Geo-referenced Database Research Articles

A comprehensive geospatial database of nearly 100 000 reservoirs in China

Abstract. With rapid population growth and socioeconomic development over the last century, a great number of dams/reservoirs have been constructed globally to meet various needs. China has strong economical and societal demands for constructing dams and reservoirs. The official statistics reported more than 98 000 dams/reservoirs in China, including nearly 40 % of the world's largest dams. Despite the availability of several global-scale dam/reservoir databases (e.g., the Global Reservoir and Dam database (GRanD), the GlObal geOreferenced Database of Dams (GOODD), and the Georeferenced global Dams And Reservoirs (GeoDAR)), these databases have insufficient coverage of the reservoirs in China, especially for small or newly constructed ones. The lack of reservoir information impedes the estimation of water budgets and the evaluation of dam impacts on hydrologic and nutrient fluxes for China and its downstream countries. Therefore, we presented the China Reservoir Dataset (CRD), which contains 97 435 reservoir polygons and fundamental attribute information (e.g., name and storage capacity) based on existing dam/reservoir products, national basic geographic datasets, multi-source open map data, and multi-level governmental yearbooks and databases. The reservoirs compiled in the CRD have a total maximum water inundation area of 50 085.21 km2 and a total storage capacity of about 979.62 km3 (924.96–1060.59 km3). The quantity of reservoirs decreases from the southeast to the northwest, and the density hotspots mainly occur in hilly regions and large plains, with the Yangtze River basin dominating in reservoir count, area, and storage capacity. We found that these spatial accumulations of reservoirs are closely related to China's socioeconomic development and the implementation of major policies. Finally, we presented the comparison of the CRD with GOODD, GeoDAR, and GRanD databases. The CRD has significantly increased the reservoir count, area, and storage capacity in China, especially for reservoirs smaller than 1 km2. The CRD database provides more comprehensive reservoir spatial and attribute information and is expected to benefit water resources managements and the understanding of ecological and environmental impacts of dams across China and its affected transboundary basins. The CRD database is publicly available at https://doi.org/10.5281/zenodo.6984619 (Song et al., 2022).

Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications

Geographical Indications (GIs), as Protected Designation of Origin (PDO)and Protected Geographical Indication (PGI), offer a unique protection scheme to preserve high-quality agri-food productions and support sustainable rural development at the territorial level. However, not all the areas with traditional agri-food products are acknowledged with a GI. Examining the Italian wine sector by a geo-referenced database and a machine learning framework, we show that municipalities which obtain a GI within the subsequent 10 year period (2002–2011) can be predicted using a large set of (lagged) municipality-level data (1981–2001). We find that the Random Forest algorithm is the best model to make out-of-sample predictions of municipalities which obtain GIs. Results show that there is a sort of optimal territorial condition characterized by the successful matching of wine-growing profession (vineyards), local actors involved (number of farmers), and physical dimension of farms (middle farms). Being in a vital economic system and the distance from major urban centers also emerges among the main relevant features in predicting the success of GIs. The methodology adopted and the evidence provided lead to policy reflections, in the light of the future Common Agricultural Policy (CAP) programming period and the scheduled reform of the GI’s quality scheme.

Relevance of soil fertility spatial databases for parameterizing APSIM-wheat crop model in Moroccan rainfed areas

Crop models have evolved over the past decade to incorporate more soil-related processes. While this may open avenues to support farmers regarding fertilization practices, it also widens the pitfalls related to model parametrization. Open-access georeferenced soil databases are often a solution for modelers to derive soil parameters. However, they can potentially add to model uncertainty depending on database resolution and the variability of the characteristics it contains. Fertimap is an online spatial database recently released in Morocco. In this study, we aim at assessing how Fertimap could support the use of crop model in the rainfed wheat production areas of Morocco. Data including local soil analysis, farmers’ practices, wheat biomass, and yield were collected on 126 farmers’ fields distributed across the rainfed wheat production area in Morocco from 2018 to 2020. Data were first used to parameterize, calibrate, and assess the model, using site-specific data to infer soil parameters. Then, the impact of soil data source on model uncertainty was assessed by rerunning the simulations while using alternatively locally measured soil inputs or inputs extracted from Fertimap. To disentangle the effect of data source from model sensitivity on model outputs, the model’s sensitivity to labile phosphorus, pH, and organic carbon parameters was also tested. The APSIM-wheat model was found to reasonably simulate wheat phenological stages, biomass, and yield. The comparison of model outputs using one or another source of soil data indicated that using Fertimap had no significant effect on the model’s outputs. This study provides the first assessment of the APSIM-wheat model for simulation of widely used wheat cultivars in Moroccan rainfed areas. It is also the first proof of the practical utility of Fertimap database for modeling purposes in Morocco. This preliminary study delivers a robust basis for model-assisted agricultural advising to take off in Morocco.

