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USGS2019

Uncertainty of reference pixel soil moisture averages sampled at SMAP core validation sites

Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active/Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to under-sampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range 0.01 to 0.02 m3/m3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

Journal of Hydrometeorology↗

USGS2016

Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors

Using fine spatial resolution (~ 7.6 m) hyperspectral AVIRIS data collected over the Deepwater Horizon oil spill in the Gulf of Mexico, we statistically estimated slick lengths, widths and length/width ratios to characterize oil slick morphology for different thickness classes. For all AVIRIS-detected oil slicks (N = 52,100 continuous features) binned into four thickness classes (≤ 50 μm but thicker than sheen, 50–200 μm, 200–1000 μm, and > 1000 μm), the median lengths, widths, and length/width ratios of these classes ranged between 22 and 38 m, 7–11 m, and 2.5–3.3, respectively. The AVIRIS data were further aggregated to 30-m (Landsat resolution) and 300-m (MERIS resolution) spatial bins to determine the fractional oil coverage in each bin. Overall, if 50% fractional pixel coverage were to be required to detect oil with thickness greater than sheen for most oil containing pixels, a 30-m resolution sensor would be needed.

Marine Pollution Bulletin↗

USGS2013

Characterization and data-gap analysis of surface-water quality data in the Piceance study area, western Colorado, 1959–2009

The U.S. Geological Survey, in cooperation with Federal, State, county, and industry partners, developed a Web-accessible common data repository to provide access to historical and current (as of August 2009) water-quality information (available on the Internet at http://rmgsc.cr.usgs.gov/cwqdr/Piceance/index.shtml ). Surface-water-quality data from public and private sources were compiled for the period 1931 to 2009 and loaded into the common data repository for the Piceance Basin. A subset of surface-water-quality data for 1959 to 2009 from the repository were compiled, reviewed, and checked for quality assurance for this report. This report contains data summaries, comparisons to water-quality standards, trend analyses, a generalized spatial analysis, and a data-gap analysis for select water-quality properties and constituents. Summary statistics and a comparison to standards were provided for 347 sites for 33 constituents including field properties, nutrients, major ions, trace elements, suspended sediment, Escherichia coli, and BTEX (benzene, toluene, ethylbenzene, and xylene). When sufficient data were available, trends over time were analyzed and loads were calculated for those sites where there were also continuous streamflow data. The majority of sites had information on field properties. Water temperature data was available for 316 sites where data were collected between 1959 and 2009. The only trend that was detected in temperature was an upward trend at the Gunnison River near Grand Junction, Colorado. There were 326 values out of a total of 32,006 values in the study area that exceeded the aquatic-life standard for daily maximum water temperature. For the entire study area, 196 sites had dissolved-oxygen data collected between 1970 and 2009, and median dissolved-oxygen concentrations ranged from 6.8 to11.2 milligrams per liter (mg/L). There were 185 concentrations that exceeded the dissolved oxygen aquatic-life standard out of a total of 11,248 values. The pH data were available for 276 sites, and median pH values ranged from 7.5 to 9.0. There were 241 values that exceeded the high pH standard and 13 values that were less than the low pH standard of the 16,790 values in the study area. Nutrients within the study area were not well represented in each basin and were often not being sampled currently. For the entire study area, 62 sites had nitrate data collected between 1958 and 2009, and median nitrate concentrations ranged from less than detection to 3.72 mg/L as nitrogen. The maximum contaminant level for domestic water supply for nitrate is 10 mg/L and was exceeded once in 3,736 samples. Total phosphorus was collected at 113 sites between 1974 and 2009, and median total phosphorus concentrations ranged from less than detection to 5.04 mg/L. The U.S. Environmental Protection Agency recommendation for phosphorus is less than 0.1 mg/L, and 1,469 of 4,842 samples exceeded this recommended standard. An upward trend in both nitrate and total phosphorus was detected in the White River above Coal Creek near Meeker, Colo. Standards for major ions exist only for chloride and sulfate. For the entire study area, 118 sites had both chloride and sulfate concentration data collected between 1958 and 2009. Median chloride concentrations ranged from 0.085 mg/L to 280 mg/L. Median sulfate concentrations ranged from 4.57 mg/L to 15,000 mg/L. Both chloride and sulfate domestic water-supply standards are 250 mg/L. There were 120 chloride concentrations and 1,111 sulfate concentration samples that exceeded these standards. A downward trend in dissolved solids was detected at the Colorado River near the Colorado-Utah state border and could be a result of salinity control work near Grand Junction, Colo. Trace elements were relatively well represented both temporally and spatially in the study area though the number of trace element samples per site was not typically enough to compute trends or loads except for selenium. There were 127 sites that had dissolved iron concentration data collected between 1961 and 2009, and median iron concentrations ranged from less than detection to 1,100 micrograms per liter (µg/L). The 30-day drinking-water standard for iron is 300 µg/L, and 203 samples exceeded the standard. Selenium was the best represented trace element with selenium concentration data collected at 197 sites between 1973 and 2009, and median selenium concentrations range from less than detection to 181 µg/L. The chronic standard of 4.6 µg/L for selenium concentrations was exceeded in 899 samples, and the acute aquatic-life standard of 18.4 µg/ for selenium was exceeded in 629 samples. High concentrations of selenium are of concern in the Lower Gunnison River Basin because of the combination of geologic formations and land use. There were significant downward trends in selenium at both main-stem sites on the Gunnison River at Delta, Colo., and the Gunnison River near Grand Junction, Colo. High selenium concentrations correlate with high salinity concentrations; thus, when salinity control efforts are conducted in selenium-rich areas in the Lower Gunnison River Basin, both salinity and selenium have the potential to decrease. Spatial, temporal, and analytical data gaps were identified in the study area. The spatial coverage of sampling sites could be expanded in the White River Basin by adding more tributary sites. No water-quality data exist for tributary streams in the area north of Rangely, Colo., where extensive energy development has occurred in a complex geologic setting. Douglas Creek has a drainage area of 425 square miles and has limited historic water-quality and water-quantity data. Limited data were available for field properties, major ions, nutrients, and trace elements on the main stem of the Colorado River between Glenwood Springs and Cameo, Colo. Nutrient data were minimally collected upstream from Colorado River at the Colorado-Utah state border and on the Gunnison River (major tributary in the reach). Approximately 30 percent of the samples for total phosphorus in the Lower Gunnison River Basin exceeded the recommended standard, yet there were insufficient data to do trends analysis in the Lower Gunnison River Basin except at the Gunnison near Grand Junction site. There is limited trace element data except for selenium in the Lower Gunnison River Basin. Additional sampling is necessary to understand the occurrence, concentrations, and loads of these constituents.

