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USGS2020

Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates

Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity.

Montana↗

USGS2018

Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks

Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions.

International Journal of Cartography↗

USGS2010

A climate trend analysis of Kenya-August 2010

Introduction This brief report draws from a multi-year effort by the United States Agency for International Development's Famine Early Warning System Network (FEWS NET) to monitor and map rainfall and temperature trends over the last 50 years (1960-2009) in Kenya. Observations from seventy rainfall gauges and seventeen air temperature stations were analyzed for the long rains period, corresponding to March through June (MAMJ). The data were quality controlled, converted into 1960-2009 trend estimates, and interpolated using a rigorous geo-statistical technique (kriging). Kriging produces standard error estimates, and these can be used to assess the relative spatial accuracy of the identified trends. Dividing the trends by the associated errors allows us to identify the relative certainty of our estimates (Funk and others, 2005; Verdin and others, 2005; Brown and Funk, 2008; Funk and Verdin, 2009). Assuming that the same observed trends persist, regardless of whether or not these changes are due to anthropogenic or natural cyclical causes, these results can be extended to 2025, providing critical, and heretofore missing information about the types and locations of adaptation efforts that may be required to improve food security.

Fact Sheet↗

USGS1981

Pattern-recognition program modified and applied to southeastern United States seismicity

The pattern-recognition technique of Bongard and others (1966), Gelfand and others (1976), and Briggs and others (1977) was modified and applied in Southeastern United States to try to detect combinations or interactions of geologic, geophysical, and topographic parameters characteristic of areas with moderate and large earthquakes. The modification involved replacing the operator-defined criteria for significance of association of parameters and earthquakes with statistical estimates of significance. This was done in order to decrease the probability of random associations and to increase the flexibility of the pattern-recognition technique. The binomial distribution was used to determine the probability that each particular association could have occurred by chance. Bonferroni's inequality was invoked to determine a conservative significance level for each of the binomial tests. However, without being able to quantify the dependence among the associations, it is not possible to statistically evaluate the group of binomial tests as a whole. Preliminary results identify four independent associations characterizing spatially independent earthquake epicenters, or "dangerous" points, and seven independent associations characterizing the intervening "safe" points. When all points were reclassified using these traits, all epicenters were correctly classified as dangerous. In addition, three "safe" points were reclassified as dangerous and appear to be potential sites for future earthquakes. However, it is not possible to determine the degree of dangerousness of the points using this reclassification scheme. Two dangerous zones emerge in the northeast and southwest portions of the study area. Epicenters outside of these zones, including those of the large historic earthquakes (MMI > VIII) in Giles County, Virginia, and Charleston, South Carolina, have not been well characterized by the significant associations and may have fundamentally different causes.

southeastern United States↗

USGS2014

Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM)

