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The analysis of the spatial and temporal patterns of low flows as well as their generation mechanisms over large geographic regions can provide valuable insights and understanding for climate change impacts, regional frequency analysis, risk assessment of extreme events, and decisionmaking regarding allowable withdrawals. The goal of this paper is to examine nonstationarity in low flow generation across the eastern US and explore the potential anthropogenic influences or climate drivers. We use nonparametric tests to identify abrupt and gradual changes in time series of low flows and their timing for 508 USGS streamflow gauging sites in the eastern US with more than 50 years of daily data, to systematically distinguish the effects of human intervention from those of climate variability. A time series decomposition algorithm was applied to 1-day, 7-day, 30-day, and 90-day annual low flow time series that combines the Box-Ljung test for detection of autocorrelation, the Pettitt test for abrupt step changes and the Mann-Kendall test for monotonic trends. Examination of the USGS notes for each site showed that many of the sites with step changes and around half of the sites with an increasing trend have been documented as having some kind of regulation. Sites with decreasing or no trend are less likely to have documented influences on flows. Overall, a general pattern of increasing low flows in the northeast and decreasing low flows in the southeast is evident over a common time period (1951- 2005), even when discarding sites with significant autocorrelation, documented regulation or other human impacts. The north-south pattern of trends is consistent with changes in antecedent precipitation. The main exception is along the mid-Atlantic coastal aquifer system from eastern Virginia northwards, where low flows have decreased despite increasing precipitation, and suggests that declining groundwater levels due to pumping may have contributed to decreased low flows. For most sites, the majority of low flows occur in one season in the late summer to fall, as driven by the lower precipitation and higher evaporative demand in this season, but this is complicated in many regions because of the presence of a secondary low flow season in the winter for sites in the extreme northeast and in the spring for sites in Florida. Trends in low flow timing are generally undetectable, although abrupt step changes appear to be associated with regulation. [ABSTRACT FROM AUTHOR]

Some expected changes in climate resulting from human greenhouse gas emissions are clear and well documented, but others may be harder to predict because they involve extreme weather events or heretofore unusual combinations of weather patterns. One recent example of unusual weather that may become more frequent with climate change occurred in early spring 2007 when a large Arctic air mass moved into the eastern United States following a very warm late winter. In this paper, we document effects of this freeze event on Walker Branch, a well-studied stream ecosystem in eastern Tennessee. The 2007 spring freeze killed newly grown leaf tissues in the forest canopy, dramatically increasing the amount of light reaching the stream. Light levels at the stream surface were sustained at levels considerably above those normal for the late spring and summer months due to the incomplete recovery of canopy leaf area. Increased light levels caused a cascade of ecological effects in the stream beginning with considerably higher (two–three times) rates of gross primary production (GPP) during the late spring and summer months when normally low light levels severely limit stream GPP. Higher rates of stream GPP in turn resulted in higher rates of nitrate (NO3−) uptake by the autotrophic community and lower NO3− concentrations in stream water. Higher rates of stream GPP in summer also resulted in higher growth rates of a dominant herbivore, the snail Elimia clavaeformis. Typically, during summer months net NO3− uptake and snail growth rates are zero to negative; however, in 2007 uptake and growth were maintained at moderate levels. These results show how changes in forest vegetation phenology can have dramatic effects on stream productivity at multiple trophic levels and on nutrient cycling as a result of tight coupling of forest and stream ecosystems. Thus, climate change-induced changes in canopy structure and phenology may lead to large effects on stream ecosystems in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT In light of numerous studies documenting a decline in the diurnal temperature range (DTR) over much of the globe, some authors have in recent years examined the annual march of the DTR in an effort to understand better the factors that influence the DTR's seasonal variations. These papers show that, over the southern two-thirds of the eastern United States, the DTR's climatology features peaks in spring and autumn and minima in winter and mid-to-late summer. However, the factors responsible for these characteristics remain uncertain. In this study, the DTR climatology of the eastern United States is analyzed in detail using daily surface and 850-mb data, with emphasis on possible relationships to seasonal changes in vegetation. It is shown that the warm season dip in the DTR deepens and widens from north to south across the study area, in accordance with a lengthening of the growing season. Furthermore, the dip is particularly prominent in the annual march of the DTR on mostly sunny days, indicating that seasonal variations in cloudiness are not responsible for this feature. The climatologies of daily maximum and daily minimum temperatures are found to be very different from each other: the former flattens out after the springtime peak in the DTR whereas the latter exhibits a pronounced mid-to-late summer maximum. These findings suggest that, by inhibiting daytime surface heating, evapotranspiration from vegetation contributes significantly to the dip in the DTR during the warm season in the eastern United States. [ABSTRACT FROM AUTHOR]

