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547 results for “rainfall”
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>
SM2RAIN-Climate (1998-2021): monthly global satellite rainfall dataset
<p><strong>SM2RAIN-Climate</strong> rainfall product is a new long-term global scale rainfall product developed by using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product v06.1 as input into the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-Climate global rainfall dataset is generated in the period 1998-2021 with monthly temporal and 1° spatial resolutions, which provide the opportunity for climatological studies.</p> <p>Four different SM2RAIN-Climate datasets are provided in NetCDF format. For each dataset, the spatial grid (latitude and longitude), the rainfall values, and the mask type is defined in each NetCDF file. Two different masks are the temperature mask in data post-processing and a threshold value (percentage of missing data) taking into account missing data within a month. Depending on the application, the user can select the more suitable product.</p> <p>Details on the dataset development is provided as:</p> <p>Mosaffa, H., Filippucci, P., Massari, C., Ciabatta, L., & Brocca, L. (2023). SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies. <em>Scientific Data</em>, <em>10</em>(1), 749. <a href="https://doi.org/10.1038/s41597-023-02654-6"><em>https://doi.org/10.1038/s41597-023-02654-6</em></a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the Open-Earth-Monitor Cyberinfrastructure project that has received funding from the European Union's Horizon Europe research and innovation programme (grant agreement no. 101059548) and by the European Space Agency through the Digital Twin Earth Hydrology project (grant no. ESA 4000129870/20/I-NB - CCN N. 1) and the 4DMED Hydrology project (grant no. ESA 4000136272/21/I-EF).</p>
Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022
<p>The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022.</p> <p>Zurich:</p> <p>Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>Milan:</p> <p>Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx</p> <p>The presented-data passed only a primilinary quality control.</p> <p>Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257</p>
WRF Forecast Data used for Verification of multi-resolution model forecasts of heavy rainfall events of 23rd-26th August 2017 over Nigeria
<p>A deterministic Weather Research and Forecasting model version 4.2 forecast of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) within the period 23<sup>rd</sup>-26<sup>th</sup> August 2017 over Nigeria. The model was setup to perform two nested domain simulations with 18 (parent domain), 6 and 2 km (hereafter WRF18, WRF6 and WRF2) horizontal resolutions. The outer domain covers West Africa and the innermost domain, which runs at convection-permitting scale, focuses on Nigeria. When interpreting the results, it is worthy of note that the data has been regridded to 18 km, which is 3 x the grid scale for WRF6 and 9 x the grid scale for WRF2. This means that there is a fair degree of smoothing that has been applied using a bilinear regridding process to get the models onto a level playing field. Only WRF18 retains its native grid and has not benefited from any additional smoothing.</p> <p>The WRF model setup is similar to the study of Gbode et al. (2019; DOI: https://doi.org/10.1007/s00704-018-2538-x) in terms of the model physics combination used in the model simulations. The parameterization schemes used are the Goddard (GD) WRF model microphysics (MP), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) and the Bett-Miller-Janjic (BMJ) cumulus convection (CU) parameterization schemes. This combination was found to reproduce realistic rainfall and temperature relative to gridded observations over West Africa. The GD is a six-class microphysics with graupel and modifications for ice/water saturation. MYJ is a local closure scheme that predicts turbulent kinetic energy and the BMJ CU is a profile adjustment scheme that relaxes both deep and shallow profiles toward a reference profile without explicit updraft, downdraft, or cloud entrainment. However, the CU scheme was turned off in the 2 km domain to explicitly represent convection.</p>
Radar rainfall event characteristics
<p>Datasets of radar-derived rainfall events and their characteristics between 01/01/2010 and 31/12/2020 for the Brisbane (Mt Stapylton), Sydney (Terrey Hills), and Melbourne (Laverton) radars. Rainfall events were identified only using data within a 100km radius of the radar, with gaps of one timestep interpolated over using the arithmetic mean of value on either side of the gap, and using an areal mean rain rate threshold of 0.1 mm/h. Created using Level 2 rain rate and Steiner classification data from the Australian Unified Radar Archive (AURA), which is available on NCI. Datasets are in csv format.</p>
2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_ER
