SEARCH · USGS Science

Results for “Land Use and Land Cover”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,693 records · Page 94Linked to original sources

USGS1981

Hydrology of area 25, Eastern Region, Interior Coal Province, Illinois

The eastern region of the Interior Coal Province has been divided into 11 hydrologic study areas. Area 25, located in west-central Illinois, includes the Spoon River and small tributaries to the Illinois River. Pennsylvanian age rocks underlie most of the study area. Illinois, with the largest reserves of bituminous coal, is second only to Montana in total coal reserves. Loess soils cover most of the study area. Agriculture is the dominant land use. Surface water provides 97% of all the water used. Precipitation averages 34 to 35 inches. Water-quality data has been collected at over 31 sites. Analysis for specific conductance, pH, alkalinity, iron, manganese, sulfate and many trace elements and other water-quality constituents have been completed. These data are available from computer storage through the National Water Data Storage and Retrieval System (WATSTORE). (USGS)

Open-File Report↗

USGS2011

Investigation of the potential for concealed base-metal mineralization at the Drenchwater Creek Zn-Pb-Ag occurrence, northern Alaska, using geology, reconnaissance geochemistry, and airborne electromagnetic geophysics

In 2005, the U.S. Geological Survey, Bureau of Land Management, and State of Alaska cooperated on an investigation of the mineral potential of a southern part of the National Petroleum Reserve in Alaska, Howard Pass quadrangle, to provide background information for future land-use decisions. The investigation incorporated an airborne electromagnetic (EM) survey covering 1,500 mi 2 (~3,900 km 2 ), including flight lines directly over the Drenchwater Creek sediment-hosted Zn-Pb-Ag occurrence, the largest known base-metal occurrence in the survey area. Samples from the mineralized outcrop and rubblecrop contain metal concentrations that can exceed 11 percent Zn+Pb, with appreciable amounts of Ag. Soil samples with anomalous Pb concentrations are distributed near the sulfide-bearing outcrops and along a >2.5 km zone comprising mudstone, shale, and volcanic rocks of the Kuna Formation. No drilling has taken place at the Drenchwater occurrence, so alternative data sources (for example, geophysics) are especially important in assessing possible indicators of mineralization. Data from the 2005 electromagnetic survey define the geophysical character of the rocks at Drenchwater and, in combination with geological and surface-geochemical data, can aid in assessing the possible shallow (up to about 50 m), subsurface lateral extent of base-metal sulfide accumulations at Drenchwater. A distinct >3-km-long electromagnetic conductive zone (observed in apparent resistivity maps) coincides with, and extends further westward than, mineralized shale outcrops and soils anomalously high in Pb concentrations within the Kuna Formation; this conductive zone may indicate sulfide-rich rock. Models of electrical resistivity with depth, generated from inversion of electromagnetic data, which provide alongflight-line conductivity-depth profiles to between 25 and 50 m below ground surface, show that the shallow subsurface conductive zone occurs in areas of known mineralized outcrops and thins to the east. Broader, more conductive rock along the western ~1 km of the geophysical anomaly does not reach ground surface. These data suggest that the Drenchwater deposit is more extensive than previously thought. The application of inversion modeling also was applied to another smaller geochemical anomaly in the Twistem Creek area. The results are inconclusive, but they suggest that there may be a local conductive zone, possibly due to sulfides.

Professional Paper↗

USGS2015

Evapotranspiration in the Nile Basin: Identifying dynamics and drivers, 2002–2011

Analysis of the relationship between evapotranspiration (ET) and its natural and anthropogenic drivers is critical in water-limited basins such as the Nile. The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p < 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction.