In 2012, the U.S. Geological Survey and the Oregon Department of Transportation began a cooperative study to demonstrate use of the Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses in Oregon. SELDM can be used to estimate stormflows, constituent concentrations, and loads from the area upstream of a stormflow discharge site, from the site of interest and in the receiving waters downstream of the discharge. SELDM also can be used to assess the potential effectiveness of best management practices (BMP) for mitigating potential effects of runoff in receiving waters. Nominally, SELDM is a highway-runoff model, but it is well suited for analysis of runoff from other land uses as well. This report provides case studies and examples to demonstrate stochastic-runoff modeling concepts and to demonstrate application of the model. Basin characteristics from six Oregon highway study sites were used to demonstrate various applications of the model. The highway catchment and upstream basin drainage areas of these study sites ranged from 3.85 to 11.83 acres and from 0.16 to 6.56 square miles, respectively. The upstream basins of two sites are urbanized, and the remaining four sites are less than 5 percent impervious. SELDM facilitates analysis by providing precipitation, pre-storm streamflow, and other variables by region or from hydrologically similar sites. In Oregon, there can be large variations in precipitation and streamflow among nearby sites. Therefore, spatially interpolated geographic information system data layers containing storm-event precipitation and pre-storm streamflow statistics specific to Oregon were created for the study using Kriging techniques. Concentrations and loads of cadmium, chloride, chromium, copper, iron, lead, nickel, phosphorus, and zinc were simulated at the six Oregon highway study sites by using statistics from sites in other areas of the country. Water‑quality datasets measured at hydrologically similar basins in the vicinity of the study sites in Oregon were selected and compiled to estimate stormflow-quality statistics for the upstream basins. The quality of highway runoff and some upstream stormflow constituents were simulated by using statistical moments (average, standard deviation, and skew) of the logarithms of data. Some upstream stormflow constituents were simulated by using transport curves, which are relations between stormflow and constituent concentrations. Stochastic analyses were done by using SELDM to demonstrate use of the model and to illustrate the types of information that stochastic analyses may provide: 1. An analysis was done to demonstrate use of dilution factors as an initial reconnaissance tool for comparing relative risk among sites. 2. An analysis of hardness-dependent, water-quality criteria was done to illustrate the effects of variations in hardness and flow on the application and interpretation of such criteria. This analysis shows that hardness-dependent criteria can vary by an order of magnitude among storm events because hardness is diluted by stormflows. 3. An analysis of uncertainties in input and output values was done to demonstrate that properly selected robust datasets are needed to represent conditions at a site of interest. This analysis shows that the rate of water-quality exceedances that are measured or simulated may depend on sample size and the luck of the draw. 4. An analysis was done to demonstrate that SELDM and other Monte Carlo models may generate extreme values from input statistics, which may or may not be feasible based on physicochemical or hydrological limits. 5. An analysis of BMP modeling methods was done to demonstrate use of the model for estimating treatment requirements for meeting water-quality objectives. 6. An analysis of the use of grab sampling and nonstochastic upstream modeling methods was done to evaluate the potential effects on modeling outcomes. Additional analyses using surrogate water-quality datasets for the upstream basin and highway catchment were provided for six Oregon study sites to illustrate the risk-based information that SELDM will produce. These analyses show that the potential effects of highway runoff on receiving-water quality downstream of the outfall depends on the ratio of drainage areas (dilution), the quality of the receiving water upstream of the highway, and the concentration of the criteria of the constituent of interest. These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important.

Oregon↗

USGS1996

Water-quality assessment of the Connecticut, Housatonic, and Thames River Basins study unit: Analysis of available data on nutrients, suspended sediments, and pesticides, 1972-92

