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151 results for “extreme event”
Figure 4 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 4. The annual changes in the mean amplitude (upper part), the number (middle part) and the mean duration (bottom part) of extreme events with positive (red lines) and negative (blue lines) air temperature anomalies in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E), exceeding two standard deviations, and their linear trends.
Figure 3 in Climate variability of extreme air temperature events in the Eastern Black Sea
Figure 3. The annual changes in the mean amplitude (upper part), the number (middle part) and the mean duration (bottom part) of extreme events with positive (red lines) and negative (blue lines) air temperature anomalies in the eastern part of the Black Sea (42° - 45°N, 37° - 42°E), exceeding one standard deviation, and their linear trends.
Extreme Erosion Events database for Eurasia
<p>Climate changes lead to an increase of extreme events frequency and intensity (hurricanes, floods, extreme<br> rainfall), and as a consequence to the intensification of erosion processes both in the<br> mountainous and plain regions. Database of extreme erosion events (EEEs) was created on the basis of literature review.</p> <p><em>This work was supported by the Russian Foundation for Basic Research under grant 16-05-00815.</em></p> <p>Изменение климата приводит к увеличение частоты и интенсивности экстремальных событий (ураганы, наводнения, экстремальные осадки), в результате происходит интенсификация эрозионных процессов как в горных так и в равнинных регионах. На основе анализа литературы была создана база данных Экстремальных эрозионных событий (ЭЭЭ) в Евразии.</p> <p><em>Работа выполнена при финансовой поддержке Российского фонда фундаментальных исследований, грант 16-05-00815.</em></p>
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO Data sets
<p>Updated 2 February 2023.<br> This archive contains the key data files (including figure data) associated with:</p> <p>Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, Nature Water, accepted.</p> <p>Figure1_time_series_data.xlsx – Data used to create the 14 inset time series plots in Figure 1.</p> <p>fig1_extents.zip – Contains spatial data used to create the "Top wet events" and "Top dry events" maps in Figure 1, in both text and NetCDF formats.</p> <p>Figure2_data.xlsx – Time series data used to create Figure 2.</p> <p>Figure3a_Koeppen-Geiger-ASCII.zip - Contains a text data file of spatial data (latitude, longitude, class) used to create the climate class map in Figure 1. Note that only the main climates (first letter of class code) are used: A = Tropical, B = Dry, C = Temperate, D = Continental, E = Polar (no data). See http://koeppen-geiger.vu-wien.ac.at/present.htm for details.</p> <p>Figure3bc_data.xlsx – Time series data used to create Figures 3b and 3c.</p> <p>Figure4_data.xlsx – Location, year, and intensity data used to create the maps in Figure 4.</p> <p>all_event_intensity.xlsx - Contains the centroid location (longitude and latitude), year, and intensity (km3mo) of all 505 wet extreme events and 551 dry extreme events identified and analyzed in this study.</p> <p>Source data and code used in this study are available as follows.</p> <p>Data Availability<br> The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas Center for Space Research (https://www2.csr.utexas.edu/grace/RL06_mascons.html). The output from a global GRACE/FO data assimilating instance of the Catchment land surface model (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11-month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GRACEDADM_CLSM025GL_7D_3.0/). The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/climateindices/list/ and https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/). The global mean temperature data are available from the NASA Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Köppen-Geiger climate map data are available for download from http://koeppen-geiger.vu-wien.ac.at/present.htm.</p> <p>Code Availability<br> The python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository, https://github.com/gitAtila/ST-DBSCAN. Statistical analyses were performed and figures were generated using NCL software.</p>
Urban resilience to extreme natural events and climate change: from Brasil to Europe
<p>Originally published at this link: <a href="https://www.iai.it/en/eventi/urban-resilience-extreme-natural-events-and-climate-change-brasil-europe">Urban resilience to extreme natural events and climate change: from Brasil to Europe | IAI Istituto Affari Internazionali</a></p> <p>The workshop will discuss best practices and innovative methodologies for urban resilience building to extreme natural events and climate change. The focus will be on participatory processes and citizen engagement practices, looking in particular at their relevance for physically and socially vulnerable urban areas. The event is designed as a knowledge-sharing opportunity for cities and will include the participation of researchers and experts working on the field with municipalities.</p> <p>The project “Waterproofing Data: Engaging Stakeholders in Sustainable Flood Risk Governance for Urban Resilience” will be discussed as a successful case study, focusing on data co-production practices and the role of digital tools. The project has been implemented in several Brazilian cities to build resilience to flooding in vulnerable communities by engaging citizens in data generation processes critical to design effective early-warning systems and strategies to reduce the risk of flood-related events. Professor João Porto De Albuquerque (University of Glasgow, UK) and Professor Maria Alexandra Viegas da Cunha (Fundação Getulio Vargas, São Paolo, Brasil) will illustrate the results of Waterproofing Data project and its innovative methodology, discussing opportunities for its implementation in cities both in Brasil and Europe and application to a broad spectrum of extreme events, from flooding to heatwaves and drought.</p>
Data from: Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards
Open the record for dataset details and reuse information.
