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USGS2022

South Carolina and Landsat

South Carolina, the eighth State admitted to the union, transcends its size with its deep, rich history; striking beauty; vast natural resources; and extensive cultural diversity. Home to part of the Blue Ridge Mountains of the Central Appalachians, the Upstate is graced with more than 100 waterfalls, while the Lowcountry borders the Atlantic Ocean with 187 miles of coastline and 35 barrier islands. Forests cover two-thirds of the State, and forestry and agriculture together, as agribusiness, make up South Carolina’s leading industry. Two historic crops—cotton and tobacco—still rank in the top 10 commodities, though corn and soybeans now rank higher. Poultry, cattle, peanuts, and flowers also make the list. South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname. South Carolina’s subtropical climate, long coastline, and lower elevations make it highly susceptible to tornado and hurricane activity and coastal flooding. Projected sea-level rise is a growing concern. A view from space can help monitor and manage natural resources on the land and in rivers, marshes, and the coast. Landsat reveals not just what an area looks like now, but also insights from decades ago.

South Carolina↗
USGS2024

Interactive effects of salinity and hydrology on radial growth of bald cypress (Taxodium distichum (L.) Rich.) in coastal Louisiana, USA

Tidal freshwater forests are usually located at or above the level of mean high water. Some Louisiana coastal forests are below mean high water, especially bald cypress ( Taxodium distichum (L.) Rich.) forests because flooding has increased due to the combined effects of global sea level rise and local subsidence. In addition, constructed channels from the coast inland act as conduits for saltwater. As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding.

Louisiana↗
USGS2024

Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions

Study region The Mississippi Alluvial Plain (MAP) in the United States (US). Study focus Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km 3 /year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R 2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.

Journal of Hydrology Regional Studies↗
USGS2026

Restoring dryland water cycles for precipitation feedback and climate stability; a review

Drylands across the globe are experiencing intensifying water scarcity, land degradation, and hydroclimatic extremes. This review integrates evidence from multidecadal field studies, hydrologic monitoring, geomorphic and ecological assessments, remote sensing, and land–atmosphere science to evaluate how restoration influences key components of the terrestrial water cycle. Low-tech natural infrastructure in dryland streams (NIDS)—including check dams, leaky weirs, one-rock dams, and gabions—has emerged as a promising but under-synthesized nature-based solution for restoring hydrologic function in these environments. We describe the mechanisms through which these interventions modify runoff detention, infiltration, sediment and alluvial storage, shallow-groundwater recharge, vegetation recovery, and surface-energy partitioning, and we summarize outcomes across diverse dryland settings. Findings consistently show increased water residence time, enhanced soil-moisture storage, expanded riparian vegetation, extended flow duration, and shifts toward greater latent-heat flux—producing localized cooling and strengthened ecohydrological feedbacks. Building on these localized effects, we articulate a hypothesis that links the spatial extent of restoration, the density of NIDS per unit drainage area, and the magnitude of the latent-to-sensible-heat contrast generated by wetter post-rainfall conditions. Specifically, we hypothesize that when NIDS are implemented at densities permitted by topography and across areas large enough to maintain elevated soil moisture after storm events, the resulting increases in latent heat flux, surface cooling, and boundary-layer moistening may enhance moisture convergence and boundary-layer development, potentially increasing the likelihood or stability of convective precipitation, analogous to how reductions in these processes have contributed to regional drought intensification. These land–atmosphere feedbacks remain untested at scale but represent an important research Frontier. By integrating hydrologic, geomorphic, ecological, and atmospheric perspectives, this review provides a comprehensive framework for considering how low-tech, landscape-scale interventions can strengthen watershed resilience and contribute to climate-relevant nature-based solutions.

