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Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

Fact Sheet

Management of ground water and evolving hydrogeologic studies in New Jersey : a heavily urbanized and industrialized state in the northeastern United States

New Jersey is the most densely populated and one of the most industrialized states in the United States. An abundance of freshwater and proximity to major northeastern metropolitan centers has facilitated this development. Pumpage of freshwater from all aquifers in the State in 1980 was 730 million gallons per day (2.76 million cubic meters per day). Management and efficient development of the ground-water resources of the State are the responsibility of the New Jersey Department of Environmental Protection. Laws have been enacted and updated by the State legislature to manage water allocation and to control the disposal of hazardous wastes. Present resource management is guided by the New Jersey Water-Supply Master Plan of 1981. Funding for management activities is partially derived from the sale of state-approved bonds. Effective planning and regional management require accurate and up-to-date hydrologic information and analyses. The U.S. Geological Survey, in cooperation with the New Jersey Geological Survey, is conducting three intensive ground-water studies involving the collection and interpretation of hydrologic data to meet the urgent water-management needs of New Jersey. These studies are part of a long-term cooperative program and are funded through the Water-Supply Bond Act of 1981. They began in 1983 and are scheduled to be completed in 1988. The project areas are situated in the New Jersey part of the Atlantic Coastal Plain in and near Atlantic City, Camden, and South River. They range in size from 400 to 1,200 mil (1,040 to 3,120 km2). The studies are designed to define the geology, hydrology, and geochemistry of the local ground-water systems. The results of these studies will enable the State to address more effectively major problems in these areas such as declining water levels, overpumping, saltwater intrusion, and ground-water contamination resulting from the improper disposal of hazardous wastes. Specific objectives of these studies by the U.S. Geological Survey are to (1) develop an accurate and up-to-date hydrogeologic data base, (2) design and implement a data-collection program and establish a computerized information management system, (3) refine the conceptualization of the ground-water flow system, and (4) define the geochemistry of the aquifer system by conducting a water-quality appraisal. The objectives are accomplished by standard hydrogeologic methods. Information concerning hydrogeologic framework, ground-water levels, water use, hydraulic characteristics, and water quality in the study areas is compiled from all available sources. Additional data needed are collected through well inventories, surface geophysical surveys, water-quality samplings, water-level measurements, and a well-drilling program. Interpretation of the flow system is based on the use of standard analytical techniques and digital flow modeling. Calibrated flow models will provide ground-water managers with a mechanism to develop and test regional water-supply strategies. Definition of the geochemistry of the aquifer system is accomplished through a variety of methods which depend on the problems and available data in the particular study area. The approach includes statistical analysis of water-quality data, reaction-path modeling, and determination of the movement of chemical constituents using analytical and numerical modeling techniques. A combined staff of 25 to 30 professionals and technicians from the New Jersey District office of the U.S. Geological Survey is committed to the three studies. The staff has specialists in geohydrology, numerical modeling, geochemistry, geophysics, and computer science. The findings of these studies will be published in data reports, interpretive reports, instructional manuals and journal articles.

New Jersey

The computer program estimate trend (ESTREND), a system for the detection of trends in water-quality data

Computerized statistical and graphical procedures were developed for use in U.S. Geological Survey (USGS) investigations of trend in stream water-quality data. These procedures, identified as EStimate TREND (ESTREND), are described in this paper to assist USGS investigators involved in multiple-station studies of water-quality trends. Additional discussion focuses on certain statistical and operational decisions required in multiple-station analysis of trends. The statistical methods used in ESTREND overcome common statistical problems encountered by conventional statistical trend techniques in the analysis of water-quality data. The problems include data that are non-normal and seasonally varying and water-quality records with missing values, 'less-than' (censored) values, and outliers, all of which adversely affect the performance of conventional statistical techniques. Parametric and nonparametric statistical trend tests are used in ESTREND. A nonparametric method, the Seasonal Kendall test, is used for data that have few less-than values or data that have been censored at only one reporting limit. A parametric test for trend involving a maximum likelihood estimation method is used for data that have been censored at multiple reporting limits. The Seasonal Kendall test for uncensored data allows for the removal of flow variability in water-quality data which improves the performance of the statistical trend tests. Menu-driven procedures in ESTREND allow the user to easily retrieve water-quality data, analyze data for trend, and view tabular and graphical results of analyses.