Australia’s Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change
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Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events
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Review of ecological research approaches for the study of extreme events in aquatic ecosystems
Extreme climatic events have increased in frequency globally, with a simultaneous surge in scientific interest about their ecological consequences, particularly in sensitive freshwater, coastal, and marine ecosystems. In this context, it is imperative that ecologists apply their expertise to understand and predict the ecological impacts of extreme events, and to collaborate across disciplines and sectors to improve socio-ecological resilience to extreme events. However, ecological research on extreme events is often opportunistic and hampered by lack of coordination, among ecologists and among interdisciplinary collaborators. We conducted a literature search to investigate the research approaches that ecologists use to study extreme events in aquatic ecosystems (including freshwater, coastal, and marine ecosystems), that is, to understand how, when, and where ecologists study these events, and to identify areas to improve research practices. We used keywords related to ecology, aquatic ecosystems, and types of extreme events to identify 215 relevant papers in the literature and we examined these papers to identify 49 studies that met our inclusion criteria of including observations of ecological responses to an extreme event occurring in an aquatic ecosystem. We then extracted information from the 49 included papers, including information on the ecosystem, the extreme event, the spatial and temporal approaches to sampling, the types of response variables sampled, and the magnitude of responses measured. This dataset collates research approaches to the study of extreme events in aquatic ecosystems at a broad scale. Based on this literature review, we identified key areas where aquatic ecologists can improve research practices, including prioritizing pre- and post-event data collection, leveraging long-term and cross-site monitoring networks, and adopting novel approaches to analysis, synthesis, and collaboration.
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
WRF-Hydro simulated hydrographs for Jan-1989 and Nov-1994 extreme events (Cyprus)
<p>WRF-Hydro simulation results for two extreme events occurred in Cyprus in January 1989 and November 1994. For specific information on simulation setup and study area, please refer to:</p> <p>Camera, C., Bruggeman A., Zittis, G., Sofokleous, I., Arnault, J., 2020. Simulation of extreme rainfall and streamflow events in small Mediterranean watersheds with a one-way coupled atmospheric-hydrologic modelling system. NHESSD, https://doi.org/10.5194/nhess-2020-43.</p>
Data from: Indirect legacy effects of an extreme climactic event on a marine megafaunal community
While extreme climactic events (ECEs) are predicted to become more frequent, reliably predicting their impacts on consumers remains challenging– particularly for large consumers in marine environments. Many studies that do evaluate ECE effects focus primarily on direct effects, though indirect effects can be equally or more important. Here, we investigate the indirect impacts of the 2011 "Ningaloo Niño" marine heatwave ECE on a diverse megafauna community in Shark Bay, Western Australia. We use an 18 year community level dataset before (1998-2010) and after (2012-2015) the heatwave to assess the effects of seagrass loss on the abundance of seven consumer groups: sharks, sea snakes (multiple species), Indo-pacific bottlenose dolphins (Tursiops aduncus), dugongs (Dugong dugon), green turtles (Chelonia mydas), loggerhead turtles (Caretta caretta), and pied cormorants (Phalacrocorax spp.). We then assess whether seagrass loss influences patterns of habitat use by the latter five groups, which are under risk of shark predation. Sharks catch rates were dominated by the generalist tiger shark (Galeocerdo cuvier) and changed little, resulting in constant apex predator density despite heavy seagrass degradation. Abundances of most other consumers declined markedly as food and refuge resources vanished, with the exception of generalist loggerhead turtles. Several consumer groups significantly modified their habitat use patterns in response to the die-off, but only bottlenose dolphins did so in a manner suggestive of a change in risk-taking behavior. We show that ECEs can have strong indirect effects on megafauna populations and habitat use patterns in the marine environment, even when direct effects are minimal. Our results also show that indirect impacts are not uniform across taxa or trophic levels and suggest that generalist marine consumers are less susceptible to indirect effects of ECEs than specialists. Such non-uniform changes in populations and habitat use patterns have implications for community dynamics, such as the relative strength of direct predation and predation risk. Attempts to predict ecological impacts of ECEs should recognize that direct and indirect effects often operate through different pathways and that taxa can be strongly impacted by one even if resilient to the other.