Arizona↗
USGS2025

Reconstruction of Holocene and Last Interglacial vegetation dynamics and wildfire activity in Southern Siberia

Wildfires are a rapidly increasing threat to boreal forests. While our understanding of the drivers behind wildfires and their environmental impact is growing, it is mostly limited to the observational period. Here we focus on the boreal forests of southern Siberia and exploit a U–Th-dated stalagmite from Botovskaya Cave, located in the upper Lena region of southern Siberia, to document wildfire activity and vegetation dynamics during parts of two warm periods: the Last Interglacial (LIG; specifically part of the Last Interglacial maximum between 124.1 and 118.8 ka) and the Holocene (10–0 ka). Our record is based on levoglucosan (Lev), a biomarker sensitive to biomass burning, and on lignin oxidation products (LOPs) that discriminate between open and closed forest and hard- or softwood vegetation. In addition, we used carbonate carbon stable isotope ratios ( δ 13 C ), which reflect a dominant control of the host rock, to evaluate soil respiration and local infiltration changes. Our LOP data suggest that, during the Last Interglacial, the region around Botovskaya Cave was characterised by open forest, which by ca. 121.5 ka underwent a transition from fire-resistant hardwood to fire-prone softwood. The Lev record indicates that fire activity was high and increased towards the end of Last Interglacial just before 119 ka. In contrast, the Holocene was characterised by a closed-forest environment with mixed hard- and softwood vegetation. Holocene fire activity varied but at a much lower level than during the Last Interglacial. We attribute the changes in wildfire activity during the intervals of interest to the interplay between vegetation and climate. The open forests of the Last Interglacial were more likely to ignite than their closed Holocene equivalents, and their flammability was aided by warmer and drier summers and a stronger seasonal temperature contrast due to the increase in seasonal insolation difference compared to the Holocene. Our comparison of the last two interglacial intervals suggests that, with increasing global temperatures, the boreal forest of southern Siberia may become progressively more vulnerable to higher wildfire activity.

Botovskaya Cave, Siberia↗
USGS2024

Characterization of change in tree cover state and condition over the conterminous United States

Variability in the effects of disturbances and extreme climate events can lead to changes in tree cover over time, including partial or complete loss, with diverse ecological consequences. It is therefore critical to identify in space and time the change processes that lead to tree cover change. Studies of change are often hampered by the lack of data capable of consistently detecting different types of change. Using the Landsat satellite record to create a long time-series of land cover and land cover change, the U.S. Geological Survey Land Change Monitoring Assessment and Projection (LCMAP) project has made an annual time series of land cover across the conterminous United States for the period 1985 to 2018. Multiple LCMAP products analyzed together with map validation reference plots provide a robust basis for understanding tree cover change. In LCMAP (Collection 1.2), annual change detection is based on harmonic model breaks calculated at each Landsat pixel from the Continuous Change Detection and Classification (CCDC) algorithm. The results showed that the majority of CCDC harmonic model breaks (signifying change) indicated partial tree cover loss (associated with management practices such as tree cover thinning) as compared to complete tree cover loss (associated with practices like clearcut harvest or fire disturbance). Substantially fewer occurrences of complete tree cover loss were associated with change in land cover state. The area of annual tree cover change increased after the late 1990s and stayed high for the rest of the study period. The reference data showed that tree harvest dominated across the conterminous United States. The majority of tree cover change occurred in evergreen forests. Large estimates of disturbance-related tree cover change indicated that tree cover loss may have previously been underreported due to omission of partial tree cover loss in prior studies. This has considerable implications for forest carbon accounting along with tracking ecosystem goods and services.

Forests↗
USGS2024

The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch

Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.

Remote Sensing of Environment↗
USGS2025

A framework for understanding the effects of subsurface agricultural drainage on downstream flows

Understanding controls on streamflow volume and magnitude is important to water resource management applications, such as critical water and transportation structure design and floodplain mapping. Changes in land use and agricultural practices, such as subsurface agricultural drainage, may be contributing to changes in streamflow characteristics. Subsurface agricultural drainage, also known as tile drainage, is the practice of installing drains in the subsurface of agricultural fields to improve productivity. Because of the complex interactions between subsurface drainage systems, precipitation, local soil conditions, and land management practices, it is difficult to determine how subsurface agricultural drainage affects downstream flow. Previously developed subsurface agricultural drainage conceptual models under dry, saturated, and winter conditions are summarized, and current literature on the effects of subsurface agricultural drainage on downstream flows, focusing on peak flow, non-event flow, and total flow to develop frameworks for discussing these systems is compiled. The effects that subsurface drainage has on hydrologic systems are expected to vary by site and are seasonally based on system design, soil type, moisture conditions, precipitation characteristics, and land conditions. Subsurface drainage can affect the magnitude of peak flow by converting surface runoff from a storm event to subsurface runoff. By increasing hydrologic connectivity of a catchment, subsurface drainage can increase non-event flow or the flow between two storm events, typically dependent on lateral flow through the subsurface and groundwater. Theoretically, by diverting water from groundwater recharge or by reducing water available for evapotranspiration, subsurface drainage may increase the total volume of flow. Precipitation changes may increase infiltration, excess overland flow, and flood risk regardless of the presence or absence of subsurface drainage.

