Geoscience Data Journal最新文献

Increased anthropogenic stressors (e.g., warming, acidification, wildfires, and other extreme events) present complex observational challenges for Earth science, and no one sensor can “do it all”. While many remote sensing technologies are available at present, scientific disciplines are often trained to use only a specific subset, greatly limiting scientific advancements. Here we present open-source software (icepyx) that lowers the barrier for entry for two remote platforms offering vertically-resolved information about the ocean's subsurface: ICESat-2 (Ice, Cloud, and land Elevation Satellite 2) and Argo floats. icepyx provides object-oriented code for querying and downloading ICESat-2 and Argo data within a single analysis workflow. icepyx natively handles ICESat-2 data access and read-in; here we introduce the Query, Unify, Explore SpatioTemporal (QUEST) module as a framework for adapting icepyx to easily access and ingest other datasets and present Argo data as the initial use case. Seamless retrieval of coincident data from ICESat-2 and Argo enables improved targeted and exploratory studies across the cryosphere and open ocean realms. We close with recommendations for future work, discussion of the value of open science, relevance of our work to upcoming satellite missions, and an invitation to join our programming community. Link to repository: https://github.com/icesat2py/icepyx/tree/main. Link to documentation: https://icepyx.readthedocs.io/en/latest/.

Weather at the mid-latitudes is governed by cyclones and anticyclones mostly migrating eastward. These weather systems cause the jet streams to undulate; the meandering patterns are known as the Rossby waves. Occasionally, Rossby waves bring forth localised extreme weather phenomena. An example of a finite-amplitude wave phenomenon is atmospheric blocking, which is often associated with heat waves and droughts. Recent development of a finite-amplitude local wave activity (FALWA) theory by Nakamura and collaborators enables comprehensive analysis of the dynamics of finite-amplitude Rossby waves observed in climate data, which helps to understand the drivers of their life cycles. Despite the simplicity of interpretation it brings about, to apply the FALWA diagnostic to climate data requires more involved calculations than the traditional Eulerian framework. This article introduces the open-source Python package falwa, which encapsulates the FALWA diagnostics implemented on gridded climate data presented in the authors' previous publications. It reviews the essence of the FALWA theory, the corresponding components in the package that implement the calculations, and where users can find sample notebooks to start with. It aims to serve as a road map for new users to easily navigate through this package. The latter half of this article documents the practices of the developers, which include the documentation tools, continuous integration practice, and repository maintenance using automated GitHub functionalities. The authors also discuss existing numerical issues and future improvement plans. This open-source project aims to promote the broader application of FALWA diagnostics on climate data and model outputs by streamlining complex numerical computations.

Versatile approaches for urban modelling need to simultaneously consider the physical characteristics of a city (urban form) and urban function as a manifestation of economically, socially, and culturally motivated human activities. Exposure and risk assessment studies concerning urban heat or air pollution can greatly benefit from modelling that dynamically connects physical and socio-economic urban spaces and represents humans as active components of the urban system (e.g., agent-based modelling). The spatio-temporal complexity and variability of urban form, function, human behaviour, and micro-climate put high demands on input data of such models. We present a general methodology for creating a suite of data connecting and harmonising available information for high-resolution modelling. This is demonstrated for London, UK. The multi-scale database covers urban neighbourhoods (at 500 m grid-cell resolution), localised microenvironments of activity, buildings, and extends down to the scale of individuals. Data include neighbourhood land-cover fractions that provide boundary conditions for urban land-surface models and building typologies generated by assessing building function, form, and materials (via building age) that are suitable for building energy modelling. Urban populations (residential, workplace) and demographic composition of households in building typologies are derived. Temporal profiles (10 min resolution) of human activities by age cohort, household size, day type, work patterns, and season derived from time-use survey data are mapped to various socio-economic microenvironments, alongside assessments of activity-dependent electrical energy consumption and human metabolic output. A transport database provides available travel options (1 min resolution) between London neighbourhoods by mode, making use of public transport schedules, road networks, and traffic speeds.

