The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agriculture Handbook Number 703 (Renard et al.,1997). Lines connecting points of equal rainfall ersoivity are called isoerodents. The iserodents plotted on a map of the coterminous U.S. were digitized, then values between these lines were obtained by linear interpolation. The final R-Factor data are in raster GeoTiff format at 800 meter resolution in Albers Conic Equal Area, GRS80, NAD83.

Abstract \r

\r The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r There are 4 csv files here:\r \r BAWAP_P_annual_BA_SYB_GLO.csv\r \r Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.\r \r Source data: annual BILO rainfall on \\wron\Project\BA\BA_N_Sydney\Working\li036_Lingtao_LI\Grids\BILO_Rain_Ann\\r \r \r \r P_PET_monthly_BA_SYB_GLO.csv\r \r long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month\r \r \r \r Climatology_Trend_BA_SYB_GLO.csv\r \r Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend\r \r \r \r Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv\r \r Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). All data used in this analysis came directly from James Risbey, CMAR, Hobart. As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).\r \r

Dataset History \r

\r Dataset was created from various BILO source data, including Monthly BILO rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET (calculated by Randall Donohue), Correlation coefficient data from James Risbey\r \r

Dataset Citation \r

\r Bioregional Assessment Programme (XXXX) SYD ALL climate data statistics summary. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/b0a6ccf1-395d-430e-adf1-5068f8371dea.\r \r

Dataset Ancestors \r

\r * Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012\r \r

ERRA is a data-driven, nonparametric, model-independent method for quantifying rainfall-runoff relationships across a spectrum of time lags, in systems that may be nonlinear, nonstationary, and spatially heterogeneous. Researchers using ERRA in published work should cite J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024 (for ERRA itself) and J.W. Kirchner, "Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and de-mixing of noisy time series", Sensors, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022 (for the underlying mathematics). This data set includes two versions of the ERRA script written in the open-source programming language R, a detailed user's guide, and sample scripts and source data for all of the results in Kirchner (2024). These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/. The data and documentation are made available under Creative Commons Attribution Share-Alike CC-BY-SA. ETH Zurich, WSL, and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

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This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset.

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.

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.

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.

Monthly rainfall erosivity maps (R-factor maps) of Switzerland with a spatial resolution of 100 m. The maps show the spatial and seasonal variability of rainfall erosivity in MJ mm ha⁻¹ h⁻¹ month⁻¹. Light shades of blue indicate a low erosive impact of rainfall and dark shades a high impact.

The monthly R-factors are based on precipitation measurements from 87 automatic gauging stations with measurement intervals of 10 minutes (average measuring period of 19.5 years per station). The stations cover all agricultural zones in Switzerland. To exclude the influence of snow, temperatures are also recorded at an hourly resolution for 71 stations or are derived from the nearest station.

A comparison of the 12 monthly R-factor maps shows that the summer months (June, July and August) have the highest rainfall erosivity values during the year. The Southern Alps (canton of Ticino), the mountain zones of the Northern Alps and parts of the valley zone have particularly high R-factors in this period. A proportion of 62% of Switzerland's annual rainfall erosivity is recorded between June and September. Identifying regions and periods in which rainfall with an increased erosive impact occurs enables targeted erosion control and a better understanding of the dynamics of erosion processes over the course of a year.

Monthly rainfall erosivity maps (R-factor maps) of Switzerland with a spatial resolution of 100 m. The maps show the spatial and seasonal variability of rainfall erosivity in MJ mm ha⁻¹ h⁻¹ month⁻¹. Light shades of blue indicate a low erosive impact of rainfall and dark shades a high impact.

The monthly R-factors are based on precipitation measurements from 87 automatic gauging stations with measurement intervals of 10 minutes (average measuring period of 19.5 years per station). The stations cover all agricultural zones in Switzerland. To exclude the influence of snow, temperatures are also recorded at an hourly resolution for 71 stations or are derived from the nearest station.

