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This study aims on an exploration of recent applications of drones in the Malaysian context. More specifically, to investigate the drone's application status and application areas. To search the academic literature and identify the included documents, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an overview study of the total number of 54 documents (out of 202) published since 2018, which were extracted from the Scopus database. The results of the bibliometric analysis of the included documents showed that drones in Malaysia are applied in different disciplines, including agriculture, mapping, forestry, river monitoring, transportation, construction, coastal management, flood management, and other applications. Among these, agriculture and mapping are the main application areas. MDPI published the most journal articles, and IGRSM conferences published the highest number of conference papers. Furthermore, scholars from the Universiti Teknologi Malaysia have the highest contribution among others. [ABSTRACT FROM AUTHOR]

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. "Restoration" encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges of implementing the CityGML standard within database environments for managing urban digital twins (UDT), with a particular focus on addressing big geodata issues. CityGML stands as a critical standard for representing and exchanging 3D urban models and thematic data, essential for effective urban planning and infrastructure management. However, integrating CityGML into databases poses challenges due to the dual requirements of geospatial and semi-structured data. While the former imposes a certain rigor in its formalism, the second benefits from more flexibility. Through a comprehensive review and benchmarking of existing database implementations, including both new distributions and those often use, 3DCityDB, CJDB, Measur3D, and Cerbere, this paper proposes a novel approach towards a multi-database CityGML environment tailored to the specific needs of UDT. The proposed solution leverages the strengths of both relational and NoSQL databases, offering a flexible and scalable architecture while ensuring data consistency, geospatial capabilities and compliance with the CityGML schema. The research hypothesis suggests integrating Cerbere, a middleware for CityGML schema compliance and transaction validation, with 3DCityDB and Measur3D (MongoDB). This approach aims to demonstrate the feasibility of managing advanced 3D data operations based on the CityGML model (3DCityDB) and scalability for big data like IoT (Measur3D) within a multi-database environment. The paper contributes to the advancement of UDT by providing a comprehensive solution for managing diverse data types, facilitating more effective urban planning, infrastructure management, and sustainable development initiatives in the context of smart cities. [ABSTRACT FROM AUTHOR]

As a single coherent model, the proposed Virtual Historic Dublin City will improve the current approaches for planning, conserving, presenting and interpreting cultural heritage buildings and their environments. The combination of digital recording, modelling and data management systems enable the interaction with complex, interlinked three-dimensional structures containing rich and diverse underlying data. End users can encompass architectural and engineering conservation, education and research, in addition to public engagement and cultural tourism. In this paper a digital design framework is presented, based on state of the art current approaches for recording, modelling and presenting Virtual Historic Dublin. The modelling sites and structures is based on remotely sensed survey data which is processed and modelled in Historic BIM or GIS allowing the addition of semantic attributes. Archiving and storage of both models and knowledge and information attributes requires open systems and server data base capable of handling vector and point cloud information in addition to other digital data. The dissemination and interaction with the models and attached knowledge attributions is based on combining game engine platforms, Historic BIM, Historic GIS and access to compatible storage and data base. [ABSTRACT FROM AUTHOR]

The Global Terrestrial Network for Permafrost (GTN-P) provides the first dynamic database associated with the Thermal State of Permafrost (TSP) and the Circumpolar Active Layer Monitoring (CALM) programs, which extensively collect permafrost temperature and active layer thickness data from Arctic, Antarctic and Mountain permafrost regions. The purpose of the database is to establish an "early warning system" for the consequences of climate change in permafrost regions and to provide standardized thermal permafrost data to global models. In this paper we perform statistical analysis of the GTN-P metadata aiming to identify the spatial gaps in the GTN-P site distribution in relation to climate-effective environmental parameters. We describe the concept and structure of the Data Management System in regard to user operability, data transfer and data policy. We outline data sources and data processing including quality control strategies. Assessment of the metadata and data quality reveals 63% metadata completeness at active layer sites and 50% metadata completeness for boreholes. Voronoi Tessellation Analysis on the spatial sample distribution of boreholes and active layer measurement sites quantifies the distribution inhomogeneity and provides potential locations of additional permafrost research sites to improve the representativeness of thermal monitoring across areas underlain by permafrost. The depth distribution of the boreholes reveals that 73% are shallower than 25 m and 27% are deeper, reaching a maximum of 1 km depth. Comparison of the GTN-P site distribution with permafrost zones, soil organic carbon contents and vegetation types exhibits different local to regional monitoring situations on maps. Preferential slope orientation at the sites most likely causes a bias in the temperature monitoring and should be taken into account when using the data for global models. The distribution of GTN-P sites within zones of projected temperature change show a high representation of areas with smaller expected temperature rise but a lower number of sites within arctic areas were climate models project extreme temperature increase. This paper offers a scientific basis for planning future permafrost research sites on large scales. [ABSTRACT FROM AUTHOR]

