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At least 127 records · Page 7Linked to original sources
USGS2026

Comparative evaluation of three extraction techniques: Validation of a novel approach for the determination of 51 Per- and Polyfluoroalkyl substances in Graminaceous plants

Per- and polyfluoroalkyl substances (PFAS) are a class of over 15,000 xenobiotic compounds that have permeated across the globe and are largely resistant to natural forms of degradation, resulting in their long-term environmental persistence. These compounds can bioaccumulate and cause toxicological effects in organisms living within PFAS contaminated ecosystems. Grasses and feedstocks grown on contaminated farmlands are intermediates in the transmission of PFAS from contaminated soil to livestock and humans and may provide a potential avenue for biomonitoring of PFAS as they permeate through agricultural food systems. However, extraction methods for these compounds in graminaceous plants are sparse, non-standardized and limited in analytical range of PFAS compounds. This study outlines the development of a novel method of extraction using an alkaline, methanol-based solid-liquid extraction technique for 51 PFAS in graminaceous plant material, including nine separate PFAS subclasses. In comparison to two other previously published methods, this alkaline, methanolic solid-liquid approach has statistically significantly improved analyte recoveries for 22 of the 51 tested PFAS, ranging from 64.7–139% recovery in previously unexposed prairie grass ( Panicum virgatum and Dactylis glomerata ). Method detection limits range from 0.091 – 53.7 parts per trillion across tested PFAS analytes, demonstrating increased precision and sensitivity when compared to two other tested extraction procedures. This method’s strategy of alkaline, methanolic SLE may serve as a foundation for further testing across new plant taxa and an increased range of PFAS analytes.

Journal of Hazardous Materials Advances↗
USGS2021

Water-resource management monitoring needs, State of Hawai‘i

In cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawaiʻi Water Resources Research Center, the U.S. Geological Survey developed a water-resource monitoring program—a rainfall, surface-water, and groundwater data-collection program—that is required to meet State needs for water-resource assessment, management, and protection in Hawai‘i. Current and foreseeable issues related to water-resource management and climate-change effects guided the evaluation of data-collection sites within the monitoring program. Data-collection sites currently (2018) being operated in Hawai‘i were evaluated, and additional data-collection sites were selected on the basis of their usefulness for characterizing anthropogenic effects on water resources or representing natural conditions. Data-collection strategies consist of a combination of continuous long-term monitoring to evaluate trends and climate-change effects and occasional and periodic intensive monitoring to enhance spatial understanding of hydrologic conditions and to address current issues in priority areas—areas that currently have water-availability issues or are expected to have the greatest socioeconomic or ecological effects because of climate change. Priority areas for rainfall monitoring consist of urban and agricultural lands, areas with high rainfall and high-rainfall gradient, and areas within the trade-wind inversion band. Surface-water priority areas consist of streams with major surface-water diversions, with established interim instream-flow standards, in a surface-water management area, that support water leases, and with uncertainties in hydrogeologic characteristics. Priority areas for groundwater monitoring consist of areas with high withdrawal, declining water levels, reduced recharge, limited alternative sources, and uncertainties in hydrogeologic characteristics. Data-quality objectives for the rainfall, surface-water, and groundwater monitoring programs that describe anticipated uses of the data were established with the goal of producing useful, reliable, and accurate water-resource information of sufficient precision to support decision making. The data-quality objectives also consider quality-assurance and quality-control programs that ensure defensible data. Establishment of common data-quality objectives not only assures comparability of data collected by multiple agencies but also allows data from academic, private, and public organizations to be useful for meeting State monitoring needs, provided the data meet appropriate data-quality objectives and data-accessibility requirements.

Hawaii↗
USGS2016

Extending ordinal regression with a latent zero-augmented beta distribution

Ecological abundance data are often recorded on an ordinal scale in which the lowest category represents species absence. One common example is when plant species cover is visually assessedwithin bounded quadrats and then assigned to pre-defined cover class categories.We present an ordinal beta hurdle model that directly models ordinal category probabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero model allows ecologists to explore distribution (absence) and abundance processes in an integrated framework. This provides an alternative to cumulative link models when data are inconsistent with the assumption that the odds ofmoving into a higher category are the same for all categories (proportional odds). Graphical tools and a deviance information criterion were developed to assess whether a hurdle-at-zero model should be used for inferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal models fit to vegetation cover class data produced substantially different conclusions.The ordinal beta hurdle model yielded more precise parameter estimates than cumulative logit models, although out-of-sample predictions were similar. The ordinal beta hurdle model provides inferences directly on the latent biological variable of interest, percent cover, and supports exploration of more realistic ecological patterns and processes through the hurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗
USGS2011

Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York

Water-resource managers in Onondaga County, New York, are faced with the challenge of improving the water quality of Onondaga Lake, which has the distinction of being one of the most contaminated lakes in the United States. To assist in this endeavor, during 2003-07 the U.S. Geological Survey (USGS), in cooperation with the Onondaga Lake Partnership, developed a precipitation-runoff model of the 285-square-mile Onondaga Lake Basin with the computer program Hydrological Simulation Program-Fortran (HSPF). The model was intended to provide a tool whereby the processes responsible for the generation of loads of sediment and nutrients that are transported to Onondaga Lake could be better understood. This objective was only partly attained because data for calibration of the model were available from monitoring sites only at or near the mouths of the major tributaries to Onondaga Lake; no calibration data from headwater subbasins, where the loads originated, were available. To address this limitation and thereby decrease the uncertainty in the simulated results that were associated with headwater processes, the USGS conducted a 3-year (2005-08) basinwide study to assess the quality of surface water in the Onondaga Lake Basin. The study quantified the relative contributions of nonpoint sources associated with the major land uses and land covers in the basin and also monitored known sources and presumed sinks of sediment and nutrient loads, which previously had not been evaluated. The use of the newly acquired data to recalibrate the HSPF model resulted in improvements in the simulation of processes in the headwater subbasins, including suspended-sediment, orthophosphate, and phosphorus generation and transport. Simulation of streamflows in small subbasins was improved by adjusting model parameter values to match base flows, storm peaks, and storm recessions more precisely than had been done with the original model. Simulated recessional and low flows were either increased or decreased as appropriate for a given stream, and simulated peak flows generally were lowered in the revised model. The use of suspended-sediment concentrations rather than concentrations of the surrogate constituent, total suspended solids, resulted in increases in the simulated low-flow sediment concentrations and, in most cases, decreases in the simulated peak-flow sediment concentrations. Simulated orthophosphate concentrations in base flows generally increased but decreased for peak flows in selected headwater subbasins in the revised model. Compared with the original model, phosphorus concentrations simulated by the revised model were comparable in forested subbasins, generally decreased in developed and wetland-dominated subbasins, and increased in agricultural subbasins. A final revision to the model was made by the addition of the simulation of chloride (salt) concentrations in the Onondaga Creek Basin to help water-resource managers better understand the relative contributions of salt from multiple sources in this particular tributary. The calibrated revised model was used to (1) compute loading rates for the various land types that were simulated in the model, (2) conduct a watershed-management analysis that estimated the portion of the total load that was likely to be transported to Onondaga Lake from each of the modeled subbasins, (3) compute and assess chloride loads to Onondaga Lake from the Onondaga Creek Basin, and (4) simulate precolonization (forested) conditions in the basin to estimate the probable minimum phosphorus loads to the lake.

Onondaga, New York↗
USGS1995

U.S. Geological Survey laboratory method for methyl tert-butyl ether and other fuel oxygenates

Methyl tert-butyl ether (MTBE) was found in shallow ground-water samples in a study of 8 urban and 20 agricultural areas throughout the United States in 1993 and 1994 (Squillace and others, 1995, p. 1). The compound is added to gasoline either seasonally or year round in many parts of the United States to increase the octane level and to reduce carbon monoxide and ozone levels in the air. The U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL), near Denver, uses state-of-the-art technology to analyze samples for MTBE as part of the USGS water-quality studies. In addition, the NWQL offers custom analyses to determine two other fuel oxygenates--ethyl tert-butyl ether (ETBE) and tert-amyl methyl ether (TAME). The NWQL was not able to obtain a reference standard for tert-amyl ethyl ether (TAEE), another possible fuel oxygenate (Shelley and Fouhy, 1994, p. 63). The shallow ground-water samples were collected as part of the USGS National Water-Quality Assessment Program. These samples were collected from 211 urban wells or springs and 562 agricultural wells sampled by the USGS in 1993 and 1994. The wells were keyed to specific land-use areas to assess the effects of different uses on ground-water quality (Squillace and others, 1995, p. 2). Ground-water samples were preserved on site to pH less than or equal to 2 with a solution of 1:1 hydrochloric acid. All samples were analyzed at the NWQL within 2 weeks after collection. The purpose of this fact sheet is to explain briefly the analytical method implemented by the USGS for determining MTBE and other fuel oxygenates. The scope is necessarily limited to an overview of the analytical method (instrumentation, sample preparation, calibration and quantitation, identification, and preservation of samples) and method performance (reagent blanks, accuracy, and precision).