Water↗

USGS2012

Groundwater quality in Coachella Valley, California

Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The Priority Basin Project of the GAMA Program provides a comprehensive assessment of the State’s groundwater quality and increases public access to groundwater-quality information. Coachella Valley is one of the study areas being evaluated. The Coachella study area is approximately 820 square miles (2,124 square kilometers) and includes the Coachella Valley groundwater basin (California Department of Water Resources, 2003). Coachella Valley has an arid climate, with average annual rainfall of about 6 inches (15 centimeters). The runoff from the surrounding mountains drains to rivers that flow east and south out of the study area to the Salton Sea. Land use in the study area is approximately 67 percent (%) natural, 21% agricultural, and 12% urban. The primary natural land cover is shrubland. The largest urban areas are the cities of Indio and Palm Springs (2010 populations of 76,000 and 44,000, respectively). Groundwater in this basin is used for public and domestic water supply and for irrigation. The main water-bearing units are gravel, sand, silt, and clay derived from surrounding mountains. The primary aquifers in Coachella Valley are defined as those parts of the aquifers corresponding to the perforated intervals of wells listed in the California Department of Public Health database. Public-supply wells in Coachella Valley are completed to depths between 490 and 900 feet (149 to 274 meters), consist of solid casing from the land surface to a depth of 260 to 510 feet (79 to 155 meters), and are screened or perforated below the solid casing. Recharge to the groundwater system is primarily runoff from the surrounding mountains, and by direct infiltration of irrigation. The primary sources of discharge are pumping wells, evapotranspiration, and underflow to the Salton Sea and Imperial Valley areas.

California↗

USGS2010

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Remote Sensing of Environment↗

USGS1998

Mapping the global land surface using 1 km AVHRR data

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Space Technology↗

USGS2018

Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems

Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R 2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r -value = 0.86), cheatgrass cover (average r -value = 0.96), and growing season proxies for vegetation productivity (R 2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.

Remote Sensing↗

USGS2025

Mapping predicted ecological states at landscape scales using remote sensing data and machine learning

Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2009

Addressing foundational elements of regional land-use change forecasting

Regional land-use models must address several foundational elements, including understanding geographic setting, establishing regional land-use histories, modeling process and representing drivers of change, representing local land-use patterns, managing issues of scale and complexity, and development of scenarios. Key difficulties include managing an array of biophysical and socioeconomic processes across multiple spatial and temporal scales, and acquiring and utilizing empirical data to support the analysis of those processes. The Southeastern and Pacific Northwest regions of the United States, two heavily forested regions with significant forest industries, are examined in the context of these foundational elements. Geographic setting fundamentally affects both the primary land cover (forest) in the two regions, and the structure and form of land use (forestry). Land-use histories of the regions can be used to parameterize land-use models, validate model performance, and explore land-use scenarios. Drivers of change in the two regions are many and varied, with issues of scale and complexity posing significant challenges. Careful scenario development can be used to simplify process-based land-use models, and can improve our ability to address specific research questions. The successful modeling of land-use change in these two areas requires integration of both top-down and bottom-up drivers of change, using scenario frameworks to both guide and simplify the modeling process. Modular approaches, with utilization and integration of existing process models, allow regional land-use modelers the opportunity to better represent primary drivers of land-use change. However, availability of data to represent driving forces remains a primary obstacle.

Landscape Ecology↗

USGS2013

Vegetation classification and mapping, Vicksburg National Military Park, Mississippi

Executive Summary The National Park Service (NPS) Gulf Coast Inventory and Monitoring Network, with the support of the National Park Service Vegetation Inventory completed vegetation classification and mapping for Vicksburg National Military Park (VICK), in Warren County, Mississippi, from 2004 to 2010. VICK is one of more than 250 NPS units to be covered by the Vegetation Inventory. Methods and procedures follow those of the NPS Vegetation Inventory as of August, 2012 ( http://science.nature.nps.gov/im/inventory/veg/index.cfm ). Ecologists collected floristic and environmental data from 45 vegetation field plots and classified and described 13 plant community types corresponding to US National Vegetation Classification (USNVC) associations from these data. This classification was used to map 712 hectares (1,698 acres) of Vicksburg National Military Park, in ten map classes corresponding to vegetation associations and two non-vegetated land cover classes, from a digital orthomosaic created from 1:24,000 color infrared aerial photographs and digital elevation model (DEM) data. Methods used to delineate stands varied according to vegetation type and were a combination of incorporating stand boundaries from an existing map, new “heads-up” interpretation and modeling of digital environmental spatial data. A thematic accuracy assessment collected vegetation data from 118 field observations stratified across the vegetation classes. The overall accuracy of the vegetation map was estimated to be 60.5% at the thematic level of USNVC association and 92.4% at the thematic level of USNVC group. Estimates of areas of vegetation types, as adjusted for mapping error, were calculated for all types mapped and/or observed during the thematic accuracy assessment. An important finding of this study is that the composition of the forests of VICK is substantially different from that described by earlier, more qualitative, accounts. Implications of these findings and possible applications of the data for resource monitoring and management are presented. This report summarizes the methods and general results of the Vegetation Inventory for Vicksburg National Military Park. Appendices include descriptions of vegetation types, a field key to the types, a list of plant species, and accuracy assessment contingency tables. This report is supplemented by the project data, which include: spatial database containing map polygon attributes. Geospatial products are in Universal Transverse Mercator (UTM) projection, Zone 15, using the North American Datum of 1983. all field data (plot, observation point, and accuracy assessment point) stored in a Microsoft Access database metadata for all digital products The data and reports produced by this investigation reside at: https://irma.nps.gov/App/ and/or at: http://biology.usgs.gov/npsveg/