Detecting urban human mobility patterns helps contributes to many urbanization issues, such as urban planning and traffic management. With the growing volume of crowd-sourced data, many studies have benefited from this data type to explore people's daily movements and track their activities. There are several published review papers examing these studies on urban human mobility, with the focus on defining models and applications. However, the absence of a review of studies on urban human mobility that considered spatial, temporal, and semantic properties as well as crowd-sourced data together has limited the proliferation of semantic content in addressing mobility issues. In response, this paper provides a review on urban human mobility, including the data, models, and applications used in the selected articles. We defined particular inclusion and exclusion criteria to select the most relevant articles. We also included metadata analysis to overview the existing relevant literature. Finally, several research challenges and open issues are discussed. [ABSTRACT FROM AUTHOR]

The Weddell Gyre plays a crucial role in the modification of climate by advecting heat poleward to the Antarctic ice shelves and by regulating the density of water masses that feed the lowest limb of the global ocean overturning circulation. However, our understanding of Weddell Gyre water mass properties is limited to regions of data availability, primarily along the Prime Meridian. The aim of this paper is to provide a dataset of the upper water column properties of the entire Weddell Gyre. Objective mapping was applied to Argo float data in order to produce spatially gridded, time composite maps of temperature and salinity for fixed pressure levels ranging from 50 to 2000 dbar, as well as temperature, salinity and pressure at the level of the subsurface temperature maximum. While the data are currently too limited to incorporate time into the gridded structure, the data are extensive enough to produce maps of the entire region across three time composite periods (2002-2005, 2006-2009 and 2010-2013), which can be used to determine how representative conclusions drawn from data collected along general RV transect lines are on a gyre scale perspective. The work presented here represents the technical prerequisite in addressing climatological research questions in forthcoming studies. These data sets are available in netCDF format at doi:10.1594/PANGAEA.842876. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a spectral surface reflectivity climatology based on observations made by TROPOMI on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1 , OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the short-wave infrared (SWIR) (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER, we made use of comparison with the MODIS surface bi-directional reflectance distribution function (BRDF) for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER, and we found good agreement with the MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including Ocean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites, Sentinel-5, and Multi-viewing Multi-channel Multi-polarisation imager (3MI) on the MetOp-SG-A1 satellite to be launched in 2025, as well as the future CO2M mission. [ABSTRACT FROM AUTHOR]

GeoAI combines artificial intelligence (AI) with geospatial data, science, and technologies. In this paper, a successful case in utilizing GeoAI to improve the data quality in the national topographic database (TDB) of Finland was introduced. The project employed a GeoAI model to identify buildings from the input data of true orthophotos, digital elevation model (DTM), and digital surface model (DSM). The GeoAI-derived buildings served as reference data, enabling a comparison with building polygons from the topographic database (TDB) to reveal TDB building location deviations, missing structures, and demolished buildings. Throughout the project, algorithms were developed to match the TDB building vectors to the GeoAI-derived building polygons. The challenges include i) the differences between the GeoAI-derived building outlines and the TDB build footprints; ii) the reliability of the GeoAI model across data over different environments: urban, suburban, rural, and forest areas. Throughout the project, the GeoAI model was continuously improved by training massive new datasets: corrected vectors from the model prediction. Testing was conducted on datasets from twelve areas of Finland, covering 2204 km2 and including hundreds of thousands of buildings. The test areas covered urban, suburban, rural, and forest areas. The evaluation was conducted by the mapping team in the organization. The results showed that our methods greatly enhanced the quality of the TDB building footprints. The challenges and lessons of the project were addressed in the paper. [ABSTRACT FROM AUTHOR]

Since the start of the 21st century, the widespread application of ion probes has promoted the mass output of high-precision and high-accuracy U–Th–Pb geochronological data. Zircon, as a commonly used mineral for U–Th–Pb dating, widely exists in the continental crust and records a variety of geological activities. Due to the universality and stability of zircon and the long half-lives of U and Th isotopes, zircon U–Th–Pb geochronology can provide nearly continuous records for almost the entirety of Earth's history and is thus essential to studying the growth and evolution of the continental crust and Earth system evolution. Here, we present a database of zircon U–Th–Pb geochronology that samples the global continental crust and spans nearly all of Earth's history. This database collects ∼2 000 000 geochronology records from ∼12 000 papers and dissertations. This paper describes the compiled raw data, presents the relationship between dating error and zircon age, compares the error levels of different dating instruments, and discusses the impact of sampling bias on data analysis as well as how to evaluate and minimize this impact. In addition, we provide an overview of the temporal and spatial distribution of global zircon ages and provide key insights into the potential research value of zircon ages for Earth system science, such as crustal evolution, plate tectonics, and paleoclimate changes, as well as commercial use in mining and energy exploration. Overall, this data collection provides us with a comprehensive platform with which to study zircon chronological data in deep time and space. The described zircon database is freely available via Zenodo at 10.5281/zenodo.7387566 (Wu et al., 2023). [ABSTRACT FROM AUTHOR]