Fact Sheet↗
USGS2018

Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15

This report was written as a collaborative effort between the U.S. Geological Survey, SilvaCarbon, and Wageningen University with funding provided by the U.S. Agency for International Development and the European Space Agency, respectively, to address a pressing need for enhanced result-based monitoring and evaluation of delivered capacity-building activities. For this report, the capacity-building activities delivered by capacity-building providers (referred to as “providers” hereafter) during 2011–15 (the study period) to support countries in building measurement, reporting, and verification (MRV) systems for reducing emissions from deforestation and forest degradation (REDD+) were assessed and evaluated. Summarizing capacity-building activities and outcomes across multiple providers was challenging. Many of the providers did not have information readily available, which precluded them from participating in this study despite the usefulness of their information. This issue led to a key proposed future action: Capacity-building providers could establish a central repository within the Global Forestry Observation Initiative (GFOI; http://www.gfoi.org/ ) where data from past, current, and future activities of all capacity-building providers could be stored. The repository could be maintained in a manner to continually learn from previous lessons. Although various providers monitored and evaluated the success of their capacity-building activities, such evaluations only assessed the success of immediate outcomes and not the overarching outcomes and impacts of activities implemented by multiple providers. Good monitoring and evaluation should continuously monitor and periodically evaluate all factors affecting the outcomes of a provided capacity-building activity. The absence of a methodology to produce quantitative evidence of a causal link between multiple capacity-building activities delivered and successful outcomes left only a plausible association. A previous publication argued that plausible association, although not a precise measurement of cause and effect, was a realistic tool. Our review of the available literature on this subject did not find another similar assessment to assess capacity-building activities for supporting the countries in building MRV system for REDD+. Four countries from the main forested regions of Africa, the Americas, and Asia were chosen as subjects for this report based on the length of time SilvaCarbon and other providers have provided capacity-building activities toward MRV system for REDD+: Colombia (the Americas), the Democratic Republic of the Congo (DRC; Africa), Peru (the Americas), and the Republic of the Philippines (referred to as “the Philippines” hereafter; Asia). Several providers were contacted for information to include in this report, but, because of various constraints, only SilvaCarbon, the Food and Agriculture Organization of the United Nations (FAO), and the World Wildlife Fund (WWF) participated in this study. These three providers supported various targeted capacity-building activities through-out Africa, the Americas, and Asia, including the following: technical workshops at national and regional levels (referred to as “workshops” hereafter), hands on training, study tours, technical details by experts, technical consultation between providers and recipients, sponsorship for travel, organizing network meetings, developing sampling protocols, assessing deforestation and degradation drivers, estimating carbon stock and flow, designing monitoring systems for multiple uses, promoting public-private partnerships to scale up investments on MRV system for REDD+, and assisting with the design of national forest monitoring systems. Their activities were planned in coordination with key partners in each country and region and with the support and assistance of other providers. Note that several other organizations and institutions assisted the providers to deliver capacity-building activities, including Boston University, Conservation International, Stanford University, University of Maryland, and Wageningen University & Research.

Open-File Report↗
USGS2018

Findings from a preliminary investigation of the effects of aquatic habitat (water) availability on giant gartersnake (Thamnophis gigas) demography in the Sacramento Valley, California, 2014–17