This retrospective report examines available nutrient, suspended sediment, and pesticide data in surface and ground water in the Connecticut, Housatonic and Thames Rivers Study Unit of the National Water-Quality Assessment Program. The purpose of this study is to improve the understanding of natural and anthropogenic factors affecting water quality in the study unit. Waterquality data were acquired from various sources, primarily, the U.S. Geological Survey and the U.S. Environmental Protection Agency. The report examines data for water years 1972-92, focusing on 1980-92, although it also includes additional data from as early as 1905. The study unit lies within the New England Physiographic Province and altitudes range from sea level in coastal Connecticut to 6,288 feet above sea level at Mount Washington, New Hampshire. Two major aquifer types underlie the study unit unconsolidated glacial deposits and fractured bedrock. The climate generally is temperate and humid, with four distinct seasons. Average annual precipitation ranges from 34 to 65 inches. The study unit has a population of about 4.5 million, which is most highly concentrated in southwestern Connecticut and along the south-central region of the Connecticut River Valley. Surface-water-quality data were screened to provide information about sites with adequate numbers of analyses (50) over sufficiently long periods (1980-90) to enable valid statistical analyses. In order to compare effects of different types of land use on surface-water quality, examination of data required application of several statistical and graphical techniques, including mapping, histograms, boxplots, concentration-discharge plots, trend analysis, and load estimation. Spatial and temporal analysis of surface-water-quality data indicated that, with a single exception, only stations in the Connecticut water-quality network had sufficient data collected over adequately long time periods to use in detailed analyses. Ground-water nutrient and pesticide data were compiled from several Federal and State agencies, primarily the U.S. Geological Survey, U.S. Environmental Protection Agency, and Connecticut Department of Health Services. Nutrient data were available for several thousand wells; nitrite plus nitrate as nitrogen was the most commonly reported constituent. Most wells with nutrient data are in Massachusetts and Connecticut. Relative to nutrient data in ground and surface water, pesticide data are less common. Pesticide data were available for slightly more than 200 surface-water sites and less than 500 wells; about 95 percent of the wells are completed in stratified-drift or till aquifers. Data for 81 pesticide compounds were available in various data bases. 2,4-D and silvex were the most commonly detected herbicides in surface water and dieldrin and diazinon were the most commonly detected insecticides. Most surface-water pesticide samples and detections are from bed sediment, but much of the data are not recent. Ethylene dibromide (EDB), a soil fumigant used in tobacco farming was detected in 268 wells in a 50 square-mile area of north-central Connecticut; EDB contamination also was detected in wells in Massachusetts. Atrazine, an herbicide commonly used in corn farming, commonly was detected in wells installed in tilled agricultural fields. Corn herbicides were commonly detected in the northern part of the study unit, although the sampling has been less frequent than in the southern part of the study unit. Pesticides were seldom detected in public-supply wells in Connecticut. Urban sites with relatively high population densities and high concentrations of dischargers were characterized by having the highest nutrient concentrations and loads when adjusted for differences in drainage area or population. Particularly, the Pequabuck, Naugatuck, and Quinnipiac River Basins were characterized by high nutrient concentrations median total nitrogen concentrations ranged from 3.3 to 4.2 mg/L (milligrams per liter) and median total phosphorus concentrations ranged from 0.42 to 0.8 mg/L. In contrast, the predominantly forested and low density residential land-use sites, such as Saugatuck and Salmon River Basins, were characterized by low nutrient concentrations median total nitrogen ranged from 0.50 to 0.60 mg/L and median total phosphorus concentrations ranged from 0.01 to 0.02 mg/L. Estimated total nitrogen loadings in median discharge years ranged from 940 kilograms per square mile at the Salmon River near East Hampton, Conn., to 5,800 kilograms per square mile at the Naugatuck River at Beacon Falls, Conn. Water quality, in terms of nutrient concentrations and areally adjusted loadings, for sites with large drainage basins integrating a wide variety of land-use categories fell between the extremes of the urban and forested sites total nitrogen was 1,400 kilograms per square mile per year at the Connecticut River at Thompsonville, Conn. Nitrate concentrations in ground water occasionally exceeded the safe drinking-water standard of 10 mg/L as nitrogen. The greatest number of detections exceeding the standard, however, were not in public-water supplies but in shallow observation wells in agricultural settings (the most frequently sampled type of well). None of the public-supply wells in Massachusetts exceeded the standard. Although nitrate concentrations for Vermont and New Hampshire generally were low, few data were available and those were seldom reported on the basis of drainage basin, making analysis difficult. Trend analysis indicated that flow-adjusted concentrations of total and dissolved phosphorus generally decreased during the period of analysis, however, total nitrogen did not change substantially. Decreases in ammonia concentrations with time were usually accompanied by increases in nitrate, suggesting improvements in sewage treatment. The lack of adequate data from more or less exclusively agricultural areas points to the need for further study of the effects of fanning on surface-water quality in the study unit. Furthermore, additional information is needed on the rates, transformations, and movements of nutrients and other materials through and between the aquatic and terrestrial components of the study unit.

Connecticut, Massachusetts, New Hampshire, Quebec,↗

USGS2017

A three-dimensional mapping of the ocean based on environmental data

The existence, sources, distribution, circulation, and physicochemical nature of macroscale oceanic water bodies have long been a focus of oceanographic inquiry. Building on that work, this paper describes an objectively derived and globally comprehensive set of 37 distinct volumetric region units, called ecological marine units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller and shallower, and occur around coastal features. We assessed the vertical distribution of EMUs in the water column and placed them into classical depth zones representing epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization of the EMUs represent a new spatial framework for organizing and understanding the physical, chemical, and ultimately biological properties and processes of oceanic water bodies. The EMUs are an initial objective partitioning of the ocean using long-term historical average data, and could be extended in the future by adding new classification variables and by introducing functionality to develop time-specific EMU distribution maps. The EMUs are an open-access resource, and as both a standardized geographic framework and a baseline physicochemical characterization of the oceanic environment, they are intended to be useful for disturbance assessments, ecosystem accounting exercises, conservation priority setting, and marine protected area network design, along with other research and management applications.