Colorado↗

USGS1996

Evaluation of the streamflow-gaging network of Alaska in providing regional streamflow information

In 1906, the U.S. Geological Survey (USGS) began operating a network of streamflow-gaging stations in Alaska. The primary purpose of the streamflow- gaging network has been to provide peak flow, average flow, and low-flow characteristics to a variety of users. In 1993, the USGS began a study to evaluate the current network of 78 stations. The objectives of this study were to determine the adequacy of the existing network in predicting selected regional flow characteristics and to determine if providing additional streamflow-gaging stations could improve the network's ability to predict these characteristics. Alaska was divided into six distinct hydrologic regions: Arctic, Northwest, Southcentral, Southeast, Southwest, and Yukon. For each region, historical and current streamflow data were compiled. In Arctic, Northwest, and Southwest Alaska, insufficient data were available to develop regional regression equations. In these areas, proposed locations of streamflow-gaging stations were selected by using clustering techniques to define similar areas within a region and by spatial visual analysis using the precipitation, physiographic, and hydrologic unit maps of Alaska. Sufficient data existed in Southcentral and Southeast Alaska to use generalized least squares (GLS) procedures to develop regional regression equations to estimate the 50-year peak flow, annual average flow, and a low-flow statistic. GLS procedures were also used for Yukon Alaska but the results should be used with caution because the data do not have an adequate spatial distribution. Network analysis procedures were used for the Southcentral, Southeast, and Yukon regions. Network analysis indicates the reduction in the sampling error of the regional regression equation that can be obtained given different scenarios. For Alaska, a 10-year planning period was used. One scenario showed the results of continuing the current network with no additional gaging stations and another scenario showed the results of adding gaging stations to the network. With the exception of the annual average discharge equation for Southeast Alaska, by adding gaging stations in all three regions, the sampling error was reduced to a greater extent than by not adding gaging stations. The proposed streamflow-gaging network for Alaska consists of 308 gaging stations, of which 32 are designated as index stations. If the proposed network can not be implemented in its entirety, then a lesser cost alternative would be to establish the index stations and to implement the network for a particular region.