ESCORREGAMENTOS NO ESTADO DE PERNAMBUCO

Even though the occurrences of social and economic losses and fatal victims caused by landslides in Pernambuco are recurrent, one must consider that there is a gap in the systematization of information pertaining to such processes in a georeferenced spatial database at a state level, especially with regard to the location and types of triggering conditioning factors. This work is intended to evaluate the occurrence of landslides in the state of Pernambuco, the conditioning factors thereof, and the most affected areas. For such, a Geographic Database (Banco de Dados Geográfico - BDG) and an inventory map were created, based on the capture of landslides in the state between 1988 and 2019, both through secondary and primary sources. Lastly, all of the occurrences were catalogued in information plans, vectorized in a GIS (Geographic Information System) environment and correlated with thematic variables of the main conditioning factors. 3,138 landslides in Pernambuco were inventoried, which enabled the verification of behavior patterns regarding the occurrence of said processes in the state. The formation of this georeferenced spatial database may contribute to better understand the incidence of these processes, and thus subsidize the establishment of investigative methodologies and strategies related to the subject. Keywords: Inventory; Database; Mass Movements; Northeastern Brazil.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.

BackgroundPublic health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts’ Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated.ObjectiveWe developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic.MethodsMAGGI uses a server-client model–based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time.ResultsWe created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts’ spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19–positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention’s social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021.ConclusionsWe developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

GIS Multisource Data for the Seismic Vulnerability Assessment of Buildings at the Urban Scale

The paper presents a methodology of extraction, integration and elaboration of data from different sources using the geographic information system (GIS), to realize a georeferenced building database (GBD) useful for the seismic vulnerability assessment of existing buildings on a large scale. Three levels of GIS entities have been defined and equipped with the related information: census section (CS), urban block (UB), and individual building (IB), depending on the level of detail of dataset. Additional information about the typological and structural features has been extracted by CARTIS catalogue, to refine the data associated with each building. The data have been validated using detailed information gathered on a proper sample of buildings, which have been filed and analyzed one-by-one. The alphanumeric format of data allows for the automatic implementation of different methods available in the literature, which provide a qualitative seismic vulnerability index at different scales (whole urban district, an urban block, and a single building). Finally, the 3D representation of data and results have been elaborated, providing a tool easily searchable and constantly implementable. An application has been developed for a case study in Puglia, Italy: Bisceglie, for which 3726 IB have been assessed. The work addresses the issue of the lack of information typical of large-scale applications, exploiting all the available data sources to achieve nearly complete knowledge of existing building stock finalized to a rapid but extensive evaluation of the seismic vulnerability on an urban scale with very low computational efforts and the use of limited resources in terms of time and cost.

GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations

Abstract. Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22 560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved ∼ 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.

Geoenvironmental mapping of the Mamanguape River estuary (PB)

Estuaries and mangroves are important breeding and feeding areas for many species of larval fish and invertebrates that usually complete their life cycles in the sea. The Mamanguape River estuary is located between parallels 06 ° 48' and06 ° 51 'south latitude, and meridians 35 ° 07' and 34 ° 54 'west longitude, it is configured as an Environmental Preservation Area (APA in Portuguese), which has the largest mangrove reserve in the state of Paraíba. Considering its importance in environmental and economic, it takes planning and management for the development of economic activities. Thus this paper aims to Thematic Mapping in the scale of 1:10000, identifying the environments and their Geoenvironmental Units, representing them individually on maps; moreover, treat their potential, suggesting skills for use and occupation. Digital image processing of QuickBird data and WordView, field trips, vectorization of geoenvironmental units and elements of interest, were used as methodology through the development of this paper. As result, the structuring of an Environmental Georeferenced Database of multisource Data mapping for storage and their further manipulation

Global field observations of tree die-off reveal hotter-drought fingerprint for Earth\u2019s forests

