Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
250
datasets available to search
ShareScore release 0.7.1
Dataset results
250 results for “hurricanes”
Laboratory measurements of wind, waves, and turbulence in hurricane conditions in the ASIST wind-wave facility
<p>Laboratory measurements of wind, waves, and turbulence in hurricane conditions, collected in September and October of 2018 and January of 2019 in the ASIST wind-wave facility, in the SUSTAIN laboratory at the University of Miami.</p> <p>This dataset includes two experiments, one with fresh water ("fresh") and another with seawater ("salt"), each in 10-m winds from 0 to approximately 42 m/s. Data include:</p> <ul> <li>3-dimensional wind velocity at 20 Hz sampling frequency from Campbell Scientific IRGASON sonic anemometer (collected in 2018)</li> <li>2-dimensional (along-tank and vertical) wind velocity at 1000 Hz sampling frequency from TSI IFA-300 hot film anemometer (collected in 2018)</li> <li>1-dimensional (along-tank) wind velocity at 10 Hz sampling frequency from a pitot anemometer (collected in 2018 and 2019)</li> <li>3-dimensional water velocity in the bottom 5 cm of the tank at 100 Hz sampling velocity from Nortek Vectrino velocimeter. (collected in 2018)</li> <li>Water elevation at 20 Hz sampling frequency at 6 locations in the tank from Senix Toughsonic 30 ultrasonic distance meters (collected in 2019)</li> <li>Along-tank static air pressure difference at 10 Hz sampling frequency from Baratron MKS 226 differential pressure transducer (collected in 2019)</li> </ul> <p>All data is in NetCDF4 format.</p> <p>Experiment set up and positions of instruments are documented in more detail in Curcic and Haus (2020), Revised estimates of ocean surface drag in strong winds, <em>Geophysical Research Letters</em>, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647</a>.</p> <p>Produced as part of the National Science Foundation Award #1745384, titled "Air-Sea Momentum Transfer in Extreme Wind Conditions"<strong>.</strong></p> <p>Contact: Milan Curcic <mcurcic@miami.edu></p>
Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020).</p> <p>All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment).</p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b).</p> <p>There are three csv files:</p> <p>ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA).</p> <p>ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was:</p> <p>`magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg`</p> <p>The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p>ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv.</p> <p>There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020).</p> <p>A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( `mogrify -resize 2000x2000`). For full size images, please download the jpegs directly from NOAA.</p>
Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018
Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota. Meteorological data characterizing tropical cyclones are also presented and are derived from: IBTrACS: International Best Track Archive for Climate Stewardship Global storm track data set for all recorded low pressure systems and tropical cyclones dating back to 1842. Data includes timestamped spatial information on storm center location as well as meteorological readings of wind speed, wind direction, and barometric pressure. Most importantly for the purposes of this data set, wind speeds at various distances from storm center are provided, which allows for the development of a model of wind speed with distance for different category storms. GRIDMET: University of Idaho Gridded Surface Meteorological Dataset The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data becom
Porewater measurements of dissolved nutrients (ammonia, nitrate/nitrite, phosphate) from core monitoring sites in the GCE-LTER domain following hurricane Irma from October 2017 to October 2018.
To access the effect of hurricane Irma on the GCE domain, porewater samples were collected to evaluate porewater nutrient concentrations at four core GCE monitoring sites (7, 8, 9 and 11). A limited number of samples were collected in October 2017 (a month after the storm surge from hurricane Irma hit the east coast of the United States) and then all sites were sampled in Nov 2017 and in Jan, Feb, Apr and Oct 2018. Porewater samples were obtained from approximately 10 cm depth using Rhizon samplers and then analyzed for ammonium, nitrate + nitrite, and phosphate concentrations.
