐term production characteristics associated with vegetation type and soil differences. Rangeland NDVI dynamics, including qualitative assessments of rangeland production, and the timing and length of the growing season in Senegal were examined by using 7.4‐km global area coverage satellite data. Analyses were based on 10‐day NDVI composite image data from 1982 through 1989. The NDVI image data were stratified by rangeland and soil polygons derived from locally available resource maps. Time‐series NDVI statistics were calculated from the resource polygons that had been interpreted into high, medium, and low production rangelands. Analysts monitoring rangeland conditions can better identify seasonal anomalies such as drought by comparing production potential within homogeneous; resource polygons with the current NDVI data.

Geocarto International↗
USGS1978

A summary of measured hydraulic data for the series of steady and unsteady flow experiments over uniform grass roughness

The primary purpose of this report is to summarize and make available to other investigators the measured hydraulic data collected in the Flood Plain Simulation Facility located at the Gulf Coast Hydroscience Center, near Bay St. Louis, Miss., during a series of experiments designed to study steady and unsteady flow over uniform grass roughness. All experiments were conducted during the 1973 and 1974 test seasons. Tables of measured ground-surface elevations, water-surface elevations, and point velocities are included for all experiments. Bed roughness data in the form of grass heights and densities are tabulated. A total of 19 steady flow experiments and 7 unsteady flow experiments for varying grass heights are included. For the steady flow experiments, an average set of water-surface elevations and point velocity measurements are tabulated. Inflow discharge hydrographs are tabulated for the unsteady flow experiments. The tabulated point velocities and water-surface elevations for the unsteady flow experiments were selected to represent the general changes in the flow variables as the flood wave passed through the facility but do not include all collected data. However, all data that were collected have been stored on computer disk storage and may be retrieved using the listing programs and memory locations.

Open-File Report↗
USGS1981

Comparison of uranium-series, radiocarbon, and amino acid data from marine molluscs, Baffin Island, Arctic Canada

Uranium-series and 14 C dates and the extent of amino acid racemization are reported for 24 marine shell samples from three areas of Baffin Island, Arctic Canada. When the radiometric dates are plotted against the ratio of D-alloisoleucene:L-isoleucene in the shells, five broad age groups are recognized. The uranium-series data indicate that 231 Pa is incompletely retained in most fossil shells and that 230 Th is lost from some of the samples. Therefore, their apparent ages are minimum. However, a few dated samples in each group have yielded useful age results, and the minimum ages of the five groups of samples are estimated as 7,000 to 11,000, ≥ 70,000, ≥ 136,000, ≥ 190,000, and > 300,000 yr. Calculated integrated thermal histories based on the epimerization reaction in the mollusc Hiatella arctica Linné give paleotemperature estimates of around −5 °C, compared to the present mean annual air temperature of about −11 °C.

Baffin Island↗
USGS2023

Gulf Islands National Seashore regional sediment budget research and data needs—Workshop series summary

