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Mangrove wetlands span broad geographical gradients, resulting in functionally diverse tree communities. We asked whether latitudinal variation, allometric scaling relationships and species composition influence mangrove forest structure and biomass allocation across biogeographical regions and distinct coastal morphologies.
The Tonga volcano eruption of 15 January 2022 unleashed a variety of atmospheric perturbations, coinciding with the recovery-phase of a geomagnetic storm. The ensuing thermospheric variations created rare display of extreme poleward-expanding conjugate plasma bubbles seen in the rate of total electron content index over 100–150°E, reaching ∼40°N geographic latitude. This is associated with fluctuations in FORMOSAT-7/COSMIC-2 (F7/C2) ion-density measurements and spread-F in ionograms. Preceding to this, an unusually strong pre-reversal enhancement (PRE) occurred in the global ionospheric specification (GIS) electron density profiles derived from F7/C2 observations. The GIS also revealed a decrease of equatorial ionization anomaly (EIA) crest density due to the storm impact. Reduced E-region conductivity by volcano-induced waves and enhanced F-region wind, further accelerated by reduced ion-drag over the EIA, apparently intensified the PRE. Accompanied with the strong PRE, volcano-induced seed perturbations triggered the super plasma bubble activity.
The South of Para, located in the heart of the Brazilian Amazon, has become notorious for violent land struggle. Although land conflict has a long history in Brazil, and today impacts many parts of the country, violence is most severe and persistent here. The purpose of this article is to examine why. Specifically, we consider how a particular Amazonian place, the so-called South of Para has come to be known as Brazil's most dangerous badland. We begin by considering the predominant literature, which attributes land conflict to the frontier expansion process with intensified struggle emerging in the face of rising property values and demand for private property associated with capitalist development. From this discussion, we distill a concept of the frontier, based on notions of property rights evolution and locational rents. We then empirically test the persistence of place-based violence in the region, and assess the frontier movement through an analysis of transportation costs. The findings from the analyses indicate that the prevalent theorization of frontier violence in Amazonia does little to explain its persistent and pervasive nature in the South of Para. To fill this gap in understanding, we develop an explanation based the geographic conception of place, and we use contentious politics theory heuristically to elucidate the ways in which general processes interact with place specific history to engender a landscape of violence. In so doing, we focus on environmental, cognitive, and relational mechanisms (and implicated structures), and attempt to deploy them in an explanatory framework that allows direct observation of the accumulating layers of the region's tragic history. We end by placing our discussion within a political ecological context, and consider the implications of the Amazon Land War for the environment.
Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R 2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R 2 = 0.23) and SoilGrids 2.0 ( R 2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.
Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO 2 sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, here we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment. IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs’ β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests.
To meet the government mandates such as Renewable Portfolio Standards (RPS), conventional synchronous generators with rotating mass are being displaced by inverter-based resources (IBRs), leading to high penetration of renewable energy sources (RES). As a result, the inherent properties and characteristics of the transformed generation mix may significantly impact the grid behaviour, and it is of paramount significance that the impact and the consequences of the resource mix change is thoroughly understood to adopt measures to maintain reliable grid operations. Of great concern is the inter-area low-frequency oscillation, which usually propagates through a large region and has a system-wide impact. Such oscillations may lead to unnecessary or inadvertent tripping of generators, that may be simply reacting to the oscillation originating from geographically remote sites. Such tripping of generators can lead to cascading outages, system split and load loss events. Over the years, several system-wide oscillation events have been observed across all three North American interconnections. This project titled Wide-area Oscillation Assessment and Trending Study, sponsored by the Office of Electricity (OE) of the Department of Energy (DOE), aims to conduct the required research to capture and investigate the potential changes in wide-area oscillatory performance of the system as a result of resource mix transition. The Pacific Northwest National Laboratory (PNNL) conducted this research and technical staff of Federal Energy Regulatory Commission (FERC) served as advisors to the project. The primary objective of this study is to assess whether there are significant trends in the power system wide-area oscillatory behaviours, as a result of the generation-mix changes due to the increased penetration of RES in the U.S. Eastern Interconnection (EI). The analysis presented in this report can serve as reference for future grid planning and operation of the EI system. This research conducted in this project evaluates the wide-area oscillatory behaviour of the EI, using a model-based approach for potential future resource mixes, and the measurement-based analysis with 21-month phasor measurement unit (PMU) time series. The oscillation modes in EI system along with the impact of the changes of the generation mix on the frequency and damping ratio (DR) of the oscillations have been studied.
