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26 results for “extreme rainfall”
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Dataset: Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management
<p>This data set is used to employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed. A case of implications for flood management. Observational raw data from Thailand were provided by the Climate Information Services (CIS) at https://www.tmd.go.th/cis/main.php.</p>
Data from: The extreme rainfall gradient of the Cape Horn Biosphere Reserve and its impact on forest bird richness. Biodiversity and Conservation
<p><strong>Description of dataset</strong></p> <p>This dataset contains information about forest bird species richness and climatic variables in 61 sample sites of the Cape Horn Biosphere Reserve. This dataset was analysed in : Quilodrán CS, Sandvig EM, Aguirre F, Rivero de Aguilar J, Barroso O, Vásquez RA, and R Rozzi. 2022. Effects of the extreme rainfall gradient in the Cape Horn Biosphere Reserve on forest bird richness. <em>Biodiversity and Conservation</em>. </p> <p> </p> <p><strong>Acknowledgments </strong></p> <p>This study was funded by grants for Technological Centers of Excellence with Basal Financing of the National Agency for Research and Development (ANID-Chile), granted to the Cape Horn International Center (CHIC- FB210018) and the Institute of Ecology and Biodiversity (IEB-AFB170008). CSQ acknowledges support from the Swiss National Science Foundation (N°P5R5PB_203169). </p>
Data and code_Ombadi et al., Nature (2023)_A warming induced reduction in snow fraction amplifies rainfall extremes
<p>This repository contains intermediary and final outputs of the analysis presented in the article: "Ombadi et al., Nature (2023), A warming-induced reduction in snow-fraction amplifies rainfall extremes.". Additionally, the repository also contains codes for analysis and visualization of results presented in this article. The codes are primarily written in Python and provided as Jupyter Notebooks (.py files) with the exception of one code written in R language. </p>
Data from: Impacts of rainfall extremes predicted by climate-change models on major trophic groups in the leaf-litter arthropod community
1. Arthropods in the leaf-litter layer of forest soils influence ecosystem processes such as decomposition. Climate-change models predict both increases and decreases in average rainfall. Increased drought may have greater impacts on the litter arthropod community. In addition to affecting survival or behavior of desiccation-sensitive species, lower rainfall may indirectly lower abundances of consumers that graze drought-stressed fungi, with repercussions for higher trophic levels. 2. We tested the hypothesis that trophic structure will differ between the two rainfall scenarios. In particular, we hypothesized that densities of several broadly defined trophic groupings of arthropods would be lower under reduced rainfall. 3. To test this hypothesis we used sprinklers to impose two rainfall treatments during three growing seasons in roofed, fenced 14-m2 plots; and documented changes in abundance from initial, pre-treatment densities of 39 arthropod taxa. Experimental plots were subjected to either LOW (fortnightly) or HIGH (weekly) average rainfall based upon climate models and the previous 100 years of regional weekly averages. Unroofed open plots, our reference treatment (REF), experienced higher-than-average rainfall during the experiment. 4. The two rainfall extremes produced clear negative effects of lowered rainfall on major trophic groups. Broad categories of fungivores, detritivores and predators were more abundant in HIGH than LOW plots by the final year. Springtails (Collembola), which graze fungal hyphae, were 3x more abundant in the HIGH-rainfall treatment. Taxa of larger-bodied fungivores and detritivores, spiders (Araneae), and non-spider predators were 2x more abundant under HIGH rainfall. Densities of mites (Acari), which include fungivores, detritivores and predators, were 1.5x greater in HIGH rainfall plots. Abundances and community structure of arthropods were similar in REF and experimental plots, showing that effects of rainfall uncovered in the experiment are applicable to nature. 5. This pattern suggests that changes in rainfall will alter bottom-up control processes in a critical detritus-based food web of deciduous forests. Our results, in conjunction with other findings on the impact of desiccation on arthropods and fungal growth, suggest that drier conditions will depress densities of fungal consumers, causing declines in higher trophic levels, with possible impacts on soil processes and the larger forest food web.
Data for "Fast Warming over the Mongolian Plateau a Catalyst for Extreme Rainfall over North China"
<p>Data and plot scripts for this manuscript <strong>"Fast Warming over the Mongolian Plateau a Catalyst for Extreme Rainfall over North China".</strong></p> <blockquote> <p>The md5 value of this compressed file is b04d6476796901687317045be4abba70</p> </blockquote>
extreme-rainfall convective feature dataset from Guangzhou SPOL
<p>Extreme-rainfall convective feature dataset built from Guangzhou SPOL, which used for publication: https://doi.org/10.1007/s00376-022-1319-8.</p>
Data files for analysis of scaling relations between relative and absolute humidity and rainfall extremes
<p>data belonging to: https://github.com/mister-superCC/CCscaling-Evaluation</p> <p>Contains:</p> <p>Dutch observarions in netcdf: KNMI_20201124_hourly.nc</p> <p>Model data as an R object file: DATA_SCALING_PRINCIPLES.tar.gz</p> <p>Processed model and observational data (including SFR) in: data_hourly_bootstrap.tar.gz and data_hourly_bootstrap_abs.tar.gz</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Supplementary figures for 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall.'
<p>Supplementary dynamics and skill plots for the paper 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall', submitted to QJRMS.</p>
Data from: Tillage intensity effects on soil abiotic and biotic characteristics determine <em>Zea mays</em> yield following extreme rainfall events
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Data from: Impacts of rainfall extremes predicted by climate-change models on major trophic groups in the leaf-litter arthropod community
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Wildfire and extreme rainfall reduce soil carbon and nitrogen pools in a semiarid shrubland ecosystem
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Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s
<p>LPS tracks for the manuscript "Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s"</p>
Tracks for "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"
<p>TempestExtremes track files for CAM5 analysis presented in "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"</p>
Distribution. Widespread in low-rainfall areas in C Australia, including C & S Northern Territory, inland Queensland, extreme WC Western Australia, N South Australia, and NW New South Wales. in Muridae
Distribution. Widespread in low-rainfall areas in C Australia, including C & S Northern Territory, inland Queensland, extreme WC Western Australia, N South Australia, and NW New South Wales.
Downscaled Extreme Rainfall in Bangladesh
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The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event
<p>The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files. </p>
Extreme rainfall events and cooling of sea turtle clutches: implications in the face of climate warming
<p class="m-4193862355675860270msoplaintext">Understanding how climate change impacts species and ecosystems is integral to conservation. When studying impacts of climate change, warming temperatures are a research focus, with much less attention given to extreme weather events and their impacts. Here we show how localized, extreme rainfall events can have a major impact on a species that is endangered in many parts of its range. We report incubation temperatures from the world's largest green sea turtle rookery, during a breeding season when two extreme rainfall events occurred. Rainfall caused nest temperatures to drop suddenly and the maximum drop in temperature for each rain-induced cooling averaged 3.6°C (n = 79 nests, min = 1.0°C, max = 7.4°C). Since green sea turtles have temperature-dependent sex determination, with low incubation temperatures producing males, such major rainfall events may have a masculinization effect on primary sex ratios. Therefore, in some cases, extreme rainfall events may provide a "get-out-of-jail-free card" to avoid complete feminization of turtle populations as climate warming continues.</p>
Supplementary material 3 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Maps of the individual potential FRES demand indicators
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.