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Evaluation of water-quality data and monitoring program for Lake Travis, near Austin, Texas

Statistical analyses were made of selected water-quality properties and constituents for Lake Travis, northwest of Austin in central Texas. Objectives for the evaluation were: (1) to provide information on levels of selected water-quality properties or constituents to use as reference values for assessing the future effectiveness of the Lake Travis Nonpoint-Source Control ordinance of the Lower Colorado River Authority; and (2) to determine whether water-quality constituents at any of the sampling sites are statistically redundant with other sites and, thus, can be discontinued without loss of information. The data were grouped into two periods—the thermally stratified period (May through November) and the mixed period (December through April). Lake Travis is a biologically unproductive reservoir with acceptable water quality for virtually all current water uses. Nutrient (nitrogen, phosphorus) concentrations tend to be small in the reservoir throughout the year, indicating nutrient limitation of maximum phytoplankton biomass. On the basis of traditional limnological properties, Lake Travis exhibits small biological productivity and exceptional water transparency. However, dissolved oxygen concentrations for bottom samples often decrease to less than 5 milligrams per liter throughout the reservoir, especially during the thermally stratified period. Statistical comparisons were made between data collected at the surface and at the bottom at each sampling site to determine statistical similarities. The available data were insufficient to perform the comparisons for nitrite nitrogen and dissolved orthophosphate phosphorus. In addition, no bottom data were available at the most upstream site because the shallow bottom was commonly above the thermocline. The multiple-comparison tests indicate that, for some constituents, a single sampling site for a constituent or property might adequately characterize the water quality of Lake Travis for that constituent or property. However, multiple sampling sites are required to provide information of sufficient temporal and spatial resolution to accurately evaluate other water-quality constituents for the reservoir. For example, the water-quality data from surface samples and from bottom samples indicate that nutrients (nitrogen, phosphorus) might require additional sampling sites for a more accurate characterization of their in-lake dynamics.

Texas↗

Effects of habitat fragmentation on passerine birds breeding in Intermountain shrubsteppe

Habitat fragmentation and loss strongly influence the distribution and abundance of passerine birds breeding in Intermountain shrubsteppe. Wildfires, human activities, and change in vegetation communities often are synergistic in these systems and can result in radical conversion from shrubland to grasslands dominated by exotic annuals at large temporal and spatial scales from which recovery to native conditions is unlikely. As a result, populations of 5 of the 12 species in our review of Intermountain shrubsteppe birds are undergoing significant declines; 5 species are listed as at-risk or as candidates for protection in at least one state. The process by which fragmentation affects bird distributions in these habitats remains unknown because most research has emphasized the detection of population trends and patterns of habitat associations at relatively large spatial scales. Our research indicates that the distribution of shrubland-obligate species, such as Brewer's Sparrows (Spizella breweri), Sage Sparrows (Amphispiza belli), and Sage Thrashers (Oreoscoptes montanus), was highly sensitive to fragmentation of shrublands at spatial scales larger than individual home ranges. In contrast, the underlying mechanisms for both habitat change and bird population dynamics may operate independently of habitat boundaries. We propose alternative, but not necessarily exclusive, mechanisms to explain the relationship between habitat fragmentation and bird distribution and abundance. Fragmentation might influence productivity through differences in breeding density, nesting success, or predation. However, local and landscape variables were not significant determinants either of success, number fledged, or probability of predation or parasitism (although our tests had relatively low statistical power). Alternatively, relative absence of natal philopatry and redistribution by individuals among habitats following fledging or post-migration could account for the pattern of distribution and abundance. Thus, boundary dynamics may be important in determining the distribution of shrubland-obligate species but insignificant relative to the mechanisms causing the pattern of habitat and bird distribution. Because of the dichotomy in responses, Intermountain shrubsteppe systems present a unique challenge in understanding how landscape composition, configuration, and change influence bird population dynamics.