Water-Resources Investigations Report↗

USGS2001

Digital data used to relate nutrient input to water quality in the Chesapeake Bay watershed

Digital data sets compiled by the U.S. Geological Survey were used as input for a collection of Spatially Referenced Regressions On Watershed (SPARROW) attributes for the Chesapeake Bay region including parts of Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia, and the District of Columbia. These regressions use a nonlinear statistical approach to relate nutrient sources and land-surface characteristics to nutrient loads of streams throughout the Chesapeake Bay watershed. A digital segmented-watershed network serves as the primary framework for spatially referencing nutrient-source and land-surface characteristic data within a geographic information system. Flow direction and flow accumulation generated from a 30-meter cell-size Digital Elevation Model and attributes from 1:500,000-scale stream data were used to generate stream and watershed networks. Spatial data sets representing nutrient inputs of total nitrogen and total phosphorus from the early 1990's were created and compiled from numerous sources. Data include atmospheric deposition, septic systems, point-source locations, land use, land cover, and agricultural sources such as commercial fertilizer and manure. Some land-surface characteristic data sets representing factors that affect the transport of nutrients also were compiled. Data sets include land use, land cover, average-annual precipitation and temperature, slope, hydrogeomorphic regions, and soil permeability. Nutrient-input and land-surface characteristic data sets merged with the segmented-watershed network provide the spatial detail by watershed segment required by SPARROW. Stream-nutrient load estimates for 132 sampling sites representing the early 1990's (103 for total nitrogen and 121 for total phosphorus) serve as the dependent variables for the regressions. These estimates were used to calibrate models of total nitrogen and total phosphorus depicting 1992 land-surface conditions. Examples of model predictions consist of stream-nutrient load and source percentages contributed locally to each stream reach, as well as percentages of the load that reach Chesapeake Bay.

Open-File Report↗

USGS2017

Emulation of long-term changes in global climate: application to the late Pliocene and future

Multi-millennial transient simulations of climate changes have a range of important applications, such as for investigating key geologic events and transitions for which high-resolution palaeoenvironmental proxy data are available, or for projecting the long-term impacts of future climate evolution on the performance of geological repositories for the disposal of radioactive wastes. However, due to the high computational requirements of current fully coupled general circulation models (GCMs), long-term simulations can generally only be performed with less complex models and/or at lower spatial resolution. In this study, we present novel longterm “continuous” projections of climate evolution based on the output from GCMs, via the use of a statistical emulator. The emulator is calibrated using ensembles of GCM simulations, which have varying orbital configurations and atmospheric CO2 concentrations and enables a variety of investigations of long-term climate change to be conducted, which would not be possible with other modelling techniques on the same temporal and spatial scales. To illustrate the potential applications, we apply the emulator to the late Pliocene (by modelling surface air temperature – SAT), comparing its results with palaeo-proxy data for a number of global sites, and to the next 200 kyr (thousand years) (by modelling SAT and precipitation). A range of CO2 scenarios are prescribed for each period. During the late Pliocene, we find that emulated SAT varies on an approximately precessional timescale, with evidence of increased obliquity response at times. A comparison of atmospheric CO2 concentration for this period, estimated using the proxy sea surface temperature (SST) data from different sites and emulator results, finds that relatively similar CO2 concentrations are estimated based on sites at lower latitudes, whereas higher-latitude sites show larger discrepancies. In our second illustrative application, spanning the next 200 kyr into the future, we find that SAT oscillations appear to be primarily influenced by obliquity for the first ∼ 120 kyr, whilst eccentricity is relatively low, after which precession plays a more dominant role. Conversely, variations in precipitation over the entire period demonstrate a strong precessional signal. Overall, we find that the emulator provides a useful and powerful tool for rapidly simulating the long-term evolution of climate, both past and future, due to its relatively high spatial resolution and relatively low computational cost. However, there are uncertainties associated with the approach used, including the inability of the emulator to capture deviations from a quasi-stationary response to the forcing, such as transient adjustments of the deep-ocean temperature and circulation, in addition to its limited range of fixed ice sheet configurations and its requirement for prescribed atmospheric CO2 concentrations.

