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

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At least 775 records · Page 43Linked to original sources
USGS1996

Development of the temporal transportation database for the analysis of urban development in the Baltimore-Washington region

The U.S. Geological Survey is participating in a joint effort with the University of Maryland Baltimore County and other government agencies to construct a dynamic database of urban development for the Baltimore-Washington region from the late-1700's to the 1990's. The multi-theme temporal database includes a principal transportation data layer that documents the primary roads, railroads, and other transportation features that provided the infrastructure for urban development. A geographic information system was used to collect from various maps at various scales. Compilation criteria, such as connectivity, mobility, lineage, and alignment, were developed to accommodate limitations in the source materials. Visualization techniques such as animation and flight simulation are used to portray the changing transportation infrastructure and its effect on urbanization. The database will be used to study the evolution of the Baltimore-Washington metropolitan area. The principal transportation data layer is essential to illustrate and analyze the regional and temporal aspects of human-induced land transformations. Modeling applications will explore the urban growth process and its correlation to key transportation development.

Maryland↗
USGS2008

Interannual streamflow variability in the United States based on principal components

Interannual modes of streamflow variation at 106 locations across the United States during the period 1931–1978 are defined by using principal components. Five statistically significant components are found to account for more than 56% of the total streamflow variance. The first principal component represents a nationwide tendency for either above- or below-mean streamflow. The second component represents a north-south opposition in departures from mean flow, and the third, an east-west opposition. Higher-order components (fourth and fifth) geographically depict regional patterns of opposition in the sign of streamflow departures between coastal-continental areas and between the northern and southern plains, respectively. Analyses using spatially and temporally modified data sets indicate that the first three components (which explain 45% of the variance) are quite stable spatially, while only the first component is stable temporally. Time series analysis of principal component scores indicates that all but the fourth component are first-order autoregressive processes, as is mean annual nationwide streamflow. The fourth component is an autoregressive (AR)(2) process. In general, the principal components of streamflow are found to exhibit more persistence over annual time scales than the mean annual flow data themselves.

Water Resources Research↗
USGS2010

Fish habitat degradation in U.S. reservoirs

As the median age of the thousands of large reservoirs (> 200 ha) in the United States tops 50, many are showing various signs of fish habitat degradation. Our goal was to identify major factors degrading fish habitat in reservoirs across the country, and to explore regional degradation patterns. An online survey including 14 metrics was scored on a 0 (no degradation) to 5 (high degradation) point scale by 221 fisheries scientists (92% response rate) to describe degradation in 482 reservoirs randomly distributed throughout the continental United States. The highest scored sources of degradation were lack of aquatic macrophytes (41% of the reservoirs scored as 4–5), lack or loss of woody debris (35% scored 4–5), mistimed water level fluctuations (34% scored 4–5), and sedimentation (31% scored 4–5). Factor analysis identified five primary degradation factors that accounted for most of the variability in the 14 degradation metrics. The factors reflected siltation, structural habitat, eutrophication, water regime, and aquatic plants. Three degradation factors were driven principally by in-reservoir processes, whereas the other two were driven by inputs from the watershed. A comparison across U.S. regions indicated significant geographical differences in degradation relative to the factors emphasized by each region. Reservoirs sometimes have been dismissed as unnatural and disruptive, but they are a product of public policy, a critical feature of landscapes, and they cannot be overlooked if managers are to effectively conserve river systems. Protection and restoration of reservoir habitats may be enhanced with a broader perspective that includes watershed management, in addition to in reservoir activities.

Fisheries↗
USGS2006

Geologic Map of Loudoun County, Virginia

Introduction The geology of Loudoun County, Va., was mapped from 1988 through 1991 under a cooperative agreement between the U.S. Geological Survey (USGS) and the Loudoun County Office of Mapping and Geographic Information. This geologic map was compiled in 1993 from a series of detailed published and unpublished field investigations at scales of 1:12,000 and 1:24,000. Some of these same data were compiled as a digital geologic map at 1:100,000 scale (Burton and others, 1992a) and were the basis for a cost-benefit analysis of the societal value of geologic maps (Bernknopf and others, 1993).