Fungal litter mat cover in Cannopy Trimming Experiment (CTE) plots responses to canopy opening, hurricanes and drought
Fungi that bind leaf litter into mats and produce white-rot via degradation of lignin and other aromatic compounds influence forest nutrient cycling and soil fertility. Over three and a half years beginning in June 2014, 6 months before the second iteration of the Canopy Trimming Experiment (CTE), we measured quarterly the extent of white-rot litter mats formed by basidiomycete fungi in the Luquillo Mountains of Puerto Rico in response to disturbances – a simulated hurricane treatment executed by canopy trimming and debris addition in December 2014 (CTE0, a mid-year drought in 2015, and two hurricanes 10 days apart in September 2017. Percent fungal litter mat cover ranged from 0.4% after hurricanes Irma and Maria to a high of 53% in forest with undisturbed canopy prior to the 2017 hurricanes, with means mostly between 10 - 45% of fungal litter mat cover in undisturbed forest. Drought decreased litter mat cover in both treatments, except in one undisturbed plot dominated by a drought-resistant fungus, Marasmius crinis-equi. Percent fungal litter mat cover sharply declined after real hurricanes and the simulated hurricane treatment (CTE). We found that solar radiation had a significant treatment effect and was strongly negatively correlated with percent litter mat cover within each of the four climatic seasons. Solar radiation was also strongly negatively correlated with relative humidity, throughfall, rain and litter wetness. However, rainfall was negatively correlated with litter mat cover, possibly due to erosion or saturation during high rainfall events. Canopy opening reduced leaf litterfall rates but did not affect litter mat cover. The main negative effect on basidiomycete fungi that bind leaf litter into mats was lower litter moisture associated with increased solar radiation from canopy opening and high leaf fall during drought. Variation in drought tolerance among basidiomycete fungal litter mat formers provided some resilience to drought. \<para\> Support f
2017 Hurricane Irma Evacuation Survey Data
<p>Following Hurricane Irma in 2017, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the storm in Florida. Collected from October to December 2017, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 1,263 valid responses, of which 921 were completed. Subsequent papers using this data retained 645 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of five $200 gift cards. The survey questions are included in a separate PDF document. Please note that Q173-Q197 in the survey questions were not asked to respondents and no data was collected. </p> <p><strong>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</strong></p> <p>Wong, S., Pel, A., Shaheen, S., Chorus, C. (2020). Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory. <em>Transportation Research Part D: Disasters and Resilience Section</em>. Retrieved from <a href="https://doi.org/10.1016/j.trd.2020.102227">https://www.sciencedirect.com/science/article/pii/S1361920919312866</a>. <strong>Note: The data file can also be accessed via this journal article. </strong></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Bridging the Gap between Evacuations and the Sharing Economy. <em>Transportation</em>.<em> </em>Retrieved from <a href="https://link.springer.com/article/10.1007/s11116-020-10101-3">https://link.springer.com/article/10.1007/s11116-020-10101-3</a>.</p> <p>Wong, S.,<strong> </strong>Shaheen, S., & Walker, J. (2018). Understanding Evacuee Behavior: A Case Study of Hurricane Irma. Report. Retrieved from <a href="https://escholarship.org/uc/item/9370z127">https://escholarship.org/uc/item/9370z127</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S.<strong> </strong>(2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. Retrieved from <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a>.</p>
Coupled atmosphere-wave-ocean simulation of Hurricane Dorian (2019)