Ensuring food security against climate risks has been a growing challenge recently. Weather index insurance has been pointed out as a tool for increasing the financial resilience of food production. However, the multi-hazard insurance design needs to be better understood. This paper aims to review weather index insurance design for food security resilience, including the methodology for calculating natural hazards' indices, vulnerability assessment, and risk pricing. We searched for relevant research papers in the Scopus database using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. Initially, 364 peer-reviewed papers from 1 January 2010 to 19 February 2022 were screened for bibliometric analysis. Then, the 26 most relevant papers from the last 5 years were systematically analyzed. Our results demonstrate that despite a significant research effort on index insurance, most papers focused on food production. However, research considering other aspects of food security, such as transportation, storage, and distribution, is lacking. Most research focuses on droughts. Other hazards, such as extreme temperature variation, excessive rainfall, and wildfires, were poorly covered. Most studies considered only single-hazard risk, and the multi-hazard risk studies assumed independence between hazards, neglecting the synergy hypothesis between hazards. Lastly, we proposed a conceptual framework that illustrates design paths for a generalized weather index insurance design and evaluation. Solutions for addressing multi-hazard problems are considered. An illustrative example demonstrates the importance of testing the multi-hazard risk hypothesis for weather-based index insurance design for soybean production in Brazil. [ABSTRACT FROM AUTHOR]

The new sea ice configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the sea ice extent, thickness and volume are compared with the previous configuration and with observationally-based datasets. In the Arctic, the sea ice is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and extent with the HadISST dataset. In the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in ice extent and volume; further work is required to rectify this in future configurations. [ABSTRACT FROM AUTHOR]

The process of digital restoration of lost cultural heritage often takes a lot of effort and it is difficult to judge the accuracy of the restoration. In this paper, we propose a set of basic methods and processes for digital restoration based on the lost heritage by mining materials and information, and can trace the evidence based on the restoration results. Taking the restoration of the South Gate of the Shangjing Palace in the Liao Dynasty as an example, this paper introduces the methods and processes of establishing a database of restoration materials and identifying relevant information, restoration design steps and accuracy assessment in the process of digital restoration, and finally realizing the digital presentation of the lost heritage. It is hoped that this paper can provide problem-solving ideas and methods for workers in the field of digital restoration of vanished heritage. [ABSTRACT FROM AUTHOR]

Earthquakes represent the highest risk in terms of potential loss of lives and economic damage for Central Asian countries. Knowledge of fault location and behavior is essential in calculating and mapping seismic hazard. Previous efforts in compiling fault information for Central Asia have generated a large amount of data that are published in limited-access journals with no digital maps publicly available, or are limited in their description of important fault parameters such as slip rates. This study builds on previous work by improving access to fault information through a webbased interactive map and an online database with search capabilities that allow users to organize data by different fields. The data presented in this compilation include fault location, its geographic, seismic and structural characteristics, short descriptions, narrative comments and references to peer-reviewed publications. The interactive map displays 1196 fault segments and 34 000 earthquake locations on a shaded-relief map. The online database contains attributes for 122 faults mentioned in the literature, with Quaternary and geodetic slip rates reported for 38 and 26 faults respectively, and earthquake history reported for 39 faults. This work has implications for seismic hazard studies in Central Asia as it summarizes important fault parameters, and can reduce earthquake risk by enhancing public access to information. It also allows scientists and hazard assessment teams to identify structures and regions where data gaps exist and future investigations are needed. [ABSTRACT FROM AUTHOR]

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive inter-comparison due to the availability of quality checked in situ databases. The CoastColour project Round Robin (CCRR) project funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). These datasets are available from doi.pangaea.de/10.1594/PANGAEA.841950. The datasets mainly consisted of 6484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters. [ABSTRACT FROM AUTHOR]

In the process of quality inspection of Remote sensing image data results, the reuse of spatial location information of multiple units, multiple projects and multiple sources can not only overcome the problems of long time to obtain control information, high cost and difficulty in obtaining some areas, but also the basis for achieving efficient and high-precision geometric correction. From the perspective of reusability of checkpoints and saving the cost of quality inspection of remote sensing images, this paper discusses the necessity of joint construction of multi-source and multi-resolution image checkpoint database. And put forward the construction principle and management objectives of checkpoint database. At last, this paper briefly introduces and prospects the application of the national multi-source and multi-resolution image checkpoint database. [ABSTRACT FROM AUTHOR]

The Varved Sediments Database (VARDA) was launched in 2020 and aimed to establish a community database for annually resolved chronological archives with their associated high-resolution proxy records. This resource would support reproducibility through accessible data for the paleoclimate and modelling communities. In this paper, VARDA has been extended by a dataset of European tephra geochemical data and metadata to enable the synchronisation of varve records during the Last Glacial–Interglacial Transition (LGIT; here defined as 25 to 8 ka; Beckett et al., 2022). Geochemical data from 49 known individual tephra layers across 19 lake records have been included, with Lago di Grande Monticchio being the single biggest contributor of geochemical data with 28 tephra layers. The Vedde Ash and Laacher See tephra are the most common layers found in six different records. This highlights the potential of refining the absolute age estimates for these tephra layers using varve chronologies and for synchronising regional paleoclimate archives. This is the first stage in a 5-year plan funded by the Past Global Changes (PAGES) Data Stewardship Scholarship to incorporate a global dataset of tephra geochemical data into varve records. Further stages of this project will focus on different regions and timescales. Data collated for this project are available open access at 10.5880/fidgeo.2023.015 (Beckett et al., 2022). [ABSTRACT FROM AUTHOR]