IMAP↗
USGS2013

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0): web-based tools to assess the impact of sea level rise in south Florida

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, due to its rich endowment of animal and plant species and its heavily populated urban areas along the coast. Rising sea levels are expected to have substantial impacts on inland flooding, the depth and extent of surge from coastal storms, the degradation of water supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners and managers responsible for mitigating these impacts require advanced tools to help them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) Web site has been developed to address these needs by providing more convenient access to projections from models that forecast the effects of sea level rise on surface water and groundwater, the extent of surge and resulting economic losses from coastal storms, and the distribution of habitats. IMMAGE not only provides an advanced geographic information system (GIS) interface to support decision making, but also includes topic-based modules that explain and illustrate key concepts for nontechnical users. The purpose of this report is to familiarize both technical and nontechnical users with the IMMAGE Web site and its various applications.

Florida↗
USGS2010

Seasonal and spatial distribution patterns of finfish and selected invertebrates in coastal lagoons of northeastern Florida, 2002-2004

The U.S. Geological Survey conducted a survey of juvenile fisheries resources, in cooperation with the St. Johns River Water Management District and Volusia County, to establish baseline data on spatial and temporal distribution patterns of estuarine fish. The survey was conducted from November 2001 to March 2005 and the baseline data established for the survey in the Northern Coastal Basins were collected from January 2002 to December 2004. The study area included the bar-built estuaries ranging from just north of St. Augustine, Florida, south to Ponce de Leon Inlet. Sampling protocols developed by the Florida Fish and Wildlife Research Institute for their statewide Fisheries Independent Monitoring (FIM) program were replicated to allow for comparability with FIM program results. Samples were collected monthly from randomly selected stations based on a geographically stratified design. Finfish and selected invertebrates were collected using a 21.3-meter center-bag seine with a 3-millimeter mesh, and a 6.1-meter otter trawl with a 3-millimeter mesh liner. Total estimated fish and selected invertebrate densities were similar to estimates from FIM projects in adjacent areas and were characterized by similar dominant species. Preliminary analysis indicates that observed species distribution patterns were mainly a function of proximity to the three inlets within the study area. The two regions encompassing the northern Tolomato River and the Tomoka River and Basin are farthest from inlets and appear to function as oligohaline nursery areas. Those two areas had the greatest estimated densities of shellfish and juvenile sciaenid (drum) species associated with oligohaline waters (for example, Micropogonias undulatus, Sciaenops ocellatus and Cynoscion nebulosus). Samples near inlets, and between the two northern inlets, had greater estimated densities of species limited to euhaline waters, including juvenile clupeids collected at relatively high abundance and species of marine strays collected in low abundance. Based on correspondence analysis, seasonal variation dominated the ordination of seine samples and spatial variation dominated the ordination of trawl samples; this was due to differences in the number and timing of taxa found near inlets versus oligohaline regions. The absence of seagrass habitat in the Northern Coastal Basins is reflected by the absence of a seagrass-fish assemblage.

Scientific Investigations Report↗
USGS2011

Shallow lithological structure across the Dead Sea Transform derived from geophysical experiments