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024. This version is an update to the previous one (2010 to 2022)</p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2024.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting . The original data is at a 0.25-degree resolution and was downscaled by ERA extraction algorithms, then downloaded at a 1 km resolution. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 (Total precipitation) 1 km imagery from 2010 to 2024. Each parameter extract dataset was then processed by a Temporal Fourier Processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and errors measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Then, sea pixels were masked with a VIIRS land/sea layer in arcmap. The E4Warning study region was a subset of global images. </p> <p> </p> <p>This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p>Projection + EPSG code:</p> <p>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <h4>File names:</h4> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 04 refers to the year timeline of 2010-2024.<br><br>The next two characters identify the channel:<br>20 Monthly Total Precipitation<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
Long-term species-level measurements of fall season aboveground net primary production in the Monsoon Rainfall Manipulation Experiment (MRME), Sevilleta National Wildlife Refuge, New Mexico, USA
Anticipated intensification of the North American Monsoon in the southwestern United States is predicted to shift growing season rainfall patterns, historically characterized by frequent small rain events, to a more extreme precipitation regime consisting of fewer, but larger rain events. Atmospheric nitrogen deposition is also increasing throughout this dryland region as a result of anthropogenic activities. Alterations in rainfall size and frequency, along with changes in nitrogen availability, are likely to have significant consequences for aboveground net primary production (ANPP) and plant community dynamics in drylands, where ecological processes are limited by water and nitrogen availability. This data package accompanies an associated manuscript in which we used fourteen years (2007-2020) of growing season ANPP measurements from the long-term Monsoon Rainfall Manipulation Experiment (MRME), located in the Sevilleta National Wildlife Refuge, to investigate how changes in rainfall regimes, along with chronic nitrogen enrichment, impact ANPP in a northern Chihuahuan Desert grassland.
Seasonal N dynamics and fluxes of nitrogen in leachate and runoff from experimental rainfalls on fertilized and unfertilized lawns in Baltimore County, Maryland
The aim of this research was to examine the spatial and temporal variation in export control points of nitrogen on residential lawns (locations prone to mobilizing nitrogen during a rain event) and to examine if previously measured hydrobiogeochemical properties were predictive of N mobilization in lawns. This data set contains measurements of saturated infiltration rates, sorptivity, soil moisture, soil organic matter, bulk density, pH, soil nitrate, soil ammonium, N2O, N2 and CO2 fluxes from soil cores, nitrogen mineralization rates and fluxes of N in runoff and leachate from fertilized and unfertilized residential and institutional lawns. Study lawns were located at homes of people who agreed to volunteer their lawn for the study from a door knocking campaign. Four sampling houses were located in an exurban neighborhood in Baisman Run. Five sampling houses were located in a suburban neighborhood in Dead Run. Two sampling locations on institutional lawns were located at University of Maryland Baltimore County. At the exurban study houses and institutional lawns sites, we identified one hillslope to conduct sampling on. At the Dead Run houses we identified one hillslope on the front yard and one in the backyard as there were distinct locations that were not present in the exurban neighborhood. Locations within the yards for sampling were selected based on sampling conducted in October 2017. Locations were grouped into four categories based on have either high or low potential denitrification rates and high or low saturated infiltration rates (n=48). These locations were also distributed across yard types (exurban, suburban or institutional), fertilizer treatments, and hillslope location (top or bottom of hillslope). At each sampling location we ran a Cornell Sprinkle Infiltrometer to generate an experimental rainfall during which we collected runoff and leachate to quantity N flux. We also measure sorptivity and saturated infiltration rates. Volumetric water conten
RMP01 Rainfall manipulation plot study at Konza Prairie
Rainfall Manipulation Plots facility (RaMPs) is a unique experimental infrastructure that allows us to manipulate precipitation events and temperature, and assess population community, and ecosystem responses in native grassland. This facility allows us to manipulate the amount and timing of individual precipitation events in replicated field plots at the Konza Prairie Long-Term Ecological Research (LTER) site. We used data from a unique 15-year long rainfall manipulation experiment at the Konza Prairie Biological Station in northeastern Kansas, USA, to determine how altered precipitation patterns (fewer, larger events) impacted plant species composition and structure in an annually burned, ungrazed, native tallgrass prairie. We tested two hypotheses. First, based on the HRF, we predicted that directional change in grass and forb cover and richness and community composition would eventually occur after a lag period under the altered precipitation treatment. Second, we predicted that change in cover and composition under altered precipitation would be driven by the response of forbs more so than grasses because the dominant grasses are reported to be buffered against precipitation variability1,44 and changes in the cover and richness of forbs contribute disproportionately to community responses to other drivers in this grassland.