Conference Paper↗

Toward improved decision-support tools for Delta Smelt management actions

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level. Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions. Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance. Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement. • DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions. • DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth. • DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt. There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address. For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models. Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time. Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires: - Engagement of a committed group of technical experts with appropriate expertise. - The development of a coordinated workplan including appropriate project management and tracking. - Dialog between potential users (i.e., managers and policy makers) and technical experts. - Resources to pursue DST development including personnel and computational resources. This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

White Paper↗

Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast

A 38-year hindcast water level product is developed for the U.S. Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

Florida, Georgia, North Carolina, South Carolina, ↗

Pattern and process of land loss in the Mississippi Delta: A spatial and temporal analysis of wetland habitat change

An earlier investigation (Turner 1997) concluded that most of the coastal wetland loss in Louisiana was caused by the effects of canal dredging, that loss was near zero in the absence of canals, and that land loss had decreased to near zero by the late 1990s. This analysis was based on a 15-min quadrangle (approximately 68,000 ha) scale that is too large to isolate processes responsible for small-scale wetland loss and too small to capture those responsible for large-scale loss. We conducted a further evaluation of the relationship between direct loss due to canal dredging and all other loss from 1933–1990 using a spatial scale of 4,100 ha that accurately captures local land-loss processes. Regressions of other wetland loss on canal area (i.e., direct loss) for the Birdfoot, Terrebonne, and Calcasieu basins were not significant. Positive relationships were found for the Breton (r 2 =0.675), Barataria (r 2 =0.47), and Mermentau (r 2 =0.35) basins, indicating that the extent of canals is significantly related to wetland loss in these basins. A significant negative relationship (r 2 =0.36) was found for the Atchafalaya coastal basin which had statistically lower loss rates than the other basins as a whole. The Atchafalaya area receives direct inflow of about one third of the Mississippi discharge. When the data were combined for all basins, 9.2% of the variation in other wetland loss was attributable to canals. All significant regressions intercepted the y-axis at positive loss values indicating that some loss occurred in the absence of canals. Wetland loss did not differ significantly from the coast inland or between marsh type. We agree with Turner that canals are an important agent in causing wetland loss in coastal Louisiana, but strongly disagree that they are responsible for the vast majority of this loss. We conclude that wetland loss in the Mississippi delta is an ongoing complex process involving several interacting factors and that efforts to create and restore Louisiana’s coastal wetlands must emphasize riverine inputs of freshwater and sediments.

Louisiana↗

Outcomes of control and monitoring of a widespread riparian invader (Tamarix spp.): A comparison of synthesis approaches

Effective ecological restoration requires empirical assessment to determine outcomes of projects, but conclusions regarding the effects of restoration treatments on the whole ecosystem remain rare. Control of invasive shrubs and trees in the genus Tamarix and associated riparian restoration in the American Southwest has been of interest to scientists and resource managers for decades; dozens of studies have reported highly variable outcomes of Tamarix control efforts, as measured by a range of response variables, temporal and spatial scales and monitoring strategies. We conducted a literature search and review, meta-analysis and vote count (comparison of numerical outcomes lacking reported variances and/or sample sizes) on published papers that quantitatively measured a variety of responses to control of Tamarix . From 96 publications obtained through a global search on terms related to Tamarix control, we found 52 publications suitable for a meta-analysis (n = 777 comparisons) and 63 publications suitable for two vote counts (n = 1,460 comparisons total; 622 comparisons reported as statistically significant) of response to Tamarix control. We estimated responses to control by treatment type (e.g. cut-stump treatment, burning, biocontrol) and ecosystem component (e.g. vegetation, fauna, fluvial processes). Finally, we compared results of the various synthesis methods to determine whether the increasingly stringent requirements for inclusion led to biased outcomes. Vegetation metrics, especially measures of Tamarix response, were the most commonly assessed. Ecosystem components other than vegetation, such as fauna, soils and hydrogeomorphic dynamics, were under-represented. The meta-analysis showed significantly positive responses by vegetation overall to biocontrol, herbicide and cut-stump treatments. This was primarily due to reduction of Tamarix cover; impacts on replacement vegetation were highly variable. We found concordance amongst our varied synthesis approaches, indicating that increased granularity from stricter quantitative techniques does not come at the cost of a biased sample. Overall, our results indicate that common control methods are generally effective for reducing Tamarix , but the indirect effects on other aspects of the ecosystem are variable and remain understudied. Given that this is a relatively well-studied invasive plant species, our results also illustrate the limitations of not only individual studies, but also of reviews for measuring the impact of invasive species control. We call on researchers to investigate the less commonly studied responses to Tamarix control and riparian restoration including the effects on fauna, soil and hydrogeomorphic characteristics.