In the framework of the DEad SEa Rift Transect (DESERT) project a 150 km magnetotelluric profile consisting of 154 sites was carried out across the Dead Sea Transform. The resistivity model presented shows conductive structures in the western section of the study area terminating abruptly at the Arava Fault. For a more detailed analysis we performed a joint interpretation of the resistivity model with a P wave velocity model from a partially coincident seismic experiment. The technique used is a statistical correlation of resistivity and velocity values in parameter space. Regions of high probability of a coexisting pair of values for the two parameters are mapped back into the spatial domain, illustrating the geographical location of lithological classes. In this study, four regions of enhanced probability have been identified, and are remapped as four lithological classes. This technique confirms the Arava Fault marks the boundary of a highly conductive lithological class down to a depth of ∼3 km. That the fault acts as an impermeable barrier to fluid flow is unusual for large fault zone, which often exhibit a fault zone characterized by high conductivity and low seismic velocity. At greater depths it is possible to resolve the Precambrian basement into two classes characterized by vastly different resistivity values but similar seismic velocities. The boundary between these classes is approximately coincident with the Al Quweira Fault, with higher resistivities observed east of the fault. This is interpreted as evidence for the original deformation along the DST originally taking place at the Al Quweira Fault, before being shifted to the Arava Fault.

Dead Sea↗
USGS2005

An analysis of region-of-influence methods for flood regionalization in the Gulf-Atlantic Rolling Plains

Region-of-influence (RoI) approaches for estimating stream flow characteristics at ungaged sites were applied and evaluated in a case study of the 50-year peak discharge in the Gulf-Atlantic Rolling Plains of the southeastern United States. Linear regression against basin characteristics was performed for each ungaged site considered based on data from a region of influence containing the n closest gages in predictor variable (PRoI) or geographic (GRoI) space. Augmentation of this count based cutoff by a distance based cutoff also was considered. Prediction errors were evaluated for an independent (split-sampled) dataset. For the dataset and metrics considered here: (1) for either PRoI or GRoI, optimal results were found when the simpler count based cutoff, rather than the distance augmented cutoff, was used; (2) GRoI produced lower error than PRoI when applied indiscriminately over the entire study region; (3) PRoI performance improved considerably when Rol was restricted to predefined geographic subregions.

Journal of the American Water Resources Associatio↗
USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗
USGS2015

Recent changes in annual area burned in interior Alaska: The impact of fire management

The Alaskan boreal forest is characterized by frequent extensive wildfires whose spatial extent has been mapped for the past 70 years. Simple predictions based on this record indicate that area burned will increase as a response to climate warming in Alaska. However, two additional factors have affected the area burned in this time record: the Pacific decadal oscillation (PDO) switched from cool and moist to warm and dry in the late 1970s and the Alaska Fire Service instituted a fire suppression policy in the late 1980s. In this paper a geographic information system (GIS) is used in combination with statistical analyses to reevaluate the changes in area burned through time in Alaska considering both the influence of the PDO and fire management. The authors found that the area burned has increased since the PDO switch and that fire management drastically decreased the area burned in highly suppressed zones. However, the temporal analysis of this study shows that the area burned is increasing more rapidly in suppressed zones than in the unsuppressed zone since the late 1980s. These results indicate that fire policies as well as regional climate patterns are important as large-scale controls on fires over time and across the Alaskan boreal forest.

Alaska↗
USGS2008

U.S.-Mexico Border Geographic Information System

Geographic Information Systems (GIS) and the development of extensive geodatabases have become invaluable tools for addressing a variety of contemporary societal issues and for making predictions about the future. The United States-Mexico Geographic Information System (USMX-GIS) is based on fundamental datasets that are produced and/or approved by the national geography agencies of each country, the U.S. Geological Survey (USGS) and the Instituto Nacional de Estadistica Y Geografia (INEGI) of Mexico, and the International Boundary and Water Commission (IBWC). The data are available at various scales to allow both regional and local analysis. The USGS and the INEGI have an extensive history of collaboration for transboundary mapping including exchanging digital technology and developing methods for harmonizing seamless national level geospatial datasets for binational environmental monitoring, urban growth analysis, and other scientific applications.