Oceanography↗

USGS2008

Landscape complexity and soil moisture variation in south Georgia, USA, for remote sensing applications

This research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t-statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil moisture, since t-test's among adjacent plots with different LULCs showed significant differences. These results confirm that a remote sensing approach that considers homogeneous LULC landscape fragments can be used to identify landscape units of similar soil moisture behavior under heterogeneous landscapes. In addition, the in situ USDA-ARS network will serve better in remote sensing studies in which sensors with fine spatial resolution are evaluated. This study is a first step towards identifying landscape units that can be monitored using the single point reading of the USDA-ARS stations network. ?? 2008 Elsevier B.V.

Journal of Hydrology↗

USGS2015

An empirical evaluation of landscape energetic models: Mallard and American black duck space use during the non-breeding period

Bird conservation Joint Ventures are collaborative partnerships between public agencies and private organizations that facilitate habitat management to support waterfowl and other bird populations. A subset of Joint Ventures has developed energetic carrying capacity models (ECCs) to translate regional waterfowl population goals into habitat objectives during the non-breeding period. Energetic carrying capacity models consider food biomass, metabolism, and available habitat to estimate waterfowl carrying capacity within an area. To evaluate Joint Venture ECCs in the context of waterfowl space use, we monitored 33 female mallards ( Anas platyrhynchos ) and 55 female American black ducks ( A. rubripes ) using global positioning system satellite telemetry in the central and eastern United States. To quantify space use, we measured first-passage time (FPT: time required for an individual to transit across a circle of a given radius) at biologically relevant spatial scales for mallards (3.46 km) and American black ducks (2.30 km) during the non-breeding period, which included autumn migration, winter, and spring migration. We developed a series of models to predict FPT using Joint Venture ECCs and compared them to a biological null model that quantified habitat composition and a statistical null model, which included intercept and random terms. Energetic carrying capacity models predicted mallard space use more efficiently during autumn and spring migrations, but the statistical null was the top model for winter. For American black ducks, ECCs did not improve predictions of space use; the biological null was top ranked for winter and the statistical null was top ranked for spring migration. Thus, ECCs provided limited insight into predicting waterfowl space use during the non-breeding season. Refined estimates of spatial and temporal variation in food abundance, habitat conditions, and anthropogenic disturbance will likely improve ECCs and benefit conservation planners in linking non-breeding waterfowl habitat objectives with distribution and population parameters. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Arkansas, Delaware, Louisiana, Michigan, New Jerse↗

USGS2012

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

Introduction Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Methods Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. Results This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change. Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. Conclusions The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Ecological Processes↗

USGS2005

Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina

Highways are one of the leading causes of wildlife habitat fragmentation and may particularly affect wide-ranging species, such as American black bears (Ursus americanus). We initiated a research project in 2000 to determine potential effects of a 4-lane highway on black bear ecology in Washington County, North Carolina. The research design included a treatment area (highway construction) and a control area and a pre- and post-construction phase. We used data from the pre-construction phase to determine whether we could detect scale dependency or directionality among allele occurrence patterns using geostatistics. Detection of such patterns could provide a powerful tool to measure the effects of landscape fragmentation on gene flow. We sampled DNA from roots of black bear hair at 70 hair-sampling sites on each study area for 7 weeks during fall of 2000. We used microsatellite analysis based on 10 loci to determine unique multi-locus genotypes. We examined all alleles sampled at ???25 sites on each study area and mapped their presence or absence at each hair-sample site. We calculated semivariograms, which measure the strength of statistical correlation as a function of distance, and adjusted them for anisotropy to determine the maximum direction of spatial continuity. We then calculated the mean direction of spatial continuity for all examined alleles. The mean direction of allele frequency variation was 118.3?? (SE = 8.5) on the treatment area and 172.3?? (SE = 6.0) on the control area. Rayleigh's tests showed that these directions differed from random distributions (P = 0.028 and P < 0.001, respectively), indicating consistent directional patterns for the alleles we examined in each area. Despite the small spatial scale of our study (approximately 11,000 ha for each study area), we observed distinct and consistent patterns of allele occurrence, suggesting different directions of gene flow between the study areas. These directions seemed to coincide with the primary orientation of the best habitat areas. Furthermore, the patterns we observed suggest directions of potential source populations beyond the 2 study areas. Indeed, nearby areas classified as core black bear habitat exist in the directions indicated by our analysis. Geostatistical analysis of allele occurrence patterns may provide a useful technique to identify potential barriers to gene flow among bear populations.