Climate of the Past↗

USGS1997

Water-quality assessment of the Rio Grande Valley, Colorado, New Mexico, and Texas: Summary and analysis of water-quality data for the basic-fixed-site network, 1993-95

The Rio Grande Valley study unit of the U.S. Geological Survey National Water-Quality Assessment Program collected monthly water- quality samples at a network of surface-water sites from April 1993 through September 1995. This basic-fixed-site network consisted of nine main-stem sites on the Rio Grande, five sites on tributaries of the Rio Grande, two sites on streams in the Rio Grande Valley study unit that are not directly tributary to the Rio Grande, and one site on a conveyance channel. During each monthly sampling, field properties were measured and samples were collected for the analysis of dissolved solids, major constituents, nutrients, selected trace elements, and suspended-sediment concentrations. During selected samplings, supplemental samples were collected for the analysis of additional trace elements, organic carbon, and/or pesticides. Spatial variations of dissolved-solids, major-constituent, and nutrient data were analyzed. The report presents summary statistics for the monthly water-quality data by sampling site and background information on the drainage basin upstream from each site. Regression equations are presented that relate dissolved-solids, major-constituent, and nutrient concentrations to streamflow, selected field properties, and time. Median instantaneous streamflow at each basic-fixed site ranged from 1.4 to 1,380 cubic feet per second. Median specific conductance at each basic-fixed site ranged from 84 to 1,680 microsiemens per centimeter at 25 degrees Celsius, and median pH values ranged from 7.8 to 8.5. The water sampled at the basic-fixed sites generally was well oxygenated and had a median dissolved-oxygen percent of saturation range from 89 to 108. With the exception of Rio Grande above mouth of Trinchera Creek, near Lasauses, Colorado, dissolved-solids concentrations in the main stem of the Rio Grande generally increased in a downstream direction. This increase is from natural sources such as ground-water inflow and evapotranspiration and from anthropogenic sources such as irrigation- return flows, urban runoff, and wastewater-treatment plant discharges. The smallest median dissolved-solids concentration detected at a basic- fixed site was 58 milligrams per liter and the largest was 1,240 milligrams per liter. The spatial distribution of calcium, magnesium, sodium, sulfate, chloride, and fluoride was similar to the spatial distribution of dissolved solids. The spatial distribution of potassium and bicarbonate varied slightly from that of dissolved solids. Median silica concentrations generally decreased in a downstream direction. Of all cations, calcium and sodium had the largest concentrations at most basic-fixed sites. Bicarbonate and sulfate were the anions having the largest concentrations at most sites. The largest median silica concentration was at Rito de los Frijoles in Bandelier National Monument, New Mexico, where silica composed approximately 50 percent of the dissolved solids. The largest concentrations and largest median concentrations of dissolved-nutrient analytes were detected at Santa Fe River above Cochiti Lake, New Mexico, and Rio Grande at Isleta, New Mexico. The relatively large dissolved-nutrient concentrations at these sites probably were due to discharges from wastewater-treatment plants and urban runoff. The largest concentrations and largest median concentrations of total ammonia plus organic nitrogen and total phosphorus were detected at Rio Puerco near Bernardo, New Mexico. The largest concentrations of these nutrients at this site were associated with runoff from summer thunderstorms. Dissolved-iron concentrations ranged from censored concentrations to 914 micrograms per liter. Median dissolved-iron concentrations ranged from 3 to 160 micrograms per liter.