Data from: Corralling a black swan: natural range of variation in a forest landscape driven by rare, extreme events
The natural range of variation (NRV) is an important reference for ecosystem management, but has been scarcely quantified for forest landscapes driven by infrequent, severe disturbances. Extreme events such as large, stand-replacing wildfires at multi-century intervals are typical for these regimes; however, data on their characteristics are inherently scarce, and, for land management, these events are commonly considered too large and unpredictable to integrate into planning efforts (the proverbial 'Black Swan'). Here, we estimate the NRV of late-seral (mature/old-growth) and early-seral (post-disturbance, pre-canopy-closure) conditions in a forest landscape driven by episodic, large stand-replacing wildfires: the Western Cascade Range of Washington, USA (2.7 million ha). These two seral stages are focal points for conservation and restoration objectives in many regions. Using a state-and-transition simulation approach incorporating uncertainty, we assess the degree to which NRV estimates differ under a broad range of literature-derived inputs regarding: a) overall fire rotations, and b) how fire area is distributed through time – as relatively frequent smaller events (less episodic), or fewer but larger events (more episodic). All combinations of literature-derived fire rotations and temporal distributions (i.e. 'scenarios') indicate that the largest wildfire events (or episodes) burned up to 10^5-10^6 hectares. Under most scenarios, wildfire dynamics produced 5th-95th percentile ranges for late-seral forests of ~47-90% of the region (median 70%), with structurally complex early-seral conditions composing ~1-30% (median 6%). Fire rotation was the main determinant of NRV, but temporal distribution was also important, with more episodic (temporally clustered) fire yielding wider NRV. In smaller landscapes (20,000 ha; typical of conservation reserves and management districts), ranges were 0-100% because fires commonly exceeded the landscape size. Current conditions are outside the estimated NRV, with the majority of the region instead covered by dense mid-seral forests (i.e. a regional landscape with no historical analog). Broad consistency in NRV estimates among widely varied fire regime parameters suggests these ranges are likely relevant even under changing climatic conditions, both historical and future. These results indicate management-relevant NRV estimates can be derived for seral stages of interest in extreme-event landscapes, even when incorporating inherent uncertainties in disturbance regimes.
Dataset for plots and results in manuscript: "Reliance on fossil fuels increases during extreme temperature events in the continental United States"
<p>These are the dataset for plots and results in the manuscript titled "Reliance on fossil fuels increases during extreme temperature events in the continental United States".</p><p>Figure 1,2,3,4,5 are the dataset used for analysis and plotting the figures in the manuscript.</p><p>Other dataset are the 34 years' air temperature and population-weighted air temperature thresholds for detecting extreme temperature events in each U.S. states. For example, Ta_threshold(1990-2023)_99th_percentiles.csv is the 99th percentiles for each U.S. states.</p><p>Any questions and further assitance or collaborations are welcome to contact through: wz2481@columbia.edu, happystillwaterzhao@gmail.com</p><p> </p><p> </p>
Supplementary data for "Increasing risks of extreme salt intrusion events across European estuaries in a warming climate", published in Communications Earth & Environment
<p>This data repository contains python scripts and post-processed climate model and salt intrusion length data to reproduce figures in the paper below.</p> <p>====================</p> <p>Title: Increasing risks of extreme salt intrusion events across European estuaries in a warming climate (<a href="https://www.nature.com/articles/s43247-024-01225-w">Link to the full paper</a>)</p> <p>Author: Jiyong Lee, Bouke Biemond, Huib de Swart, and Henk A. Dijkstra</p> <p>Journal: Communications Earth & Environment</p> <p>Year: 2024</p> <p>Publisher: Nature</p> <p>====================</p>
Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil
<p>Data used in the paper "Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil" from Theoretical and Applied Climatology</p>
Extreme events in Indian Monsoon linked to Global warming scenario during Bølling–Allerød
<p>Stable Oxygen isotope data from stalagmite samples of Kailash cave, Central India, during the Bølling-Allerød warmth </p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows:</p> <p>In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.</p>
Disentangling the impact of event- and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess: A case study in apple tree plantation
<p>The dataset is the basic data of the author's paper ' Disentangling the impact of event-and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess - a case study in apple tree plantation '. The main content of this paper is to study the hydrological effect of extreme precipitation on the critical area of semi-arid loess. Taking apple plantation as an example, the data set includes the soil moisture and soil temperature data monitored in the field and the apple tree transpiration data. The measured data are used to calibrate and verify the model used in this paper. The water vapor flux, apple tree evapotranspiration and soil leakage data of the simulated soil profile are also included to analyze the hydrological effect of extreme precipitation on the critical area of loess.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.