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At least 1,171 records · Page 65Linked to original sources

USGS2014

Mobilization of selenium from the Mancos Shale and associated soils in the lower Uncompahgre River Basin, Colorado

This study investigates processes controlling mobilization of selenium in the lower part of the Uncompahgre River Basin in western Colorado. Selenium occurs naturally in the underlying Mancos Shale and is leached to groundwater and surface water by limited natural runoff, agricultural and domestic irrigation, and leakage from irrigation canals. Soil and sediment samples from the study area were tested using sequential extractions to identify the forms of selenium present in solid phases. Selenium speciation was characterized for nonirrigated and irrigated soils from an agricultural site and sediments from a wetland formed by a leaking canal. In nonirrigated areas, selenium was present in highly soluble sodium salts and gypsum. In irrigated soils, soluble forms of selenium were depleted and most selenium was associated with organic matter that was stable under near-surface weathering conditions. Laboratory leaching experiments and geochemical modeling confirm that selenium primarily is released to groundwater and surface water by dissolution of highly soluble selenium-bearing salts and gypsum present in soils and bedrock. Rates of selenium dissolution determined from column leachate experiments indicate that selenium is released most rapidly when water is applied to previously nonirrigated soils and sediment. High concentrations of extractable nitrate also were found in nonirrigated soils and bedrock that appear to be partially derived from weathered organic matter from the shale rather than from agricultural sources. Once selenium is mobilized, dissolved nitrate derived from natural sources appears to inhibit the reduction of dissolved selenium leading to elevated concentrations of selenium in groundwater. A conceptual model of selenium weathering is presented and used to explain seasonal variations in the surface-water chemistry of Loutzenhizer Arroyo, a major tributary contributor of selenium to the lower Uncompahgre River.

Colorado↗

USGS2008

Seasonal temperature responses to land-use change in the western United States

In the western United States, more than 79 000??km2 has been converted to irrigated agriculture and urban areas. These changes have the potential to alter surface temperature by modifying the energy budget at the land-atmosphere interface. This study reports the seasonally varying temperature responses of four regional climate models (RCMs) - RSM, RegCM3, MM5-CLM3, and DRCM - to conversion of potential natural vegetation to modern land-cover and land-use over a 1-year period. Three of the RCMs supplemented soil moisture, producing large decreases in the August mean (- 1.4 to - 3.1????C) and maximum (- 2.9 to - 6.1????C) 2-m air temperatures where natural vegetation was converted to irrigated agriculture. Conversion to irrigated agriculture also resulted in large increases in relative humidity (9% to 36% absolute change). Modeled changes in the August minimum 2-m air temperature were not as pronounced or consistent across the models. Converting natural vegetation to urban land-cover produced less pronounced temperature effects in all models, with the magnitude of the effect dependent upon the preexisting vegetation type and urban parameterizations. Overall, the RCM results indicate that the temperature impacts of land-use change are most pronounced during the summer months, when surface heating is strongest and differences in surface soil moisture between irrigated land and natural vegetation are largest. ?? 2007 Elsevier B.V. All rights reserved.

Global and Planetary Change↗

USGS2020

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2019

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

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

International Journal of Applied Earth Observation↗

USGS2021

Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Michigan↗

USGS2022

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km 2 ) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

Chesapeake Bay Watershed↗

USGS2008

Effects of climate and land management change on streamflow in the driftless area of Wisconsin

Baseflow and precipitation in the Kickapoo River Watershed, located in the Driftless Area of Wisconsin, exhibit a step increase around 1970, similar to minimum and median flows in many other central and eastern USA streams. Potential effects on streamflow due to climatic and land management changes were evaluated by comparing volumetric changes in the hydrologic budget before and after 1970. Increases in precipitation do not fully account for the increase in baseflow, which appears to be offset by a volumetric decrease in stormflow. This suggests that factors that influence the partitioning of precipitation into overland runoff or infiltration have changed. A transition from relatively more intensive to relatively less intensive agricultural land use is generally associated with higher infiltration rates, and likely influences partitioning of flow. Changes in agricultural land management practices in the Driftless Area, which began in the mid-1930s, do not coincide with the abrupt increase in baseflow around 1970. Instead, the timing of hydrologic change appears to coincide with changes in precipitation, whereas the magnitude of the change in baseflow and stormflow was likely amplified by changes in agricultural land management. ?? 2008 Elsevier B.V. All rights reserved.

Journal of Hydrology↗

USGS2009

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service-Southeast Watershed Research Lab (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2-4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity.