Water-Resources Investigations Report

A geographic information system tool to solve regression equations and estimate flow-frequency characteristics of Vermont Streams

Estimates of the magnitude and frequency of streamflow are needed to safely and economically design bridges, culverts, and other structures in or near streams. These estimates also are used for managing floodplains, identifying flood-hazard areas, and establishing flood-insurance rates, but may be required at ungaged sites where no observed flood data are available for streamflow-frequency analysis. This report describes equations for estimating flow-frequency characteristics at ungaged, unregulated streams in Vermont. In the past, regression equations developed to estimate streamflow statistics required users to spend hours manually measuring basin characteristics for the stream site of interest. This report also describes the accompanying customized geographic information system (GIS) tool that automates the measurement of basin characteristics and calculation of corresponding flow statistics. The tool includes software that computes the accuracy of the results and adjustments for expected probability and for streamflow data of a nearby stream-gaging station that is either upstream or downstream and within 50 percent of the drainage area of the site where the flow-frequency characteristics are being estimated. The custom GIS can be linked to the National Flood Frequency program, adding the ability to plot peak-flow-frequency curves and synthetic hydrographs and to compute adjustments for urbanization.

Open-File Report

Simulating realistic predator signatures in quantitative fatty acid signature analysis

Diet estimation is an important field within quantitative ecology, providing critical insights into many aspects of ecology and community dynamics. Quantitative fatty acid signature analysis (QFASA) is a prominent method of diet estimation, particularly for marine mammal and bird species. Investigators using QFASA commonly use computer simulation to evaluate statistical characteristics of diet estimators for the populations they study. Similar computer simulations have been used to explore and compare the performance of different variations of the original QFASA diet estimator. In both cases, computer simulations involve bootstrap sampling prey signature data to construct pseudo-predator signatures with known properties. However, bootstrap sample sizes have been selected arbitrarily and pseudo-predator signatures therefore may not have realistic properties. I develop an algorithm to objectively establish bootstrap sample sizes that generates pseudo-predator signatures with realistic properties, thereby enhancing the utility of computer simulation for assessing QFASA estimator performance. The algorithm also appears to be computationally efficient, resulting in bootstrap sample sizes that are smaller than those commonly used. I illustrate the algorithm with an example using data from Chukchi Sea polar bears ( Ursus maritimus ) and their marine mammal prey. The concepts underlying the approach may have value in other areas of quantitative ecology in which bootstrap samples are post-processed prior to their use.

Ecological Informatics

Characterization and analysis of temporal and spatial variations in habitat and macroinvertebrate community structure, Fountain Creek basin, Colorado Springs and vicinity, Colorado, 1998-2001

The Fountain Creek Basin in and around Colorado Springs, Colorado, is affected by various land- and water-use activities. Biological, hydrological, water-quality, and land-use data were collected at 10 sites in the Fountain Creek Basin from April 1998 through April 2001 to provide a baseline characterization of macroinvertebrate communities and habitat conditions for comparison in subsequent studies; and to assess variation in macroinvertebrate community structure relative to habitat quality. Analysis of variance results indicated that instream and riparian variables were not affected by season, but significant differences were found among sites. Nine metrics were used to describe and evaluate macroinvertebrate community structure. Statistical analysis indicated that for six of the nine metrics, significant variability occurred between spring and fall seasons for 60 percent of the sites. Cluster analysis (unweighted pair group method average) using macroinvertebrate presence-absence data showed a well-defined separation between spring and fall samples. Six of the nine metrics had significant spatial variation. Cluster analysis using Sorenson?s Coefficient of Community values computed from macroinvertebrate density (number of organisms per square meter) data showed that macroinvertebrate community structure was more similar among tributary sites than main-stem sites. Canonical correspondence analysis identified a substrate particle-size gradient from site-specific species-abundance data and environmental correlates that decreased the 10 sites to 5 site clusters and their associated taxa.