<p><strong>Description</strong></p> <p>This dataset provides the output of the coupled atmosphere-wave-ocean simulation of Hurricane Dorian from August 29 to September 7, 2019. The simulation is a composite of two separate simulations:</p> <ol> <li>From 00 UTC August 29 to 00 UTC September 1, 2019</li> <li>From 00 UTC September 1 to 00 UTC September 7, 2019</li> </ol> <p>The first simulation serves as "spin-up" for the hurricane and its environment prior to landfall. The second simulation is initialized from the output of the first simulation, while relocating the Dorian vortex to its correct position on September 1. Due to the size of the dataset only the surface fields are made available.</p> <p><strong>Model configuration</strong></p> <ul> <li><strong>Atmosphere</strong>: Weather Research and Forecasting (WRF, https://github.com/wrf-model/WRF) model v4.2.2, with the Advanced Research WRF (ARW) dynamical core. The model has a 3-km resolution grid over the parent domain and a 1-km resolution nest over the Bahamas region (September 1-7 only), both with 45 vertical layers. Initial and boundary conditions are based on 6-hourly ERA-5 dataset.</li> <li><strong>Ocean Waves</strong>: University of Miami Wave Model (UMWM, https://umwm.org). The model is configured at the same 3-km as the atmosphere model, and has 36 directional bins and 37 frequency bins that are logarithmically spaced from 0.0313 to 2 Hz.</li> <li><strong>Ocean Circulation</strong>: HYbrid Coordinate Ocean Model (HYCOM, https://github.com/HYCOM) v2.3.01, configured at 0.01 degree resolution and 41 vertical layers. Initial and boundary conditions are based on daily GOFS 3.1 41-layer HYCOM + NCODA Global 1/12° Analysis, daily. K-Profile Parameterization for vertical mixing.</li> <li><strong>Coupling</strong>: Earth System Modeling Framework (ESMF, https://github.com/esmf-org/esmf) v8.0.1</li> </ul> <p><strong>File Description</strong></p> <ul> <li>blkdat.input - HYCOM (ocean circulation) configuration file</li> <li>dorian2019_atmosphere_1km_2019090100.nc - Atmosphere at 1-km resolution dataset</li> <li>dorian2019_atmosphere_waves_3km_2019082900.nc - Atmosphere and waves at 3-km resolution dataset, Aug 29 - Sep 1.</li> <li>dorian2019_atmosphere_waves_3km_2019090100.nc - Atmosphere and waves at 3-km resolution dataset, Sep 1-7</li> <li>dorian2019_ocean_1km_2019082900.nc - Ocean circulation at 1-km resolution dataset</li> <li>main.nml - UMWM (waves) configuration file</li> <li>namelist.input - WRF (atmosphere) configuration file</li> <li>regional.depth.[ab] - HYCOM (ocean circulation) bathymetry files</li> <li>regional.grid.[ab] - HYCOM (ocean circulation) grid files</li> <li>umwm.gridtopo - UMWM (waves) grid and bathymetry file</li> <li>wrfbdy_d01 - WRF (atmosphere) boundary conditions file</li> <li>wrfinput_d01.2019082900 - WRF (atmosphere) initial conditions file for parent domain on Aug 29</li> <li>wrfinput_d01.2019090100 - WRF (atmosphere) initial conditions file for parent domain on Sep 1</li> <li>wrfinput_d02.2019090100 - WRF (atmosphere) initial conditions file for inner nest on Sep 1</li> </ul> <p><strong>Coupled model source code</strong></p> <p>The model source code has not yet been released. We plan to open source it upon publication of the paper describing the simulation. When the source code is released, we will add the link to this repository.</p>
Climatology of rainfall from Atlantic hurricanes in the USA from radar data
<p>Atlantic Tropical Cyclone Rainfall Climatology in the USA<br> Data sources (see references): NEXRAD level III data, hourly precipitation; IBtracs best track data; University of Colorado extended best track data<br> Available as NetCDF files and Matlab structure</p> <p>Classification as TC precipitation criteria: within radius of outermost closed isobar of a TC at a given time</p> <p>Scope: 100km radius around corresponding radar station</p> <p>Dealing with radar outages: up to 2h gap - interpolation of precipitation, larger gaps - rescaling of frequency with fraction of available data (see formulas)</p> <p>Available variables per radar station:</p> <ul> <li>Location: name [ ], coordinates [°N, °W]</li> <li>Grid: lat [°N], lon [°E]</li> <li>Frequency rescaling: re_freq [ ]</li> </ul> <p>Available variables per event:</p> <ul> <li>Storm identifiers: name [ ], year [a]</li> <li>Storm total precipitation <ul> <li>Area distribution: Ptot [kg/m<sup>2</sup>], gridded (0.1x0.1°)</li> <li>Area average: Ptot_av [kg/m<sup>2</sup>]</li> <li>Area maximum within 0.5x0.5°: Ptot_max [kg/m<sup>2</sup>]</li> </ul> </li> <li>Annual exceedance frequency: f(Ptot_max) [a^-1]</li> </ul> <p>Relevant formulas:</p> <p>re_freq = total duration of storm exposure / duration of viable measurements<br> f (Ptot_max) = (number of events exceeding Ptot_max / length of observation) * re_freq</p> <p>Matlab structure:</p> <ul> <li>Level 1: TCP_climatology</li> <li>Level 2: station variables -> station_event_data leads to event variables</li> <li>Level 3: event variables -> Ptot leads to spatially gridded precipitation</li> <li>Level 4: Ptot-grid</li> </ul>