Journal of Hydrology↗

USGS2009

A simple approach to distinguish land-use and climate-change effects on watershed hydrology

Impacts of climate change on watershed hydrology are subtle compared to cycles of drought and surplus precipitation (PPT), and difficult to separate from effects of land-use change. In the US Midwest, increasing baseflow has been more attributed to increased annual cropping than climate change. The agricultural changes have led to increased fertilizer use and nutrient losses, contributing to Gulf of Mexico hypoxia. In a 25-yr, small-watershed experiment in Iowa, when annual hydrologic budgets were accrued between droughts, a coupled water-energy budget (ecohydrologic) analysis showed effects of tillage and climate on hydrology could be distinguished. The fraction of PPT discharged increased with conservation tillage and time. However, unsatisfied evaporative demand (PET - Hargreaves method) increased under conservation tillage, but decreased with time. A conceptual model was developed and a similar analysis conducted on long-term (>1920s) records from four large, agricultural Midwest watersheds underlain by fine-grained tills. At least three of four watersheds showed decreases in PET, and increases in PPT, discharge, baseflow and PPT:PET ratios (p < 0.10). An analysis of covariance showed the fraction of precipitation discharged increased, while unsatisfied evaporative demand decreased with time among the four watersheds (p < 0.001). Within watersheds, agricultural changes were associated with ecohydrologic shifts that affected timing and significance, but not direction, of these trends. Thus, an ecohydrologic concept derived from small-watershed research, when regionally applied, suggests climate change has increased discharge from Midwest watersheds, especially since the 1970s. By inference, climate change has increased susceptibility of nutrients to water transport, exacerbating Gulf of Mexico hypoxia.

Journal of Hydrology↗

USGS2012

Land change variability and human-environment dynamics in the United States Great Plains

Land use and land cover changes have complex linkages to climate variability and change, biophysical resources, and socioeconomic driving forces. To assess these land change dynamics and their causes in the Great Plains, we compare and contrast contemporary changes across 16 ecoregions using Landsat satellite data and statistical analysis. Large-area change analysis of agricultural regions is often hampered by change detection error and the tendency for land conversions to occur at the local-scale. To facilitate a regional-scale analysis, a statistical sampling design of randomly selected 10 km × 10 km blocks is used to efficiently identify the types and rates of land conversions for four time intervals between 1973 and 2000, stratified by relatively homogenous ecoregions. Nearly 8% of the overall Great Plains region underwent land-use and land-cover change during the study period, with a substantial amount of ecoregion variability that ranged from less than 2% to greater than 13%. Agricultural land cover declined by more than 2% overall, with variability contingent on the differential characteristics of regional human–environment systems. A large part of the Great Plains is in relatively stable land cover. However, other land systems with significant biophysical and climate limitations for agriculture have high rates of land change when pushed by economic, policy, technology, or climate forcing factors. The results indicate the regionally based potential for land cover to persist or fluctuate as land uses are adapted to spatially and temporally variable forcing factors.

Colorado;Iowa;Kansas;Minnesota;Missouri;Montana;Ne↗

USGS2014

Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI

Agricultural production capacity contributes to food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by snowmelt. Because of the high contribution of irrigated crops (> 80%) to total agricultural production, knowing the spatial distribution and year-to-year variability in irrigated areas is imperative to monitoring food security for the country. We used 16-day composites of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to create 23-point time series for each year from 2000 through 2013. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm to map irrigated areas in Afghanistan for the last 14 years. In the absence of ground reference irrigated area information, we evaluated these maps with the irrigated areas classified from multiple snapshots of the landscape during the growing season from Landsat 5 optical and thermal sensor images. We were able to identify irrigated areas using Landsat imagery by selecting as irrigated those areas with Landsat-derived NDVI greater than 0.30–0.45, depending on the date of the Landsat image and surface temperature less than or equal to 310 Kelvin (36.9 ° C). Due to the availability of Landsat images, we were able to compare with the MODIS-derived maps for four years: 2000, 2009, 2010, and 2011. The irrigated areas derived from Landsat agreed well r 2 = 0.91 with the irrigated areas derived from MODIS, providing confidence in the MODIS NDVI threshold approach. The maps portrayed a highly dynamic irrigated agriculture practice in Afghanistan, where the amount of irrigated area was largely determined by the availability of surface water, especially snowmelt, and varied by as much as 30% between water surplus and water deficit years. During the past 14 years, 2001, 2004, and 2008 showed the lowest levels of irrigated area (~ 1.5 million hectares), attesting to the severe drought conditions in those years, whereas 2009, 2012 and 2013 registered the largest irrigated area (~ 2.5 million hectares) due to record snowpack and snowmelt in the region. The model holds promise the ability to provide near-real-time (by the end of the growing seasons) estimates of irrigated area, which are beneficial for food security monitoring as well as subsequent decision making for the country. While the model is developed for Afghanistan, it can be adopted with appropriate adjustments in the derived threshold values to map irrigated areas elsewhere.