Water-Resources Investigations Report

Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013

Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales. In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed. The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013. Individual Small Watershed Summaries Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail. Smith Creek Smith Creek is a 105.39-mi 2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ 15 N of nitrate, pH, bicarbonate, calcium, and δ 18 O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin. Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods. Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time. Upper Chester River The Upper Chester River watershed includes the 36-square-mile (mi 2 ) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi 2 ) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff. Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ 15 N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season. Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load. Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N 2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ 15 N and δ 18 O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer. The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature. Conewago Creek Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi 2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year. Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed. Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time. Difficult Run The Difficult Run watershed is a 57.82-mi 2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ 15 N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ 15 N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin. Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.

Maryland, Pennsylvania, Virginia

Hierarchical models and the analysis of bird survey information

Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.

Ornis Hungarica

Evaluation of four structural best management practices for highway runoff in Beaufort and Colleton Counties, South Carolina, 2005–2006

From 2005 to 2006, the U.S. Geological Survey worked cooperatively with the South Carolina Department of Transportation in Beaufort and Colleton Counties, South Carolina, to assess the performance of four different structural devices that served as best management practices (BMPs). These structural devices were installed to mitigate the effects of stormwater runoff on waterways near State roads. The South Carolina Department of Transportation is required to address the quality of stormwater runoff from State-maintained roadways as part of the National Pollutant Discharge Elimination System stormwater program mandated in the Clean Water Act. The performance assessment of the four structural best management practices was based on stormflow measurements and chemical analyses of stormwater-quality samples collected during a 20-month period from March 2005 through October 2006, which represented a range of seasons and rainfall intensities. A total of 49 sample sets that included stormwater from the inlet and outlet of each of the four structural devices were collected as flow-weighted composites to provide event-mean concentrations of suspended sediment, nutrients, and trace metals. In addition, each set included grab samples that were collected to provide the first flush concentrations of oil and grease and fecal-indicator bacteria. A tiered statistical approach was used in the data analysis. Performances of the four structural BMPs were assessed individually based on how well the BMPs were able to reduce the selected constituents. Descriptive statistics and nonparametric Wilcoxon signed rank tests were applied to event-mean concentrations and loads in the water entering the inlet and in the water leaving the outlet of each BMP for each constituent to identify if significant reductions occurred. If significant reductions occurred, the BMP was considered efficient at reducing the constituent. To quantify efficiency, a simplistic approach was applied to compute mean and geometric mean efficiency ratios for the significantly reduced constituents in each BMP. Each BMP performance was ranked based on its computed efficiency ratios, however, the computed efficiency ratios were not sufficient to determine if statistical differences occurred among the performances of the four BMPs. Consequently, a more complex approach was used to apply statistical comparison tests to reduction percentages computed for individual storms (a modified removal efficiency of individual storm-load approach) to determine if differences in event-mean concentrations, loads, and reduction percentages for significantly reduced constituents occurred among the four structural BMPs. Overall, the four BMPs were efficient in reducing suspended-sediment event-mean concentrations and loads in the stormwater entering the inlets of the BMPs to significantly lower event-mean concentrations before discharging the stormwater from the outlets. The cumulative suspended-sediment event-mean load in stormwater entering the BMPs from the storms sampled during the data-collection period was 1,026 kilograms (1.13 tons). The BMPs removed a cumulative suspended-sediment load of 558 kilograms (0.62 ton). The BMPs tended to preferentially trap the sand-size fraction of the sediment, thereby releasing a greater percentage of fine-grained (silt and clay) sediment in the water discharging from the outlet. The preferential trapping of fine-grained sediment by the BMPs could explain, at least in part, why the BMPs were not successful at significantly reducing these constituents. In general, the four BMPs were not successful at significantly reducing fecal bacteria, nutrients, and total organic carbon (including associated properties of biochemical oxygen demand and chemical oxygen demand). Three of the four BMPs significantly lowered oil and grease concentrations before the stormwater discharged from the outlet. Additionally, only one BMP was effective at reducing all total and particulate trace-metal event-mean concentrations and particulate trace-metal event-mean loads in stormwater entering the inlet. With respect to trace-metal event-mean concentrations, however, minimal or no improvement in outlet water quality was observed for the four BMPs, and the majority of the outlet concentrations were above the established acute and chronic aquatic-life criteria by the South Carolina Department of Health and Environmental Control. No statistical differences among the removal-efficiency of the four BMPs were determined for suspended-sediment event-mean concentrations, total suspended solids event-mean concentrations, or oil and grease concentrations. These statistical findings indicated that differences among the mean efficiency ratios were not significant among the BMPs for these properties. Additionally, one BMP generally had statistically greater removal efficiency for total and particulate cadmium, copper, lead, and zinc than one or more of the other three BMPs. Statistical correlation tests were unable to identify a single major factor that would explain the high variability in inlet and outlet water concentrations and in removal efficiencies estimated by reduction percentage. Highly variable inlet and outlet concentrations for each BMP that produced highly variable reduction percentages were probably the result of multiple interacting factors, particularly rainfall intensity, the amount of rainfall between sampling events, traffic density, and the period of time since the last maintenance (clean out) of the BMP.

