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At least 757 records · Page 42Linked to original sources

USGS2012

Modeling habitat dynamics accounting for possible misclassification

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.

Landscape Ecology↗

USGS2007

Factors affecting herbicide yields in the Chesapeake Bay watershed, June 1994

Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 199094 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 1990-94 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.

Water Resources Bulletin↗

USGS1997

Classification and Mapping of Agricultural Land for National Water-Quality Assessment

Agricultural land use is one of the most important influences on water quality at national and regional scales. Although there is great diversity in the character of agricultural land, variations follow regional patterns that are influenced by environmental setting and economics. These regional patterns can be characterized by the distribution of crops. A new approach to classifying and mapping agricultural land use for national water-quality assessment was developed by combining information on general land-use distribution with information on crop patterns from agricultural census data. Separate classification systems were developed for row crops and for orchards, vineyards, and nurseries. These two general categories of agricultural land are distinguished from each other in the land-use classification system used in the U.S. Geological Survey national Land Use and Land Cover database. Classification of cropland was based on the areal extent of crops harvested. The acreage of each crop in each county was divided by total row-crop area or total orchard, vineyard, and nursery area, as appropriate, thus normalizing the crop data and making the classification independent of total cropland area. The classification system was developed using simple percentage criteria to define combinations of 1 to 3 crops that account for 50 percent or more or harvested acreage in a county. The classification system consists of 21 level I categories and 46 level II subcategories for row crops, and 26 level I categories and 19 level II subcategories for orchards, vineyards, and nurseries. All counties in the United States with reported harvested acreage are classified in these categories. The distribution of agricultural land within each county, however, must be evaluated on the basis of general land-use data. This can be done at the national scale using 'Major Land Uses of the United States,' at the regional scale using data from the national Land Use and Land Cover database, or at smaller scales using locally available data.

Circular↗

USGS2017

The landscapes of West Africa—40 years of change

What has driven changes in land use and land cover in West Africa over the past 40 years? What trends or patterns can be discerned in those changes? To answer these questions, the U.S. Geological Survey West Africa Land Use Dynamics project partnered with the Permanent Interstate Committee for Drought Control in the Sahel and the U.S. Agency for International Development/West Africa to map land use and land cover across the region for three time periods (years): 1975, 2000, and 2013. This cooperative effort has resulted in the publication of a 219-page atlas, “Landscapes of West Africa: A Window on a Changing World.” The atlas uses satellite imagery, maps, and pictures to tell a complex story of landscape change at regional and national scales. It includes a collection of focused studies, some of which raise cause for concern, and others that provide considerable hope.

Fact Sheet↗

USGS1994

Identification of tundra land cover near Teschekpuk Lake, Alaska using SPOT satellite data

Tundra vegetation in the Teshekpuk Lake area of the Alaskan Arctic Coastal Plain was mapped to assess distribution and abundance of waterfowl habitats. Three SPOT satellite scenes were acquired and registered to a 20 m Universal Transverse Mercator grid. Two clustering techniques were used to develop statistical parameters by which the SPOT data were spectrally classified. A maximum likelihood algorithm that correlated spectral classes with land cover types was applied to the SPOT data. Field data were used to assist in spectral class labeling and vegetation descriptions. Twelve cover classes were mapped. The most common type was moist sedge meadow tundra (13.5%); the least common was moss/peat shoreline (0.2%). The moss/peat shoreline type, important to moulting geese and other waterfowl, was spectrally identified using supervised clustering techniques. All other land cover types were identified using unsupervised clustering techniques. Cover classes were described, and a tundra landscape profile produced.

Alaska↗

USGS2013

Assessment of the NASA-USGS Global Land Survey (GLS) Datasets

The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.

Remote Sensing of Environment↗

USGS2005

Dynamic modeling of Tampa Bay urban development using parallel computing

Urban land use and land cover has changed significantly in the environs of Tampa Bay, Florida, over the past 50 years. Extensive urbanization has created substantial change to the region's landscape and ecosystems. This paper uses a dynamic urban-growth model, SLEUTH, which applies six geospatial data themes (slope, land use, exclusion, urban extent, transportation, hillside), to study the process of urbanization and associated land use and land cover change in the Tampa Bay area. To reduce processing time and complete the modeling process within an acceptable period, the model is recoded and ported to a Beowulf cluster. The parallel-processing computer system accomplishes the massive amount of computation the modeling simulation requires. SLEUTH calibration process for the Tampa Bay urban growth simulation spends only 10 h CPU time. The model predicts future land use/cover change trends for Tampa Bay from 1992 to 2025. Urban extent is predicted to double in the Tampa Bay watershed between 1992 and 2025. Results show an upward trend of urbanization at the expense of a decline of 58% and 80% in agriculture and forested lands, respectively.