Remote Sensing of Environment↗

USGS2020

A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery

Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Maryland↗

USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗

USGS2020

Influence of land use and region on glyphosate and aminomethylphosphonic acid in streams in the USA

Glyphosate is the most widely used herbicide in the United States for agricultural and non-agricultural weed control. Many studies demonstrate possible effects of glyphosate and its degradate AMPA on human and ecological health. Although glyphosate is thought to have limited mobility in soil, it is found year-round in many rivers and streams throughout the world in both agricultural and developed environments. It is vitally important to continue to increase the knowledge base of glyphosate use, distribution, transport, and impacts on human health and the environment. Here we show that glyphosate and AMPA are found in nearly all of 70 streams throughout the United States at concentrations far below human health or ecological benchmarks, with less occurrence in the Northeast and that undeveloped land, classified as such by land use near the sampling station, has lower concentrations compared to other types of land. Results also show that sites with large watersheds tend to have more AMPA than glyphosate and the opposite is true for small watersheds. Travel times and opportunity for glyphosate to degrade to AMPA and for reservoirs of AMPA to grow are greater in large watersheds. Factors that promoted quick movement of glyphosate to streams, such as subsurface tile or storm drains, sewers, overland flow from developed landscapes, and arid landscapes were associated with sites that had greater concentrations of glyphosate compared to AMPA. These results contribute contemporary information and generalized interpretations adding to the knowledge base of the fate of glyphosate on a national scale and provide a springboard for further exploration of technical processes controlling transport to streams.

Science of the Total Environment↗

USGS2020

Trihalomethane precursors: Land use hot spots, persistence during transport, and management options

To meet drinking water regulations, rather than investing in costly treatment plant operations, managers can look for ways to improve source water quality; this requires understanding watershed sources and fates of constituents of concern. Trihalomethanes (THMs) are one of the major classes of regulated disinfection byproducts, formed when a specific fraction of the organic carbon pool—referred to as THM precursors—reacts with chorine and/or bromine during treatment. Understanding the source, fate, timing and duration of the organic compounds that react to form THMs will allow identification of targeted and effective management actions. In this study we evaluated THM precursor contributions from multiple land use categories and hydrologic contexts, including novel data for urban land uses that demonstrate strong potential to release water with high THM formation potential (THMFP; median 618 μg L −1 ): greater than storm runoff integrated across a mixed-use (1/3 natural, 2/3 agricultural) watershed (median 460 μg L −1 ), irrigation runoff from agricultural systems (357 μg L −1 ), or runoff from a natural forested (median 123 μg L −1 ) and shrubland/grassland (median 259 μg L −1 ) watersheds. While individual storm events released high THM precursor concentrations over short periods, dry season agricultural irrigation as well as urban landscapes have the potential to release water high in THM precursors for several months. Experimental bioassays and sampling along 333 miles of the California Aqueduct confirmed bioavailability and photooxidation potential of less than 10% for THM precursors, suggesting that rivers with residence times of days to weeks may act as THM precursor conduits, shuttling THM precursors from hundreds of miles away to drinking water intakes with minimal degradation. This finding has considerable implications for water managers, who may therefore consider THM precursor management strategies that target even sources located far upstream.

California↗

USGS2021

Evidence that watershed nutrient management practices effectively reduce estrogens in environmental waters