NeoBiota↗

Phosphorus and suspended sediment load estimates for the Lower Boise River, Idaho, 1994-2002

The U.S. Geological Survey used LOADEST, newly developed load estimation software, to develop regression equations and estimate loads of total phosphorus (TP), dissolved orthophosphorus (OP), and suspended sediment (SS) from January 1994 through September 2002 at four sites on the lower Boise River: Boise River below Diversion Dam near Boise, Boise River at Glenwood Bridge at Boise, Boise River near Middleton, and Boise River near Parma. The objective was to help the Idaho Department of Environmental Quality develop and implement total maximum daily loads (TMDLs) by providing spatial and temporal resolution for phosphorus and sediment loads and enabling load estimates made by mass balance calculations to be refined and validated. Regression models for TP and OP generally were well fit on the basis of regression coefficients of determination (R2), but results varied in quality from site to site. The TP and OP results for Glenwood probably were affected by the upstream wastewater-treatment plant outlet, which provides a variable phosphorus input that is unrelated to river discharge. Regression models for SS generally were statistically well fit. Regression models for Middleton for all constituents, although statistically acceptable, were of limited usefulness because sparse and intermittent discharge data at that site caused many gaps in the resulting estimates. Although the models successfully simulated measured loads under predominant flow conditions, errors in TP and SS estimates at Middleton and in TP estimates at Parma were larger during high- and low-flow conditions. This shortcoming might be improved if additional concentration data for a wider range of flow conditions were available for calibrating the model. The average estimated daily TP load ranged from less than 250 pounds per day (lb/d) at Diversion to nearly 2,200 lb/d at Parma. Estimated TP loads at all four sites displayed cyclical variations coinciding with seasonal fluctuations in discharge. Estimated annual loads of TP ranged from less than 8 tons at Diversion to 570 tons at Parma. Annual loads of dissolved OP peaked in 1997 at all sites and were consistently higher at Parma than at the other sites. The ratio of OP to TP varied considerably throughout the year at all sites. Peaks in the OP:TP ratio occurred primarily when flows were at their lowest annual stages; estimated seasonal OP:TP ratios were highest in autumn at all sites. Conversely, when flows were high, the ratio was low, reflecting increased TP associated with particulate matter during high flows. Parma exhibited the highest OP:TP ratio during all seasons, at least 0.60 in spring and nearly 0.90 in autumn. Similar OP:TP ratios were estimated at Glenwood. Whereas the OP:TP ratio for Parma and Glenwood peaked in November or December, decreased from January through May, and increased again after June, estimates for Diversion showed nearly the opposite pattern ? ratios were highest in July and lowest in January and February. This difference might reflect complex biological and geochemical processes involving nutrient cycling in Lucky Peak Lake, but further data are needed to substantiate this hypothesis. Estimated monthly average SS loads were highest at Diversion, about 400 tons per day (ton/d). Average annual loads from 1994 through 2002 were 144,000 tons at Diversion, 33,000 tons at Glenwood, and 88,000 tons at Parma. Estimated SS loads peaked in the spring at all sites, coinciding with high flows. Increases in TP in the reach from Diversion to Glenwood ranged from 200 to 350 lb/d. Decreases in TP were small in this reach only during high flows in January and February 1997. Decreases in SS, were large during high-flow conditions indicating sediment deposition in the reach. Intermittent data at Middleton indicated that increases and decreases in TP in the reach from Glenwood to Middleton were during low- and high-flow conditions, respectively. All constituents increased in the r

Scientific 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↗

Interannual streamflow variability in the United States based on principal components

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

Water Resources Research↗

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

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

Alaska↗

Ground-water vulnerability to nitrate contamination in the mid-atlantic region

The U.S. Environmental Protection Agency?s (USEPA) Regional Vulnerability Assessment (ReVA) Program has developed a set of statistical tools to support regional-scale, integrated ecological risk-assessment studies. One of these tools, developed by the U.S. Geological Survey (USGS), is used with available water-quality data obtained from USGS National Water-Quality Assessment (NAWQA) and other studies in association with land cover, geology, soils, and other geographic data to develop logistic-regression equations that predict the vulnerability of ground water to nitrate concentrations exceeding specified thresholds in the Mid-Atlantic Region. The models were developed and applied to produce spatial probability maps showing the likelihood of elevated concentrations of nitrate in the region. These maps can be used to identify areas that currently are at risk and help identify areas where ground water has been affected by human activities. This information can be used by regional and local water managers to protect water supplies and identify land-use planning solutions and monitoring programs in these vulnerable areas.