Colorado, New Mexico, Texas↗

USGS2015

Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

Iowa, Minnesota↗

USGS2011

Assessment of surface-water quantity and quality, Eagle River watershed, Colorado, 1947-2007

From the early mining days to the current tourism-based economy, the Eagle River watershed (ERW) in central Colorado has undergone a sequence of land-use changes that has affected the hydrology, habitat, and water quality of the area. In 2000, the USGS, in cooperation with the Colorado River Water Conservation District, Eagle County, Eagle River Water and Sanitation District, Upper Eagle Regional Water Authority, Colorado Department of Transportation, City of Aurora, Town of Eagle, Town of Gypsum, Town of Minturn, Town of Vail, Vail Resorts, City of Colorado Springs, Colorado Springs Utilities, and Denver Water, initiated a retrospective analysis of surface-water quantity and quality in the ERW. Surface-water quantity data and surface-water quality data were obtained from local, State, and Federal agencies to assist in the analysis of surface-water conditions in the ERW 1947-2007. Surface-water-quality data from 293 sites and 12 different source agencies were compiled into 192 unique sites located on streams and rivers in the ERW. Approximately 39 percent of the unique sites had fewer than 5 samples; while 23 percent of the sites had more than 100 samples. Physical properties were the most abundant type of samples collected, with major ions, nutrients, and trace elements also commonly collected. For selected water-quality properties and constituents in the watershed, this report: (1) characterizes available water quantity and water-quality data, (2) identifies spatial and seasonal variability in water quantity and water quality, (3) provides comparisons to Federal and State water-quality standards or recommendations, (4) characterizes temporal changes in water quality, and (5) where possible, identifies potential causes of these changes. This report provides reconnaissance-level statistical summaries and comparisons of water-quality conditions and characteristics using available data within the ERW. The report also includes streamflow statistics such as: mean annual runoff totals, peak-flood-frequency recurrence intervals, and minimum 7-day mean streamflows for selected sites within the watershed. The spatial patterns for concentrations of trace metals (aluminum, cadmium, copper, iron, manganese, and zinc) indicate an increase in dissolved concentrations of these metals near historical mining areas in the Eagle River and several tributaries near Belden. In general, concentrations decrease downstream from mining areas. Concentrations typically are near or below reporting limits in Gore Creek and other tributaries within the watershed. Concentrations for trace elements (arsenic, selenium, and uranium) in the watershed usually are below the reporting limit, and no prevailing spatial patterns were observed in the data. Step-trend analysis and temporal-trend analysis provide evidence that remediation of historical mining areas in the upper Eagle River have led to observed decreases in metals concentrations in many surface-waters. Comparison of pre- and post-remediation concentrations for many metals indicates significant decreases in metals concentrations for cadmium, manganese, and zinc at sites downstream from the Eagle Mine Superfund Site. Some sites show order of magnitude reductions in median concentrations between these two periods. Evaluation of monotonic trends for dissolved metals concentrations show downward trends at numerous sites in, and downstream from, historic mining areas. The spatial pattern of nutrients shows lower concentrations on many tributaries and on the Eagle River upstream from Red Cliff with increases in nutrients downstream of major urban areas. Seasonal variations show that for many nutrient species, concentrations tend to be lowest May-June and highest January-March. The gradual changes in concentrations between seasons may be related to dilution effects from increases and decreases in streamflow. Upward trends in nutrients between the towns of Gypsum and Avon were detected for nitrate, orthophosphate, and total phosphorus. An upward trend in nitrite was detected in Gore Creek. No trends were detected in un-ionized ammonia within the ERW. Exceedances of State water-quality standards (nitrite, nitrate, and un-ionized ammonia) and levels higher than U.S. Environmental Protection Agency recommendations (total phosphorus) occur in several areas within the ERW. The majority of the exceedances are from comparisons to the U.S. Environmental Protection Agency total phosphorus recommendations. A positive correlation was observed between suspended sediment and total phosphorus. An upward trend in total dissolved solids in Gore Creek may be the result of increases in chloride salts. Highly significant trends were detected in sodium, potassium, and chloride with a significant upward trend in magnesium and a weakly significant upward trend in calcium. A quantitative analysis of the relative abundance of calcium, magnesium, sodium, and potassium to the available anions suggests that chloride salts likely are the source for the detected upward trends because chloride is the only commonly occurring anion with a trend in Gore Greek. A potential source for the observed chloride salts may be the chemical anti-icing and deicing products used during winter road maintenance in municipal areas and on Interstate-70. A downward trend in dissolved solids in the Eagle River between Gypsum and Avon may be contributing to the detected trend on the Eagle River at Gypsum. Significant downward trends were detected in specific ions such as calcium, magnesium, sulfate, and silica. Measures of total dissolved solids as well as comparisons to specific ions show that in water-quality samples within the ERW concentrations generally are lower in the headwaters, with increases downstream from Wolcott. Differences in concentrations likely result from increased abundance of salt-bearing geologic units downstream from Avon. Few sites had measured concentrations that exceeded the State standards for chloride.