The giant gartersnake ( Thamnophis gigas ) is a semi-aquatic species of snake precinctive to the Central Valley of California. Because the Central Valley has experienced a substantial loss of wetland habitat, giant gartersnake populations are largely found in aquatic habitats associated with rice agriculture. In dry years, less water may be available for rice agriculture, resulting in less aquatic habitat, which could have cascading effects on giant gartersnake populations. We present 2 years of data intended to examine how the demography of giant gartersnakes is affected by the availability of aquatic habitat on the landscape (2016–17), along with 2 years of (sparse) preliminary data (2014–15) collected as part of an earlier radio-telemetry study on giant gartersnake movement behavior. We sampled agricultural canals near rice fields for giant gartersnakes at 8 sites distributed throughout the Sacramento Valley. Five sites were sampled from 2014–17, and 3 sites were sampled from 2015–17. In total, we made 2,995 captures of 1,011 snakes from 2014–17. We used these capture data to fit a multi-site Jolly-Seber model to estimate the abundance of giant gartersnakes as well as the daily and annual probability of capture at each site. We used remotely sensed Landsat data to characterize the extent of flooded rice fields surrounding each site in each year. In addition, we collected 175 females from 2014–17 and delivered them to the Sacramento Zoo for health assessments and reproductive exams. The abundance of giant gartersnakes varied among sites, and abundance estimates were more precise in 2016 and 2017 when sampling effort was greatest. The probability of a giant gartersnake being captured at least once in a year was higher in 2016 and 2017 than 2014 and 2015, and recaptures of snakes marked the previous year were highest in 2016 and 2017 as well. Mean annual apparent survival was estimated to be 0.40 but varied among sites from a low of 0.14 to a high of 0.63. Five sites had diverse size distributions that included abundant sub-adult and large adult female snakes. One site had a truncated size distribution with few large adult female snakes, and 2 sites had mostly large adult-sized snakes and few small individuals. Both the probability a female was gravid and a female’s litter size were positively related to the female’s snout-vent length. Somatic growth rates varied more among years than among sites, and females grew faster (in millimeters per day) than male snakes. The proportion of the landscape around each site under active rice cultivation fluctuated over time (generally between 60–90 percent of the landscape was active rice growing, although this proportion was lower for some sites in some years), and variation in rice growing was asynchronous among sites. This study demonstrates that intensive demographic sampling enables estimation of several key demographic variables at each study site. Continued sampling would allow for investigating potential relationships between the amount of rice growing at a site and demographic parameters such as growth, survival, and reproduction.

California↗
USGS2025

Sundial: A method for inferring image acquisition time from shadow orientation

Aerial photography and satellite imagery can be used to characterize landscape change over time and help to understand how these changes are related to climate and hydrology. Publicly available optical imagery from sources such as the United States National Agricultural Imagery Program (NAIP) is particularly valuable in this context due to its high temporal and spatial resolution. However, the exact time an image was acquired is often unknown, which complicates, if not precludes, linking images with other types of high temporal resolution data, such as streamflow records. In this letter, we propose a ‘sundial method’ to infer image acquisition time from shadow orientation. This approach involves measuring the direction of a shadow on the image and using solar geometry calculated for the known image date and location to infer the former sun position. Time estimates for 16 Worldview satellite and six NAIP aerial images based on 407 independent measurements of shadow orientation demonstrate the sundial method had an error of 2.1 ± 3.4 min, indicating that image acquisition times can be inferred with a high degree of accuracy and precision. Sensitivity analyses confirm the robustness of the method across different object types, shadow lengths, and solar zenith angles, while also providing practical guidelines regarding the number of measurements required and errors associated with uncertainty in the image date.

Earth Surface Processes and Landforms↗
USGS2016

Integrating seasonal information on nutrients and benthic algal biomass into stream water quality monitoring

Benthic chlorophyll a (BChl a ) and environmental factors that influence algal biomass were measured monthly from February through October in 22 streams from three agricultural regions of the United States. At-site maximum BChl a ranged from 14 to 406 mg/m 2 and generally varied with dissolved inorganic nitrogen (DIN): 8 out of 9 sites with at-site median DIN >0.5 mg/L had maximum BChl a >100 mg/m 2 . BChl a accrued and persisted at levels within 50% of at-site maximum for only one to three months. No dominant seasonal pattern for algal biomass accrual was observed in any region. A linear model with DIN, water surface gradient, and velocity accounted for most of the cross-site variation in maximum chlorophyll a (adjusted R 2 = 0.7), but was no better than a single value of DIN = 0.5 mg/L for distinguishing between low and high-biomass sites. Studies of nutrient enrichment require multiple samples to estimate algal biomass with sufficient precision given the magnitude of temporal variability of algal biomass. An effective strategy for regional stream assessment of nutrient enrichment could be based on a relation between maximum BChl a and DIN based on repeat sampling at sites selected to represent a gradient in nutrients and application of the relation to a larger number of sites with synoptic nutrient information.