Improved line positions and intensities have been generated for the 7.6 μm spectral region of nitric acid. They were obtained relying on a recent reinvestigation of the nitric acid band system at 7.6 μm and comparisons of HNO3 volume mixing ratio profiles retrieved from the "Michelson Interferometer for Passive Atmospheric Sounding" (MIPAS) limb emission radiances in the 11 and 7.6μm domains. This has led to an improved database called "MIPAS-2015". Comparisons with available laboratory information (individual line intensities, integrated absorption cross sections, and absorption cross sections) show that MIPAS-2015 provides an improved description of the 7.6 μm region of nitric acid. This study should help to improve HNO3 satellite retrievals by allowing measurements to be performed simultaneously in the 11 and 7.6 μm micro-windows. In particular, it should be useful to analyze existing MIPAS and IASI spectra as well as spectra to be recorded by the forthcoming "Infrared Atmospheric Sounding Interferometer- New Generation" (IASI-NG) instrument. [ABSTRACT FROM AUTHOR]

Central Asian countries, which include Kazakhstan, the Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan, are known to be highly exposed to natural hazards, particularly earthquakes, floods, and landslides. With the aim of enhancing financial resilience and risk-based investment, planning to promote disaster and climate resilience in Central Asia, the European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), launched the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) regional programme. Within this framework, a consortium of national and international scientific institutions was established and tasked with developing a regionally consistent multi-hazard and multi-asset probabilistic risk assessment. The overall goal was to improve scientific understanding on local perils and to provide local stakeholders and governments with up-to-date tools to support risk management strategies. However, the development of a comprehensive risk model can only be done with the basis of an accurate hazard evaluation, the reliability of which depends significantly on the availability of local data and direct observations. This paper describes the preparation of the input datasets required for the implementation of a probabilistic earthquake model for the Central Asian countries. In particular, it discusses the preparation of a new regional earthquake catalogue harmonized between countries and homogenized in moment magnitude (Mw), as well as the preparation of a regional database of selected active faults with associated slip rate information to be used for the construction of the earthquake source model. The work was carried out in collaboration with experts from the local scientific community, whose contribution proved essential for the rational compilation of the two harmonized datasets. [ABSTRACT FROM AUTHOR]

The Eastern Tropical Pacific (ETP) is believed to be one of the largest marine sources of the greenhouse gas nitrous oxide (N2O). Future N2O emissions from the ETP are highly uncertain because oxygen minimum zones are expected to expand, affecting both regional production and consumption of N2O. Here we assess three primary uncertainties in how N2O may respond to changing O2 levels: (1) the relationship between N2O production and O2 (is it linear or exponential at low O2 concentrations?), (2) the cutoff point at which net N2O production switches to net N2O consumption (uncertainties in this parameterization can lead to differences in model ETP N2O concentrations of more than 20%), and (3) the rate of net N2O consumption at low O2. Based on the MEMENTO database, which is the largest N2O dataset currently available, we find that N2O production in the ETP increases linearly rather than exponentially with decreasing O2. Additionally, net N2O consumption switches to net N2O production at -10 µM O2, a value in line with recent studies that suggest consumption occurs on a larger scale than previously thought. N2O consumption is on the order of 0.129mmol N2O myr-3 yr-1 in the Peru--Chile Undercurrent. Based on these findings, it appears that recent studies substantially overestimated N2O production in the ETP. In light of expected deoxygenation, future N2O production is still uncertain, but due to higher-than-expected consumption levels, it is possible that N2O concentrations may decrease rather than increase as oxygen minimum zones expand. [ABSTRACT FROM AUTHOR]

Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate-fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997-2010) and Monitoring Trends in Burn Severity (MTBS, 1984-2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5° resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE and fire activity in the Alaska, while water deficit (precipitation - PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6-12 months). Fire season PE in- creased from the 1980s-2000s, enhancing climate-driven fire risk in the southern and western US where PE-BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990s-2000s highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climate-fire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity. [ABSTRACT FROM AUTHOR]

The article discusses a study which aims to examine the consistency of Global Ozone Monitoring Experiment-2/Metop-A (GOME-2) total ozone columns and the long-term total ozone satellite monitoring database. It performs an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. A very brief description of the four different instruments is provided.

The article presents a study which compiles a dataset of macrozooplankton abundance and biomass observations in the world ocean. The study used q standard deviation, biogeochemical models and Plankton Functional Type models, and Chauvenet's criterion, Results show that biomass if highest in the tropic areas and biomass conservation coverage is only good at high latitudes.

The article presents a study which compiles the largest body of data from different databases or unpublished datasets on diatom distribution in the world ocean. The study used a single standardized method, Continuous Plankton Recorder (CPR) data, and quantitative analysis. Results show that a total of 607 different species and 136 different genera have been determined after name correction and spell checking.