Earth’s forests face grave challenges in the Anthropocene, including hotter droughts increasingly associated with widespread forest die-off events. But despite the vital importance of forests to global ecosystem services, their fates in a warming world remain highly uncertain. Lacking is quantitative determination of commonality in climate anomalies associated with pulses of tree mortality—from published, field-documented mortality events—required for understanding the role of extreme climate events in overall global tree die-off patterns. Here we established a geo-referenced global database documenting climate-induced mortality events spanning all tree-supporting biomes and continents, from 154 peer-reviewed studies since 1970. Our analysis quantifies a global “hotter-drought fingerprint” from these tree-mortality sites—effectively a hotter and drier climate signal for tree mortality—across 675 locations encompassing 1,303 plots. Frequency of these observed mortality-year climate conditions strongly increases nonlinearly under projected warming. Our database also provides initial footing for further community-developed, quantitative, ground-based monitoring of global tree mortality.

Meta-Analysis of a New Georeferenced Database on Polycyclic Aromatic Hydrocarbons in Western and Central Mediterranean Seafood

The aim of this work was to collect and harmonize the results of several studies achieved over the years, in order to obtain a database of georeferenced observations on polycyclic aromatic hydrocarbons (PAHs) in Western and Central Mediterranean seafood. For each observation, some information on the taxonomy and the ecology of the sampled species are reported, as well as details on the investigated hydrocarbon, and spatial and temporal information on sampling. Moreover, two health risk indexes were calculated for each record and included in the database. Through several statistical methods, we conducted a meta-analysis of the data on some of the species in this database, identifying trends that could be related to the biology of the investigated organisms, as well as to the physico-chemical properties of each hydrocarbon and to the oceanographic characteristic of this part of the Mediterranean. The analysis of the data showed that, at a consumption rate like the one typical of the Italian population, seafood caught from the area considered in the present work seems to pose a minimal risk to health. However, we also found evidence of an increasing trend of PAH concentrations in Mediterranean mussels, pointing to the need for constant monitoring.

FAST-NEPAL: Regionally Calibrated Spectral Method for Reinforced Concrete With Masonry Infills

Reinforced concrete (RC) with masonry infill is one of the most common structural typologies in Nepal, especially in the Kathmandu Valley. Masonry infills are typically made of solid clay bricks produced locally in Nepal. This study aims to calibrate the spectral-based analytical method, namely, FAST, for Nepalese RC-infilled buildings. The FAST method has been initially conceived for Southern European RC buildings with hollow clay brick infills. The calibration is achieved by reviewing code prescriptions and construction practices for RC masonry infills in Nepal and updating the FAST method. The variables of FAST method are calibrated using different information sources and a Bayesian updating procedure to consider the global and local material properties for solid clay bricks. The FAST-NEPAL method obtained is then verified, considering a single school design, for which a detailed state-of-the-art vulnerability assessment is available. Being particularly suitable for large-scale assessment, the method is further validated using data from Ward-35 of Kathmandu Metropolitan City (in the vicinity of Tribhuvan International Airport) obtained from photographic documentation included in a geo-referenced database of buildings collected after the 2015 Nepal earthquake and prepared for census purposes. The comparisons show that the FAST-NEPAL method can be conservative relative to the other data sources for vulnerability and is more accurate at capturing low-level damage. This makes the approach suitable for large-scale preliminary assessment of vulnerability for prioritisation purposes.

Integrated assessment and prospectivity mapping of geothermal resources for EGS in Brazil

Studies in Brazil related to geothermal resources have been carried out mainly by the National Observatory. Based on the results vast areas of Brazil have been delimited with a surface heat flow of over 60 mW/m2 and an excess temperature above 100 °C at depths between 3 and 6 km. The nature of the heat source and the mode of heat dissipation in these areas are largely unknown. Geothermal resources that might be associated with these areas can occur in hot sedimentary aquifers (HSA) and units of hot dry rocks (HDR) that are hosted, respectively, in intracratonic sedimentary basins and high heat producing granites (HHPG). The focus of our study is on finding resources of high enthalpy (above 150 °C) in HHPG, which regardless of their hydrological properties (porosity, permeability) could be considered as potential targets for enhanced geothermal systems (EGS). The structural provinces of Tocantins and Borborema were chosen as pilot regions based on both the indication of HDR and the wealth of open access georeferenced information. A comprehensive database was compiled and processed under GIS that includes geology, faults, soil typology, radiogenic heat, heat flow and geothermal gradient from wells, thermal spring locations, airborne gamma-ray spectrometry and magnetic surveys, terrestrial gravimetric surveys, and earthquake locations. In parallel, various critical factors were defined primarily to identify and characterize the most promising HHPG under insulating sedimentary rocks for EGS. These critical factors were grouped into three main categories labelled as: ‘thermal source’, ‘thermal insulation’ and ‘active pathways’. In the absence of exploratory data on existing EGS in Brazil these critical factors were used in this study along three separate work streams to develop a knowledge-driven model based on fuzzy logic for improving the knowledge on the prospectivity of geothermal resources. The developed model was further tested for its validation. The selected approach is expected to be applicable to mapping areas with EGS potential in other regions with similar geology and open access georeferenced databases.