Illinois, Iowa, Michigan, Minnesota, Missouri, Mon↗
USGS2024

Earthquake effects surveyed during the nineteenth century as ecological features of Chinookan tidelands

Lasting effects of a Cascadia earthquake in 1700 were documented during surveys of Chinookan tidelands near the mouth of the Columbia River between 1805 and 1868. The effects resemble estuarine consequences, near Anchorage, of the 1964 Alaska earthquake: fatal drowning of subsided meadows and forests by post-earthquake tides, rebirth of marshes and forests through post-earthquake sedimentation, and uplift. Chinookan remains of killed forests were recorded by James Graham Cooper, John J. Lowell, and Cleveland Rockwell. Cooper, attached to a railroad survey and the Smithsonian Institution, wrote of redcedar stumps and trunks standing dead in tidal marshes of Shoalwater (now Willapa) Bay. Two such snags served as bearing trees for Lowell as he platted a Shoalwater Bay township under contract with the General Land Office. Rockwell, of the US Coast Survey, flecked landward edges of tidal flats west of Astoria with symbols that evoke remains of a bygone spruce forest. The Lewis and Clark Expedition, while in that area in 1805–1806, mapped and puzzled over tideland vegetation that post-1700 succession helps explain.

Alaska, British Columbia, California, Oregon, Wash↗
USGS2024

Comparison of water quality in shallow groundwater near agricultural areas in the Delaware Coastal Plain, 2014 and 2019

The State of Delaware has encouraged agricultural conservation practices to improve nutrient uptake by crops and mitigate nutrient transport to groundwater in the surficial aquifer. To study recent changes in groundwater quality, the U.S. Geological Survey and the Delaware Department of Agriculture (DDA) developed a network of shallow wells near agricultural areas throughout the Delaware Coastal Plain. This network was designed to characterize water quality related to agricultural practices and to detect any recent changes in shallow groundwater quality, in particular groundwater nitrate concentrations. The shallow well network was first sampled in 2014 and resampled in 2019. In 2019, field parameters (including dissolved oxygen, pH, specific conductance, and temperature), major ions, nutrients, stable isotopes of water, and isotopes of nitrate were measured in groundwater samples collected between October and December. Wells were organized into three groups based on their geochemical characteristics measured in 2014: the Agricultural, Urban, and Mixed Groups. Results from the 2019 sampling showed little change in water quality from the 2014 sampling. Land-use factors continued to be the driving influence between groups. Groundwater moves slowly and changes in groundwater quality are likely to respond slowly to changes in conservation practices. Continued sampling of both groundwater quality in this network and monitoring land management practices can help detect groundwater quality trends in the future.

Delaware↗
USGS1991

Reconnaissance of the occurrence of agricultural chemicals in ground water in Haywood, Lake, Obion and Shelby Counties, Tennessee

Data on the occurrence of agricultural chemicals in ground wafer in Tennessee are sparse. The surficial alluvial aquifer is an important source of domestic water supply in West Tennessee, and potentially is subject fo contamination from the application of agricultural chemicals in the area. Nineteen shallow wells completed in the alluvial aquifers in areas of high density agricultural use were sampled in the winter and again in the summer of 1988 to ascertain the occurrence of agricultural chemical in ground water. Although no triazine herbicides or organophosphorus insecticides were detected in any of the wells sampled, elevated nitrite plus nitrate (as nitrogen) concentrations were detected. Results from the winter sampling period indicate a range of nitrite plus nitrate (as nitrogen) concentrations of less than 0.1 to 7.8 milligrams per liter with a median concentration of 2.6 milligrams per liter. Results from the summer sampling period indicate a range of nitrite plus nitrate (as nitrogen) concentrations of less than 0.1 to 8.9 milligrams per liter, median, 2.5 milligrams per liter. The highest concentrations occurred in the shallowest wells, and, in one instance, in a shallow well near a heavily irrigated field.