Chamber measurements of carbon dioxide (CO2) and methane (CH4) in Everglades following Hurricane Irma: 2017 - 2019
On September 9, 2017, highwinds from Hurricane Irma impacted the Florida Everglades. With 24 hours of heavy rain along with strong winds and storm surge, the storm caused higher than normal water levels, wind-induced defoliation, uprooting of plants and soil disturbance in Everglades short-stature freshwater wetlands. The hurricane redistributed short-stature vegetation into dead mats in Everglades National Park. The high post-storm water levels saturated many of these dead mats, slowing decomposition and increasing their persistence on the landscape. In this study, we measure carbon dioxide (CO2) and methane (CH4) fluxes at dead mats and compared them to the ridge and slough of freshwater marsh and to the marl prairie.
Surface and SubSurface Soil Organic Matter Processing following Hurricane Harvey, Texas, USA
Coastal wetland plant identity and cover is changing, as many subtropical salt marshes dominated by low-stature herbaceous species transition to woody mangroves. How changes in dominant plant species affect carbon processing in coastal wetlands during storms is uncertain. We experimentally manipulated patch-scale (3 × 3 m) cover of black mangroves (Avicennia germinans) and saltmarsh plants (e.g., Spartina alterniflora, Batis maritima) in fringe and interior locations of ten plots (24 × 42 m) to create a gradient in mangrove cover in coastal Texas, USA. Hurricane Harvey made direct landfall over our site on 25 August 2017. To test how mangrove cover affected carbon retention after the storm, we measured litter breakdown rates (k) of A. germinans and S. alterniflora in surface soils and fast- and slow-decomposing standard litter substrates (green and red tea, respectively) in subsurface soils (15 cm depth). Soil temperatures were lower in mangrove than marsh patches, and prior microclimate measurements showed non-linear increases in air and soil temperatures with increasing mangrove cover (highest temperatures at intermediate % cover). Litter breakdown rates (k) were 2 higher in surface than in subsurface soils. Avicennia germinans litter k increased linearly in surface soils with plot-level mangrove cover, whereas slow-decomposing red tea had similar k in subsurface soils of all plots. Litter k of S. alterniflora in surface soils and fast-decomposing green tea in subsurface soils increased non-linearly with mangrove cover (highest k at intermediate % cover), explained largely by temperature. Microbial respiration rates (R) were highest in interior marsh patches for S. alterniflora litter and increased with plot-level mangrove cover, whereas R associated with A. germinans litter was similar among fringe and interior patches and highest at higher mangrove cover. Despite widespread declines in soil nutrient concentrations throughout marsh and mangrove patches in all pl
Sediment and nutrient deposition and plant-soil phosphorus interactions associated with Hurricane Irma (2017) in mangroves of the Florida Coastal Everglades (FCE LTER), Florida
We quantified how Hurricane Irma influenced soil nutrient pools, vertical accretion, and plant phosphorus (P) uptake after its passage across the Florida Coastal Everglades in September 2017. Mangrove leaf litter data from three years (2008, 2014, 2018) were selected for each site at Shark River estuary to identify species-specific foliar P responses post-Wilma’s impact in 2005 and immediate post-Irma’s impact in 2017. We also monitored porewater SRP concentrations in the Shark River mangrove sites to evaluate the effect of Hurricane Irma on soil chemistry. The data in this data package were used in the following paper: Castañeda-Moya, E., V.H. Rivera-Monroy, R.M. Chambers, X. Zhao, L. Lamb-Wotton, A. Gorsky, E.E. Gaiser, T.G. Troxler, J.S. Kominoski, and M. Hiatt. 2020. Hurricanes fertilize mangrove forests in the Gulf of Mexico (Florida Everglades, USA). PNAS. In Press.