Low-Cost Geographic Information System for Municipal Road Signs Management in Depopulated and Low-Density Areas

The current national and regional economical conjuncture in many countries, requires a sustainable management of different urban infrastructures, including road signs. From the set of possible solutions to improve the existing infrastructure’s management, analysis and spatial representation, Geographic Information Systems (GIS) have proved to be an essential tool for the functional competencies of local administration (municipalities), managers of municipal road networks. This study presents a framework of existing national regulations on road signs, as well as the shortcomings in the definition of methodologies for GIS-based management systems implementation. The paper continues with the critical assumptions considered in their definition and the municipalities specific competences needed for their implementation. A low-cost methodology is proposed and applied to a case study in a small Portuguese village: Belmonte. The results have shown the potential of these low-cost systems, revealing relevant time and economical gains, providing municipalities with fundamental information for the definition of realistic and well-founded strategic plans and budgets, and allowing better information to their citizens. The gathered information can help in inventory, inspections, maintenance and replacement allowing detailed reports and geo-referenced electronic database and maps of a municipality's road signs at reasonable costs. These maps and reports are the starting point to forecast the projected lifespan of the municipality's signage allowing more accurate project sign management budgets for future years. Aspects that need further development in order to improve the proposed system are also addressed.

Investigating the origins of ivory recovered in the United Kingdom

• Concordance study of stable isotope and mtDNA African elephant databases. • Ivory present in the United Kingdom analysed and geographic assignments evaluated. • UK seizure contained modern ivory and regional geographic distribution was inferred. • Antique and modern ivory surrendered in the UK included novel mtDNA haplotypes. • Novel haplotypes & moderate isotopic fits offer more complex geographic provenance. Over recent years, mounting pressure has been placed on countries to assess their role in the ivory trade, with a view to tackling the rapidly declining numbers of elephants, due to poaching. The United Kingdom has been identified as a large re-exporter of ivory. Despite much of this trade being reported as legal or antique ivory, such provision of ivory to meet demand is known to fuel illegal markets and provide trade routes for modern ivory sales. Aside from ivory species and age, further analysis to evaluate geographic provenance, can inform where an elephant had lived, and so identify a source region or population where poaching occurred. The purpose of this study was to determine the age and species of ivory objects surrendered or seized in the UK and assess their likely geographic provenance through comparison of results from mitochondrial DNA and stable isotope analysis to publicly accessible georeferenced African elephant databases. The results demonstrated that the objects tested from an airport seizure were modern and matched existing haplotypes allowing for regional geographic inferences (supported by both techniques) to be obtained for most of these objects. In contrast, antique and modern ivory was detected amongst the amnesty objects, and several new mtDNA haplotypes were identified. Regional geographic inferences were achieved for some but not all of the objects tested. Our findings show this combination of methods provides a wealth of information which, could provide insight into targeted elephant populations and assist in disrupting international wildlife trade networks.

The long-term fortunes of territories as a route for agri-food policies: evidence from Geographical Indications

Once the EU has perceived the strategic importance of local peculiarities to support rural development and high-quality productions, it has emphasized the need for more place-sensitive agri-food policies. The importance of socio-economic, historical and cultural factors as transfers of intangible value-added is particularly evident in the agri-food sector. Place-blind and sectorial-oriented approaches have indeed not succeeded in dealing with the territorial heterogeneity of agri-food systems. By delving into the longstanding debate on the conceptualizations of territory and focusing on the territories of origin of the most economically performant Italian Protected Designation of Origins (PDOs), this paper empirically investigates what are the contextual conditions that have mainly contributed in the economic success of local productions. Drawing on an original geo-referenced database, the analysis is conducted on a panel of Italian municipalities and exploits non-linear dynamic panel models. Findings point out the heterogeneity of affecting territorial factors. Imbalances come from both socio-economic conditions (food PDOs) and socio-cultural knowledge (wine PDOs). This paper informs the evidence-based debate on the relevance of territorially-sensitive interventions for the future of EU agri-food and rural development policies. In the case of GIs, it should consider being more place-sensitive as well as more integrated with other agricultural and regional policies to meet the EU’s socio-economic objectives.