This manuscript helps bridge a gap between theoretical work that advocates for a broad view of forest dependence, and empirical work that has focused narrowly on economic measures. Background: Forest dependence has been widely recognized as a valuable concept for understanding human communities’ well-being and vulnerability to shocks and changes. Past theoretical literature has highlighted the importance of recognizing various types of dependence—environmental, economic, and social—yet past empirical literature on the topic in the United States has almost exclusively relied on measures of economic dependence such as employment and earnings from the traditional forest products sector. Objective and Methods: As a first step to bridge the gap between the theoretical and empirical, we reviewed the existing, publicly available, reliable, wall-to-wall data sources to identify alternate proxy measures for forest dependence. Data availability made the analysis feasible only at the county level—the administrative subdivisions of the state—or higher. Results and Conclusions: We created environmental, economic, and social criteria based on threshold levels of the following proxy variables: forest area, earnings, employment, and indigenous population. Using these criteria, we identified 524 counties to be potentially forest-dependent of 3140 total counties in the United States. The largest concentration was in the Pacific Northwest and Southeast regions, and a higher proportion were non-metro counties than metro. Varying the threshold levels significantly changes the number of counties identified but does not alter the overall geographic trends.
A ground-based, portable microwave radiometer that will be used to measure water vapor in the 30-80-km altitude region, and is to operate 24 hr a day, is described. The thermally excited 22.235-GHz rotational-transition line of water vapor is employed. The emission from this region produces a signal with an apparent brightness temperature of the order 0.1 to 0.5 K. A steerable reflector is used to provide optimal viewing angles, depending on the geographic location and season. Periodic tipping curve scans by this reflector permit determination of the amount of tropospheric correction that is applied to the data. All local oscillators in the receiver are crystal-controlled so that narrow-band spectral analysis of the received line shape can be performed.
The first thermal images of Phobos' shadow on the surface of Mars, in addition to simultaneous visible images, were obtained by the Phobus '88 Termoskan instrument. The best observed shadow occurence was on the flanks of Arsia Mons. For this occurence, we combined the observed decrease in visible illumination of the surface with the observed decrease in brightness temperature to calculate thermal inertias of the Martian surface. The most realistic of our three models of eclipse cooling improves upon our preliminary model by including nonisothermal initial conditions and downward atmospheric flux. Most of our derived inertias fall within the range 38 to 59 J/Sq m/S(exp 0.5)K (0.9 to 1.4 10(exp -3)Cal/Sq m/S(exp 0.5)/K), corresponding to dust-sized particles (for a homogeneous surface), consistent with previous theories of Tharsis as a currrent area of dust deposition. Viking infrared thermal mapper (IRTM) inertias are diurnally derived and are sensitive to centimeter depths, whereas the shadow-derived inertias sample the upper tenths of a millimeter of the surface. The shadow-derived inertias are lower than those derived from Viking IRTM measurements (84 to 147), however, uncertainties in both sets of derived inertias make conclusions about layering tenuous. Thus, near-surface millimeter versus centimeter layering may exist in this region, but if it does, it is likely not very significant. Both eclipse and diurnal inertias appear to increase near the eastern end of the shadow occurence. We also analyzed a shadow occurence near the crater Herschel that showed no observed cooling. This analysis was limited by cool morning temperatures and instrument sensitivity, but yielded a lower bound of 80 on eclipse inertias in that region. Based upon our results, we strongly recommend future spacecraft thermal observations of Phobus' shadow, and suggest that they will be most useful if they improve upon Termoskan's geographic and temporal coverage and its accuracy.
The first thermal images of Phobos' shadow on the surface of Mars, in addition to simultaneous visible images, were obtained by the Phobos'88 Termoskan instrument. The best observed shadow occurrence was on the flanks of Arsia Mons. For this occurrence, we combined the observed decrease in visible illumination of the surface with the observed decrease in brightness temperature to calculate thermal inertias of the Martian surface. The most realistic of our three models of eclipse cooling improves upon our preliminary model by including nonisothermal initial conditions and downward atmospheric flux. Most of our derived inertias fall within the range 38 to 59 J/(sq m s(exp 1/2) K), (0.9 to 1.4 x 10(exp -3) cal/(sq cm s(exp 1/2) K)) corresponding to dust-sized particles (for a homogeneous surface), consistent with previous theories of Tharsis as a current area of dust deposition. Viking infrared thermal mapper (IRTM) inertias are diurnally derived and are sensitive to centimeter depths, whereas the shadow-derived inertias sample the upper tenths of a millimeter of the surface. The shadow-derived inertias are lower than those derived from Viking IRTM measurements (84 to 147), however, uncertainties in both sets of derived inertias make conclusions about layering tenuous. Thus, near-surface millimeter versus centimeter layering may exist in this region, but if it does, it is likely not very significant. Both eclipse and diurnal inertias appear to increase near the eastern end of the shadow occurrence. We also analyzed a shadow occurrence near the crater Herschel that showed no observed cooling. This analysis was limited by cool morning temperatures and instrument sensitivity, but yielded a lower bound of 80 on eclipse inertias in that region. Based upon our results, we strongly recommend future spacecraft thermal observations of Phobos' shadow, and suggest that they will be most useful if they improve upon Terinoskan's geographic and temporal coverage and its accuracy.
Science Objectives for off-nadir imaging: a) Daily observations of high activity and high interest targets in the Polar Regions; b) Daily imaging of regions of gas jetting through vents and the formation of dark spots and fans; c) Increases likelihood of observing these processes in an active phase; d) Stereo imaging for geographical analysis and landing site characterization; and e) Fill in existing gaps and gores.
Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.
Abstract We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.