South Carolina

Statistical power for detecting trends with applications to seabird monitoring

Power analysis is helpful in defining goals for ecological monitoring and evaluating the performance of ongoing efforts. I examined detection standards proposed for population monitoring of seabirds using two programs (MONITOR and TRENDS) specially designed for power analysis of trend data. Neither program models within- and among-years components of variance explicitly and independently, thus an error term that incorporates both components is an essential input. Residual variation in seabird counts consisted of day-to-day variation within years and unexplained variation among years in approximately equal parts. The appropriate measure of error for power analysis is the standard error of estimation (S.E.est) from a regression of annual means against year. Replicate counts within years are helpful in minimizing S.E.est but should not be treated as independent samples for estimating power to detect trends. Other issues include a choice of assumptions about variance structure and selection of an exponential or linear model of population change. Seabird count data are characterized by strong correlations between S.D. and mean, thus a constant CV model is appropriate for power calculations. Time series were fit about equally well with exponential or linear models, but log transformation ensures equal variances over time, a basic assumption of regression analysis. Using sample data from seabird monitoring in Alaska, I computed the number of years required (with annual censusing) to detect trends of -1.4% per year (50% decline in 50 years) and -2.7% per year (50% decline in 25 years). At ??=0.05 and a desired power of 0.9, estimated study intervals ranged from 11 to 69 years depending on species, trend, software, and study design. Power to detect a negative trend of 6.7% per year (50% decline in 10 years) is suggested as an alternative standard for seabird monitoring that achieves a reasonable match between statistical and biological significance.

Alaska

Trends in pesticide concentrations in corn-belt streams, 1996-2006

Trends in the concentrations of commonly occurring pesticides in the Corn Belt of the United States were assessed, and the performance and application of several statistical methods for trend analysis were evaluated. Trends in the concentrations of 11 pesticides with sufficient data for trend assessment were assessed at up to 31 stream sites for two time periods: 1996–2002 and 2000–2006. Pesticides included in the trend analyses were atrazine, acetochlor, metolachlor, alachlor, cyanazine, EPTC, simazine, metribuzin, prometon, chlorpyrifos, and diazinon. The statistical methods applied and compared were (1) a modified version of the nonparametric seasonal Kendall test (SEAKEN), (2) a modified version of the Regional Kendall test, (3) a parametric regression model with seasonal wave (SEAWAVE), and (4) a version of SEAWAVE with adjustment for streamflow (SEAWAVE-Q). The SEAKEN test is a statistical hypothesis test for detecting monotonic trends in seasonal time-series data such as pesticide concentrations at a particular site. Trends across a region, represented by multiple sites, were evaluated using the regional seasonal Kendall test, which computes a test for an overall trend within a region by computing a score for each season at each site and adding the scores to compute the total for the region. The SEAWAVE model is a parametric regression model specifically designed for analyzing seasonal variability and trends in pesticide concentrations. The SEAWAVE-Q model accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic trends from changes caused by other factors, such as pesticide use. There was broad, general agreement between unadjusted trends (no adjustment for streamflow effects) identified by the SEAKEN and SEAWAVE methods, including the regional seasonal Kendall test. Only about 10 percent of the paired comparisons between SEAKEN and SEAWAVE indicated a difference in the direction of trend, and none of these had differences significant at the 10-percent significance level. This consistency of results supports the validity and robustness of all three approaches as trend analysis tools. The SEAWAVE method is favored, however, because it has less restrictive data requirements, enabling analysis for more site/pesticide combinations, and can incorporate adjustment for streamflow (SEAWAVE-Q) with substantially fewer measurements than the flow-adjustment procedure used with SEAKEN. Analysis of flow-adjusted trends is preferable to analysis of non-adjusted trends for evaluating potential effects of changes in pesticide use or management practices because flow-adjusted trends account for the influence of flow-related variability. Analysis of flow-adjusted trends by SEAWAVE-Q showed that all of the pesticides assessed, except simazine and acetochlor, were dominated by varying degrees of concentration downtrends in one or both analysis periods. Atrazine, metolachlor, alachlor, cyanazine, EPTC, and metribuzin—all major corn herbicides, as well as prometon and chlorpyrifos, showed more prevalent concentration downtrends during 1996–2002 compared to 2000–2006. Diazinon had no clear trends during 1996–2002, but had predominantly downward trends during 2000–2006. Acetochlor trends were mixed during 1996–2002 and slightly upward during 2000–2006, but most of the trends were not statistically significant. Simazine concentrations trended upward at most sites during both 1996–2002 and 2000–2006. Comparison of concentration trends to agricultural-use trends indicated similarity in direction and magnitude for acetochlor, metolachlor, alachlor, cyanazine, EPTC, and metribuzin. Concentration downtrends for atrazine, chlorpyrifos, and diazinon were steeper than agricultural-use downtrends at some sites, indicating the possibility that agricultural management practices may have increasingly reduced transport to streams (particularly atrazine) or, for chlorpyrifos and diazinon, that nonagricultural uses declined substantially. Concentration uptrends for simazine generally were steeper than agricultural-use uptrends, indicating the possibility that nonagricultural uses of this herbicide increased during the study period.