Mississippi↗

USGS2014

Climate change implications and use of early warning systems for global dust storms

With increased changes in land cover and global climate, early detection and warning of dust storms in conjunction with effective and widespread information broadcasts will be essential to the prevention and mitigation of future risks and impacts. Human activities, seasonal variations and long-term climatic patterns influence dust storms. More research is needed to analyse these factors of dust mobilisation to create more certainty for the fate of vulnerable populations and ecosystems in the future. Early warning and communication systems, when in place and effectively implemented, can offer some relief to these vulnerable areas. As an issue that affects many regions of the world, there is a profound need to understand the potential changes and ultimately create better early warning systems for dust storms.

Book chapter↗

USGS2013

A new map of standardized terrestrial ecosystems of Africa

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

African Geographical Review↗

USGS2010

Modeling amphibian energetics, habitat suitability, and movements of western toads, Anaxyrus (=Bufo) boreas, across present and future landscapes

Effective conservation of amphibian populations requires the prediction of how amphibians use and move through a landscape. Amphibians are closely coupled to their physical environment. Thus an approach that uses the physiological attributes of amphibians, together with knowledge of their natural history, should be helpful. We used Niche Mapper™ to model the known movements and habitat use patterns of a population of Western toads ( Anaxyrus (= Bufo ) boreas ) occupying forested habitats in southeastern Idaho. Niche Mapper uses first principles of environmental biophysics to combine features of topography, climate, land cover, and animal features to model microclimates and animal physiology and behavior across landscapes. Niche Mapper reproduced core body temperatures ( T c ) and evaporation rates of live toads with average errors of 1.6 ± 0.4 °C and 0.8 ± 0.2 g/h, respectively. For four different habitat types, it reproduced similar mid-summer daily temperature patterns as those measured in the field and calculated evaporation rates (g/h) with an average error rate of 7.2 ± 5.5%. Sensitivity analyses indicate these errors do not significantly affect estimates of food consumption or activity. Using Niche Mapper we predicted the daily habitats used by free-ranging toads; our accuracy for female toads was greater than for male toads (74.2 ± 6.8% and 53.6 ± 15.8%, respectively), reflecting the stronger patterns of habitat selection among females. Using these changing to construct a cost surface, we also reconstructed movement paths that were consistent with field observations. The effect of climate warming on toads depends on the interaction of temperature and atmospheric moisture. If climate change occurs as predicted, results from Niche Mapper suggests that climate warming will increase the physiological cost of landscapes thereby limiting the activity for toads in different habitats.