Journal of the American Water Resources Associatio↗
USGS2002

How sedge meadow soils, microtopography, and vegetation respond to sedimentation

The expansion of urban and agricultural activities in watersheds of the Midwestern USA facilitates the conversion of species-rich sedge meadows to stands of Phalaris arundinacea and Typha spp. We document the role of sediment accumulation in this process based on field surveys of three sedge meadows dominated by Carex stricta , their adjacent Phalaris or Typha stands, and transitions from Carex to these invasive species. The complex microtopography of Carex tussocks facilitates the occurrence of other native species. Tussock surface area and species richness were positively correlated in two marshes (r 2 =0.57 and 0.41); on average, a 33-cm-tall tussock supported 7.6 species. Phalaris also grew in tussock form in wetter areas but did not support native species. We found an average of 10.5 Carex tussocks per 10-m transect, but only 3.5 Phalaris tussocks. Microtopographic relief, determined with a high-precision GPS, measured 11% greater in Carex meadows than Phalaris stands. Inflowing sediments reduced microtopographic variation and surface area for native species. We calculated a loss of one species per 1000 cm 2 of lost tussock surface area, and loss of 1.2 species for every 10-cm addition of sediment over the sedge meadow surface. Alluvium overlying the sedge meadow soil had a smaller proportion of organic matter content and higher dry bulk density than the buried histic materials. We conclude that sedimentation contributes to the loss of native species in remnant wetlands.

Wetlands↗
USGS2017

Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.

The national network of weather surveillance radars (NEXRAD) detects birds in flight, and has proven to be a useful remote-sensing tool for ornithological study. We used data collected during Fall 2008 to 2014 by 16 NEXRAD and four terminal Doppler weather radars (TDWR) in the northeastern U.S. to map and study the spatial distribution of landbirds shortly after they leave daytime stopover sites to embark on nocturnal migratory flights. Given observed variability in the precise timing of migratory exodus, we developed a new method to sample the onset of migration at the point of maximum rate of increase in bird densities aloft to consistently sample exodus across radars and days. The mean linear trend in aggregate stopover densities of migrants indicated a 4% decline per year from the 2008 baseline density (29% decline over the seven years). Regionally, coastal Virginia and Maine had the steepest declines. The steepest increases in migrant densities across years occurred within the Delmarva Peninsula and in coastal Connecticut. We used NEXRAD observations to develop models to predict potentially important stopover sites throughout USFWS Region 5. Observed NEXRAD data were positively correlated to observations from TDWR and NASA’s S-Band Dual-Polarimetric Radar (NPOL), though not strongly. Predicted densities increased with increasing hardwood cover across multiple scales and with vegetation productivity. Contrastingly, predicted densities decreased with increasing agricultural, emergent marsh and coniferous land cover, but did not change with fraction of urban cover. Stopover density increased closer to bright areas and the Atlantic coast. Moreover, interactive effects indicated that migrants were more concentrated in forested areas that were both brightly lit and near the Atlantic coast. Large areas of predicted regionally important stopover sites were located along the coastlines of Maine, Long Island Sound, New Jersey, the lower Delmarva Peninsula, within the Adirondack Mountains, Catskill Mountains, and eastern Virginia. We also created maps of classified stopover use during bimonthly periods and at multiple-scales. Migrant densities peaked along the Adirondack Mountains early in September, and along the Atlantic coast in late September with the passage of Neotropical migrants. Stopover densities peaked in the most northern extent of Maine and New England States in late October with the departure of temperate migrants. Ground surveys conducted at 48 forested sites within the Delmarva Peninsula and Tidewater Virginia during Fall 2013 and 2014 revealed that nocturnal migrant densities pooled across species and for 14 individual species, after accounting for temporal phenology in their passage timing, were related to factors operating at multiple scales including food resources (primarily arthropod abundance in understory) and understory shrub density at a patch scale, and latitude and proximity to the Atlantic coast at a regional scale. We integrated field survey and radar data to estimate relative stopover duration and to identify stopover functional types among 45 sites that included data from a past study near the Gulf of Mexico. We identified four functional types spanning the gradient of short rest stops to refueling stops with variable duration of stopover in relation to food abundance. The Mid-Atlantic sites were dominated by rest stops near coastal areas and lacked quick refueling stops due to low overall food abundance. The maps and ecological understanding produced can help inform conservation planning to protect and enhance stopover sites for migratory landbirds in the future.