Methane and carbon dioxide flux in a tidal freshwater marsh recovering from three years of experimental seawater additions and following the Hurricane Irma storm surge
Methane (CH4) and carbon dioxide (CO2) flux rates were measured in a tidal freshwater marsh using static flux chambers. The experimental field site, SALTEx (Seawater Addition Long-Term Experiment) is part of the Georgia Coastal Ecosystems (GCE) LTER and is located on the Altamaha River, GA. The marsh was experimentally dosed with brackish water additions for 3 years, from 2014 – 2017. There are three treatments groups (Press, Pulse, and Fresh) and two control groups (with and without siding on the plots), each with six replicates. Press treatment plots received brackish water throughout the year, Pulse plots received brackish water in September and October and fresh water the rest of the year, Fresh plots received fresh river water throughout the year. The two control groups, one with siding on the plots and one without, received no water addition manipulations. All dosing ceased in January 2018, at which point we began to study the recovery of the marsh. In this study, the Hurricane Irma Rapid Grant evaluated additional effects of the Hurricane Irma storm surge that occurred in October 2017. Greenhouse gas measurements were taken seasonally beginning in March 2018 and ending in March 2019.
Quarterly plant monitoring survey -- shoot height and flowering status and calculated biomass of plants in three GCE LTER permanent monitoring plots (7,8,9) and three Altamaha River plant transition sites (SCSA,ZSC1,ZSC2) following hurricane Irma from October 2017 through October 2018.
The biomass of plants surveyed in permanent plots at 3 GCE LTER sampling sites (7,8,9) and 3 Altamaha River plant transition sites (SCSA, ZSC1, ZSC2) following hurricane Irma from October 2017 through October 2018. October data are duplicates of data from the annual fall monitoring data sets but are included here for completeness. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set (PLT-IRMA-2002a). Biomass twas estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1711a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b and PLT-GCEM-0812a. NOTE: These relationships were measured in the fall and may not be valid at other seasons.
Quarterly disturbance observations to three GCE LTER permanent monitoring plots (7,8,9) and three Altamaha River plant transition sites (SCSA,ZSC1,ZSC2) following hurricane Irma from October 2017 through October 2018.
To access the effect of hurricane Irma on the GCE domain, yearly monitoring of species and size distribution of plants at three GCE LTER sampling sites (7, 8, 9) and three Altamaha plant transition sites (SCSA, ZSC1, ZSC2) was expanded to quarterly sampling between October 2017 and October 2018. We established permanent vegetation monitoring plots in creekbank and midmarsh at GCE sites 1-10 in 2000. A dedicated Juncus zone was later added at sites 10 and 9. We established creekbank permanent vegetation monitoring plots in three plant transition zones along the Altamaha River in 2012. When we recorded plant sizes, we also noted any disturbance to the plots. Plots were scored as normal (no visible disturbance), disturbed by wrack (wrack present in plots and stems dead or broken), disturbed by snails (>100 Littoraria per square meter and plant biomass low), disturbed by pigs (animal trail through the plot, this mostly happened at site 8 mid-marsh), initial slump (plot at the creekbank sliding into the creek based on movement of pvc poles or formation of a crevasse), and terminal slump (plot had slid far enough down that vegetation had drowned). Plots that experienced terminal slump or could not be found for any reason were scored as lost. Lost plots were replaced with a new plot in the same general area with the plot code incremented by 10. For example if plot 3 was lost, it was replaced by 13, and then in turn by 23. October data are duplicates of data from the annual fall monitoring data sets but are included here for completeness.