ASR01 Short-term assessment of effects of burning on infiltration, runoff, and sediment and nutrient loss on Tallgrass Prairie using rainfall simulation, 1989
Rainfall simulation and overland flow experiments were performed on four plots at a single site on Konza from May to August, 1989. Two plots were treated with a late spring burn and two plots were left unburned. Five simulations were performed on burned plots and three simulatons on unburned plots. Each simulation consisted of a “dry run” followed 24 hours later by a 'wet run'. The dry run consisted of rainfall applied at an intesity of approximately 60 mm/hour. The wet run was the same as a dry run, except when the rainfall was complete, overland flow was applied directly at the top of the plots to simulate run off coming from upslope. Measurements taken include overland flow velocity, water application rate, runoff, hydrograph, water flow depth, sediment content, nitrogen and phosphorus content and percent ground cover (See A.B. Duell, Effects of burning on infiltration, overland flow, and sediment loss on tallgrass prairie, M.S. thesis, Kansas State University, 82pp. for further details).
WAT02 Climate legacies determine grassland responses to future rainfall regimes
Climate variability and periodic droughts have complex effects on carbon (C) fluxes, with uncertain implications for ecosystem C balance under a changing climate. Responses to climate change can be modulated by persistent effects of climate history on plant communities, soil microbial activity, and nutrient cycling (i.e., legacies). To assess how legacies of past precipitation regimes influence tallgrass prairie C cycling under new precipitation regimes, we modified a long-term irrigation experiment that simulated a wetter climate for >25 years. We reversed irrigated and control (ambient precipitation) treatments in some plots and imposed an experimental drought in plots with a history of irrigation or ambient precipitation to assess how climate legacies affect aboveground net primary productivity (ANPP), soil respiration, and selected soil C pools. Legacy effects of elevated precipitation (irrigation) included higher C fluxes and altered labile soil C pools, and in some cases altered sensitivity to new climate treatments. Indeed, decades of irrigation reduced the sensitivity of both ANPP and soil respiration to drought compared with controls. Positive legacy effects of irrigation on ANPP persisted for at least 3 years following treatment reversal, were apparent in both wet and dry years, and were associated with altered plant functional composition. In contrast, legacy effects on soil respiration were comparatively short-lived and did not manifest under natural or experimentally-imposed “wet years,” suggesting that legacy effects on CO2 efflux are contingent on current conditions. Although total soil C remained similar across treatments, long-term irrigation increased labile soil C and the sensitivity of microbial biomass C to drought. Importantly, the magnitude of legacy effects for all response variables varied with topography, suggesting that landscape can modulate the strength and direction of climate legacies. Our results demonstrate the role of climate his
Rainfall at El Verde Field Station, Rio Grande, Puerto Rico since 1975
Rainfall at El Verde Field Station is measured manually using a rain collector placed on the station roof. The collector is mostly open, but some degree of interference from tall trees around the station is possible. A technician measures rainfall daily during workdays. For weekends and holidays, the amount of precipitation is measured on the next working day and divided equally among the days since the last reading. As of July 1st, 2022 we stopped the practice of redistributing rainfall totals after gaps in rainfall collection. Previously for example, If on a Monday rainfall of 15 millimeters was collected and rainfall wasn't collected on Saturday or Sunday but had been on Friday then Monday, Saturday, and Sunday would each have 5 millimeters. Since July 1st, 2022 rainfall is reported as the totals actually collected on the dates they are collected. Rainfall has been measured at the El Verde Field Station since 1964. McDowell and Estrada-Pinto, 1988 presents a description of the collection procedures, raw data from 1964 to 1986, and some summary statistics for this period of record. Precipitation for this period showed some seasonality in monthly means, with a peak in May. Monthly averages for the period of 1975 to current chart can be found at this site. The highest values for the monthly averages for the period of 1975 to 2000 are from August to December with a low in October and November the highest. In this period the highest amount of total annual rainfall was in 1998 with 5293.61 mm and the minimum in 1994 with 1402.87 mm. 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.