This paper presents version 1.0 of the World Atlas of Last Interglacial Shorelines (WALIS), a global database of sea-level proxies and samples dated to marine isotope stage 5 (∼ 80 to 130 ka). The database includes a series of datasets compiled in the framework of a special issue published in this journal (https://essd.copernicus.org/articles/special%5fissue1055.html , last access: 15 December 2022). This paper collates the individual contributions (archived in a Zenodo community at https://zenodo.org/communities/walis%5fdatabase/ , last access: 15 December 2022) into an open-access, standalone database (Rovere et al., 2022, https://doi.org/10.5281/zenodo.7348242). The release of WALIS 1.0 includes complete documentation and scripts to download, analyze, and visualize the data (https://alerovere.github.io/WALIS/ , last access: 15 December 2022). The database contains 4545 sea-level proxies (e.g., marine terraces or fossil beach deposits), 4110 dated samples (e.g., corals dated with U-series), and 280 other time constraints (e.g., biostratigraphic constraints or tephra layers) interconnected with several tables containing accessory data and metadata. By creating a centralized database of sea-level proxy data for the Last Interglacial, the WALIS database will be a valuable resource to the broader paleoclimate community to facilitate data–model integration and intercomparisons, assessments of sea-level reconstructions between different studies and different regions, as well as comparisons between past sea-level history and other paleoclimate proxy data. [ABSTRACT FROM AUTHOR]

Data are a key component for many applications and methods in the domain of photogrammetry and remote sensing. Especially data-driven approaches such as deep learning rely heavily on available annotated data. The amount of data is increasing significantly every day. However, reference data is not increasing at the same rate and finding relevant data for a specific domain is still difficult. Thus, it is necessary to make existing reference data more accessible to the scientific community as far as possible in order to make optimal use of it. In this paper we provide an overview of the development of our photogrammetry and remote sensing specific Benchmark Metadata Database (BeMeDa). BeMeDa is based on MongoDB, a NoSQL database system. In addition, the development of a user-oriented metadata schema serves for data structuring. BeMeDa enables easy searching of benchmark datasets in the field of photogrammetry and remote sensing. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with data pertinent to transportation networks, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with sensors that monitor the network and the objects moving along it. These sensors produce time-series data resulting in sensor networks. Examples are river-, road- and electricity networks. Geographical information systems are used to gather, store and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time-series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time-series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time-series and measurement patterns may be combined with graph patterns to describe, retrieve and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin. [ABSTRACT FROM AUTHOR]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (10.57760/sciencedb.09858, Hoinaski et al., 2023a; 10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (10.57760/sciencedb.09857, Hoinaski and Will, 2023a; 10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (10.57760/sciencedb.09859, Hoinaski and Will, 2023b; 10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​ [ABSTRACT FROM AUTHOR]

In soil sciences, parametric models known as constitutive models (e.g., the Modified Cam Clay and the NorSand) are used to represent the behavior of natural and artificial materials. In contexts where liquefaction may occur, the NorSand constitutive model has been extensively applied by both industry and academia due to its relatively simple critical state formulation and low number of input parameters. Despite its suitability as a good modeling framework to assess static liquefaction, the NorSand model still is based on premises which may not perfectly represent the behavior of all soil types. In this context, the creation of data-driven and physically informed metamodels emerges. The literature suggests that data-driven models should initially be developed using synthetic datasets to establish a general framework, which can later be applied to experimental datasets to enhance the model's robustness and aid in discovering potential mechanisms of soil behavior. Therefore, creating large and reliable synthetic datasets is a crucial step in constructing data-driven constitutive models. In this context, the NorSand model comes in handy: by using NorSand simulations as the training dataset, data-driven constitutive metamodels can then be fine-tuned using real test results. The models created that way will combine the power of NorSand with the flexibility provided by data-driven approaches, enhancing the modeling capabilities for liquefaction. Therefore, for a material following the NorSand model, the present paper presents a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin hypercube sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160 000 triaxial test results. The second one considers nested quasi-Monte Carlo sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172 032 triaxial test results. By using the quasi-Monte Carlo dataset and 49 of its subsamples, it is shown that the dataset of 2000 soil types and 40 initial test configurations is sufficient to represent the general behavior of the NorSand model. In this process, four machine learning algorithms (Ridge Regressor, KNeighbors Regressor and two variants of the Ridge Regressor which incorporate nonlinear Nystroem kernel mappings of the input and output values) were trained to predict the constitutive and test parameters based solely on the triaxial test results. These algorithms achieved 13.91 % and 16.18 % mean absolute percentage errors among all 14 predicted parameters for undrained and drained triaxial test inputs, respectively. As a secondary outcome, this work introduces a Python script that links the established Visual Basic implementation of NorSand to the Python environment. This enables researchers to leverage the comprehensive capabilities of Python packages in their analyses related to this constitutive model. [ABSTRACT FROM AUTHOR]

China stands out as a major contributor to anthropogenic methane (CH 4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH 4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the progress made, the consistent acquisition of independent CH 4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH 4 observations from May 2018 to May 2023, coupled with ERA5 wind and a bottom-up inventory dataset based on the IPCC (Intergovernmental Panel on Climate Change) Tier 2 approach covering the Changzhi, Jincheng, and Yangquan regions of the Shanxi province. The derived emission strengths are 8.4 × 10 26 molec. s -1 (0.706 Tg yr -1 , ± 25 %), 1.4 × 10 27 molec. s -1 (1.176 Tg yr -1 , ± 20 %), and 4.9 × 10 26 molec. s -1 (0.412 Tg yr -1 , ± 21 %), respectively. Our results exhibit biases of - 18 %, 8 %, and 14 %, respectively, when compared to the IPCC Tier 2 bottom-up inventory. Larger discrepancies are found when comparing the estimates to the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT) and Emissions Database for Global Atmospheric Research (EDGARv7.0) inventories (64 %–176 %), suggesting that the two inventories may be overestimating CH 4 emissions from the studied coal mining regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and provide additional insights into CMM emission mitigation. [ABSTRACT FROM AUTHOR]