Scientific Investigations Report

A velocimetric survey of the Lower Missouri River from river mile 492.38 to 290.20, July-October 2011 and July 2012

Velocimetric surveys were made by the U.S. Geological Survey in 2011 and 2012 to provide data for the U.S. Army Corps of Engineers’ ongoing study of bed degradation in the Lower Missouri River. Using Acoustic Doppler Current Profile technology, velocity data were collected at 87 river miles along the Lower Missouri River from Rulo, Nebraska to Waverly, Missouri, from July to October 2011 and in July 2012, for a total of 118 velocimetric surveys. Multiple-repeat velocimetric surveys were done eight times at three river miles from July to October 2011. Synoptic velocimetric surveys spanning 2–4 days were done twice at ten river miles, once in July 2011 and once in October 2011. Additional synoptic velocimetric surveys were done at proximal river miles in October 2011 and July 2012. Main-channel, near-bed, near-bank, and whole-river velocities were extracted from the Acoustic Doppler Current Profile data using AdMap and compiled as an average of reciprocal pairs for each survey. In addition, the mean velocity computed by the Winriver II software for each survey was integrated with the extracted data. Multiple-repeat velocimetric surveys in the vicinity of Kansas City and Waverly, Missouri, in 2011 indicated that main-channel, near-bed, near-bank, and whole-river velocities generally declined with respect to declining daily mean discharges at the St. Joseph and Waverly, Missouri streamgaging stations. Statistical analysis of the four extracted velocity types indicated that multiple-repeat velocimetric surveys were strongly correlated with daily mean discharges at nearby streamgaging stations (coefficient of determination greater than 0.75). Main-channel velocity exceeded whole-river velocity by an average of 25 percent at river mile 357.70, 22 percent at river mile 357.09, and 6.8 percent at river mile 290.20 for all velocimetric surveys at each location, respectively. Mainchannel, near-bed, near-bank, and whole-river velocities declined about 48 percent from July to October 2011 at the Kansas City sites and about 38 percent at the Waverly site. Winriver II mean velocity from multiple-repeat velocimetric surveys indicated that the relation between all velocities was inconsistent from July to October 2011. Percent changes in channel width from July to October 2011 were typically less than concurrent changes in channel area and instantaneous discharge. The combined synoptic and multiple-repeat survey data for July 2011 did not indicate a clear longitudinal trend of velocity as instantaneous discharge increased downstream. Main-channel velocity exceeded whole-river velocity by an average of 22 percent during July 2011 and in some cases by more than 40 percent (river miles 452.50 and 308.80). Evaluation of 10 pairs of synoptic and synoptic-repeat velocimetric surveys with multiple-repeat velocimetric surveys from July to October 2011 indicated that all velocity types and channel width decreased by about one-third. Channel area and instantaneous discharge decreased by more than 50 percent from July to October 2011 and the percent by which main-channel velocity exceeded whole-river velocity decreased slightly from 22 percent in July to 19 percent. Comparing high (July 2011) to low (October 2011) discharge, where the channel width and area expanded by a factor of nearly 3.0 or more at high discharge (river miles 492.38 to 452.50), main-channel, near-bed, near-bank, and whole-river velocities increased by factors in the range of 1.1 to 1.7 and Winriver II mean velocity decreased. At the Kansas City river miles, all velocity types and channel areas nearly doubled at high discharge and channel widths remained similar to those at low discharge. Multiple-repeat and synoptic velocimetric surveys evaluated in October 2011 indicated that main-channel, near-bed, nearbank, and whole-river velocities generally increased downstream from river miles 424.20 to 404.70 and then decreased, until river mile 290.20, where they increased slightly. Of the July 2012 synoptic velocimetric surveys, velocities near St. Joseph, Missouri, indicated no longitudinal trends in the main-channel, near-bed, near-bank, and whole-river velocities. The Kansas City and Waverly synoptic velocimetric surveys indicated a general decrease in these velocities proceeding downstream. For all 2012 surveys, near-bed velocity was closest in magnitude to Winriver II mean velocity and near-bed and whole-river velocities decreased with increasing channel area. For the entire study, variations in near-bank velocity may have been due to the influence of channel structures and their diversion of higher velocities away from the channel edges.

