Due to its substantial role on the Earth’s biogeochemical cycles and human health, nitrogen is recognized as one of the major water quality indicators of Sustainable Development Goal 6.3.2. Quantifying these potential impacts in large spatial scales still appears to be a grand challenge because of the high computational demand required by the distributed physically based global models and their intensive data requirements for calibration and validation. The former prevents a comprehensive analysis of the full spectrum of the model behavior under different conditions, and the latter impinges on the reliability of model-based inference. To tackle this problem, we developed a data-driven model using a spatio-temporal Random Forest algorithm to predict levels of nitrogen at 0.5-degree spatial resolution from 1992 to 2010 across the world. Several variables representing livestock, climate, hydrology, topography, etc. have been selected as predictors. The response variable of interest was nitrate–nitrite, which is responsible for the high risk of infant methemoglobinemia. Our results indicate that changes in the nitrogen concentration is mainly driven by cattle and sheep population, fertilizer application, precipitation, and temperature variability, implying livestock population, climate change, and anthropogenic forces can be important risk factors for global water quality deterioration. Furthermore, using the predicted levels of nitrogen, we characterized large-scale water quality patterns, and thus identified a few major ‘hot spots’ of water quality. The proposed model can also help assess potential impacts of future scenarios (e.g., livestock production or land use change) on global water quality conditions for better development of effective policy strategies.
Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil’s 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (Rt) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire fifteen-month period and several shorter, three-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and specific humidity with increased transmission. However, the impact of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.
In light of the increased availability of massive point cloud data, acquired by advanced remote sensing techniques, software tools for their efficient representation and processing are needed. Triangulated Irregular Networks (TINs) are an effective way to represent point clouds without the need to interpolate them into raster-based terrain models. However, GISs tools have limited support for TINs due to large storage costs. For this reason, we present the Terrain Trees Library (TTL), a library for terrain analysis based on a new scalable data structure named Terrain trees. A Terrain tree relies on a hierarchical spatial index where each leaf block encodes the minimum amount of connectivity information for the TIN. Connectivity relations among the elements of the TIN are extracted locally within each leaf block at run-time and discarded when no longer needed. Moreover, the hierarchical domain decomposition makes the library well-suited for parallel processing. TTL contains a kernel for connectivity and spatial queries, and modules for extracting morphological features, including edge and triangle slopes, roughness, curvature. It also contains modules for extracting topological structures, like critical point, critical net, watershed segmentation, based on the discrete Morse gradient, and a technique for multivariate data visualization, which enables the analysis of multiple scalar fields defined on the same terrain. To evaluate the effectiveness and scalability of TTL, we compared it against the most compact state-of-the-art data structure for TINs, the IA data structure. When encoded by Terrain trees, a TIN requires 36% less storage than when encoded by the IA data structure. Beyond this storage reduction, Terrain trees also show better performance than the IA data structure in most terrain analysis operations. This speedup is obtained since Terrain trees enable 57% to 72% faster extraction of the triangles incident in a vertex. Extracting the triangles incident in vertices as well as the adjacent vertices on the mesh is a key task in most terrain feature extraction operations on a TIN. Using Terrain trees, we achieved 36% to 55% less time consumption computing morphological features and 20% less time consumption computing the discrete Morse gradient than using the IA data structure.
It is generally accepted that the subduction polarity reversal (SPR) results from the strong collision of two plates. Yet, the SPR of the Solomon Back-arc Basin is started in the “soft docking” stage, and the mechanism by which the “soft docking” induced subduction initiation (SI) remains elusive. We find that the mass depletion of the plateau influences the evolution of the subduction patterns during SI. And the island arc rheological strength affects the development of the shear zone between an island arc and back-arc basin which favors SI. What’s more, with the increase of the rheological strength difference, the SI is more easily to occur, and the contribution of the plateau collision to SI weakens. Hence, by combining the available geological evidence, we suggest that the Solomon Island Arc rheological strength and the Ontong-Java plateau collision jointly controlled the SI during the “soft docking” stage.