The Ice Cloud Imager (ICI) aboard the Second Generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 GHz to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional variability of clouds. The radiative transfer calculations use empirically-based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for polarisation to be considered. The degrees of freedom of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm) and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101 kg m-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals on real observations will be possible from day one of the ICI operational phase. [ABSTRACT FROM AUTHOR]

The technical provisions for protecting built cultural heritage originate from the interdisciplinary disciplines of architecture and engineering, which mutually operate despite encountering specific challenges. Therefore, the lack of adaptable applications capable of integrating the management of complex data originating from various sources has prompted the development of an Internet-of- Things-based application that enables "on-the-fly" data acquisition and information processing from distant locations, records the timeline on a remote desktop, and sends email and brief early warning text messages of alarm when a programmed threshold is exceeded. Furthermore, a dedicated platform is designed to administer and grant users access to a database containing data gathered through the analysis of diagnostic non-destructive methods (i.e., archival documentation, image processing for infrared thermography, acoustic and ultrasonic tomography, and so forth). The system architecture is distinguished by a distributed intelligence consisting of multiple nodes, which enables the remote processing of locally acquired information. The arrangement was strategically planned to optimize the utilization of the wireless digital bus connecting sensors and data storage units. This enables the examination of the case study to be conducted remotely, with all collected data accessible in a suitable semantic web environment. The data can then be analysed and interpreted following the investigation's context. Hence, the present paper aims to provide a detailed vision of the tests conducted on the case study, showcasing the prototype demonstrator's stress test, the analysis layout, and the project's architecture. [ABSTRACT FROM AUTHOR]

Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at 10.23708/02POK6 (Bathelemy et al., 2023). [ABSTRACT FROM AUTHOR]

The Unified North American Soil Map (UNASM) was developed to provide more accurate regional soil information for terrestrial biosphere modeling. The UNASM combines information from state-of-the-art US STATSGO2 and Soil Landscape of Canada (SLCs) databases. The area not covered by these datasets is filled with the Harmonized World Soil Database version 1.1 (HWSD1.1). The UNASM contains maximum soil depth derived from the data source as well as seven soil attributes (including sand, silt, and clay content, gravel content, organic carbon content, pH, and bulk density) for the top soil layer (0-30 cm) and the sub soil layer (30-100 cm) respectively, of the spatial resolution of 0.25° in latitude and longitude. There are pronounced differences in the spatial distributions of soil properties and soil organic carbon between UNASM and HWSD, but the UNASM overall provides more detailed and higher-quality information particularly in Alaska and Central Canada. To provide more accurate and up-to-date estimate of soil organic carbon stock in North America, we incorporated Northern Circumpolar Soil Carbon Database (NCSCD) into the UNASM. The estimate of total soil organic carbon mass in the upper 100 cm soil profile based on the improved UNASM is 347.70 Pg, of which 24.7% is under trees, 14.2% is under shrubs, and 1.3% is under grasses and 3.8% under crops. This UNASM data will provide a resource for use in land surface and terrestrial biogeochemistry modeling both for input of soil characteristics and for benchmarking model output. [ABSTRACT FROM AUTHOR]

The article discusses a study conducted to update ECOCLIMAP-I in Europe. It is evident that ECOCLIMAP-I is a database designed to cater needs of meteorological community to examine natural and managed ecosystems in association with weather forecasting and climate change modelling. It discusses methods, datasets and characteristics of producing ECOCLIMAPII/Europe.

The article discusses the history and development of the Surface Ocean Carbon Dioxide (CO2) Atlas (SOCAT) project initiated by international marine carbon science community in 2007 aiming to provide data set of CO2. It states that SOCAT includes 6.3 million data points measured in all ocean areas worldwide from 1968-2007. It also explores the establishment by Taro Takahashi from the Lamont-Doherty Earth Observatory (LDEO) of an initial data on marine carbon.

The article presents a study on the development of the Northern Circumpolar Soil Carbon Database (NCSCD), a polygon-based digital database of soil carbon storage and soil coverage in North America and Eurasia. The study used sampling techniques, Walkley-Black method, and quantitative research. Results show that NCSCD helps in the estimation of global soil organic carbon (SOC) stocks.

The article focuses on the database that have time-referenced sea ice draft values from upward looking sonar (ULS) measurements in the Weddell Sea, Antarctica. It says that the ice sea draft data can be utilized to infer the ice thickness, which were collected in 1990-2008. it states that the database includes measurements from 13 locations and generated from over 3.7 million measurements of sea ice draft. Moreover, the data processing and quality control were discussed in detail.