Fact Sheet↗
USGS2014

Methods for estimating annual exceedance-probability discharges and largest recorded floods for unregulated streams in rural Missouri

Regression analysis techniques were used to develop a set of equations for rural ungaged stream sites for estimating discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. Basin and climatic characteristics were computed using geographic information software and digital geospatial data. A total of 35 characteristics were computed for use in preliminary statewide and regional regression analyses. Annual exceedance-probability discharge estimates were computed for 278 streamgages by using the expected moments algorithm to fit a log-Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data from water year 1844 to 2012. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized multiple Grubbs-Beck test was used to detect potentially influential low floods. Annual peak flows less than a minimum recordable discharge at a streamgage were incorporated into the at-site station analyses. An updated regional skew coefficient was determined for the State of Missouri using Bayesian weighted least-squares/generalized least squares regression analyses. At-site skew estimates for 108 long-term streamgages with 30 or more years of record and the 35 basin characteristics defined for this study were used to estimate the regional variability in skew. However, a constant generalized-skew value of -0.30 and a mean square error of 0.14 were determined in this study. Previous flood studies indicated that the distinct physical features of the three physiographic provinces have a pronounced effect on the magnitude of flood peaks. Trends in the magnitudes of the residuals from preliminary statewide regression analyses from previous studies confirmed that regional analyses in this study were similar and related to three primary physiographic provinces. The final regional regression analyses resulted in three sets of equations. For Regions 1 and 2, the basin characteristics of drainage area and basin shape factor were statistically significant. For Region 3, because of the small amount of data from streamgages, only drainage area was statistically significant. Average standard errors of prediction ranged from 28.7 to 38.4 percent for flood region 1, 24.1 to 43.5 percent for flood region 2, and 25.8 to 30.5 percent for region 3. The regional regression equations are only applicable to stream sites in Missouri with flows not significantly affected by regulation, channelization, backwater, diversion, or urbanization. Basins with about 5 percent or less impervious area were considered to be rural. Applicability of the equations are limited to the basin characteristic values that range from 0.11 to 8,212.38 square miles (mi 2 ) and basin shape from 2.25 to 26.59 for Region 1, 0.17 to 4,008.92 mi 2 and basin shape 2.04 to 26.89 for Region 2, and 2.12 to 2,177.58 mi 2 for Region 3. Annual peak data from streamgages were used to qualitatively assess the largest floods recorded at streamgages in Missouri since the 1915 water year. Based on existing streamgage data, the 1983 flood event was the largest flood event on record since 1915. The next five largest flood events, in descending order, took place in 1993, 1973, 2008, 1994 and 1915. Since 1915, five of six of the largest floods on record occurred from 1973 to 2012.

Missouri↗
USGS1977

Tertiary and Quaternary deposits at The Palisades, central Alaska

The Palisades of the Yukon River, located near the geographic center of Alaska, has long been an attraction to people traveling along the river. Numerous scientific field parties have viewed or visited the locality and published short accounts of their observations. The river bluffs are as much as 90 meters high and are composed of Tertiary lignite-rich sedimentary rocks and frozen Quaternary sand, silt, and gravel. Resampling and analysis of pollen show that the Tertiary rocks are Miocene rather than Miocene and Pliocene as previously thought. The Miocene flora, possessing a number of conifer and hardwood-tree types, was more varied than the present vegetation and indicates a warmer and wetter climate than at present. The area is frequently referred to as the boneyard because of the common occurrence of large bones of Pleistocene mammals within the frozen silts.

Alaska↗
USGS2000

Atmospheric nitrogen in the Mississippi River Basin: Amissions, deposition and transport