Computers & Geosciences↗

USGS2013

Stochastic empirical loading and dilution model (SELDM) version 1.0.0

The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.

Techniques and Methods↗

USGS2014

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Remote Sensing↗

USGS2009

Quantifying terrestrial ecosystem carbon dynamics in the Jinsha watershed, Upper Yangtze, China from 1975 to 2000

Quantifying the spatial and temporal dynamics of carbon stocks in terrestrial ecosystems and carbon fluxes between the terrestrial biosphere and the atmosphere is critical to our understanding of regional patterns of carbon storage and loss. Here we use the General Ensemble Biogeochemical Modeling System to simulate the terrestrial ecosystem carbon dynamics in the Jinsha watershed of China's upper Yangtze basin from 1975 to 2000, based on unique combinations of spatial and temporal dynamics of major driving forces, such as climate, soil properties, nitrogen deposition, and land use and land cover changes. Our analysis demonstrates that the Jinsha watershed ecosystems acted as a carbon sink during the period of 1975–2000, with an average rate of 0.36 Mg/ha/yr, primarily resulting from regional climate variation and local land use and land cover change. Vegetation biomass accumulation accounted for 90.6% of the sink, while soil organic carbon loss before 1992 led to lower net gain of carbon in the watershed, and after that soils became a small sink. Ecosystem carbon sinks/source pattern showed a high degree of spatial heterogeneity, Carbon sinks were associated with forest areas without disturbances, whereas carbon Sources were primarily caused by stand-replacing disturbances. This highlights the importance of land-use history in determining the regional carbon sinks/source pattern.

Jinsha watershed, Yangtze River↗

USGS2009

Quantifying terrestrial ecosystem carbon dynamics in the Jinsha watershed, Upper Yangtze, China from 1975 to 2000

Quantifying the spatial and temporal dynamics of carbon stocks in terrestrial ecosystems and carbon fluxes between the terrestrial biosphere and the atmosphere is critical to our understanding of regional patterns of carbon budgets. Here we use the General Ensemble biogeochemical Modeling System to simulate the terrestrial ecosystem carbon dynamics in the Jinsha watershed of China’s upper Yangtze basin from 1975 to 2000, based on unique combinations of spatial and temporal dynamics of major driving forces, such as climate, soil properties, nitrogen deposition, and land use and land cover changes. Our analysis demonstrates that the Jinsha watershed ecosystems acted as a carbon sink during the period of 1975–2000, with an average rate of 0.36 Mg/ha/yr, primarily resulting from regional climate variation and local land use and land cover change. Vegetation biomass accumulation accounted for 90.6% of the sink, while soil organic carbon loss before 1992 led to a lower net gain of carbon in the watershed, and after that soils became a small sink. Ecosystem carbon sink/source patterns showed a high degree of spatial heterogeneity. Carbon sinks were associated with forest areas without disturbances, whereas carbon sources were primarily caused by stand-replacing disturbances. It is critical to adequately represent the detailed fast-changing dynamics of land use activities in regional biogeochemical models to determine the spatial and temporal evolution of regional carbon sink/source patterns.

Jinsha watershed, Yangtze River↗

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↗

USGS1983

Use of digital land-cover data from the Landsat satellite in estimating streamflow characteristics in the Cumberland Plateau of Tennessee

Characteristics of Streamflow at ungaged sites in coal-mining areas of the Cumberland Plateau may be estimated with regression equations that relate streamflow characteristics to the physiographic and climatic characteristics of the corresponding drainage basins. An experiment was performed to compare the accuracy of equations using basin characteristics derived from maps and climatological records (control group equations) with the accuracy of equations using basin characteristics derived from digital processing of Landsat spectral data as well as maps and climatological records (experimental group equations). Results of this experiment show that (with the exception of low flows and four of six annual peak logarithms) drainage area can explain more than 90 percent of the variance in all streamflow characteristics in both groups of equations. Seventeen of 39 experimental group equations that have two basin characteristics each are different from the corresponding control group equations. Five of the 17 differing experimental group equations have no counterpart in the control group because of lack of significance in control group basin characteristics, seven are slightly more accurate, and five are not measurably different in accuracy. When the equations in both groups are arranged into six flow categories, there is no substantial difference in accuracy between equations using basin characteristics derived from maps and climatological records (control group) and equations using basin characteristics derived from Landsat tapes as well as maps and climatological records (experimental group) for this particular study area, the Cumberland Plateau of Tennessee.