Tennessee↗
USGS2026

Microclimate mediates the strength and direction of avian biotic interactions

Theory predicts that that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities. We capitalized on a 15-year, landscape-scale dataset, collected annually in a relatively stable old-growth forest environment to test the long-held hypothesis that the strength and direction of species interactions might be mediated by climatic conditions. It is generally accepted that competitive and facilitative interactions drive the distributions of many species. Using multi-species dynamic occupancy models applied to long-term data, we tested whether annual settlement by bird species could affect either the persistence or settlement by other phylogenetically related species, and whether these interactions are mediated by microclimate. We found that species interactions were influenced by microclimate for some, but not all avian species pairs. Related species pairs more often showed settlement dynamics that were indicative of attraction rather than repulsion. In some cases, competitive interactions at warmer microclimates flipped to become facilitative in colder ones. However, the reverse was also true: facilitative interactions amplified for some species under warm conditions. Furthermore, species pairs that were closely related were more likely to exhibit competitive relationships along at least part of the microclimatic gradient. Our results highlight the importance of using long-term data to incorporate competitive and facilitative interactions into species distribution models and support the notion that the strength and direction of species interactions can be dependent on microclimatic environmental conditions.

Oregon↗
USGS2026

Multi-objective optimization of a hydro-economic model in an over-allocated agricultural basin

Groundwater depletion for agricultural irrigation poses significant environmental and economic challenges. This study introduces a proof-of-concept that combines hydro-economic modeling, scenario-based modeling, and multi-objective optimization to manage pumping curtailment in an over-allocated basin in the western United States. Three optimization scenarios were evaluated, each offering different degrees of management flexibility. Results reveal that scenarios with finer spatial resolution achieved greater environmental benefits per unit profit loss. Additionally, strategies allowing fractional reductions in curtailed wells–rather than complete shutdowns based on water rights seniority–substantially improved efficiency, highlighting the value of increased decision-making flexibility. Although scenario testing can aid stakeholder engagement and strategy exploration, multi-objective optimization provides a systematic framework to quantify tradeoffs between competing objectives. This combined approach demonstrates promise for building consensus and supporting the design of sustainable water management strategies that balance agricultural livelihoods with ecosystem preservation.

Oregon↗
USGS2025

Data gap analysis for estimation of agricultural return flows in the Upper Gunnison River Basin, Colorado

The Gunnison River and many tributaries in the Upper Gunnison River Basin provide water to irrigate agricultural crops. The application of irrigation water can recharge some aquifers locally by water percolating below the root zone and eventually flowing back to the stream or river through the subsurface. Diverting surface water for irrigation reduces streamflow during the irrigation season but can provide temporary storage of water and supplement streamflow after the snowmelt runoff season. Understanding the timing and quantity of agricultural return flows could help resource managers make informed decisions and adapt to potential changes in water management and availability that could affect irrigation practices. In 2024, the U.S. Geological Survey, in cooperation with the Upper Gunnison River Water Conservancy District, began a study to characterize agricultural return flows in the Upper Gunnison River Basin by using endmember mixing analysis and developing a groundwater model. Both approaches require data from multiple sources, but data gaps exist in the East River study reach and other reaches of interest (Ohio Creek, Tomichi Creek, and Cochetopa Creek). The East River Basin, which is the initial focus of the study, has fewer data gaps than the other basins. Data gaps could be addressed by installing additional surface water and groundwater monitoring sites, making regular streamflow measurements on tributaries, and completing tests to characterize local aquifer properties.