We present EuropeAgriDB v1.0, a dataset of crop production and nitrogen (N) flows in European cropland 1961–2019. The dataset covers 26 present-day countries, detailing the cropland N harvests in 17 crop categories as well as cropland N inputs in synthetic fertilizers, manure, symbiotic fixation, and atmospheric deposition. The study builds on established methods but goes beyond previous research by combining data from FAOSTAT, Eurostat, and a range of national data sources. A key contribution is the comprehensive and detailed coverage of crop production, in particular fodder crops such as temporary grassland, green maize, and forage legumes. For these crops, we have combined the Eurostat crop production statistics database with a range of national databases, statistical yearbooks, and other sources. For other arable and permanent crops, we use the FAOSTAT database which apart from fodder crops offers the longest and most complete time series of crop production. Our crop production dataset, divided into 17 crop categories, provides a solid basis for understanding how crop mix and productivity have varied over time. A second key contribution is the detailed estimation of synthetic N fertilizer application to cropland and permanent grassland. We have assembled a comprehensive dataset based on a wide range of data sources and devised a rigorous method to process it. The result, we believe, is to date the most comprehensive and consistent estimate of the allocation of synthetic N fertilizer between cropland and permanent grassland in Europe. In summary, EuropeAgriDB v1.0 is a detailed, complete, and consistent dataset which will be useful both to understand Europe’s recent agricultural history and to make informed decisions about its future. This is particularly relevant in the current context of the EU Farm to Fork strategy, which requires major reduction in N inputs and surpluses and therefore the best possible quantification.
Multiplicity of open source remote sensing date platforms help in bringing various opportunities. Spatio-temporal analysis ofa region can help in analysing changes in regional climate over different constituent land use/land cover (LU/LC). This studyderives a pattern of Land Surface Temperature (LST) over a period of 10 years in 11 smart cities of Uttar Pradesh using opensource data and software programs only. Smart cities namely Agra, Aligarh, Bareilly, Jhansi, Kanpur, Lucknow, Moradabad,Prayagraj, Rampur, Saharanpur and Varanasi are studied for LST in year 2010, 2015 and 2019 by using data from BHUVAN,NRSC and Copernicus Global Land Service: Land Cover (CGLS: LC-100) products. Boundary of the smart cities aredigitized form maps of various local authorities. Land use maps are obtained as Annual Landuse Land Cover 250k scaleproducts for year 2010 & 2015 from BHUVAN, NRSC but CGLS: LC-100 products are of resolution 100 m for year 2019.Both the Land use products are having 12 classes in region of smart cities which are reclassified into 5 LU classes of Built-up, Vegetation, Crop land, Barren land and Water. USGS Earth Explore is used to generate LST for year 2010 throughLandsat-5 ETM images by At-Surface Brightness Temperature & for year 2015 and 2019 through Landsat-8 TIRS bandimages by Radiative Transfer equation. Analysis of LST over years and LU classes show that smart cities of Aligarh andJhansi are dominantly warm over other smart cities of Uttar Pradesh. Capital city of Lucknow and Moradabad smart city arerelatively cooler than other smart cities. Rampur and Jhansi are having the lowest and highest standard deviation in LSTrespectively. Difference in LST over smart cities can be in range of 10-15 °C. Barren Land in these smart cities is found to behotter than Built-up land use class and vegetation is having lowest LST in all 11 smart cities. Range between LST values indifferent years over different LU classes vary between 28-35 °C. In Year 2019 LST statistics seem to be cooled down afteryear 2015 being worst in terms of LST range, maximum value and standard deviation of 6.12 °C. Percentage of vegetationhelping in reducing LST is surely a motivation to apply concept of Urban Green Space (UGS) in these 11 smart cities.