Fact Sheet↗

Temporal and spatial trends of chloride and sodium in groundwater in New Hampshire, 1960–2011

Data on concentrations of chloride and sodium in groundwater in New Hampshire were assembled from various State and Federal agencies and organized into a database. This report provides documentation of many assumptions and limitations of disparate data that were collected to meet wide-ranging objectives and investigates temporal and spatial trends of the data. Data summaries presented in this report and analyses performed for this study needed to take into account the 27 percent of chloride and 5 percent of sodium data that were censored (less than a reporting limit) at multiple reporting limits that systematically decreased over time. Throughout New Hampshire, median concentrations of chloride were significantly greater during 2000-2011 than in every decade since the 1970s, and median concentrations of sodium were significantly greater during 2000-2011 than during the 1990s. Results of summary statistics showed that the 50th, 75th, and 90th percentiles of the median concentrations of chloride and sodium by source (well) from Rockingham and Strafford counties were the highest in the State; and the 75th and 90th percentiles from Carroll, Coos, and Grafton counties were the lowest. Large increases in median concentrations of chloride and sodium for individual wells after 1995 compared with concentrations for years before were found in parts of Belknap and Rockingham counties and in small clusters within Carroll, Hillsborough, and Merrimack counties.

New Hampshire↗

Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.

Forest Ecology and Management↗

Comparison of data handling techniques for modeling bat acoustic activity

With the proliferation of acoustic sampling to investigate bat distribution and ecology, researchers have implemented a myriad of statistical modeling approaches to interpret findings. Bats are taxa of high conservation concern; therefore, ensuring the accuracy of species-level habitat association models is critical for informing management. We sought to determine prediction differences among statistical approaches to modeling counts of acoustic detections, using generalized linear mixed models with 8 acoustic data-handling techniques. We applied each approach or combination of approaches to a rare species, the northern long-eared bat ( Myotis septentrionalis ), and a common species, the eastern red bat ( Lasiurus borealis ), from summer survey results on a landscape in south-central Pennsylvania, USA, 2024. We evaluated the accuracy of habitat association models of bat acoustic activity at the species level using cross-validation and compared resulting predictions of models using spatial correlations. We determined that filtering data by automated identification software (Kaleidoscope Pro), the maximum likelihood estimate (MLE) P -value thresholds reduced relative mean absolute error (rMAE) in cross-validation of northern long-eared bat models. Using the MLE-retained data produced the most accurate predictions over using raw data or the overly conservative match ratio data. However, for the eastern red bat, the results from the most conservative approach of only retaining data with at least a 90% match ratio from software development training sets had the lowest rMAE. We have provided evidence that the current standard of filtering data by nightly MLE can result in more accurate and informative habitat-use acoustic activity models for rare bat species.

Pennsylvania↗

Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data

Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We found that Landsat Enhanced Vegetation Index (EVI), growing season AI, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r2 of 0.45 and root mean squared error (RMSE) of roughly one-quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005 and 2006 (150-d averages of 542.8, 544.3, and 536.5 g C m-2, respectively). Yet, we observed substantial variability in spatial patterns of soil respiration predictions that varied between years, suggesting that our method is sensitive to changes in respiration drivers. We compared our estimates to MODIS GPP and nocturnal net ecosystem exchange (NEE) derived from eddy covariance towers as a proxy for ecosystem respiration. Averaged across the predictive region, mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal NEE from eddy covariance towers. This study demonstrated that geospatial and remotely-sensed datasets can be used in a statistical modeling framework to estimate soil respiration at landscape scales.

Colorado, Wyoming↗

The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data

We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.

Arizona, California, Colorado, Idaho, Montana,Neva↗

Estimating relative abundance from count data

Much of the available information on large-scale patterns of animal abundance is based on count surveys. The data provided by such surveys are often influenced by nuisance factors affecting the numbers of animals counted, but unrelated to population size. Temporal and spatial patterns in nuisance factors may exist, causing simple summaries of counts to give a misleading view of patterns in population size. We develop models for count data that allows the incorporation of such factors, and describe methods for estimating spatial patterns of relative abundance from counts. We carry out spatial analyses of North American Breeding Bird Survey data, in which observer ability is a nuisance parameter nested within sites. In light of evidence that new observers tend to count more birds than the observers they replace, we model observer ability as a random effect with mean depending on observer initiation year.

Austrian Journal of Statistics↗

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling.

International Journal of Coal Geology↗