A comparison of the 12 monthly R-factor maps shows that the summer months (June, July and August) have the highest rainfall erosivity values during the year. The Southern Alps (canton of Ticino), the mountain zones of the Northern Alps and parts of the valley zone have particularly high R-factors in this period. A proportion of 62% of Switzerland's annual rainfall erosivity is recorded between June and September. Identifying regions and periods in which rainfall with an increased erosive impact occurs enables targeted erosion control and a better understanding of the dynamics of erosion processes over the course of a year.

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The dataset provided includes information on district-wise rainfall (R/F) and percentage departure (%DEP) from long-term averages for each month from January to December, spanning the years 2009 to 2022 across different districts (East, West, North, South)of Sikkim State. Description of Dataset:

Columns:
  Year: The year for which the data is recorded.
  District: Geographic region or district where the rainfall data is collected.
  January R/F, January %DEP: Rainfall (R/F) and percentage departure (%DEP) for January.
  February R/F, February %DEP: Rainfall (R/F) and percentage departure (%DEP) for February.
  March R/F, March %DEP: Rainfall (R/F) and percentage departure (%DEP) for March.
  April R/F, April %DEP: Rainfall (R/F) and percentage departure (%DEP) for April.
  May R/F, May %DEP: Rainfall (R/F) and percentage departure (%DEP) for May.
  June R/F, June %DEP: Rainfall (R/F) and percentage departure (%DEP) for June.
  July R/F, July %DEP: Rainfall (R/F) and percentage departure (%DEP) for July.
  August R/F, August %DEP: Rainfall (R/F) and percentage departure (%DEP) for August.
  September R/F, September %DEP: Rainfall (R/F) and percentage departure (%DEP) for September.
  October R/F, October %DEP: Rainfall (R/F) and percentage departure (%DEP) for October.
  November R/F, November %DEP: Rainfall (R/F) and percentage departure (%DEP) for November.
  December R/F, December %DEP: Rainfall (R/F) and percentage departure (%DEP) for December.

Districts:
  East: Contains data for the East district across all years.
  West: Contains data for the West district across all years.
  North: Contains data for the North district across all years.
  South: Contains data for the South district across all years.

Missing Values:
  Some entries are marked as "N/A" where data is missing. This could be due to various reasons such as lack of data availability or specific conditions where data couldn't be recorded.

Data Variability:
  There is significant variability in rainfall (R/F) and percentage departure (%DEP) across months and years within each district.
  %DEP indicates how much the observed rainfall deviates from the long-term average for that district and month. Positive %DEP indicates above-average rainfall, while negative %DEP indicates below-average rainfall.

Temporal Trends: The dataset spans from 2009 to 2022, allowing for analysis of temporal trends and patterns in rainfall across different districts. Each year's data can be analyzed to understand seasonal variations, anomalies, and potential impacts of climate patterns on regional rainfall.

Usage:

This dataset is useful for climate scientists, meteorologists, and researchers studying regional climate patterns, rainfall trends, and the impact of variability in rainfall on agriculture, water resources, and local ecosystems. Analyzing this data can provide insights into climate change effects, drought or flood risk assessments, and planning for water management strategies.

Dataset (GIS map) (2015) and associated products for the "Rainfall erosivity" (R-factor), one of the input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28+Switzerland; R-factor map at resolutions of 500m. Users can downloads Raw data, Baseline map (2010), Monthly erosivity, Future projections (2050), Past erosivity (1961-2000).

The Rainfall Rate Quantitative Precipitation Estimate (QPE) product contains an image with pixel values identifying the rainfall rate. The product includes data quality information that provides an assessment of the rainfall rate data values for on-earth pixels. The units of measure for the rainfall rate value is millimeters per hour. The Rainfall Rate (QPE) product image is produced on the ABI fixed grid at 2 km resolution for the Full Disk coverage region. Product data is produced for geolocated source data to local zenith angles of 90 degrees for both daytime and nighttime conditions.