Understanding the impacts of meteorological and climatic conditions on agriculture in the black soil area of northeast China is important. In contrast with long- or mid-term forecasts, the meteorological disaster warning signal, which is triggered and graded using a predefined threshold, provides information close to the time of disaster occurrence. The associated Agrometeorological Hazard Warning Dataset is compiled by extracting key information, that is, the date and time, affected county, disaster type, warning level, and the timeliness and details of the information from the text description of the publicly issued warning signal associated with agriculture. It is quality controlled and structured. The dataset covers the black soil area of northeast China from 2021 to 2023.

Concentration estimates for ambient air pollution are used widely in fields such as environmental epidemiology, health impact assessment, urban planning, environmental equity and sustainability. This study builds on previous efforts by developing an updated high-resolution geospatial database of population-weighted annual-average concentrations for six criteria air pollutants (PM_2.5, PM₁₀, CO, NO₂, SO₂, O₃) across the contiguous U.S. during a five-year period (2016–2020). We developed Land Use Regression (LUR) models within a partial-least-squares–universal kriging framework by incorporating several land use, geospatial and satellite–based predictor variables. The LUR models were validated using conventional and clustered cross-validation, with the former consistently showing superior performance in capturing the variability of air quality. Most models demonstrated reliable performance (e.g., mean squared error—based R² > 0.8, standardised root mean squared error < 0.1). We used the best modelling approach to develop estimates by Census Block, which were then population-weighted averaged at Census Block Group, Census Tract and County geographies. Our database provides valuable insights into the dynamics of air pollution, with utility for environmental risk assessment, public health, policy and urban planning.

Landslides represent one of the most prevalent natural disasters worldwide, exerting significant adverse effects on social stability and economic development. Timely and accurate monitoring of landslide changes is crucial for disaster prevention and mitigation. Unlike traditional change detection, which often focus on broad environmental changes, landslide monitoring specifically aims to capture critical parameters such as the precise location of deformation, the direction of movement, and the rate of displacement associated with landslide events. Conventional monitoring techniques are typically constrained to fixed-point observations or are limited to the collection of deformation location data, which may not provide a comprehensive understanding of the landslide's behaviour. To address these limitations, this study proposes an innovative approach for detecting landslide deformation utilising multi-temporal imagery acquired through Unmanned Aerial Vehicles (UAVs). Initially, UAVs are deployed to perform multi-temporal photogrammetric surveys of the landslide-affected area, enabling the construction of high-resolution 3D models. These models facilitate the extraction of the exposed surface by employing advanced vegetation segmentation techniques. Following this, the generated 3D models undergo surface segmentation and normal direction projection, resulting in the creation of orthoimages that accurately represent the slope surface. Subsequently, feature matching is conducted between the orthoimages of the slope surface to identify corresponding points across different temporal datasets. Utilising the forward and inverse transformation relationships of these orthoimages, the deformation direction and velocity of the identified deformation points are calculated. This methodology ultimately enables precise and comprehensive monitoring of landslide deformation. To validate the efficacy of the proposed method, a longitudinal study spanning 4 years was conducted at the Che Yiping landslide site located in western Yunnan Province, China. The findings from this extensive experiment indicate that the proposed approach effectively captures the deformation characteristics of the entire landslide, with point displacement accuracy at specific locations comparable to Global Navigation Satellite System (GNSS) measurements. Furthermore, a detailed analysis of the deformation characteristics within the landslide area revealed significant displacement variations at multiple deformation sites, thereby elucidating the overarching deformation trends present at the landslide location. Through this research, we aim to provide critical data support and a scientific foundation for the prevention of landslide disasters and the management of geological hazards. The insights gained from this study are intended to inform relevant decision-making processes, thereby contributing to enhanced safety and resilience in landslide-prone regions.