Scientific Investigations Report↗

USGS2020

Maximum entropy derived statistics of sound speed structure in a fine-grained sediment inferred from sparse broadband acoustic measurements on the New England continental shelf

Marginal probability distributions for parameters representing an effective sound-speed structure of a fine-grained sediment are inferred from a data ensemble maximum entropy method that utilizes a sparse spatially distributed set of received pressure time series resulting from multiple explosive sources in a shallow-water ocean environment possessing significant spatial variability of the seabed. A remote sensing seabed acoustics experiment undertaken in March 2017 off the New England Shelf was designed so that multiple independent analyses could infer the statistical properties of the seabed. The current analysis incorporates the measured horizontal variability from interpretations of a subbottom profiling survey of the experimental area. An idealized range- and azimuth-dependent parameterization of the seabed is derived from identification of horizons within the seabed that define multiple sediment layers. A sparse set of explosive charges were deployed on circular tracks with radii of about 2, 4, and 6.5 km with an acoustic array at the center to correlate a set of random measurements to physical acoustic processes that characterize the seabed. The mean values of a surface sound speed ratio and a linear sound speed gradient for the fine-grained sediment layer derived from 12 data samples processed in the 25–275-Hz band provide an estimate of the effective sound-speed structure in a 130-km $^2$ area. The inferred sediment sound speed values are evaluated by predicting measured time series data not used in the statistical inference, and are also compared to historical measurements. Finally, the low-frequency maximum entropy estimate of the sediment sound speed along with physical measurements derived from piston core measurements are utilized to estimate the sediment grain bulk modulus. predictions made by the viscous grain shearing model.

Maine, Vermont, New Hampshire, Massachusetts, Rhod↗

USGS2004

Fecal-indicator bacteria in the Allegheny, Monongahela, and Ohio Rivers, near Pittsburgh, Pennsylvania, July-September 2001