Ursus↗

USGS2004

Trends in Streamflow Characteristics at Long-Term Gaging Stations, Hawaii

The surface-water resources of Hawaii have significant cultural, aesthetic, ecologic, and economic importance. Proper management of the surface-water resources of the State requires an understanding of the long- and short-term variability in streamflow characteristics that may occur. The U.S. Geological Survey maintains a network of stream-gaging stations in Hawaii, including a number of stations with long-term streamflow records that can be used to evaluate long-term trends and short-term variability in flow characteristics. The overall objective of this study is to obtain a better understanding of long-term trends and variations in streamflow on the islands of Hawaii, Maui, Molokai, Oahu, and Kauai, where long-term stream-gaging stations exist. This study includes (1) an analysis of long-term trends in flows (both total flow and estimated base flow) at 16 stream-gaging stations, (2) a description of patterns in trends within the State, and (3) discussion of possible regional factors (including rainfall) that are related to the observed trends and variations. Results of this study indicate the following: 1. From 1913 to 2002 base flows generally decreased in streams for which data are available, and this trend is consistent with the long-term downward trend in annual rainfall over much of the State during that period. 2. Monthly mean base flows generally were above the long-term average from 1913 to the early 1940s and below average after the early 1940s to 2002, and this pattern is consistent with the detected downward trends in base flows from 1913 to 2002. 3. Long-term downward trends in base flows of streams may indicate a reduction in ground-water discharge to streams caused by a long-term decrease in ground-water storage and recharge. 4. From 1973 to 2002, trends in streamflow were spatially variable (up in some streams and down in others) and, with a few exceptions, generally were not statistically significant. 5. Short-term variability in streamflow is related to the seasons and to the EL Ni?o-Southern Oscillation phenomenon that may be partly modulated by the phase of the Pacific Decadal Oscillation. 6. At almost all of the long-term stream-gaging stations considered in this study, average total flow (and to a lesser extent average base flow) during the winter months of January to March tended to be low following El Ni?o periods and high following La Ni?a periods, and this tendency was accentuated during positive phases of the Pacific Decadal Oscillation. 7. The El Ni?o-Southern Oscillation phenomenon occurs at a relatively short time scale (a few to several years) and appears to be more strongly related to processes controlling rainfall and direct runoff than ground-water storage and base flow. Long-term downward trends in base flows of streams may indicate a reduction in ground-water storage and recharge. Because ground water provides about 99 percent of Hawaii's domestic drinking water, a reduction in ground-water storage and recharge has serious implications for drinking-water availability. In addition, reduction in stream base flows may reduce habitat availability for native stream fauna and water availability for irrigation purposes. Further study is needed to determine (1) whether the downward trends in base flows from 1913 to 2002 will continue or whether the observed pattern is part of a long-term cycle in which base flows may eventually return to levels measured during 1913 to the early 1940s, (2) the physical causes for the detected trends and variations in streamflow, and (3) whether regional climate indicators successfully can be used to predict streamflow trends and variations throughout the State. These needs for future study underscore the importance of maintaining a network of long-term-trend stream-gaging stations in Hawaii.

Scientific Investigations Report↗

USGS2006

Cluster analysis of water-quality data for Lake Sakakawea, Audubon Lake, and McClusky Canal, central North Dakota, 1990-2003

As a result of the Dakota Water Resources Act of 2000, the Bureau of Reclamation, U.S. Department of the Interior, identified eight water-supply alternatives (including a no-action alternative) to meet future water needs in portions of the Red River of the North (Red River) Basin. Of those alternatives, four include the interbasin transfer of water from the Missouri River Basin to the Red River Basin. Three of the interbasin transfer alternatives would use the McClusky Canal, located in central North Dakota, to transport the water. Therefore, the water quality of the McClusky Canal and the sources of its water, Lake Sakakawea and Audubon Lake, is of interest to water-quality stakeholders. The Bureau of Reclamation collected water-quality samples at 23 sites on Lake Sakakawea, Audubon Lake, and the McClusky Canal system from 1990 through 2003. Physical properties and water-quality constituents from these samples were summarized and analyzed by the U.S. Geological Survey using hierarchical agglomerative cluster analysis (HACA). HACA separated the samples into related clusters, or groups. These groups were examined for statistical significance and relation to structure of the McClusky Canal system. Statistically, the sample groupings found using HACA were significantly different from each other and appear to result from spatial and temporal water-quality differences corresponding with different sections of the canal and different operational conditions. Future operational changes of the canal system may justify additional water-quality sampling to characterize possible water-quality changes.