Land-cover change detection using satellite remote sensing is largely confined to the era of Landsat satellites, from 1972 to present. However, the Corona, Argon, and Lanyard intelligence satellites operated by the U.S. government between 1960 and 1972 have the potential to provide an important extension of the long-term record of Earth’s land surface. Recently declassified, the archive of images recorded by these satellites contains hundreds of thousands of photographs, many of which have very high ground resolution- 6–9 ft (1.8–2.7 m) even by today’s standards. This paper demonstrates methods for extending the span of forest-cover change analysis from the Landsat-5 and -7 era (1984 to present) to the previous era covered by the Corona archive in two study areas: one area covered predominantly by urban and sub-urban land uses in the eastern US and another area by tropical forest in central Brazil. We describe co-registration of Corona and Landsat images, extraction of texture features from Corona images, classification of Corona and Landsat images, and post-classification change detection based on the resulting thematic dataset. Second-order polynomial transformation of Corona images yielded geometric accuracy relative to Landsat-7 of 18.24 m for the urban area and 29.35 m for the tropical forest study area, generally deemed adequate for pixel-based change detection at Landsat resolution. Classification accuracies were approximately 95% and 96% for forest/non-forest discrimination for the temperate urban and tropical forest study areas, respectively. Texture within 7 × 7- to 9 × 9-pixel (∼13.0–16.5 m) neighborhoods and within 11 × 11-pixel (∼30 m) neighborhoods were the most informative metrics for forest classification in Corona images in the temperate and tropical study areas, respectively. The trajectory of change from the 1960s to 2000s differed between the two study areas: the average annual forest loss rate in the urban area doubled from 0.68% to 1.9% from the 1960s to the mid-1980s and then decreased during the following decade. In contrast, deforestation in the Brazilian study area continued at a slightly increased pace between the 1960s and 1990s at annual loss rate of 0.62–0.79% and quickly slowed down afterward. This study demonstrates the strong potential of declassified Corona images for detecting historical forest changes in these study regions and suggests increased utility for retrieving a wide range of land cover histories around the world. [ABSTRACT FROM AUTHOR]

Nutria (Myocastor coypus) are an introduced invasive species expanding their range in North America and may have detrimental effects on wetlands. Little is known about habitat covariates that may limit or enhance range expansion of nutria, especially when combined with predicted changes in climate. We used a hierarchical modeling approach to develop broad‐ and local‐scale maximum entropy (MaxEnt) models based on nutria harvest records in the eastern United States from 2006–2015. We developed models for current conditions and for climate change projections in 2050. At a broad scale, nutria habitat was located in areas with ≤80 annual freezing days. At local scales, nutria were found in areas with high proportions of freshwater forested‐shrub wetlands close to other wetlands. Managers who are concerned about possible nutria range expansion can use this hierarchical modeling framework to identify areas for surveillance and prioritize control efforts. © 2019 The Wildlife Society. At a broad scale, nutria habitat was located in areas with ≤80 annual freezing days, whereas at local scales, they were associated with areas having high proportions of freshwater forested‐shrub wetlands close to other wetlands. Managers who are concerned about possible nutria range expansion can use this hierarchical modeling framework to identify areas for surveillance and prioritize control efforts. [ABSTRACT FROM AUTHOR]

