Geoscience Data Journal最新文献

Although the Carpathian Mts. area is considered as extremely prone to surface erosion which results in capacity loss of the dammed reservoirs, a lack of data to follow details of this process is perceivable. The research of the selected sediment fractions transport tracking was conducted using the capabilities of the digital platform—Macromodel DNS (Discharge-Nutrient-Sea) for the catchment with drinking water reservoir in the Polish part of Western Carpathian. The continuity of sediment transport simulation in two hydrologically different elements of the catchment—the river and the reservoir—was possible due to consolidation of two models in the platform—SWAT (Soil & Water Assessment Tool) and AdH/PTM (Adaptive Hydraulics Model/Particle Tracking Model). The result of those modules' integration was a database for tracking the individual sediment fractions delivered to the reservoir and deposited in specific reservoir zones. The implementation of climate and land use change scenarios allowed additionally to analyse the estimation of those processes in the future. The simulation outcomes consist of daily flows and monthly sediment loads at the reservoir inflow and the individual sediment particle fractions deposition location inside of the reservoir.

Chert is a sedimentary rock abundant and conspicuous throughout the geologic record. It serves as an essential geological archive and is vital for palaeogeographic reconstruction. The Chert Database Working Group is part of the OneSediment Working Groups of the Deep-time Digital Earth (DDE) Big Science Program. To facilitate the sharing of chert information and promote chert research, we have compiled a summary of literature containing chert information worldwide and established the Global Chert Database (GCDB). The main body of the current database consists of seven data tables, each providing details on references, lithology, depositional age, geographic location, depositional environment and geochemical information for each sample. As of December 2023, the GCDB contains 8417 sample data from 617 pieces of literature, which can be downloaded from the ‘DDE Data Publish & Repository’.

Studying a changing world requires observations going back in time to extend and contextualize our latest scientific knowledge. Old legacy data exist in non-digital formats. Thus, techniques and methodologies for the preservation, dissemination, interpretation, homogenization, calibration, and use of such legacy data and their associated metadata, as well as for their present scientific use are important topics for advancing our understanding of the changing Earth and of past extreme events. The articles presented in this special issue review different issues involved in these diverse topics, including the importance of preserving old data and metadata, the actors involved in the task, the problems in converting them to digital files and databases, as well as to point some hints for the future.

This paper introduces a community-accessible dataset comprising daily hydroclimatic variables (precipitation, temperature, and streamflow) observed in 50 catchments in Sweden (median size of 1019 km²). The dataset covers a 60-year period (1961–2020) and includes information on geographical location, landcover, soil classes, hydrologic signatures, and regulation for each catchment. Data were collected from various sources, such as the Swedish Meteorological and Hydrological Institute, the Swedish Geological Survey, and several Copernicus products provided by the European Environment Agency. The compiled, spatially-matched, and processed data are publicly available online through the Swedish National Data Service (https://snd.se/en), contributing a new region to the collection of existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets. The CAMELS-SE dataset spans a wide range of hydroclimatic, topographic, and environmental catchment properties, making it a valuable resource for researchers and practitioners to study hydrological processes, climate dynamics, environmental impacts, and sustainable water management strategies in Nordic regions.

The transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake-induced landslides, landslide-dammed lakes and landslide-induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high-resolution satellite imagery and open-source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality-controlled inventory represents a yearly record of landslides from 2011 to 2020. A data-driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation-based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development.

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold-based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi-model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5 and SSP5-8.5. Each ensemble member was bias-adjusted and statistically downscaled using the Multivariate bias correction—N-dimensional probability density function transform (MBCn) with hourly data from ERA5-Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century.

In this article, a regional ocean surface waves dataset from Sentinel-1 A and B Synthetic Aperture Radar (SAR) satellites has been described. The ocean wave data have been extracted from the Sentinel-1 level-2 OCN (ocean) product as provided by the European Space Agency and downloadable for this region from the Copernicus Australasia regional data hub. The source OCN data have been produced by evolving versions of Sentinel-1 Instrument Processing Facility (IPF). The structure of the source OCN NetCDF files changes over time and presents a challenge in performing long duration, time series analyses, including the examination of potential inconsistencies in OCN wave data, due to employment of different IPF versions over the duration of the satellite missions. Here, the input OCN wave data have been homogenized to a single, easily usable standard format after applying a quality assurance and control procedure that removes various inconsistencies in variables, coordinates, dimensions and land flag, and through the addition of new auxiliary variables. The new format has the desirable properties of being compact in size, consistent in structure, and scalable in temporal and spatial coverage. It is also convenient to use and offers opportunities to perform fast, multi-year regional processing and analysis for calibration and validation studies and scientific applications. No re-processing of Sentinel-1 level-1 data has been carried out in this work.

We describe the transcription and quality control processes for rescuing around 570,000 sub-daily and daily weather observations which were recorded in the UK Met Office Daily Weather Reports during the 1861–1875 period. These data are from the start of coordinated weather observations and were collected with the aim of making the first-ever weather forecasts. The observations were rescued thanks to 3500 volunteers and include sub-daily sea-level pressure, dry and wet bulb temperatures, daily maximum and minimum temperatures, and daily rainfall amounts from 70 different locations across Western Europe, and one in Canada. We highlight how these observations will be used to fill gaps in existing pressure and temperature datasets and use two case studies to show how the pressure observations will likely better constrain the atmospheric circulation during two severe storms. We also compare a sub-sample of the newly rescued observations with data that were previously digitized for a small number of locations for the same dates, finding good agreement in general, although some discrepancies remain.

The exchange of energy and water fluxes between the Earth's surface and the atmosphere is crucial to a series of processes that impact human life. Noteworthy examples are agriculture yields, water availability, intensity and extent of droughts and the ability of ecosystems to provide services to society. The relevance of these processes has motivated the Satellite Application Facility on Land Surface Analysis (LSA SAF) programme to set up an operational framework to estimate—among other variables—evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the Meteosat Second Generation (MSG) satellite. The LSA SAF programme has recently launched the reprocessing of the ET and SEF datasets on the basis of the most recent version of the algorithm and homogenous forcing datasets. This article features the resulting ET/SEF dataset, a Data Record that encompasses the period from the start of the operational life of the MSG satellite (2004) till 2020 and covers the field of view of the MSG satellite (i.e. Europe, Africa and Eastern South America). Details on the algorithm and the datasets driving the ET/SEF estimates are also provided as well as a quality assessment.

To prepare for a hotter future, information on intra-urban temperature distributions is crucial for cities worldwide. In recent years, different methods to compute high-resolution temperature datasets have been developed. Such datasets commonly originate from downscaling techniques, which are applied to enhance the spatial resolution of existing data. In this study, we present an approach based on a fine-scaled low-cost urban temperature measurement network and a formerly developed land use regression approach. The dataset covers mean nocturnal temperatures of 16 summers (2007–2022) of a medium-sized urban area with adapted land cover data for each year. It has a high spatial (50 m) and temporal (daily) resolution and performs well in validation (RMSEs of 0.70 and 0.69 K and mean biases of +0.41 and −0.19 K for two validation years). The dataset can be used to examine very detailed statistics in space and time, such as first heatwave per year, cumulative heat risks or inter-annual variability. Here, we evaluate the dataset with two application cases regarding urban planning and heat risk assessment, which are of high interest for both researchers and practitioners. Due to potential biases of the low-cost measurement devices during daytime, the dataset is currently limited to night-time temperatures. With minor adaptions, the presented approach is transferable to cities worldwide in order to set a basis for researchers, city administrations and private stakeholders to address their heat mitigation and adaptation strategies.