1)The data includes average rainfall erosivity raster data for 65 countries, with a spatial resolution of 1 kilometers. 2）The 0.5°×0.5° grid daily rainfall data generated by the Climate Prediction Center (CPC) based on global site data was used to calculate the rainfall erosivity R factor of 20 countries in key regions. 3）The R value was calculated using the daily rainfall data from 2358 weather stations of the China Meteorological Administration from 1986 to 2015, and the R value calculated by establishing the CPC data source was reviewed and revised. The quality of the finally obtained data was good . 4）Rainfall erosivity R factor is used as the driving factor of the CSLE model, and its data is the basis of soil erosion simulation and spatial pattern analysis in 65 countries, and it is of great importance for the study of soil erosion mechanism.

The GOES-R Advanced Baseline Imager (ABI) Rainfall Rate Quantitative Precipitation Estimate (RRQPE) product contains an image with pixel values identifying the rainfall rate. The product includes data quality information that provides an assessment of the rainfall rate data values for on-earth pixels. The units of measure for the rainfall rate value is millimeters per hour. The product image is produced on the ABI fixed grid at 2 km resolution for the Full Disk coverage region. Product data is produced for geolocated source data to local zenith angles of 90 degrees for both daytime and nighttime conditions.

This landing page is the collection of data packages that support the Modelled Annual Rainfall Erosivity (R-factor) over New South Wales for years commencing 2001. Mean Annual Rainfall Erosivity (R-factor) is calculated as the Mean Rainfall Erosivity (R-factor) across year range.

Soil erosion induced by water has been identified as one of the major environmental problems worldwide. The erosive force of rainfall, also known as rainfall erosivity (R-factor), is the potential of rain to cause soil degradation and one of the factors in the widely adopted RUSLE (Revised Universal Soil Loss Equation) empirical soil erosion estimation model. About 68.4% of total eroded soil in India is eroded due to erosion by water, and rainfall erosivity is one of the major factors. The past assessments of rainfall erosivity in India were however largely based on rain-gauge recordings and surveys which hinders its understanding and estimation over large areas. Growing availability of gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based studies. IRED (Indian Rainfall Erosivity Dataset) is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will be helpful for agricultural experts, watershed managers, agronomists, and soil-conservational experts in order to understand and mitigate rainfall-induced erosion. In this dataset, long term yearly average R-factor, Fourier Index (FI), and Modified Fourier Index (MFI) maps have been included with a distributional analysis over IMD (India Metrological Department) defined regions, states and districts of India.

Table with the data of annual rainfall erosivity and auxiliary information for 3,939 stations.

Table with the data of monthly erosivity.

Shape file with all the stations and their erosivity values.

12 Raster (GeoTIFF) with global monthly erosivity at 1km x 1km resolution

Relevant publication to cite for those datasets:

Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M.B., Yu, B., Yang, J.E., Miao, C., Chattopadhyay, N., Sadeghi, S.H. and Levi, Y., et al. 2023. Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution. Data in Brief, 50, Art.no.109482. DOI: 10.1016/j.dib.2023.109482

This landing page is the collection of data packages that support the Modelled Annual Rainfall Erosivity (R-factor) over New South Wales for years commencing 2001. Mean Annual Rainfall Erosivity (R-factor) is calculated as the Mean Rainfall Erosivity (R-factor) across year range.

OceanRAIN version 1.0, OceanRAIN-R - ODM470 Raw number count Particle Size Distribution and Precipitation Microphysics, 37 along-track parameters plus 128 size bins for 8 ships, 692.000 precipitation minutes in total, temporally discontinuous data for each ship, 1-minute-resolution

1)The datase includes a 30-year (1986-2015) average rainfall erosivity raster data for 20 countries in key regions, with a spatial resolution of 300 meters. 2）The 0.5°×0.5° grid daily rainfall data generated by the Climate Prediction Center (CPC) based on global site data was used to calculate the rainfall erosivity R factor of 20 countries in key regions. 3）The daily rainfall data of 2358 weather stations nationwide from China Meteorological Administration from 1986 to 2015 was used to calculate the R value, and the R value calculated by establishing the CPC data source was rechecked and verified. It is found that the R value calculated by the CPC data system was low, and then it was revised, and the final data obtained was of good quality. 4）Rainfall erosivity R factor can be used as the driving factor of the CSLE model, and the data is of great significance for the simulation of soil erosion in 20 countries in key regions and the analysis of its spatial pattern.

The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agricultu...