While the Emissions Database for Global Atmospheric Research (EDGAR) focuses on global estimates for the full set of anthropogenic activities, the Land-Use, Land-Use Change and Forestry (LULUCF) sector might be the most diverse and most challenging to cover consistently for all world countries. Parties to UNFCCC are required to provide periodic estimates of GHG emissions, following the latest approved methodological guidance by the International Panel on Climate Change (IPCC). The aim of the current study is comparing the IPCC GPG 2003 and the IPCC AFOLU 2006 by calculating the C stock changes in living forest biomass, and then using computed results to extend the EDGAR database. For this purpose, we applied the IPCC Tier 1 method at global level, i.e. using spatially coarse activity data (i.e. area, obtained combining two different global forest maps: the Global Land Cover map and the eco-zones subdivision of the GEZ Ecological Zone map) in combination with the IPCC default C stocks and C stock change factors. Results for the C stock changes were calculated separately for Gains, Harvest, Net Deforestation and Fires (GFED3), for the years 1990, 2000, 2005 and 2010. At the global level, results obtained with the two set of IPCC guidance differed by about 40 %, due to different assumptions and default factors. The IPCC Tier 1 method unavoidably introduced high uncertainties due to the "globalization" of parameters. When the results using IPCC AFOLU 2006 for Annex I countries are compared to other international datasets (UNFCCC, FAO) or scientific publications, it emerges a significant overestimation of the sink. For developing countries, we conclude that C stock change in forest remaining forest can hardly be estimated with Tier 1 method. Overall, confronting the IPCC 2003 and 2006 methodologies we conclude that IPCC 2006 suits best the needs of EDGAR and provide a consistent global picture of C stock changes in living forest biomass independent of country estimates. [ABSTRACT FROM AUTHOR]

A web-based validation platform has been developed at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for the Near Real Time validation of the MyOcean- Mediterranean Monitoring and Forecasting Centre products (Med-MFC). A network for the collection of the in-situ observations, the nested sub-basin forecasting systems model data (provided by the partners of the Mediterranean Operational Oceanography Network, MOON) and the Sea Surface Temperature (SST) satellite data has been developed and is updated every day with the new available data. The network collects temperature, salinity, currents and sea level data. The validation of the biogeochemical forecast products is done by use of ocean colour satellite data produced for the Mediterranean Sea. All the data are organized in an ad hoc database interfaced with a dedicated software which allows interactive visualizations and statistics (CalVal SW). This tool allows to evaluate NRT products by comparison with independent observations for the first time. The heterogeneous distribution and the scarcity of moored observations reflect with large areas uncovered with measurements. Nevertheless, the evaluation of the forecast at the locations of observations could be very useful to discover sub-regions where the model performances can be improved, thus yielding an important complement to the basin-mean statistics regularly calculated for the Mediterranean MFC products using semi-independent observations. [ABSTRACT FROM AUTHOR]

The article presents a study which compiles a database of picophytoplankton biomass distribution in the world ocean with 40,946 data points on the World Ocean Atlas grid. The study used average conversion factors for the groups of synechococcus, prochlorococcus, and picoeukaryotes. Results show that the average biomass of picophytoplanktons is somewhat higher in the tropics, while the standard deviation within latitude bands is high.

To learn, humans observe and experience the world, collect data, and establish patterns through repetition. In scientific discovery, these patterns and relationships are expressed as laws and equations, data as properties and variables, and observations as events. Data-driven techniques aim to provide an impartial approach to learning using raw data from actual or simulated observations. In soil science, parametric models known as constitutive models are used to represent the behavior of natural and artificial materials. Creating data-driven constitutive models using deep learning techniques requires large and consistent datasets, which are challenging to acquire through experiments. Synthetic data can be generated using a theoretical function, but there is a lack of literature on high-volume and robust datasets of this kind. Digital soil models can be utilized to conduct numerical simulations that produce synthetic results of triaxial tests, which are regarded as the preferred tests for assessing soil's constitutive behavior. Due to its limitations for modeling real sands, the Modified Cam Clay model has been replaced by the NorSand model in some situations where sand-like materials need to be modelled. Therefore, for a material following the NorSand model, the present paper presents a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests of 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160000 triaxial test results. Each simulation dataset comprises a 4000 × 10 matrix that can be used for general multivariate forecasting benchmarks, in addition to direct geotechnical and soil science applications. [ABSTRACT FROM AUTHOR]