Colorado↗
USGS2025

Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches

Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗
USGS2025

Irrigated agriculture influences selenium levels in an endangered marsh bird

Selenium bioaccumulation in aquatic food webs poses risks to wildlife, particularly in wetlands receiving irrigation runoff. The Salton Sea, California’s largest lake, is primarily sustained by agricultural drainage. This drainage creates wetland habitat along the lakeshore that many bird species depend on, including the federally endangered Yuma Ridgway’s rail ( Rallus obsoletus yumanensis ). However, these marshes may pose an ecological trap – attracting rails despite high selenium exposure. We captured rails during the 2020–2023 breeding seasons and compared rail selenium levels within three types of marshes (fed with irrigation runoff, Colorado River water, or groundwater). We collected blood, breast feathers, and head feathers of rails in all three water sources for selenium comparisons. We tagged adult rails with GPS transmitters to locate nests and foraging locations where we collected eggshells, unhatched eggs, and prey. We assessed selenium exposure by collecting multiple prey species commonly eaten by rails in all three water sources. Selenium concentrations varied among sampling locations. Selenium concentrations in most sample types were predominately influenced by water source and marsh inflow velocity (sometimes in combination with marsh size). Distance to inflow, however, did not influence selenium concentrations in any sample type. Selenium concentrations were highest in agricultural-fed marshes compared to river-fed and spring-fed marshes. Increased marsh inflow velocities resulted in lower selenium concentrations. Given the risk of an ecological trap, our results suggest that supplementing wetlands with Colorado River water could mitigate selenium bioaccumulation in Yuma Ridgway’s rails.

California↗
USGS2024

Agricultural tile drains increase the susceptibility of streams to longer and more intense streamflow droughts

Streamflow droughts are receiving increased attention worldwide due to their impact on the environment and economy. One region of concern is the Midwestern United States, whose agricultural productivity depends on subsurface pipes known as tile drains to improve trafficability and soil conditions for crop growth. Tile drains accomplish this by rapidly transporting surplus soil moisture and shallow groundwater from fields, resulting in reduced watershed storage. However, no work has previously examined the connection between tile drainage and streamflow drought. Here, we pose the question: does the extent of watershed-level tile drainage lead to an increased susceptibly and magnitude of streamflow droughts? To answer this, we use daily streamflow data for 122 watersheds throughout the Midwestern United States to quantify streamflow drought duration, frequency, and intensity. Using spatial multiple regression models, we find that agricultural tile drainage generates statistically significant ( p < 0.05) increases in streamflow drought duration and intensity while significantly reducing drought frequency. The magnitude of the effect of tile drainage on streamflow drought characteristics is similar to that of water table depth and precipitation seasonality, both of which are known to influence streamflow droughts. Furthermore, projected changes in regional precipitation characteristics will likely drive the installation of additional tile drainage. We find that for each 10% increase in tile-drained watershed area, streamflow drought duration and intensity increase by 0.03 d and 12%, respectively, while frequency decreases by 0.10 events/year. Such increases in tile drainage may lead to more severe streamflow droughts and have a detrimental effect on the socio-environmental usage of streams throughout the Midwest.

Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio↗
USGS2024

Pesticide occurrence in shallow groundwater in three regions of agricultural land use: Baldwin County, the Wiregrass region, and the Tennessee River valley region of Alabama, 2009–20

As part of a cooperative investigation between the U.S. Geological Survey and the Alabama Department of Agriculture and Industries, a network of 22 groundwater wells were sampled from 2014 through 2020 for about 230 pesticide and pesticide degradate compounds. Wells were located in three regions of intensive agricultural land use in Alabama: Baldwin County, the Wiregrass region, and the Tennessee River valley region. Metolachlor sulfonic acid, a degradate of the herbicide metolachlor, was the most frequently detected compound, occurring in about 70 percent of the samples. Three other compounds, metolachlor, atrazine, and 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in more than half of the samples. Metolachlor and its degradates accounted for 33 of the 50 greatest compound concentrations study-wide, including the maximum pesticide concentration across all compounds (62,500 nanograms per liter). The frequency and magnitude of detections of many specific pesticide compounds varied among the three regions, but all detected pesticide concentrations were well below the U.S. Environmental Protection Agency maximum contaminant levels and applicable human health benchmarks. Sample results were combined with results of previous (2009–13) sampling to provide a continuous time-series of data for 2009–20. More than half of the 289 pesticide compounds analyzed during 2009–20 were not detected in any samples. Only four compounds were detected at great enough frequency throughout the 10 sampling years to evaluate patterns of change through time. Metolachlor and its degradate, metolachlor sulfonic acid, were frequently detected in all regions. Atrazine and its degradate, 2-chloro-4-isopropylamino-6-amino-s-triazine, were also detected in wells from all regions, but the variability and magnitude of concentrations were greatest in the Tennessee River valley region. No apparent temporal pattern in concentrations was found.

Alabama↗