Global correlations of the regions in which three Martian wind streak types (bright, dark, splotch-related) vary according to latitude, elevation, albedo, color, and predawn residual temperature provide constraints on models of streak formation and modification. Bright streaks are relatively stable and occur over a wide range of geographical surfaces. They are elevation-independent, suggesting that their formation is due to atmospheric dust fallout. Dark streaks appear variable from 20 to 40 deg S and at elevations between 3 and 7 km. They are associated with dark surfaces, having high thermal inertias. Splotch-related streaks occur at elevations between 0 and 6 km and at areas of either low or high thermal inertias, which is the cause of its modifying surface winds. Data are graphically presented, and the methods of data collection are fully explained. Regional studies of various types of streaks in Syrtis Major, Syria Planum, Oxia Palus, Mesogea and Pettit Craters, and Noachis confirm that the correlations found at the global level also occur at regional scales.
This document summarizes the major findings of a Gulf of Mexico Application Pilot project led by NASA Stennis Space Center (SSC) in conjunction with a regional collaboration network of the Gulf of Mexico Alliance (GOMA). NASA researchers processed and analyzed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL between 1974 and 2008. Our goal was to create satellite-based LULC data products using methods that could be transferable to other coastal areas of concern within the Gulf of Mexico. The Mobile Bay National Estuary Program (MBNEP) is the primary end-user, however, several other state and local groups may benefit from the project s data products that will be available through NOAA-NCDDC s Regional Ecosystem Data Management program. Mobile Bay is a critical ecologic and economic region in the Gulf of Mexico and to the entire country. Mobile Bay was designated as an estuary of national significance in 1996. This estuary receives the fourth largest freshwater inflow in the United States. It provides vital nursery habitat for commercially and recreationally important fish species. It has exceptional aquatic and terrestrial bio-diversity, however, its estuary health is influenced by changing LULC patterns, such as urbanization. Mobile and Baldwin counties have experienced a population growth of 1.1% and 20.5% from 2000-2006. Urban expansion and population growth are likely to accelerate with the construction and operation of the ThyssenKrupp steel mill in the northeast portion of Mobile County. Land-use and land-cover change can negatively impact Gulf coast water quality and ecological resources. The conversion of forest to urban cover types impacts the carbon cycle and increases the freshwater and sediment in coastal waters. Increased freshwater runoff decreases salinity and increases the turbidity of coastal waters, thus impacting the growth potential of submerged aquatic vegetation (SAV), which is critical nursing ground for many Gulf fish species. A survey of Mobile Bay SAV showed widespread decreases since the 1940s. Prior to our project, coastal environmental managers in Baldwin and Mobile counties needed more understanding of the historical LULC for properly assessing the impacts of urbanization. In particular, more information on the location and extent of changing urbanization LULC patterns was needed to aid LULC planning and to assess predictions of future LULC patterns. Our products will assist the coastal environmental managers and land-use planners in making better community growth planning decisions. Our project also will help to establish a historical baseline of LULC distributions, which is a fundamental need in any stewardship plan. The primary research objective of our project was to produce historic and current geospatial LULC change products across a 34-year time frame. A multi-decadal coastal LULC change product was the major project deliverable. The geographic extent and nature of change was quantified and assessed for the upland herbaceous, barren, open water, urban, upland forest, woody wetland, and non-woody wetlanddominated land cover types. We focused on regional analyses of decadal-scale urban expansion and watershed-scaled analyses of LULC change for multiple areas of concern to the Mobile Bay NEP (Figure A). We used the following dates to derive LULC classification products from Landsat data: 1974, 1979, 1984, 1988, 1991, 1996, 2001, 2005, and 2008. We assessed the accuracy of our products using randomly sampled locations and digital geospatial reference data including field survey data, high resolution orthorectified aerial photography, high resolution multispectral and panchromatic satellite data displays (from QuickBird and Corona sensors), digital elevation model data, and National Wetlands Inventory wetland cover type data. NOAA s Coastal Change Assessment Program s (C-CAP) and National Land Cover Database (NLCD) procts were used for qualitative comparison in assessing map accuracy. We calculated an average overall classification accuracy of 87% with similar overall accuracies for the older (MSS) and newer (TM and ETM) Landsat LULC products.
Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.
This report is on the Forestry Electric Vehicle Energy Routing (FEVER) Tool, a novel software system developed to support heavy-duty electric vehicle (EV) operations in remote, forested, and mountainous regions. The Phase I project aimed to demonstrate the feasibility of modeling EV energy consumption using terrain elevation, road conditions, and route features specific to forestry logistics. The tool combines geographic information systems (GIS), electric motor physics, and vehicle-specific data to calculate feasible, energy-efficient routes. Collaborations with Oregon State University’s Research Forests and Titan Freight Systems enabled collection and validation of GPS and elevation-based trip data. The FEVER Tool offers substantial opportunities for the efficient management of medium- and heavy-duty electric vehicles in sectors like forestry, agriculture, mining, defense and waste management—areas which are beginning to adopt HDEVs. The project demonstrated technical feasibility and lays the groundwork for commercial development and deployment in other industries and environmental conditions in Phase II.