Tennessee↗

USGS2006

Evaluation of osprey habitat suitability and interaction with contaminant exposure

Ospreys (Pandion haliaetus) have been the focus of conservation efforts since their dramatic population decline attributed to dichlorodiphenyltrichloroethane and related chemicals in the 1960s. Several recent studies of ospreys nesting in the United States have indicated improved reproduction. However, the density of breeding ospreys varies greatly among locations, with some areas seemingly habitable but not occupied. Because of concerns about pollution in the highly industrialized portions of the Delaware River and Bay, USA, we evaluated contaminant exposure and productivity in ospreys nesting on the Delaware River and Bay in 2002. We characterized habitat in the coastal zone of Delaware, USA, and the area around the river in Pennsylvania, USA, using data we collected as well as extant information provided by state and federal sources. We characterized habitat based on locations of occupied osprey nests in Delaware and Pennsylvania. We evaluated water clarity, water depth, land use and land cover, nest availability, and contaminants in sediment for use in a nest-occupancy model. Our results demonstrated that the presence of occupied nests was associated with water depth, water clarity, distance to an occupied osprey nest, and presence of urban land use, whereas a companion study demonstrated that hatching success was associated with the principal components derived from organochlorine-contaminant concentrations in osprey eggs (total polychlorinated biphenyls, p,p'-dichlorodiphenylethylene, chlordane and metabolites, and heptachlor epoxide). Our study provides guidelines for resource managers and local conservation organizations in management of ospreys and in development of habitat models that are appropriate for other piscivorous and marsh-nesting birds.

Journal of Wildlife Management↗

USGS2002

A strategy for estimating the rates of recent United States land-cover changes

Information on the rates of land-use and land-cover change is important in addressing issues ranging from the health of aquatic resources to climate change. Unfortunately, there is a paucity of information on land-use and land-cover change except at very local levels. We describe a strategy for estimating land-cover change across the conterminous United States over the past 30 years. Change rates are estimated for 84 ecoregions using a sampling procedure and five dates of Landsat imagery. We have applied this methodology to six eastern U.S. ecoregions. Results show very high rates of change in the Plains ecoregions, high to moderate rates in the Piedmont ecoregions, and moderate to low rates in the Appalachian ecoregions. This indicates that ecoregions are appropriate strata for capturing unique patterns of land-cover change. The results of the study are being applied as we undertake the mapping of the rest of the conterminous United States.

Photogrammetric Engineering and Remote Sensing↗

USGS2011

Past and predicted future changes in the land cover of the Upper Mississippi River floodplain, USA

This study provides one historical and two alternative future contexts for evaluating land cover modifications within the Upper Mississippi River (UMR) floodplain. Given previously documented changes in land use, river engineering, restoration efforts and hydro-climatic changes within the UMR basin and floodplain, we wanted to know which of these changes are the most important determinants of current and projected future floodplain land cover. We used Geographic Information System data covering approximately 37% of the UMR floodplain (3232 km 2 ) for ca 1890 (pre-lock and dam) and three contemporary periods (1975, 1989 and 2000) across which river restoration actions have increased and hydro-climatic changes have occurred. We further developed two 50-year future scenarios from the spatially dependent land cover transitions that occurred from 1975 to 1989 (scenario A) and from 1989 to 2000 (scenario B) using Markov models.Land cover composition of the UMR did not change significantly from 1975 to 2000, indicating that current land cover continues to reflect historical modifications that support agricultural production and commercial navigation despite some floodplain restoration efforts and variation in river discharge. Projected future land cover composition based on scenario A was not significantly different from the land cover for 1975, 1989 or 2000 but was different from the land cover of scenario B, which was also different from all other periods. Scenario B forecasts transition of some forest and marsh habitat to open water by the year 2050 for some portions of the northern river and projects that some agricultural lands will transition to open water in the southern portion of the river. Future floodplain management and restoration planning efforts in the UMR should consider the potential consequences of continued shifts in hydro-climatic conditions that may occur as a result of climate change and the potential effects on floodplain land cover.

Illinois;Iowa;Minnesota;Missouri;Wisconsin↗

USGS2015

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

AIMS Environmental Science↗

USGS2016

Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013

The U.S. Geological Survey, in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board, developed and calibrated a Soil and Water Assessment Tool watershed model of the Double Mountain Fork Brazos River watershed in western Texas to simulate monthly mean streamflow and to evaluate the effects of brush management on water yields in the watershed, particularly to Lake Alan Henry, for calendar years 1994–2013. Model simulations were done to quantify the possible change in water yield of individual subbasins in the Double Mountain Fork Brazos River watershed as a result of the replacement of shrubland (brush) with grassland. The simulation results will serve as a tool for resource managers to guide brush-management efforts. The model was calibrated from 1994 through 2008 and validated from 2009 through 2013 with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08079600 Double Mountain Fork Brazos River at Justiceburg, Texas (hereinafter referred to as the “Justiceburg gage”). Simulated monthly mean streamflow showed agreement with measured monthly mean streamflow for the 1994–2013 study period: the percentage bias was +6, the coefficient of determination was 0.73, and the Nash–Sutcliffe coefficient of model efficiency was 0.71. The calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).

Texas↗