Executive Summary The National Park Service (NPS), in collaboration with the U.S. Geological Survey (USGS), recognizes the need to quantify the sediment budget of the barrier islands within the Gulf Islands National Seashore (GINS) to understand the coastal processes affecting island resiliency. To achieve this goal, identifying and quantifying the physical parameters that drive long-term change is necessary to model the processes that are both generative and terminal in island evolution and capture island response to long-term human alteration and climatic patterns. For example, measuring change across periods of storminess is more effective at assessing island resiliency than measuring change resulting from a single storm impact. Understanding changes to the physical environment over time is key to successfully predicting island responses to future storm impacts, human alteration, and sea-level rise and is necessary for effective decision making and management response. Yet, the diversity of factors affecting natural and cultural resources necessitates a strategic approach to data collection priorities that can inform sediment budget quantification and integrated resource management. This study sought to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” at the GINS. The purpose of the workshop was to identify and prioritize the specific research and data needs regarding the sediment budget at the GINS that can enhance the NPS efforts to conserve the islands’ natural resources, cultural resources, and the facilities and infrastructure that support both conservation and visitor use of those resources. This effort explored two research questions: (1) “what research and data needs exist for the sediment budget at Gulf Islands National Seashore” (research question 1) and (2) “how can research to address these needs capitalize on regional partnerships to advance natural and cultural resource conservation at Gulf Islands National Seashore” (research question 2)? The workshop was conducted virtually in a two-part, two-day series. The workshop series was organized by researchers from North Carolina State University in collaboration with NPS and USGS staff and was facilitated by National Oceanographic and Atmospheric Administration staff. The workshop series (two paired, sequential, partial-day workshops) addressed two target audiences: (1) NPS and USGS staff (April workshop) and (2) regional Federal, State, county, and nongovernmental organization staff, including NPS and USGS staff (May workshop). A total of four workshop sessions were held, comprising two sessions with each target audience. The workshop series intended to identify sediment management research and data needs that could enhance natural and cultural resource stewardship at the GINS. One objective was to share information about regional sediment transport and management, available sediment management plans, and predictive modeling capabilities, including geomorphologic and hydrodynamic predictive models. This information was shared through a series of presentations by park managers and NPS and USGS researchers that identified park issues and available capabilities and data. The second objective was to elicit research and data needs, with a primary goal of assessing the importance and urgency of the identified needs. This assessment was partly determined by requesting that the workshop participants identify and prioritize research themes through polls, comments, and discussion. The polls explicitly asked participants to qualitatively evaluate the importance (not at all, slightly, somewhat, very, or extremely) and urgency (not at all, slightly, somewhat, very, or extremely) of the thematically grouped research and data needs. These evaluations were plotted and shared during the workshop to visualize how the relative importance ( x -axis) and relative urgency ( y -axis) of each “need,” relative to other needs, to identify the most necessary (importance) and time-sensitive (urgent) items, thereby allowing an enhanced, holistic understanding of the sediment budget at GINS. Results of the poll are published as a USGS data release. The assessment results revealed that the most important and urgent research and data needs included mapping (for example, elevation, habitat, and cultural resources), a regional sediment budget and management plan, and the dynamic modeling of sediment processes. During the workshop, these issues were visualized using scatter plots to demonstrate the relative importance and urgency of each theme, provide descriptive statistics, and elicit discussion. This format of iterative presentation, discussion, and prioritization allowed the project team to effectively accomplish their objective of identifying important and urgent research needs for natural and cultural resource stewardship at the GINS. Through the workshop, it was determined that expanded communication with the broader research community was needed to coordinate research activities and streamline potential funding opportunities and that research and policy should be integrated through a structured decision-making process. At the conclusion of the workshop, an administered poll showed that the presentations effectively identified data and research needs and that the goals of the workshop were achieved. The results suggest that this type of needs-assessment workshop can effectively identify existing research capabilities and data, determine and prioritize research and data needs, and address how these efforts can use regional partnerships to aid natural and cultural resource conservation and management at National Parks.

Florida, Mississippi↗
USGS2012

Data visualization, time-series analysis, and mass-balance modeling of hydrologic and water-quality data for the McTier Creek watershed, South Carolina, 2007-2009

The McTier Creek watershed is located in the headwaters of the Edisto River Basin, which is in the Coastal Plain region of South Carolina. The Edisto ecosystem has some of the highest recorded fish-tissue mercury concentrations in the United States. In an effort to advance the understanding of the fate and transport of mercury in stream ecosystems, the U.S. Geological Survey, as part of its National Water-Quality Assessment Program, initiated a field investigation of mercury in the McTier Creek watershed in 2006. The initial efforts of the investigation included the collection of extensive hydrologic and water-quality field data, along with the development of several hydrologic and water-quality models. This series of measured and modeled data forms the primary source of information for this investigation to assess the fate and transport of mercury within the McTier Creek watershed.