Idaho↗

USGS2017

Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production

Executive Summary The use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management. Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections: Estimating Crop Production .—Provides an overview of how Landsat satellite imagery is used to estimate crop production, including the spectral bands most frequently utilized in this application. Monitoring Consumptive Water Use .—Highlights the role of Landsat imagery in monitoring consumptive water use for agricultural production. Globally, a significant amount of agricultural production relies on irrigation, so monitoring water resources is a critical component of agricultural monitoring. National Agricultural Statistics Service —Cropland Data Layer.—Highlights the use of Landsat imagery in developing the annual Cropland Data Layer, a crop-specific land cover classification product that provides information on more than 100 crop categories grown in the United States. Foreign Agricultural Service —Global Agricultural Monitoring.—Highlights Landsat’s role in monitoring global agricultural production. The USDA has been using Landsat imagery to monitor global agricultural production since the launch of Landsat 1 in 1972. Landsat imagery provides objective, global input for a number of USDA agricultural programs and plays an important role in economic and food security forecasting. U.S. Department of Agriculture —Satellite Imagery Archive.—Highlights a number of the experiences of the USDA in acquiring, sharing, and managing moderate resolution imagery to support the diversity of USDA operational programs. Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections: Field-Level Management .—Provides an introduction to what field-level production management is and how it can be applied to agricultural management. This section explores the concept of zone mapping and how Landsat imagery can be used to identify different conditions within a field. The section also provides a case study of zone-mapping software, developed by GK Technology, Inc., that is used by numerous agricultural consultants. Putting Zone Maps to Work .—Highlights several case studies of private agricultural consultants who have been using Landsat imagery to develop zone maps for farmers. Landsat imagery is helping consultants and farmers optimize agricultural inputs, including fertilizer and seed, which leads to higher yield and economic return for the farmer. Increasing Yield .—Highlights the primary benefit of zone mapping using Landsat imagery. Using 5-year market average prices for a number of commodities, this section provides examples of how yield increases translate into higher returns for farmers.

Open-File Report↗

USGS2022

Stormwater quantity and quality in selected urban watersheds in Hampton Roads, Virginia, 2016–2020

Urbanization can substantially alter sediment and nutrient loadings to streams. Although a growing body of literature has documented these processes, conditions may vary widely by region and physiographic province (PP). Substantial investments are made by localities to meet federal, state, and local water-quality goals and locally relevant monitoring data are needed to appropriately set standards and track progress. In 2016, a long-term stormwater monitoring program was initiated to characterize water-quality and streamflow conditions and compute average annual nutrient- and sediment-loading rates across the three dominant land-use types—commercial (COM), high-density residential, and single-family residential (SFR)—in the Hampton Roads metropolitan region within the Coastal Plain PP in southeastern Virginia. This report summarizes the first five years of data collection to (1) assess patterns in streamflow and water chemistry across the three major land-use types in the region; (2) compute annual sediment and nutrient loads; and (3) compare annual loading rates to those in other urbanized regions. Patterns in watershed hydrology characteristics and conditions were similar to those observed in other urban monitoring studies. Base-flow indices were lower and stream flashiness indices were higher in the study watersheds compared to those in less developed reference watersheds. These patterns reflect a decrease in infiltration and consequent increase in storm runoff as a result of urbanization. Stream flashiness was strongly positively related to degree of impervious land cover and negatively to watershed area. Hydrologic metrics varied across the land-use gradient, reflecting greater and more rapid runoff in the COM watersheds than in SFR watersheds. Event-based analyses conducted exclusively on periods of runoff highlight longer duration events, longer time-to-peak streamflow, and a longer lag between peak precipitation and peak streamflow in SFR watersheds, and higher stormflow yields, runoff ratios, and peak flows in COM watersheds. Event-based metrics varied seasonally because of regional meteorological patterns. Concentrations of total suspended solids (TSS) and total phosphorus (TP) were positively correlated to streamflow, whereas concentrations of total nitrogen (TN) varied little across the hydrologic regime. Phosphorus composition varied spatially and seasonally—the proportion of orthophosphate (PO 4 3- ) was highest in samples collected from stations draining residential land-use types and was elevated in summer and fall. Nitrogen composition varied with hydrologic condition: nitrate plus nitrite (NO 3 - ) dominance during base flow shifted to total organic nitrogen (TON) dominance during periods of runoff. For all three major constituents (TSS, TP, and TN), concentrations were highest in SFR watersheds, whereas yields were greatest in COM watersheds. This seeming contradiction in concentration and yield across land-use types occurred because of spatial differences in streamflow yield. The network average TSS yield in Hampton Roads was lower than that in comparable networks in Fairfax County, Virginia, and Gwinnett County, Georgia, a difference that may reflect dissimilarities in the topographic and soil characteristics of the Coastal Plain versus those in Piedmont PPs, as well as differences in engineered concrete stormwater conveyances versus earthen streams. The average annual TP yield in Hampton Roads was higher than averages reported in comparison studies and was primarily driven by elevated PO 4 3- . Elevated PO 4 3- yields may be related to unique soil and geological features of the Coastal Plain PP that limit phosphorus retention. Total nitrogen yields in the Hampton Roads and Fairfax County networks were similar; however, composition did vary, with greater total organic nitrogen yields in Hampton Roads and greater NO 3 - yields in Fairfax County. Cross-correlation analyses and mass-volume curves were used to assess the timing of sediment and nutrient loadings. The majority of TSS and TP was typically transported during the initial phase of a storm-runoff event, a phenomenon commonly termed the “first flush.” Although TN concentrations typically peaked within an hour of peak streamflow, reflecting the particulate dominance of TN during stormflows, and loadings were greater during the early phase of most storm events, the stricter first-flush criterion was rarely met. This suggests that the most abundant sources of TN in these watersheds are not as directly connected to the stormwater-conveyance system as are TSS and TP.