Bisley rainfall and throughfall, and chemistry of rainfall and throughfall
This data set contain summaries and analyses mean of collected weekly measurements expressed as mm per day, and calculation of fluxes, rates and means calculated after water chemistry analyses are conducted. Rainfall and throughfall are collected weekly at the Bisley LEF site. These data sets begin March 1988 and ends December 2003. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rainfall Collected in Bisley (RCB) are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Sub samples for NH4 analysis were also preserved with 1 molar HCl. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Nutrient fluxes in rainfall and throughfall were measured weekly in a mature subtropical wet forest in NE Puerto Rico over a 15-year period that included the effects of five hurricanes and several prolonged droughts. Annual inputs of K, Ca, Mg, Cl, Na, and SO4-S are similar to those reported from other marine-influenced tropical forests. Rainfall input of nitrogen is comparatively low and reflects the relative isolation of the air shed. Mean annual rainfall and throughfall were 3482 and 2131 mm yr-1 respectively. On average, rainfall, throughfall, rainfall pH, and rainfall flux NH4-N and NO3-N had small but significant decreases throughout the study period. More nutrients fluxes had seasonal differences in rainfall (6 out of 12) than throughfall (4 out of 12). All volume weighted enrichment ratios calculated f
Chemistry of rainfall and throughfall from El Verde and Bisley
Rain, throughfall, and stream water are collected weekly at the LEF sites listed below. Samples are collected by USDA Forest Service technicians Carlos Estrada in the field filtered in the lab by Miriam Salgado. These data sets begin as early as 1983; LTER sampling began in 1988. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rain samples from WDEV are wet only from an automatically-closing collector that prevents any dry deposition (Aerochem Metrics NADP collector). RCEV and RCB are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Subsamples for NH4 analysis were also preserved with 1 molar H2SO4. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Rain and Throughfall Sampling SitesDescriptions of LTER LUQ rain and throughfall weekly sample chemistry data from 1988 onwards. Chemical concentrations are recorded as mg/L or ug/L as appropriate. Values below detection limits are recorded as 1/2 the detection limit. Site Abbreviation Description Comments Rain collector Bisley. RCB Bulk collector. Rain collector El Verde RCEV Bulk collector. Wet/dry El Verde. WDEV. Wet only collector. Throughfall Bisley. TFB=TCB TF bulk 10-collector composite Bisley gap= BGAP, TF bulk 10-collector composite 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
Rainfall and ion composition data from multiple weather stations along an elevation gradient in northeastern Puerto Rico (2009-2018)
The data archive is here: https://doi.org/10.2737/RDS-2021-0013 please use this DOI when citing this dataset. Rainfall and ionic composition data were collected at 21 sites along the elevational gradient of the Luquillo Mountains, in Puerto Rico. Stations were selected along the east coast of the island and follow the steep slope of the mountains until the highest peaks. Rainfall data were collected every two weeks and are provided in this data publication as monthly rainfall from January 2009 through May 2019. Also included are pH and conductivity which are provided monthly starting roughly in November 2011 and continue through May 2019. Monthly ionic composition data from rainwater samples collected during the last two weeks of each month are also included from January 2009 through December 2017. 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.
Bisley daily rainfall (Bisley weekly environmental data)
Data set includes all available daily, weekly, and monthly rainfall from several climate stations in the northeast section of the Luquillo Experimental Forest. These stations are surround the Bisley Experimental watersheds and the Sabana Field Station are are operated by the USFS and the USGS. Weekly canopy throughfall is also collected weekly from the Bisley experimental watersheds. 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.
Monsoon Rainfall Manipulation Experiment (MRME): Soil Carbon Dioxide Concentrations from the Sevilleta National Wildlife Refuge, NM
The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. These soil carbon dioxide data were collected at three depths.
Monsoon Rainfall Manipulation Experiment (MRME): Soil Temperature Data from the Sevilleta National Wildlife Refuge, NM
The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. These data are soil temperature data collected at two depths.
Monsoon Rainfall Manipulation Experiment (MRME) Soil Temperature, Moisture and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico
The Monsoon Rainfall Manipulation Experiment (MRME) is designed to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, by altering rainfall pulses, and thus soil moisture, that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and frequency will alter grassland productivity, ecosystem processes, and plant community dynamics. Treatments include (1) a monthly addition of 20 mm of rain in addition to ambient, and a weekly addition of 5 mm of rain in addition to ambient during the months of July, August and September. It is predicted that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. In particular, we predict that many small events will increase soil CO2 effluxes by stimulating microbial processes but not plant growth, whereas a small number of large events will increase aboveground NPP and soil respiration by providing sufficient deep soil moisture to sustain plant growth for longer periods of time during the summer monsoon.
REDB-BR: Rainfall Erosivity Database for Brazil
<p>This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset.</p> <p>It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset.</p> <p>The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. </p> <p>The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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