Atmospheric deposition of nitrogen has been cited as a major factor in the nitrogen saturation of forests in the north-eastern United States and as a contributor to the eutrophication of coastal waters, including the Gulf of Mexico near the mouth of the Mississippi River. Sources of nitrogen emissions and the resulting spatial patterns of nitrogen deposition within the Mississippi River Basin, however, have not been fully documented. An assessment of atmospheric nitrogen in the Mississippi River Basin was therefore conducted in 1998-1999 to: (1) evaluate the forms in which nitrogen is deposited from the atmosphere; (2) quantify the spatial distribution of atmospheric nitrogen deposition throughout the basin; and (3) relate locations of emission sources to spatial deposition patterns to evaluate atmospheric transport. Deposition data collected through the NADP/NTN (National Atmospheric Deposition Program/National Trends Network) and CASTNet (Clean Air Status and Trends Network) were used for this analysis. NO(x) Tier 1 emission data by county was obtained for 1992 from the US Environmental Protection Agency (Emissions Trends Viewer CD, 1985-1995, version 1.0, September 1996) and NH3 emissions data was derived from the 1992 Census of Agriculture (US Department of Commerce. Census of Agriculture, US Summary and County Level Data, US Department of Commerce, Bureau of the Census. Geographic Area series, 1995:1b) or the National Agricultural Statistics Service (US Department of Agriculture. National Agricultural Statistics Service Historical Data. Accessed 7/98 at URL, 1998. http://www.usda.gov/nass/pubs/hisdata.htm). The highest rates of wet deposition of NO3- were in the north-eastern part of the basin, downwind of electric utility plants and urban areas, whereas the highest rates of wet deposition of NH4+ were in Iowa, near the center of intensive agricultural activities in the Midwest. The lowest rates of atmospheric nitrogen deposition were on the western (windward) side of the basin, which suggests that most of the nitrogen deposited within the basin is derived from internal sources. Atmospheric transport eastward across the basin boundary is greater for NO3- than NH4+, but a significant amount of NH4+ is likely to be transported out of the basin through the formation of (NH4)2SO4 and NH4NO3 particles - a process that greatly increases the atmospheric residence time of NH4+. This process is also a likely factor in the atmospheric transport of nitrogen from the Midwest to upland forest regions in the North-East, such as the western Adirondack region of New York, where NH4+ constitutes 38% of the total wet deposition of N.

Science of Total Environment↗
USGS2025

Apparent annual survival of adult Golden-winged Warblers (Vermivora chrysoptera) may not differ by sex or region

Understanding range-wide demographic, spatial, and temporal variation in annual survival is essential for managing species of conservation concern. Multi-population models are useful tools for integrating diverse datasets, reducing biases, and deriving survival estimates across differing spatial scales. We conducted a range-wide, multi-population apparent annual survival analysis for a declining songbird, Vermivora chrysoptera (Golden-winged Warbler), using data from 18 sites across its breeding and nonbreeding grounds. This Nearctic-Neotropical migrant breeds in 2 disjunct regional populations, the Great Lakes and Appalachian Mountains, which are experiencing different rates of decline. We aimed to quantify regional-, site-, and sex-specific apparent annual survival estimates to identify geographic patterns or demographic factors influencing population declines. We used simulations to assess the precision of our estimates. Our models did not reveal a substantial difference in apparent annual survival between the Great Lakes (0.41, 95% credible interval (CrI):0.31–0.50) and the Appalachian regions (0.49, 95% CrI: 0.36–0.60), as CrIs overlapped. Site-specific estimates also showed no clear differences in apparent annual survival among sites representing both regional populations. Male apparent annual survival tended to be greater than female apparent annual survival in both regions, though CrI’s overlapped. Our study suggests demographic factors other than adult annual survival likely play a larger role in recent regional and range-wide population declines, such as productivity, juvenile/immature survival, or recruitment. Simulations indicate that improving recapture probability and study duration of datasets could lead to more precise apparent annual survival estimates. However, our model produced CrI ranges comparable to the most ideal data collection scenario, suggesting the lack of trends we found was not due to variability in our estimates. We stress the importance of addressing inherent biases in survival datasets and the need for standardized collaborative efforts to inform species conservation on a range-wide scale.