Virginia↗

USGS2019

Simulation of water availability in the Southeastern United States for historical and potential future climate and land-cover conditions

A study was conducted by the U.S. Geological Survey (USGS), in cooperation with the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) and the Department of the Interior Southeast Climate Adaptation Science Center, to evaluate the hydrologic response of a daily time step hydrologic model to historical observations and projections of potential climate and land-cover change for the period 1952–2099. The model simulations were used to compute the potential changes in hydrologic response and streamflow statistics across the Southeastern United States, using historical observations of climate and streamflow. Thirteen downscaled general circulation models with four representative concentration pathways were used to represent a range of potential future changes in climate (a total of 45 future simulations) from the Coupled Model Intercomparison Project Phase 5. The streamflow statistics were selected to describe streamflow conditions that may be most useful in defining the suitability for each river or stream to support sustaining populations of priority aquatic species across the GCPO LCC. An application of the Precipitation-Runoff Modeling System (included as part of the USGS National Hydrologic Model) was used to develop the hydrologic simulations. The results showed increases in air temperature across the study area, with the highest increases occurring in the northern part of the study area during July to September. The results showed a mix of increases and decreases in precipitation accumulation across the study area and across seasons, with decreases in precipitation accumulation across all seasons for the southwestern part of the study area. Actual evapotranspiration decreased for the southeastern part of the study area and increased for the northwestern part of the study area. The results showed general decreases in runoff across the study area, with increases in runoff in areas surrounding large metropolitan regions where potential future increases in impervious area occur. Results from a statistical analysis (Kolmogorov-Smirnov test) showed that the downscaled general circulation models generally have more skill in producing historical streamflow statistics in the duration and magnitude categories and less skill in producing historical streamflow statistics in the frequency, rate of change, and timing categories for this study area. The potential changes in the streamflow statistics and the results of the Kolmogorov-Smirnov test are available through the GCPO LCC Conservation Planning Atlas, an online science-based mapping platform built specifically for land managers and planners.

Alabama, Arkansas, Florida, Georgia, Illinois, Kan↗

USGS2018

Drought and land-cover conditions in the Great Plains

Land–atmosphere interactions play a critical role in the Earth system, and a better understanding of these interactions could improve weather and climate models. The interaction among drought, vegetation productivity, and land cover is of particular significance. In a semiarid environment, such as the U.S. Great Plains, droughts can have a large influence on the productivity of agriculture and grasslands, with serious environmental and economic impacts. Here, we used the vegetation drought response index (VegDRI) drought indicator to investigate the response of vegetation to weather and climate for land-cover types in the Great Plains in the United States from 1989 to 2012. We found that analysis that focused on land-cover types within ecoregion divisions provided substantially more and land-cover-based detail on the timing and intensity of drought than did summarizing across the entire Great Plains region. In the northern Great Plains, VegDRI measured more frequent drought impacts on vegetation in the western ecoregions than in the eastern ecoregions. Across the ecoregions of the Great Plains, drought impacts on vegetation were more commonly found in grassland than in cropland. For example, in the “Northwestern Great Plains” ecoregion (which encompasses areas of Montana, Wyoming, North Dakota, South Dakota, and Nebraska), grassland and nonirrigated cropland were observed in VegDRI to have historical fractional drought coverages in the growing season of 17% and 11%, respectively.

Great Plains↗

USGS2012

Comparison of stream invertebrate response models for bioassessment metric

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R 2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R 2 values than RICHTOL for the two regions tested. Modeled O/E R 2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

California, Oregon↗