The traditional ocean color remote sensing usually focuses on using optical inversion models to estimate the properties of in-water components from the above-surface spectra, so we call it the spectrum-concentration (SC) scheme. Unlike the SC scheme, this study proposed a new research scheme, distribution-distribution (DD) scheme, which uses statistical inference models to estimate the possibility distribution of these in-water components, based on the possibility distribution of the observed spectra. The DD scheme has the advantages that (1) it can rapidly give the key and overview information of the interest water, instead of using the SC scheme to compute each image pixel, (2) it can assist the SC scheme to improve their models and parameters, and (3) it can provide more valuable information for better understanding and indicating the features and dynamics of aquatic environment. In this study, based on Landsat-8 images, we analyzed the spectral possibility distributions (SPD) of 688 global water and found many of them were normal, lognormal, and exponential distributions, but with diverse patterns in distribution parameters such as the mean, standard deviation, skewness and kurtosis. Furthermore, we used Monte-Carlo and Hydrolight simulations to study the theoretical and statistical connections between the possibility distributions of in-water components and SPDs. The simulation results were basically consistent with the observations on the real water. Then by using the simulation and field measured data, we proposed a bootstrap-based DD scheme and developed some simple statistical inference models to estimate the distribution parameters of yellow substance in lakes. Since DD scheme is still on its early stage, we also suggested some potential and useful topics for the future work.
One third of all coastlines worldwide consist of permafrost. Many of these permafrost coasts are presently exposed to greater environmental forcing as a consequence of climate change, such as a lengthening of the open water season, intensified storms, and higher water and air temperatures. As a result, increasing erosion rates are currently reported from various sites across the Arctic. It is crucial to synthetize these data on Arctic shoreline dynamics in order to improve our understanding on present coastal dynamics on the pan-Arctic scale. A first synthesis product was released in form of the Arctic Coastal Dynamics databse in 2012, which included data published until 2009 (Lantuit et al., 2012). Since then, numerous publications and data products were published on short and long term changes of Arctic coasts across a wide range of study sites. We made an extensive literature review of publications released within the last 10 years and updated the shoreline change data section in the Arctic Coastal Dynamics database. While in 2009 for one percent of the Arctic shoreline data on coastal dynamics was available, the addition of new data leads to a broader data coverage, which is mainly the effect of the greater availability of remotely sensed products for analyses conducted in these remote regions. Further, the additional data allow us to update the current mean rate of Arctic shoreline change.
Rangelands cover over 50% of the land surface area in the western US, providing important economic, social and environmental benefits. The resilience of western rangelands, however, is threatened by climate change, including altered phenology and precipitation patterns, increased frequency and intensity of drought and forest fires, heightened pressure from invasive plants, and reduced water storage in winter snowpack. Climate adaptation strategies are available to ranchers, yet uptake varies substantially. Rancher decision-making is a complex function of their beliefs, knowledge, skill level, risk perceptions, and the institutions supporting them. Semi-structured interviews, focus groups and workshops will be utilized to examine how ranchers in Idaho, Montana, Wyoming perceive and respond to climate change, and the opportunities and barriers these social processes create for climate change adaptation.
Hillslopes are responsible for the production and transport of sediments within a landscape (Gilbert 1877). Since the hillslope gradient and morphology tend to vary across a landscape, it is expected that the erosion and sediment delivery would also be non-uniform. In this study, we explore the probability of the flux at a particular point in the catchment reaching the river mouth using connectivity and the Revised Universal Soil Loss Equation (RUSLE) in the Pranmati river catchment (a small 3rd order Himalayan river catchment within the Ganga River system). Methodology involves characterising the hillslopes of Pranmati river catchment centered on land use and land cover units. Using RUSLE, the sediment yielding capacity of various land cover units are estimated based on which potential source areas are marked. The sediment connectivity within the basin is also calculated by generating a sediment connectivity map of the area using method given by Borcelli et al. (2008). The catchment is categorized into four classes – (A) Highly connected zones with high sediment yielding capacity (B) highly connected zones but low yielding capacity (C) poorly connected zones but high yielding capacity (D) poorly connected zones and low yielding capacity. The area is then mapped on the basis of the defined classes and potential areas of erosion and storage are identified. Our results show that about 62% of the catchment area has low connectivity implying sediment flux generated in these zones have a low probability of leaving the catchment. Only 11% of the catchment area has sediment yield greater than the mean yield per hectare. The sediment generated from this small area of the catchment contributes 93% of the total sediment production of the catchment. References Borselli, L., Cassi, P., & Torri, D. (2008). Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. Catena, 75(3), 268-277. Gilbert, G. K. (1877). Geology of the Henry mountains (pp. i-160). Government Printing Office.
Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.