Aim We aim to assess the temporary transferability of species distribution models ( SDMs) for stream fish species in terms of discrimination power and calibration. Location New River basin, eastern United States. Methods In this study, we used Lasso-regularized logistic regression ( LLR), boosted regression trees ( BRT), MaxEnt, and ensemble model ( ENS) to evaluate the habitat suitability of 16 fish species with different rarity and temperature preference based on historical species occurrences obtained during 1950-1990. These SDMs were used to make probabilistic predictions of species presence in the independent datasets sampled during 2012-2014. We evaluated the temporal transferability of these SDMs in terms of discrimination power and calibration with the temporarily independent datasets. Results The area under the receiver-operating-characteristic curve ( AUC) was over 0.6 for 13 (81%) of the species in the evaluations of ENS models with the independent datasets. Cool-water species and species with small local population size traits tended to have good temporal transferability. With observed species prevalence as the discrimination cut-off, LLR had the highest overall accuracy for 13 of the 16 species and highest specificity for 10 species, whereas MaxEnt had the highest sensitivity for 14 species. Biases, under- or over-fitting problems were common in the temporal model transfers, among the modelling approaches used here. Main conclusions SDMs developed with historical data generally had moderate to good discrimination power but they tended to systematically underestimate current probability of species presence. To predict species distribution shifts over time, SDMs should be well-calibrated with high discrimination power. We suggest reclassifying predicted probability of species occurrence to ordinal ranks to deal with (under- and over-estimation) bias, and fine-tuning variable selection with regularization or cross validation to remedy under- and over-fitting. [ABSTRACT FROM AUTHOR]

Fire-maintained ecosystems and associated species are becoming increasingly rare in the southern Appalachian Mountains because of fire suppression policies implemented in the early 20th century. Restoration of these communities through prescribed fire has been hindered by a lack of information on historical fire regimes. To characterize past fire regimes, we collected and absolutely dated the tree rings on cross sections from 242 fire-scarred trees at three different sites in the southern Appalachian Mountains of Tennessee and North Carolina. Our objectives were to (1) characterize the historical frequency of fire in southern Appalachian mixed pine-oak forests, (2) assess the impact of interannual climatic variability on the historical occurrence of fire, and (3) determine whether changes in human culture and land use altered the frequency of fire. Results demonstrate that fires burned frequently at all three sites for at least two centuries prior to the implementation of fire suppression and prevention in the early to mid 20th century. Composite mean fire return intervals were 2 - 4 yr, and point mean fire return intervals were 9-13 yr. Area-wide fires that burned across multiple stands occurred at 6-13-yr intervals. The majority of fires were recorded during the dormant season. Fire occurrence exhibited little relationship with reconstructed annual drought conditions. Also, fire activity did not change markedly during the transition from Native American to Euro- American settlement or during the period of industrial logging at the start of the 20th century. Fire activity declined significantly, however, during the fire suppression period, with a nearly complete absence of fire during recent decades. The characterization of past fire regimes should provide managers with specific targets for restoration of fire-associated communities in the southern Appalachian Mountains. The fire chronologies reported here are among the longest tree-ring reconstructions of fire history compiled for the eastern United States and support the hypothesis that frequent burning has played a long and important role in the development of forests in the southern Appalachian Mountains. [ABSTRACT FROM AUTHOR]

Changes of snow occurrence across the central and eastern United States under future warming for the late 21st century are investigated by applying an empirical hyperbolic tangent function to both observed and downscaled high spatial resolution (~12.5 km) daily temperature and precipitation, to compare the historical (1981-2000) and future (2081-2100) snow occurrence. The observed distributions of snow frequency show that snow-rain transition zones are mainly zonally distributed, since they are largely determined by temperature, with slight shifts to the south over the Appalachian Mountains. The snow-rain transition zone is located around 38-46°N for November and March, and 32-42°N for winter months (DJF). These observed patterns are reproduced well for the historical period by an ensemble average of multiple general circulation models (GCMs). The probabilistic projections show that the snow-rain transition zone will shift to the north under the background of global warming at magnitudes of 2-6 °C, indicating that large areas will experience a partial, or even a very large, loss of snow occurrence in the future. The northward shifts are about 2° latitude under the representative concentration pathways 4.5 (RCP4.5) scenario and 4° latitude under the RCP8.5 scenario. The percentages of the area losing snow occurrence are also assessed. [ABSTRACT FROM AUTHOR]