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
Wood to Soil 0-10 cm data and Wood to Soil 10-20 cm data to detect the imprint of decaying logs (30-80 cm diameter) from two hurricane cohorts (Hugo, 1989, and Georges, 1998)
Many trees fell during Hurricanes Hugo (1989) and Georges (1998) in Puerto Rico. A debris removal experiment suggested that coarse woody hurricane debris slowed canopy recovery by fueling microbial nitrogen immobilization. We analyzed C, N, microbial biomass C and root length in paired soil samples taken under versus 20-50 cm away from large trunks of two species felled by Hugo and Georges three times during wet and dry seasons during the two years after Georges. Data on soil P and other nutrients have not yet been analyzed. Soil microbial biomass, C and N were higher under than near logs of both age cohorts. Frass from wood boring beetles may induce the early effects. Root length was greater under logs at 0-10 cm depth during the dry season, and away from logs in the wet season, but varied independently of microbial biomass. Thus decaying wood can provide resources exploited by tree roots. Percent soil C and N were significantly higher under than near logs in both the 0-10 and 10-20 cm samples. Microbial biomass C varied significantly among seasons at 0-10 cm depth but differences between positions (under vs away) were only suggestive. Surface soil on the upslope side of the logs had significantly more N and microbial biomass, likely from accumulation of leaf litter above the logs on steep slopes. This study shows that C and N accumulate significantly more in soil under than near decaying logs, even in logs that had only decayed for 7 months, and thus contributes to soil heterogeneity. Tree roots track and exploit resource and nutrient hotspots as they change locations between seasons, so the soil heterogeneity in soil fertility is important for forest productivity. Soil phosphorus (P) availability is most often the most limiting nutrient in wet tropical forests. Total soil P was measured by complete digestion in samples from the upper 10 cm; Olsen extractable P (available) was also measured. Total soil P concentrations were significantly greater under than away fr
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
Soil phosphorus, litterfall mass, nitrogen and phosphorus in eight Puerto Rico forests affected by hurricanes Hugo, Bertha, Georges, Irma, and Maria.
Tropical cyclones are intensifying and occurring at higher latitudes in recent decades, but the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (pace of return to pre-disturbance reference values) of tropical forests to cyclones remains largely unexplored at the pantropical scale. We conducted a meta-analysis to investigate the role of soil resource availability (i.e., total soil phosphorus concentration) in mediating site-level forest canopy resistance and resilience to cyclones pan-tropically. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2/day), phosphorus (P) and nitrogen (N) fluxes (mg/m2/day), as well as concentrations (mg/g) across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan (Bomfim et al., in review). This dataset includes information related to 42 case studies in eight Puerto Rico forests affected by hurricanes Hugo, Bertha, Georges, Irma, and Maria between 1989 and 2017. This dataset also includes data for the canopy trimming experiment (CTE) in El Verde. - The compiled Litterfall Mass and Nitrogen and Phosphorus Flux and Concentration data from natural forest ecosystems across Puerto Rico prior to and after eight hurricanes are provided in Forest-Soil-Litterfall-Hurricane_Puerto-Rico.csv. This data file also includes site location, geographical characteristics, elevation, soil phosphorus concentration, geology, and several variables related to each hurricane disturbance. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Regeneration after Hurricane Hugo, woody species > 10 cm tall (9Ha grid, El Verde) (9Ha Plots Small Data Set)
The purpose of this data set is to document vegetation damage and recovery following Hurricane Hugo, a borderline category 3-4 hurricane with winds from 130 to 160mph (110kts) and a pressure of 945 to 946.1 mb, which hit Puerto Rico in September 18th, 1989 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Regeneration after Hurricane Hugo woody species more than 1m tall (9ha grid, El Verde) (Large 9Ha Grid)
The purpose of this data set is to document vegetation damage and recovery following Hurricane Hugo, a borderline category 3-4 hurricane with winds from 130 to 160mph (110kts) and a pressure of 945 to 946.1 mb, which hit Puerto Rico in September 18th, 1989. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.