We evaluate the impacts of different nutrient management strategies on the potential for co-managing estrogens and nutrients in environmental waters of the Potomac watershed of the Chesapeake Bay. These potential co-management approaches represent agricultural and urban runoff, wastewater treatment plant effluent, and combined sewer overflow replacements. Twelve estrogenic compounds and their metabolites were analysed by gas chromatography–mass spectrometry. Estrogenic activity (E 2 Eq) was measured by in vitro bioassay. We detected estrone E 1 (0.05–6.97 ng L −1 ) and estriol E 3 (below detection-8.13 ng L −1 ) and one conjugated estrogen (estrone-3-sulfate E 1 -3S; below detection-8.13 ng L −1 ). E 1 was widely distributed and positively correlated with E 2 Eq, water temperature, and dissolved organic carbon (DOC). Among nonpoint sources, E 2 Eq, and concentrations of E 1 , soluble reactive phosphorus (SRP) and total dissolved nitrogen (TDN) decreased by 51–61%, 77–82%, 62–64%, 4–16% in restored urban and agricultural streams with best management practices (BMPs) relative to unrestored streams without BMPs. In a wastewater treatment plant (Blue Plains WWTP), >94% of E 1 , E 1 -3S, E 3 , E 2 Eq and TDN were removed while SRP increased by 305% during nitrification/denitrification as a part of advanced wastewater treatment. Consequently, E 1 and TDN concentrations in WWTP effluents were comparable or even lower than those observed in the receiving stream or river waters, and the effects of wastewater discharges on downstream E 1 and TDN concentrations were minor. Highest E 2 Eq value and concentrations of E1, E3, and TDN were detected in combined sewer overflow (CSO). This study suggests that WWTP upgrades with biological nutrient removal, CSO management, and certain agricultural and urban BMPs for nutrient controls have the potential to remove estrogens from point and nonpoint sources along with other contaminants in streams and rivers.

Maryland↗

USGS2021

Pesticides in US Rivers: Regional differences in use, occurrence, and environmental toxicity, 2013 to 2017

Pesticides pose a threat to the environment, but because of the substantial number of compounds, a comprehensive assessment of pesticides and an evaluation of the risk that they pose to human and aquatic life is challenging. In this study, improved analytical methods were used to quantify 221 pesticide concentrations in surface waters over the time period from 2013 to 2017. Samples were collected from 74 river sites in the conterminous US (CONUS). Potential toxicity was assessed by comparing surface water pesticide concentrations to standard concentrations that are considered to have adverse effects on human health or aquatic organisms. The majority of pesticide use is related to agriculture, and agricultural production varies across the CONUS. Therefore, our results were summarized by region (Northeast, South, Midwest, West and Pacific), with the expectation that crop production differences would drive variability in pesticide use, detection frequency, and benchmark exceedance patterns. Although agricultural pesticide use was at least 2.5 times higher in the Midwest (49 kg km −2 ) than in any of the other four regions (Northeast, South, West, and Pacific, 3 to 21 kg km −2 ) and the average number of pesticides detected in the Midwest was at least 1.5 higher ( n = 25) than the other four regions ( n = 8 to n = 16), the potential toxicity results were more evenly distributed. At least 50% of the sites within each of the 5 regions had at least 1 chronic benchmark exceedance. Imidacloprid posed the greatest potential threat to aquatic life with a total of 245 benchmark exceedances at 60 of the 74 sites. These results show that pesticides persist in the environment beyond the site of application and expected period of use. Continued monitoring and research are needed to improve our understanding of pesticide effects on aquatic and human life.

Science of the Total Environment↗

USGS2023

Multi-omic responses of fish exposed to complex chemical mixtures in the Shenandoah River watershed

To evaluate relationships between different anthropogenic impacts, contaminant occurrence, and fish health, we conducted in situ fish exposures across the Shenandoah River watershed at five sites with different land use. Exposure water was analyzed for over 500 chemical constituents, and organismal, metabolomic, and transcriptomic endpoints were measured in fathead minnows. Adverse reproductive outcomes were observed in fish exposed in the upper watershed at both wastewater treatment plant (WWTP) effluent- and agriculture-impacted sites, including decreased gonadosomatic index and altered secondary sex characteristics. This was accompanied with increased mortality at the site most impacted by agricultural activities. Molecular biomarkers of estrogen exposure were unchanged and consistent with low or non-detectable concentrations of common estrogens, indicating that alternative mechanisms were involved in organismal adverse outcomes. Hepatic metabolomic and transcriptomic profiles were altered in a site-specific manner, consistent with variation in land use and contaminant profiles. Integrated biomarker response data were useful for evaluating mechanistic linkages between contaminants and adverse outcomes, suggesting that reproductive endocrine disruption, altered lipid processes, and immunosuppression may have been involved in these organismal impacts. This study demonstrated linkages between human-impact, contaminant occurrence, and exposure effects in the Shenandoah River watershed and showed increased risk of adverse outcomes in fathead minnows exposed to complex mixtures at sites impacted by municipal wastewater discharges and agricultural practices.

Virginia, West Virginia↗