Missouri;Nebraska

External quality-assurance results for the National Atmospheric Deposition Program/National Trends Network, 2002-03

Six external quality-assurance programs were operated by the U.S. Geological Survey (USGS) External Quality-Assurance (QA) Project for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) from 2002 through 2003. Each program measured specific components of the overall error inherent in NADP/NTN wet-deposition measurements. The intersite-comparison program assessed the variability and bias of pH and specific conductance determinations made by NADP/NTN site operators twice per year with respect to accuracy goals. The percentage of site operators that met the pH accuracy goals decreased from 92.0 percent in spring 2002 to 86.3 percent in spring 2003. In these same four intersite-comparison studies, the percentage of site operators that met the accuracy goals for specific conductance ranged from 94.4 to 97.5 percent. The blind-audit program and the sample-handling evaluation (SHE) program evaluated the effects of routine sample handling, processing, and shipping on the chemistry of weekly NADP/NTN samples. The blind-audit program data indicated that the variability introduced by sample handling might be environmentally significant to data users for sodium, potassium, chloride, and hydrogen ion concentrations during 2002. In 2003, the blind-audit program was modified and replaced by the SHE program. The SHE program was designed to control the effects of laboratory-analysis variability. The 2003 SHE data had less overall variability than the 2002 blind-audit data. The SHE data indicated that sample handling buffers the pH of the precipitation samples and, in turn, results in slightly lower conductivity. Otherwise, the SHE data provided error estimates that were not environmentally significant to data users. The field-audit program was designed to evaluate the effects of onsite exposure, sample handling, and shipping on the chemistry of NADP/NTN precipitation samples. Field-audit results indicated that exposure of NADP/NTN wet-deposition samples to onsite conditions tended to neutralize the acidity of the samples by less than 1.0 microequivalent per liter. Onsite exposure of the sampling bucket appeared to slightly increase the concentration of most of the analytes but not to an extent that was environmentally significant to NADP data users. An interlaboratory-comparison program was used to estimate the analytical variability and bias of the NADP Central Analytical Laboratory (CAL) during 2002-03. Bias was identified in the CAL data for calcium, magnesium, sodium, potassium, ammonium, chloride, nitrate, sulfate, hydrogen ion, and specific conductance, but the absolute value of the bias was less than analytical minimum detection limits for all constituents except magnesium, nitrate, sulfate, and specific conductance. Control charts showed that CAL results were within statistical control approximately 90 percent of the time. Data for the analysis of ultrapure deionized-water samples indicated that CAL did not have problems with laboratory contamination. During 2002-03, the overall variability of data from the NADP/NTN precipitation-monitoring system was estimated using data from three collocated monitoring sites. Measurement differences of constituent concentration and deposition for paired samples from the collocated samplers were evaluated to compute error terms. The medians of the absolute percentage errors (MAEs) for the paired samples generally were larger for cations (approximately 8 to 50 percent) than for anions (approximately 3 to 33 percent). MAEs were approximately 16 to 30 percent for hydrogen-ion concentration, less than 10 percent for specific conductance, less than 5 percent for sample volume, and less than 8 percent for precipitation depth. The variability attributed to each component of the sample-collection and analysis processes, as estimated by USGS quality-assurance programs, varied among analytes. Laboratory analysis variability accounted for approximately 2 percent of the