Ornithology↗
USGS1995

Map showing location of observation wells in Massachusetts and Rhode Island

This map shows the locations of the 136 observation wells from the observation-well network maintained by the U.S. Geological Survey in Massachusetts and Rhode Island. The wells are identified by town name and well number. The map shows the location of the 10 observation wells that have digital recorders and the 126 observation wells that are measured by local observers. The aquifer material (sand, till, or bedrock) in which a well is located is noted. County and town boundaries are shown on the map. These features are presented at a scale of 1:400,000 (map size is about 38 by 30 inches). The map includes textual information describing the uses of observation-well data. The information is organized by construction, water supply, water quality, and statistical analysis. The map also presents observation well information, which was obtained from the annual data report of the Massachusetts--Rhode Island District. This infor- mation is presented in tabular form and includes town name, well number, aquifer material in which the well is located, and well depth below the land surface. The map was produced from a digital data base using a Geographic Information System. State boundaries were generated from digital line graphs maintained by the U.S. Geological Survey. Town and county boundaries were digitized from stable-base materials maintained by State agencies. The map was prepared in cooperation with State agencies of Massachusetts and Rhode Island.

Massachusetts, Rhode Island↗
USGS2004

Biomonitoring of Environmental Status and Trends (BEST) Program: Environmental contaminants and their effects on fish in the Rio Grande Basin

We collected, examined, and analyzed 368 fish of seven species from 10 sites in the Rio Grande Basin (RGB) during late 1997 and early 1998. Four sites were National Contaminant Biomonitoring Program (NCBP) stations where organochlorine ad elemental contaminants in fish had been monitored from 1969 and through 1986. The other six were USGS-National Stream Quantity Accounting Network (NASQAN) stations where water quality is monitored. The objectives were to document temporal and geographic trends in the concentrations of accumulative organic and inorganic contaminants in RGB fish and the effects of contaminants on the fish; to continue testing the feasibility of incorporating biomarkers (that is, biochemical, histopathological, and other biological indicators of contaminant exposure or effects) into a monitoring program for large U.S. rivers; and to evaluate the compatibility of monitoring methods based on the analysis of fish with those used to monitor water by NASQAN. Common carp ( Cyprinus carpio ; carp) and black basses ( Micropterus sp.; bass) were the targeted species; together, they represented 77% of the fish collected. Each fish was examined in the field for externally and internally visible gross lesions, selected organs were weighed to compute various ponderal and organosomatic indices, and samples of tissues and fluids were obtained and preserved for analysis of fish health and reproductive biomarkers. Composite samples of whole fish from each station were grouped by species and gender and analyzed by instrumental methods for persistent organic and inorganic contaminants and for dioxin-like activity (TCDD-EQ) using the H4IIE rat hepatoma cell bioassay.

Rio Grande basin↗
USGS2004

Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model

Researchers have been coupling geographic information systems (GIS) data handling and processing capability to watershed and waterquality models for many years. This capability is suited for the development of databases appropriate for water modeling. However, it is rare for GIS to provide direct inputs to the models. To demonstrate the logical procedure of coupling GIS for model parameter extraction, we selected the Agricultural Non-Point Source (AGNPS) pollution model. Investigators can generate data layers at various resolutions and resample to pixel sizes to support models at particular scales. We developed databases of elevation, land cover, and soils at various resolutions in four watersheds. The ability to use multiresolution databases for the generation of model parameters is problematic for grid-based models. We used database development procedures and observed the effects of resolution and resampling on GIS input datasets and parameters generated from those inputs for AGNPS. Results indicate that elevation values at specific points compare favorably between 3- and 30-m raster datasets. Categorical data analysis indicates that land cover classes vary significantly. Derived parameters parallel the results of the base GIS datasets. Analysis of data resampled from 30-m to 60-, 120-, 210-, 240-, 480-, 960-, and 1920-m pixels indicates a general degradation of both elevation and land cover correlations as resolution decreases. Initial evaluation of model output values for soluble nitrogen and phosphorous indicates similar degradation with resolution. ?? Springer-Verlag 2004.

Journal of Geographical Systems↗