North Dakota↗

USGS2016

Evaluating alternative methods for biophysical and cultural ecosystem services hotspot mapping in natural resource planning

Context Data for biophysically modeled and Public Participatory GIS (PPGIS)-derived cultural ecosystem services have potential to identify natural resource management synergies and conflicts, but have rarely been combined. Ecosystem service hot/coldspots generated using different methods vary in their spatial extent and connectivity, with important implications. Objectives We map biophysically modeled and PPGIS-derived cultural services for six U.S. national forests using six hot/coldspot delineation methods. We evaluate the implications of hotspot methods for management within and outside of designated wilderness areas. Methods We used the ARIES and SolVES modeling tools to quantify four biophysically modeled and 11 largely cultural ecosystem services for six national forests in Colorado and Wyoming, USA. We mapped hot/coldspots using two quantile methods (top and bottom 10 and 33 % of values), two area-based methods (top and bottom 10 and 33 % of area), and two statistical methods (Getis-Ord Gi* at α = 0.05 and 0.10 significance level) and compare results within and outside wilderness areas. Results Delineation methods vary in their degree of conservatism for hot/coldspot extents and spatial clustering. Hotspots were more common in wilderness areas in national forests near the more densely populated Colorado Front Range, while coldspots were more common in wilderness areas in more urban-distant forests in northwest Wyoming. Conclusions Statistical hotspot methods of intermediate conservatism (i.e., Getis-Ord Gi*, α = 0.10 significance) may be most useful for ecosystem service hot/coldspot mapping to inform landscape scale planning. We also found spatially explicit evidence in support of past findings about public attitudes toward wilderness areas.

Landscape Ecology↗

USGS2019

A multidisciplinary framework to derive global river reach classifications at high spatial resolution

Projected climate and environmental change are expected to increase the pressure on global freshwater resources. To prepare for and cope with the related risks, stakeholders need to devise plans for sustainable management of river systems, which in turn requires the identification of management-appropriate operational units, such as groups of rivers that share similar environmental and biological characteristics. Ideally, these units are of a manageable size, and are biotically or abiotically distinguishable across a variety of river types. Here, we aim to address this need by presenting a new global river classification framework (GloRiC) to establish a common vocabulary and standardized approach to the development of globally comprehensive and integrated river classifications that can be tailored to different goals and requirements. We define the GloRiC conceptual framework based on five categories of variables: (1) hydrology; (2) physiography and climate; (3) fluvial geomorphology; (4) water chemistry; and (5) aquatic biology. We then apply the framework using hydro-environmental attributes provided by a seamless high-resolution river reach database to create initial instances of three sub-classifications (hydrologic, physio-climatic, and geomorphic) which we ultimately combine into 127 river reach types at the global scale. These supervised classifications utilize a mix of statistical analyses and expert interpretation to identify the classifier variables, the number of classes, and their thresholds. In addition, we also present an unsupervised, multivariable k-means statistical clustering of all river reaches into 30 groups. These first-of-their-kind global river reach classifications at high spatial resolution provide baseline information for a total of 35.9 million kilometers of rivers that have been assessed in this study, and are expected to be particularly useful in remote or data-poor river basins. The GloRiC framework and associated data are primarily designed for broad and rapid applicability in assessments that require stratified analyses of river ecosystem conditions at global and regional scales; smaller-scale applications could follow the same conceptual framework yet use more detailed data sources.

Environmental Research Letters↗

USGS2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush ( Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Wyoming↗

USGS1993

"Relative CIR": an image enhancement and visualization technique

Many techniques exist to spectrally and spatially enhance digital multispectral scanner data. One technique enhances an image while keeping the colors as they would appear in a color-infrared (CIR) image. This "relative CIR" technique generates an image that is both spectrally and spatially enhanced, while displaying a maximum range of colors. The technique enables an interpreter to visualize either spectral or land cover classes by their relative CIR characteristics. A relative CIR image is generated by developed spectral statistics for each class in the classifications and then, using a nonparametric approach for spectral enhancement, the means of the classes for each band are ranked. A 3 by 3 pixel smoothing filter is applied to the classification for spatial enhancement and the classes are mapped to the representative rank for each band. Practical applications of the technique include displaying an image classification product as a CIR image that was not derived directly from a spectral image, visualizing how a land cover classification would look as a CIR image, and displaying a spectral classification or intermediate product that will be used to label spectral classes.