Report↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2011

Rapid assessment of rice seed availability for wildlife in harvested fields

Rice seed remaining in commercial fields after harvest (waste rice) is a critical food resource for wintering waterfowl in rice-growing regions of North America. Accurate and precise estimates of the seed mass density of waste rice are essential for planning waterfowl wintering habitat extents and management. In the Sacramento Valley of California, USA, the existing method for obtaining estimates of availability of waste rice in harvested fields produces relatively precise estimates, but the labor-, time-, and machineryintensive process is not practical for routine assessments needed to examine long-term trends in waste rice availability. We tested several experimental methods designed to rapidly derive estimates that would not be burdened with disadvantages of the existing method. We first conducted a simulation study of the efficiency of each method and then conducted field tests. For each approach, methods did not vary in root mean squared error, although some methods did exhibit bias for both simulations and field tests. Methods also varied substantially in the time to conduct each sample and in the number of samples required to detect a standard trend. Overall, modified line-intercept methods performed well for estimating the density of rice seeds. Waste rice in the straw, although not measured directly, can be accounted for by a positive relationship with density of rice on the ground. Rapid assessment of food availability is a useful tool to help waterfowl managers establish and implement wetland restoration and agricultural habitat-enhancement goals for wintering waterfowl. ?? 2011 The Wildlife Society.

Wildlife Society Bulletin↗
USGS2008

Reducing sedimentation of depressional wetlands in agricultural landscapes

Depressional wetlands in agricultural landscapes are easily degraded by sediments and contaminants accumulated from their watersheds. Several best management practices can reduce transport of sediments into wetlands, including the establishment of vegetative buffers. We summarize the sources, transport dynamics, and effect of sediments, nutrients, and contaminants that threaten wetlands and the current knowledge of design and usefulness of grass buffers for protecting isolated wetlands. Buffer effectiveness is dependent on several factors, including vegetation structure, buffer width, attributes of the surrounding watershed (i.e., area, vegetative cover, slope and topography, soil type and structure, soil moisture, amount of herbicides and pesticides applied), and intensity and duration of rain events. To reduce dissolved contaminants from runoff, the water must infiltrate the soil where microbes or other processes can break down or sequester contaminants. But increasing infiltration also diminishes total water volume entering a wetland, which presents threats to wetland hydrology in semi-arid regions. Buffer effectiveness may be enhanced significantly by implementing other best management practices (e.g., conservation tillage, balancing input with nutrient requirements for livestock and crops, precision application of chemicals) in the surrounding watershed to diminish soil erosion and associated contaminant runoff. Buffers require regular maintenance to remove sediment build-up and replace damaged or over-mature vegetation. Further research is needed to establish guidelines for effective buffer width and structure, and such efforts should entail a coordinated, regional, multi-scale, multidisciplinary approach to evaluate buffer effectiveness and impacts. Direct measures in "real-world" systems and field validations of buffer-effectiveness models are crucial next steps in evaluating how grass buffers will impact the abiotic and biotic variables attributes that characterize small, isolated wetlands. ?? 2008 The Society of Wetland Scientists.

Wetlands↗
USGS2011

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples ( n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R 2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Applied and Environmental Soil Science↗
USGS2012

Evaluating prediction uncertainty of areas contributing recharge to well fields of multiple water suppliers in the Hunt-Annaquatucket-Pettaquamscutt River Basins, Rhode Island