In this work, we present results of the development of an observation-based gridded climate dataset (ClimUAd), which covers the territory of Ukraine for the period of 1946–2020. The spatial resolution of the developed data is 0.1° × 0.1° (approximately 10 km in both longitude and latitude directions), with a 1-day time step. Four essential climate variables are included in the dataset, namely daily sums of atmospheric precipitation and daily minimum, mean, and maximum air temperature. The created gridded product is based on the complete collection of station measurements performed at 178 weather stations in Ukraine. Quality control, homogenisation, and gridding of the station time series were performed by means of the widely used software, INQC, Climatol, and MISH, respectively. The created gridded time series were statistically compared with several existing datasets that have the same spatial resolution (i.e., previously developed gridded monthly data of Ukraine, ERA5-Land and E-OBS) on monthly and daily scales. The comparison showed good accordance with the Ukrainian monthly data (partly obtained from other paper sources than the daily data and homogenised with HOMER software) and acceptable agreement with ERA5-Land and E-OBS data. The developed data are of great importance as they were built with the involvement of as many real weather measurements as possible, representing a denser network than those included in continental/global gridded products. They can be used for regional climate monitoring and as the reference for a wide variety of climatological applications for the territory of Ukraine. The dataset is freely available for research purposes and can be downloaded from the data repository of the Ukrainian Hydrometeorological Institute.

Recent decline of sea ice in the Barents Sea represents a clear manifestation of the ongoing Arctic warming. This study presents a compilation of data sets on sea ice physics acquired during 2018–2022 during Nansen Legacy—a Norwegian multidisciplinary national research programme that focused on the northern Barents Sea. The data were acquired using a variety of methods such as sea ice coring, thickness drillings, snow pits, snow depth surveys, drone flights, on-ice and helicopter-borne electromagnetic measurements of sea ice thickness, and ice draft measurements by bottom-anchored moorings. The collected data sets cover several key physical parameters describing the sea ice cover and encompass a range of spatial (local to regional) and temporal (daily to annual) scales. These data sets aid in filling a substantial knowledge gap of recent sea ice conditions in the rapidly changing northern Barents Sea region.

This study focuses on rescuing and analysing historical monthly rainfall data in Istanbul from 1846 to 1917 for the first time. Rainfall records from various stations, collected by foreign scientists, engineers and officials during the last century of the Ottoman Empire, were digitised in accordance with the Guidelines on Best Practices for Climate Data Rescue by the World Meteorological Organisation and assessed for homogeneity. The Pettitt test was employed to identify and address inhomogeneities and detect any potential change points at each station. Monthly and annual rainfall time series (1846–2023) were reconstructed, and long-term trends were analysed by using the Hamed and Rao Modified Mann–Kendall test to evaluate changes in Istanbul's rainfall patterns over time. Comparisons between historical data (1846–1923) and recent data (1946–2023) reveal a shift towards increased early-year rainfall and decreased late-year rainfall in recent decades. The study also identifies significant variability in observation data prior to 1937 compared to 20th CRv3 reanalysis data, attributed to the limited early records, with improved consistency in recent years. An analysis of reconstructed rainfall data from 1846 to 2023 revealed a significant annual decrease, with decreasing trends in August, September and November. In contrast, the 20thCRv3 reanalysis data indicates a significant annual increase with increasing trends in October over the same period.

This dataset contains hydrogeological cross-sections for 3D modelling in the Mexico Basin, developed using Python scripts and GIS tools. The cross-sections are based on existing geological studies and integrate a variety of lithologies and structural features, including volcanic and sedimentary units. While the dataset provides comprehensive coverage, it does acknowledge limitations in geological and structural resolution due to the availability of data. The dataset includes shapefiles representing hydrogeological units in both line and polygon formats, alongside topographic sections, surface hydrogeological distribution and regional fault systems. Although modifications may be required for specific applications, it serves as a strong foundation for multidisciplinary studies in groundwater and geological modelling. Hosted on open-source repositories, the data can be easily adapted for use in 3D modelling frameworks like GemPy and FloPy. This dataset is a valuable resource for understanding groundwater dynamics in the Mexico Basin and offers flexibility for future updates as new data become available or project needs evolve.