South Carolina↗
USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗
USGS1981

Preliminary data from a series of artificial recharge experiments at Stanton, Texas

A series of artificial recharge experiments was conducted by the U.S. Geological Survey at an experimental site located in Stanton, Texas. Five tests were performed from March 1977 through December 1978 to: (1) Evaluate the hydraulic properties of the aquifer; (2) test sampling and monitoring equipment; (3) compare tracers for future use in hydrologic investigations; and (4) determine the radial and vertical distribution of hydraulic properties at the site. Suites of inorganic, and both volatile and nonvolatile organic tracers were used in the tests, and comparative data were obtained from sampling points at several radial distances and depths from the injection well. Hydraulic data from aquifer tests and geologic data from core material also were obtained during the investigation.

Texas↗
USGS2014

Annual floods and the partial‐duration flood series

Flood data are ordinarily listed either in annual‐flood series or in a partial‐duration series. If the expectancy of a flood in the duration series ϵ is known, then the probability of that flood being an annual flood is shown to be e −ϵ . From this relationship it is possible to transform recurrence intervals in the partial duration series to those in the annual‐flood series. It is shown that for equivalent floods, the recurrence intervals in the partial‐duration series are smaller than in the annual‐flood series, but that the difference becomes inconsequential for floods greater than about five‐year recurrence interval.

Eos, Transactions, American Geophysical Union↗
USGS2009

Geologic Map of the Yukon-Koyukuk Basin, Alaska

This map and accompanying digital files represent part of a systematic effort to release geologic data for the United States in a uniform manner. All the geologic data in this series will be published as parts of the U.S. Geological Survey Data Series. The geologic data in this series have been compiled from a wide variety of sources, ranging from state and regional geologic maps to large-scale field mapping. The data are presented for use at a nominal scale of 1:500,000, although individual datasets may contain data suitable for use at larger scales. The metadata associated with each release will provide more detailed information on sources and appropriate scales for use. Associated attribute databases accompany the spatial database of the geology and are uniformly structured for ease in developing regional- and national-scale maps. The 1:500,000-scale geologic map of the Yukon-Koyukuk Basin, Alaska, covers more than 200,000 square kilometers of western Alaska or nearly 15 percent of the total land area of the state. It stretches from the Brooks Range on the north to the Kuskokwim River and lower reaches of the Yukon River on the south and from Kotzebue Sound, Seward Peninsula, and Norton Sound on the west to the Yukon-Tanana Uplands and Tanana-Kuskokwim Lowlands on the east. It includes not only the northern and central part of the basin, but also the lands that border the basin. The area is characterized by isolated clusters of hills and low mountain ranges separated by broad alluviated interior and coastal lowlands. Most of the lowlands, except those bordering Kotzebue Sound and Norton Sound, support a heavy vegetation cover. Exposures of bedrock are generally limited to rubble-strewn ridgetops and to cutbanks along the rivers. The map of the Yukon-Koyukuk Basin was prepared largely from geologic field data collected between 1953 and 1988 by the U.S. Geological Survey and published as 1:250,000-scale geologic quadrangle maps. Additional data for parts of the Wiseman, Ruby, Medfra, and Ophir quadrangles came from 1:63,360-scale quadrangle maps published by the Alaska Division of Geological and Geophysical Surveys. The map also incorporates some unpublished field data for the Ruby quadrangle collected by R.M. Chapman between 1944 and 1977 and for parts of the Tanana, Bettles, Norton Bay, and Candle quadrangles collected by W.W. Patton, Jr. and others between 1954 and 1985. Sources of geologic map data for each of the eighteen 1:250,000-scale quadrangles used in compiling this 1:500,000-scale map of the Yukon-Koyukuk Basin as well as sources of general geologic information pertaining to the entire map area are provided in the 'Sources of Information' section.

Scientific Investigations Map↗
USGS1999

Evaluating data worth for ground-water management under uncertainty

A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models-a chance-constrained ground-water management model and an integer-programing sampling network design model-to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information-i.e., the projected reduction in management costs-with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models - a chance-constrained ground-water management model and an integer-programming sampling network design model - to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information - i.e., the projected reduction in management costs - with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.

Journal of Water Resources Planning and Management↗