This report presents the results of a study by the Allegheny County Health Department (ACHD) and the U.S. Geological Survey (USGS) to determine the concentrations of fecal-indicator bacteria in the Allegheny, Monongahela, and Ohio Rivers (Three Rivers) in Allegheny County, Pittsburgh, Pa. Water-quality samples and river-discharge measurements were collected from July to September 2001 during dry- (72-hour dry antecedent period), mixed-, and wet-weather (48-hour dry antecedent period and at least 0.3 inch of rain in a 6-hour period) conditions at five sampling sites on the Three Rivers in Allegheny County. Water samples were collected weekly to establish baseline conditions and during successive days after three wet-weather events. Water samples were analyzed for fecal-indicator organisms including fecal-coliform (FC) bacteria, Escherichia coli (E. coli), and enterococci bacteria. Water samples were collected by the USGS and analyzed by the ACHD Laboratory. At each site, left-bank and right-bank surface-water samples were collected in addition to a composite sample (discharge-weighted sample representative of the channel cross section as a whole) at each site. Fecal-indicator bacteria reported in bank and composite samples were used to evaluate the distribution and mixing of bacteria-source streams in receiving waters such as the Three Rivers. Single-event concentrations of enterococci, E. coli, and FC during dry-weather events were greater than State and Federal water-quality standards (WQS) in 11, 28, and 28 percent of the samples, respectively; during mixed-weather events, concentrations of fecal-indicator bacteria were greater than WQS in 28, 37, and 43 percent of the samples, respectively; and during wet-weather events, concentrations of fecal-indicator bacteria were greater than WQS in 56, 71, and 81 percent of samples, respectively. Single-event, wet-weather concentrations exceeded those during dry-weather events for all sites except the Allegheny River at Oakmont. For this site, dilution during wet-weather events or the lack of source streams upgradient of the site may have caused this anomaly. Additionally, single-event concentrations of E. coli and FC frequently exceeded the WQS reported during wet-weather events. It is difficult to establish a short-term trend in fecal-indicator bacteria concentrations as a function of time after a wet-weather event due to factors including the spatial variability of sources contributing fecal material, dry-weather discharges, resuspension of bottom sediments, and flow augmentation from reservoirs. Relative to E. coli and enterococci, FC concentrations appeared to decrease with time, which may be attributed to the greater die-off rate for FC bacteria. Fecal-indicator bacteria concentrations at a site are dependent on the spatial distribution of point sources upstream of the station, the time-of-travel, rate of decay, and the degree of mixing and resuspension. Therefore, it is difficult to evaluate whether the left, right, and composite concentrations reported at a particular site are significantly different. To evaluate the significance of the fecal-indicator bacteria concentrations and turbidity reported in grab and composite samples during dry-, mixed-, and wet-weather events, data sets were evaluated using Wilcoxon rank sum tests. Tests were conducted using the fecal-indicator bacteria colonies and turbidity reported for each station for a given weather event. For example, fecal coliform counts reported in the left-bank sample were compared against the right-bank and composite samples, respectively, for the Ohio River at Sewickley site during dry-, mixed-, and wet-weather events. The statistical analyses suggest that, depending on the sampling site, the fecal-bacteria concentrations measured at selected locations vary spatially within a channel (left bank compared to right, right bank compared to composite). The most significant differences occurred between feca

Scientific Investigations Report↗

USGS2013

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Ohio↗

USGS2021

Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area. The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection. Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study. The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats ( https://streamstats.usgs.gov/ss/ ). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

Puerto Rico↗

USGS1986

Predictive spatial modeling of narcotic crop growth patterns

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

Open-File Report↗

USGS2012

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data ( R 2 = 0.90).