Scientific Investigations Report

Regression method for estimating long-term mean annual ground-water recharge rates from base flow in Pennsylvania

A method was developed for making estimates of long-term, mean annual ground-water recharge from streamflow data at 80 streamflow-gaging stations in Pennsylvania. The method relates mean annual base-flow yield derived from the streamflow data (as a proxy for recharge) to the climatic, geologic, hydrologic, and physiographic characteristics of the basins (basin characteristics) by use of a regression equation. Base-flow yield is the base flow of a stream divided by the drainage area of the basin, expressed in inches of water basinwide. Mean annual base-flow yield was computed for the period of available streamflow record at continuous streamflow-gaging stations by use of the computer program PART, which separates base flow from direct runoff on the streamflow hydrograph. Base flow provides a reasonable estimate of recharge for basins where streamflow is mostly unaffected by upstream regulation, diversion, or mining. Twenty-eight basin characteristics were included in the exploratory regression analysis as possible predictors of base-flow yield. Basin characteristics found to be statistically significant predictors of mean annual base-flow yield during 1971-2000 at the 95-percent confidence level were (1) mean annual precipitation, (2) average maximum daily temperature, (3) percentage of sand in the soil, (4) percentage of carbonate bedrock in the basin, and (5) stream channel slope. The equation for predicting recharge was developed using ordinary least-squares regression. The standard error of prediction for the equation on log-transformed data was 9.7 percent, and the coefficient of determination was 0.80. The equation can be used to predict long-term, mean annual recharge rates for ungaged basins, providing that the explanatory basin characteristics can be determined and that the underlying assumption is accepted that base-flow yield derived from PART is a reasonable estimate of ground-water recharge rates. For example, application of the equation for 370 hydrologic units in Pennsylvania predicted a range of ground-water recharge from about 6.0 to 22 inches per year. A map of the predicted recharge illustrates the general magnitude and variability of recharge throughout Pennsylvania.

Scientific Investigations Report

Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming

Executive Summary The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.

Wyoming

A 125 year history of topographic mapping and GIS in the U.S. Geological Survey 1884-2009, part 2: 1980-2009

The United States Geological Survey (USGS) entered the mainstream of developments in computer-assisted technology for mapping during the 1970s. The introduction by USGS of digital line graphs (DLGs), digital elevation models (DEMs), and land use data analysis (LUDA) nationwide land-cover data provided a base for the rapid expansion of the use of GIS in the 1980s. Whereas USGS had developed the topologically structured DLG data and the Geographic Information Retrieval and Analysis System (GIRAS) for land-cover data, the Map Overlay Statistical System (MOSS), a nontopologically structured GIS software package developed by Autometric, Inc., under contract to the U.S. Fish and Wildlife Service, dominated the use of GIS by federal agencies in the 1970s. Thus, USGS data was used in MOSS, but the topological structure, which later became a requirement for GIS vector datasets, was not used in early GIS applications. The introduction of Esri's ARC/INFO in 1982 changed that, and by the end of the 1980s, topological structure for vector data was essential, and ARC/INFO was the dominant GIS software package used by federal agencies.

ArcNews

Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs

As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially different results and/or computational instability. However, when only fixed effects are of interest, the survey package (svyglm and svyolr) may be suitable for a model-assisted analysis for trend. We provide possible directions for future research into combined analysis for ordinal and continuous vital sign indictors.

Natural Resource Report

Partly cloudy with a chance of lava flows: Forecasting volcanic eruptions in the 21st century

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long‐term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter‐term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations—particularly when combined in integrative, fully probabilistic forecasting frameworks—should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames.

Hawaii