Pecora 12 Symposium↗

USGS2017

Evaluation of the streamgage network for estimating streamflow statistics at ungaged sites in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York

The current (2015) streamgage network in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York was evaluated in order to design a network that would meet the hydrologic needs of many partners and serve a variety of purposes and interests, including estimation of streamflow statistics at ungaged sites. This study was done by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission. The study area includes the Commonwealth of Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York. For this study, 229 streamgages were identified as reference streamgages that could be used to represent ungaged watersheds. Criteria for a reference streamgage are a minimum of 10 years of continuous record, minimally altered streamflow, and a drainage area less than 1,500 square miles. Some of the reference streamgages have been discontinued but provide historical hydrologic information valuable in the determination of streamflow characteristics of ungaged watersheds. Watersheds in the study area not adequately represented by a reference streamgage were identified by examining a range of basin characteristics, the extent of geographic coverage, and the strength of estimated streamflow correlations between gaged and ungaged sites. Basin characteristics were determined for the reference streamgage watersheds and the 1,662 12-digit hydrologic unit code (HUC12) subwatersheds in Pennsylvania and the Susquehanna River Basin using a geographic information system (GIS) spatial analysis and nationally available GIS datasets. Basin characteristics selected for this study include drainage area, mean basin elevation, mean basin slope, percentage of urbanized area, percentage of forested area, percentage of carbonate bedrock, mean annual precipitation, and soil thickness. A GIS spatial analysis was used to identify HUC12 subwatersheds outside the range of basin characteristics of the reference streamgages. There were 320 HUC12 subwatersheds, or 19 percent of the study area, with basin characteristics outside the range represented by the reference streamgage watersheds. A GIS spatial analysis was used to identify geographic gaps in the streamgage network. For each streamgage, a watershed area, called the gage statistical area (GSA), was delineated. The GSA shows the drainage area within a specific drainage-area ratio of the streamgage for transfer of streamflow statistics from that streamgage to ungaged sites on the valid statistical reach of the GSA for a streamgage. In Pennsylvania, a drainage-area ratio of 0.33–3 times the drainage area of the ungaged site was found to perform as well as, if not better than, more traditional ratios such as 0.5–1.5 (or 2) for transfer of selected streamflow statistics. A total of 1,102 HUC12 subwatersheds, or 66 percent of the study area, are outside the GSA for a reference streamgage. The USGS Baseline Streamflow Estimator (BaSE) program was used to determine how well HUC12 subwatersheds outside the streamgage GSAs are represented by the reference streamgage network in Pennsylvania, based on estimated streamflow correlation. The centroid of each HUC12 subwatershed was run through the BaSE program to determine the reference streamgage with the highest estimated streamflow correlation. There were 929 HUC12 subwatersheds in Pennsylvania, or 56 percent of the State, with an estimated correlation coefficient less than 0.96. The results from the basin characteristic, geographic, and streamflow correlation analyses were combined to identify 1,405 HUC12 subwatersheds in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York that lack a representative reference, based on at least one identified gap. Of the 1,405 HUC12 subwatersheds, 139 exhibited all three gaps, indicating a 8-percent gap in the reference streamgage network. Streamgages in areas with similar hydrologic characteristics and in close proximity to one another can potentially provide similar information (termed streamgages with high substitution potential). Streamgages were considered to have a high substitution potential with a nearby streamgage(s) if (1) the streamflow correlation coefficient was equal to or greater than 0.96, (2) the streamgages had 10 years of concurrent record, and (3) the streamgages are in the same watershed within the GSA of the streamgage. Seventy-four current (2015) streamgages with high substitution potential with at least one other streamgage were identified in the study area. Although these identified streamgages have a high substitution potential, they provide valuable streamflow information to a stakeholder. Selected primary uses of these streamgages were identified to determine the overall need for an individual streamgage.

New York, Pennsylvania↗