Three river basins in central Rhode Island-the Hunt River, the Annaquatucket River, and the Pettaquamscutt River-contain 15 production wells clustered in 4 pumping centers from which drinking water is withdrawn. These high-capacity production wells, operated by three water suppliers, are screened in coarse-grained deposits of glacial origin. The risk of contaminating water withdrawn by these well centers may be reduced if the areas contributing recharge to the well centers are delineated and these areas protected from land uses that may affect the water quality. The U.S. Geological Survey, in cooperation with the Rhode Island Department of Health, began an investigation in 2009 to improve the understanding of groundwater flow and delineate areas contributing recharge to the well centers as part of an effort to protect the source of water to these well centers. A groundwater-flow model was calibrated by inverse modeling using nonlinear regression to obtain the optimal set of parameter values, which provide a single, best representation of the area contributing recharge to a well center. Summary statistics from the calibrated model were used to evaluate the uncertainty associated with the predicted areas contributing recharge to the well centers. This uncertainty analysis was done so that the contributing areas to the well centers would not be underestimated, thereby leaving the well centers inadequately protected. The analysis led to contributing areas expressed as a probability distribution (probabilistic contributing areas) that differ from a single or deterministic contributing area. Groundwater flow was simulated in the surficial deposits and the underlying bedrock in the 47-square-mile study area. Observations (165 groundwater levels and 7 base flows) provided sufficient information to estimate parameters representing recharge and horizontal hydraulic conductivity of the glacial deposits and hydraulic conductance of streambeds. The calibrated value for recharge to valley-fill deposits was 27.3 inches per year (in/yr) and to upland till deposits was 18.7 in/yr. Calibrated values for horizontal hydraulic conductivity of the valley-fill deposits ranged from 20 to 480 feet per day (ft/d) and of the upland till deposits was 16.2 ft/d. Calibrated values of streambed hydraulic conductance ranged from 10,000 to 52,000 feet squared per day. Values of recharge and horizontal hydraulic conductivity of the valley-fill deposits were the most precisely estimated, whereas the horizontal hydraulic conductivity of till deposits was the least precisely estimated. Simulated areas contributing recharge to the well centers on the basis of the calibrated model ranged from 0.19 to 1.12 square miles (mi 2 ) and covered a total area of 2.79 mi 2 for average well center withdrawal rates during 2004-08 (235 to 1,858 gallons per minute (gal/min)). Simulated areas contributing recharge for the maximum well center pumping capacities (800 to 8,500 gal/min) ranged from 0.37 to 3.53 mi2 and covered a total area of 7.99 mi2 in the modeled area. Simulated areas contributing recharge extend upgradient of the well centers to upland till and to groundwater divides. Some areas contributing recharge include small, isolated areas remote from the well centers. Relatively short groundwater traveltimes from recharging locations to discharging wells indicated the wells are vulnerable to contamination from land-surface activities: median traveltimes ranged from 2.9 to 5.0 years for the well centers, and 78 to 93 percent of the traveltimes were 10 years or less for the well centers. Land cover in the areas contributing recharge includes a substantial amount of urban land use for the two well centers in the Hunt River Basin, agriculture and sand and gravel mining uses for the well center in the Annaquatucket River Basin, and, for the well center in the Pettaquamscutt River Basin, land use is primarily undeveloped. Model-prediction uncertainty was evaluated using a Monte Carlo analysis. The parameter variance-covariance matrix from nonlinear regression was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters. The remaining parameters representing the glacial deposits (vertical anisotropy of valley-fill deposits and of till deposits, maximum groundwater evapotranspiration, and hydraulic conductance for headdependent cells representing a groundwater divide) that could not be estimated with nonlinear regression were incorporated into the variance-covariance matrix using prior information on parameters. Thus the uncertainty analysis was an outcome of calibrating the parameters to available observations and to information that the modeler provided. A water budget and model-fit statistical criteria were used to assess parameter sets so that prediction uncertainty was not overestimated. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas for each well center for both average and maximum pumping rates was larger than the size of the deterministic contributing areas for the well center. Thus, some areas not in the deterministic contributing area may actually be in the contributing area, including additional areas of urban and agricultural land use. Generally, areas closest to the well centers with short groundwater traveltimes are associated with higher probabilities, whereas areas distant from the well centers with long groundwater traveltimes are associated with lower probabilities. The deterministic contributing areas generally corresponded to areas associated with high probabilities (greater than 50 percent). Areas associated with low probabilities extended long distances along groundwater divides in the uplands remote from the well centers.