It is with great pleasure that we introduce palsys.org (https://palsys.org/genus/ , last access: 8 December 2023), a fully open-access taxonomic, stratigraphic and image database of organic-walled dinoflagellate cysts. Palsys.org started as the in-house database of the Laboratory of Palaeobotany and Palynology (LPP) Foundation over 30 years ago. It is now owned by Utrecht University and has been expanded and transformed into a public online platform for use in research and education. Palsys.org includes the taxonomic descriptions of genera and species of organic walled dinoflagellate cysts, from the (often translated) literature, and emendations and synonymy, mainly following Williams et al. (2017) and the stratigraphic calibrations from DINOSTRAT (Bijl, 2022), and has around 25 000 images of species. Here, in this launch paper, we explain the history of the database, present its current functionalities and explain our set-up of the data quality control. We call upon the community to help us keep palsys.org up to date and complete by, for example, by sending additional information, imagery and feedback in general through the platform. Palsys.org brings dinoflagellate micropaleontology in line with the open-science principles of modern academia. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard that can cause a wide range of impacts affecting the environment, society, and the economy. Assessing and reducing vulnerability to these impacts for regions beyond the local scale, spanning political and sectoral boundaries, requires systematic and detailed data regarding impacts. This study presents an assessment of the diversity of drought impacts across Europe based on the European Drought Impact report Inventory (EDII), a unique research database that has collected close to 5000 impact reports from 33 European countries. The reported drought impacts were classified into major impact categories, each of which had a number of subtypes. The distribution of these categories and types was then analyzed over time, by country, across Europe and for particular drought events. The results show that impacts on agriculture and public water supply dominate the collection of drought impact reports for most countries and for all major drought events since the 1970s, while the number and relative fractions of reported impacts in other sectors can vary regionally and from event to event. The data also shows that reported impacts have increased over time as more media and website information has become available and environmental awareness has increased. Even though the distribution of impact categories is relatively consistent across Europe, the details of the reports show some differences. They confirm severe impacts in southern regions (particularly on agriculture and pub20 lic water supply) and sector-specific impacts in central and northern regions (e.g. on forestry or energy production). As a text-based database, the EDII presents a new challenge for quantitative analysis; however, the EDII provides a new and more comprehensive view on drought impacts. Related studies have already developed statistical techniques to evaluate the link between drought indices and impacts using the EDII. The EDII is a living database and is a promising source for further research on drought impacts, vulnerabilities, and risks across Europe. A key result is the extensive variety of impacts found across Europe and its documentation. This data coverage may help drought policy planning at national to international levels. [ABSTRACT FROM AUTHOR]

Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale, and this limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is due to its simple structure and empirical basis a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial scale applications often rely on coarse data input, which is not compatible with the local scale at which the model is parameterized. This study aimed at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting soil erosion rates to extensive empirical databases on soil erosion from the USA and Europe. Adjusting the topographical factor required scaling of slope according to the fractal method, which resulted in improved topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that are in good comparison with high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted RUSLE model not only shows a global higher mean soil erosion rate but also more variability in the soil erosion rates. Comparison to empirical datasets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional differences with the empirical databases, the results indicate that the methods used here seem to be a promising tool in improving the applicability of the RUSLE model on a coarse resolution on global scale. [ABSTRACT FROM AUTHOR]

This work is an extended evaluation of near surface ozone as part of the global reanalysis of atmospheric composition, produced within the European Funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003-2012 and provides an overall assessment of the modelling system performance with respect to near surface ozone for specific European subregions. Measurements at rural locations from the European Monitoring and Evaluation Program (EMEP) and the European Air Quality Database (AirBase) were used for the evaluation assessment. The annual overall error of near surface ozone reanalysis is on average 24% over Europe, the highest found over Scandinavia (27%) and the lowest over the Mediterranean marine stations (21 %). Near surface ozone shows mostly a negative bias in winter and a positive bias during warm months. Assimilation reduces the bias in near surface ozone and its impact is mostly notable in winter. With respect to the seasonal cycle, the MACC reanalysis reproduces the photochemically driven broad spring-summer maximum of surface ozone of central and south Europe. However, it does not capture adequately the early spring peak and the shape of the seasonality at northern and north-eastern Europe. The diurnal range of surface ozone, which is an indication of the local photochemical production processes, is reproduced fairly well, with a tendency for a small overestimation during the warm months for most subregions (especially in central and southern Europe). Possible reasons leading to discrepancies between the MACC reanalysis and observations are discussed. [ABSTRACT FROM AUTHOR]

The article discusses the study which develops new resolution finite models in determining the thermal structure of the Japan subduction system with a high precision hypocenter database. The study says that seismicity's disappearance overlaps with the predicted phase change of blueschist to hydrous eclogite. Results show that the thermal structure below Western Hokkaido, Japan is affected by the three-dimensional (3-D) dynamics of the subducting slab.

The article discusses the study which quantifies and explores the role of land use and soil as determinants of soils' ability to store carbon (C) along Mediterranean systems. The study investigated the soil organic C (SOC) contents from different soil and land use types in Andalusia, Spain. The study used soil information from different databases, digital maps, and digital elevation models. Results show that SOC stocks were calculated for each combination of land use and soil type.

A global database of nitrogen isotope data from marine sediments is presented, including both seafloor and sub-seafloor sediment samples. The data synthesis reveals regionally and globally consistent patterns and trends, with good agreement between neighbouring seafloor sites. The spatial coverage of seafloor δ15 N data is heterogenous, with excellent coverage in the eastern tropical Pacific, South China Sea and Arabian Sea, while large regions of the globe remain unsampled. The sub-seafloor δ15 N records are mostly from the late Pleistocene, with >90 coeval records during the last ~10 kiloyears (kyr), before which the number of records at any time decreases, with <10 coeval records at any time prior to 300 kyr. There is a good correlation between seafloor and shallow-subseafloor δ15 N measurements within a 100 km radius, which suggest a reliable translation of sediments into the buried sediment record. We suggest that regional discrepancies between seafloor and late Holocene subseafloor δ15 N indicate nitrogen cycle changes during the late Holocene period, rather than systematic diagenetic changes. [ABSTRACT FROM AUTHOR]