Greater Platte River Basin↗

USGS2001

Spatial paradigms of lotic diatom distribution: A landscape ecology perspective

Spatial distributional patterns of benthic diatoms and their relation to current velocity were investigated in an unshaded cobble-bottom reach of White Creek (Washington County, NY). On 27 August 1999, diatoms were sampled and current velocity and depth were measured on a regular square sampling grid with a grain size of 0.01 m2, interval of 0.5 m, and extent of 16 m2. The relative abundance of the 18 common diatom species enumerated in the 81 samples was subjected to detrended correspondence analysis (DCA). The first axis (DCA1) explained 51% of the variance in diatom data and separated the samples according to current regimes. The spatial autocorrelation of DCA1 sample scores in deposition and erosion regions of White Creek was determined by Moran's I statistic to indicate patch size. In White Creek the patch length of all diatom communities was more than 3.1 m, whereas the patch width was 1 m in the deposition region and 0.5 m in the erosion region. There were 5 dominant diatom taxa, Achnanthes minutissima Ku??tz. et vars, Fragilaria capucina Dezmazie??res et vars, F. crotonensis Kitt., Diatoma vulgaris Bory, and Synedra ulna (Nitz.) Ehr. et vars. The patch length of the dominant species varied from 1 to more than 4.1 m, whereas the patch width, if defined, was 0.5 m. Achnanthes minutissima and F. capucina, the two diatom species with the highest relative abundance, displayed spatially structured patches of low abundance and comparatively random patches of high abundance, suggesting broad scale abiotic control of species performance in low abundance regions and finer scale biotic control of high abundance areas. Another objective of this study was to test the hypothesis that higher current velocities, which generally impede immigration, would increase randomness and complexity (i.e. homogeneity of diatom distributional patterns). The spatial complexity in low versus high velocity transects was determined by calculating the respective fractal dimension (D) of DCA1 scores. D of DCA1 was higher in the higher current velocity transects, suggesting that spatial complexity and homogeneity of diatom communities increased in faster currents. Partial canonical correspondence analysis was conducted on diatom, environmental, and spatial data to assess how much of the variance in species distribution could be attributed to environmental (current velocity and depth) versus spatial factors. The variance of species data, explained by the environment (exclusively current velocity), was 38%; whereas space alone contributed only 10%, indicating that 1) current velocity was the major factor that controlled diatom distribution in streams and 2) there were other spatially dependent variables, most likely biotic, but their role in shaping diatom communities was minor.

Journal of Phycology↗

USGS2012

A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring

Aim: To develop a novel global spatial framework for the integration and analysis of ecological and environmental data. Location: The global land surface excluding Antarctica. Methods: A broad set of climate-related variables were considered for inclusion in a quantitative model, which partitions geographic space into bioclimate regions. Statistical screening produced a subset of relevant bioclimate variables, which were further compacted into fewer independent dimensions using principal components analysis (PCA). An ISODATA clustering routine was then used to classify the principal components into relatively homogeneous environmental strata. The strata were aggregated into global environmental zones based on the attribute distances between strata to provide structure and support a consistent nomenclature. Results: The global environmental stratification (GEnS) consists of 125 strata, which have been aggregated into 18 global environmental zones. The stratification has a 30 arcsec resolution (equivalent to 0.86 km2 at the equator). Aggregations of the strata were compared with nine existing global, continental and national bioclimate and ecosystem classifications using the Kappa statistic. Values range between 0.54 and 0.72, indicating good agreement in bioclimate and ecosystem patterns between existing maps and the GEnS. Main conclusions: The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non-commercial use through the GEO portal (http://www.geoportal.org).

Global Ecology and Biogeography↗

USGS2015

Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir

Subyearling fall Chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin exhibit a transient rearing strategy and depend on connected shoreline habitats during freshwater rearing. Impoundment has greatly reduced the amount of shallow-water rearing habitat that is exacerbated by the steep topography of reservoirs. Periodic dredging creates opportunities to strategically place spoils to increase the amount of shallow-water habitat for subyearlings while at the same time reducing the amount of unsuitable area that is often preferred by predators. We assessed the amount and spatial arrangement of subyearling rearing habitat in Lower Granite Reservoir on the Snake River to guide future habitat improvement efforts. A spatially explicit habitat assessment was conducted using physical habitat data, two-dimensional hydrodynamic modelling and a statistical habitat model in a geographic information system framework. We used field collections of subyearlings and a common predator [smallmouth bass (Micropterus dolomieu)] to draw inferences about predation risk within specific habitat types. Most of the high-probability rearing habitat was located in the upper half of the reservoir where gently sloping landforms created low lateral bed slopes and shallow-water habitats. Only 29% of shorelines were predicted to be suitable (probability >0.5) for subyearlings, and the occurrence of these shorelines decreased in a downstream direction. The remaining, less suitable areas were composed of low-probability habitats in unmodified (25%) and riprapped shorelines (46%). As expected, most subyearlings were found in high-probability habitat, while most smallmouth bass were found in low-probability locations. However, some subyearlings were found in low-probability habitats, such as riprap, where predation risk could be high. Given their transient rearing strategy and dependence on shoreline habitats, subyearlings could benefit from habitat creation efforts in the lower reservoir where high-probability habitat is generally lacking. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Lower Snake river reservior↗