Rhode Island↗
USGS2026

Estimating submersed aquatic vegetation biomass using a hierarchical Bayesian model of ordinal measurements and structural zeros

This study explores the prediction of a continuous target variable that is primarily measured as an ordinal outcome. Interest in such prediction may occur given data from double- or two-phase sampling designs, where an auxiliary variable is measured at all sampling units and a target and predictor variable, X , at a subset of those units. This study focuses on the case where X is nonnegative, is conditional on a zero process, and is sampled using a cluster sampling design. We model the ordinal outcome using cumulative logistic regression and the conditional regressor X , following log transformation, in a Bayesian setting. Estimation of model parameters and prediction of missing X is generally accurate and precise, particularly with sufficiently large Pr(X > 0) and associated variances. This study is motivated by a need to accurately estimate the biomass of submersed aquatic vegetation on the Upper Mississippi River, where two types of measurements for biomass are available: inexpensive, ordinal biomass scores and expensive, continuous diver-harvested biomass data. Here, X represents plant biomass and Pr(X > 0) is the usual species site occupancy measure. When fit using biomass data from the submerged aquatic vegetation species Vallisneria americana Michx, our model estimates parameters within the parameter space region where the model performed acceptably using synthetic data. We expect this model to appeal to investigators with ordered outcomes, cluster designs, and one or more continuous, positive predictors.

Journal of Agricultural, Biological and Environmen↗
USGS2003

Ground-water resources in the lower Milliken--Sarco--Tulucay Creeks area, southeastern Napa County, California, 2000-2002

Ground water obtained from individual private wells is the sole source of water for about 4,800 residents living in the lower Milliken-Sarco-Tulucay Creeks area of southeastern Napa County. Increases in population and in irrigated vineyards during the past few decades have increased water demand. Estimated ground-water pumpage in 2000 was 5,350 acre-feet per year, an increase of about 80 percent since 1975. Water for agricultural irrigation is the dominant use, accounting for about 45 percent of the total. This increase in ground-water extraction has resulted in the general decline of ground-water levels. The purpose of this report is to present selected hydrologic data collected from 1975 to 2002 and to quantify changes in the ground-water system during the past 25 years. The study area lies in one of several prominent northwest-trending structural valleys in the North Coast Ranges. The area is underlain by alluvial deposits and volcanic rocks that exceed 1,000 feet in thickness in some places. Alluvial deposits and tuff beds in the volcanic sequence are the principal source of water to wells. The ground-water system is recharged by precipitation that infiltrates, in minor amounts, directly on the valley floor but mostly by infiltration in the Howell Mountains. Ground water moves laterally from the Howell Mountains into the study area. Although the area receives abundant winter precipitation in most years, nearly half of the precipitation is lost as surface runoff to the Napa River. Evapotranspiration also is high, accounting for nearly one-half of the total precipitation received. Because of the uncertainties in the estimates of precipitation, runoff, and evapotranspiration, a precise estimate of potential ground-water recharge cannot be made. Large changes in ground-water levels occurred between 1975 and 2001. In much of the western part of the area, water levels increased; but in the central and eastern parts, water levels declined by 25 to 125 feet. Ground-water extraction produced three large pumping depressions in the northern and east-central parts of the area. The general decline in ground-water levels is a result of increases in ground-water pumpage and possibly changes in infiltration capacity caused by changes in land use. Ground-water-level declines during 1960-2002 are evident in the records for 9 of 10 key monitoring wells. In five of these wells, water levels dropped by greater than 20 feet since the 1980s. The largest water-level declines have occurred since the mid 1970s, corresponding with a period of accelerated well construction and ground-water extraction. Analysis of samples from 15 wells indicates that the chemical quality of ground water in the study generally is acceptable. However, arsenic concentrations in samples from five wells exceed the U.S. Environmental Protection Agency primary drinking-water standard of 10 micrograms per liter, and iron concentrations in samples from five wells exceed the U.S. Environmental Protection Agency and the California Department of Health Services secondary drinking-water standard of 300 micrograms per liter. Water from 12 of 15 wells sampled contained concentrations of manganese that exceed the U.S. Environmental Protection Agency and the California Department of Health Services secondary drinking-water standard of 50 micrograms per liter. Two wells produced water that had boron in excess of the California Department of Health Services action level of 1 milligram per liter. Stable isotope, chlorofluorocarbon, and tritium data indicate that ground water in the area is a mixture of waters that recharged the aquifer system at different times. The presence of chlorofluorocarbons and tritium in water from the study area is evidence that modern recharge (post 1950) does take place. Water-temperature logs indicate that ground-water temperatures throughout the study area exceed 30?C at depths in excess of 600 feet. Further, water at

Water-Resources Investigations Report↗