A cartographic approach coupled with optimized sizing and management of an on-grid hybrid PV-solar-battery-group based on the state of the sky: An african case study

A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO2-related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own.

Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran

Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R2 = 0.88) and distribution of neighborhood centers (R2 = 0.59), drugstores (R2 = 0.64), and chain stores (R2 = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.

Análise da Estrutura Fundiária do Município de Montes Claros-MG por Meio dos Dados do Cadastro Ambiental Rural (CAR)

O Cadastro Ambiental Rural (CAR) é um registro público eletrônico georreferenciado de âmbito nacional, obrigatório para todos os imóveis rurais. O CAR, enquanto banco de dados georrefenciados, torna-se um instrumento de monitoramento da estrutura fundiária rural. Portanto, a análise de seus dados em ambiente SIG, cruzados com dados provenientes do sensoriamento remoto, permite melhor compreensão da realidade de uso da terra. Nesse contexto, este trabalho analisou a estrutura fundiária rural de Montes Claros. Para isso, em ambiente do Sistema de Informação Geográfica (SIG), foram cruzados os dados do CAR com informações extraídas de imagens de satélite. Os resultados mostram que a estrutura fundiária ainda está moldada no processo de exclusão social.

Spatiotemporal Analysis of Tropical Cyclone Landfalls in Northern Bay of Bengal, India and Bangladesh

In the Northern Bay of Bengal (NBoB), cyclonic storms (wind speed: ≥34 knots) inflict considerable losses in coastal Bangladesh and India. By building a georeferenced panel database of storm landfalls for this region, one can obtain spatial information on period-wise strike intensities. In this study, 168 cyclone strike locations along a 1259-km impact zone were analyzed for seven historical periods between 1877 and 2016. Cyclone landfall trends for this region denote a gradual reduction in storm frequencies since peaking in 1961–1980, which is generally in line with already established trends for the Northern Indian Ocean and Bay of Bengal basins. One notable exception for the NBoB in this is the unimodal seasonality of storm landfalls during 1877–1960, but that also conformed to the bimodal regional trends in the later periods. Over time, the median location of storm strikes in the NBoB has shifted eastward into westernmost Bangladesh, with a particularly marked variability in location after 1960. Northern Odisha and West Bengal, India, experienced more cyclone strikes over the past 140 years; but the highest mean wind speeds occurred along Bangladesh’s eastern coast. Also, storm impacts have varied considerably within and across zones; northern Odisha and the Sundarban region of West Bengal have withstood the greatest impacts. The pronounced spatial and temporal variations in cyclone impacts evinced in this study can provide valuable information for relief prioritization and community adaptation to cyclones in the region. The general method developed for this analysis can be applied to other coastal locations for management of storm impacts.

Decarbonizing District Heating in EU-27 + UK: How Much Excess Heat Is Available from Industrial Sites?

Energy-intensive industries across the EU-28 release unused heat into the environment. This excess heat can be utilized for district heating systems. However, this is the exception today, and the potential contribution to the decarbonization of district heating is not well quantified. An estimation of excess heat, based on industrial processes, and spatial matching to district heating areas is necessary. We present a georeferenced industrial database with annual production and excess heat potentials at different temperature levels matched with current and possible district heating areas. Our results show a total potential of 960 PJ/a (267 TWh/a) of excess heat when the exhaust gases are cooled down to 25 °C, with 47% of the 1.608 studied industrial sites inside or within a 10 km distance of district heating areas. The calculated potentials reveal that currently 230 PJ/a (64 TWh/a) of excess heat is available for district heating areas, about 17% of today’s demand of buildings for district heating. In the future, widespread and low-temperature district heating areas increase the available excess heat to 258 PJ/a (72 TWh/a) at 55 °C or 679 PJ/a (189 TWh/a) at 25 °C. We show that industrial excess heat can substantially contribute to decarbonize district heating, however, the major share of heat will need to be supplied by renewables.