Note: Version 3.1 supersedes all previous releases. Version 3.0 has been deprecated due to the discovery of a data inconsistency in the calculation of net longwave radiation in the source code used to generate the dataset. As a result, there is a general positive bias in potential evapotranspiration (ET0) and a consequent lower (drier) bias in the Aridity Index (AI) in affected outputs. This issue has been fully corrected in v3.1, and all ET0 and AI products have been recomputed using the corrected method. We are grateful for the numerous feedback from users and in particular to Dr. Pushpendra Raghav, Research Scientist, Department of Civil Engineering, University of Alabama, for identifying and bringing this issue to our attention. We recommend all users migrate to Version 3.1 and discontinue use of the previous v3.0.***********************************************************************************************************************************NOTE: The recently released Future Global Aridity Index and PET Database (CMIP_6) is now available at:https://doi.org/10.57760/sciencedb.nbsdc.00086High-resolution (30 arc-seconds) global raster datasets of average monthly and annual potential evapotranspiration (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CIMP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:**************************************************************************************************************************The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3.1 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3.x1) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET0) equation.Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global-AI geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Readme File is provided with a detailed description of the dataset files. A peer-reviewed article is now available with a description of the methodology and a technical evaluation.The Global-AI_PET_v3 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589Peer-Review Reference and Proper Citation:Zomer, R.J.; Xu, J.; Trabuco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1

The Global Aridity Index (Global-Aridity_ET0) and Global Reference Evapotranspiration (Global-ET0) Version 2 dataset provides high-resolution (30 arc-seconds) global raster climate data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon the implementation of a Penman Monteith Evapotranspiration equation for reference crop. The dataset follows the development and is based upon the WorldClim 2.0: http://worldclim.org/version2 Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global Aridity Index_ET0 geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Global-Aridity_ET0 and Global-ET0 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.

Global Aridity Index and Potential Evapotranspiration Database: CMIP_6 Future Projections(Future_Global_AI_PET)Robert J. Zomer 1, 2, 3, Antonio Trabucco1,41. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 2. Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China3. CIFOR-ICRAF China Program, World Agroforestry (ICRAF), Kunming, Yunnan. China4. National Biodiversity Future Center (NBFC), Palermo, ItalyThe Global Aridity Index and Potential Evapotranspiration (Global AI-PET) Database: CMIP_6 Future Projections – Version 1 (Future_Global_AI_PET) provides a high-resolution (30 arc-seconds) global raster dataset of average monthly and annual potential evapotransipation (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CMIP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:· All Models: includes all of the 22 ESM, as available within a particular SSP.· High Risk: includes 5 ESM identified as projecting the highest increases in temperature and precipitation and lying outside and significantly higher than the majority of estimates.· Majority Consensus: includes 15 ESM, that is, all available ESM excluding the ESM in the “High Risk” category, and those missing data across all of the 4 SSP. Further herein referred to as the “Consensus” category.These geo-spatial datasets have been produced with the support of Euro-Mediterranean Center on Climate Change, IAFES Division; Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science; CIFOR-ICRAF China Program, World Agroforestry (CIFOR-ICRAF) and the National Biodiversity Future Center (NBFC).These datasets are provided under a CC_BY 4.0 License (please attribute), in standard GeoTiff format, WGS84 Geographic Coordinate System, 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change, among other global, regional, national, and local concerns.The Future_Global_AI_PET is available online from the Science Data Bank (ScienceDB) at: https://doi.org/10.57760/sciencedb.nbsdc.00086Previous versions of the Global Aridity Index and PET Database are available online here:https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/6Technical questions regarding the datasets can be directed to Robert Zomer: r.zomer@mac.com or Antonio Trabucco: antonio.trabucco@cmcc.it Methods:Based on the results of comparative validations, the Hargreaves model has been evaluated as one of the best fit to model PET and Aridity index globally with the available high resolution downscaled and bias corrected climate projections and chosen for the implementation of the Global-AI_PET- CMIP6 Future Projections. This method performs almost as well as the Penman-Monteith method, but requires less parameterization, and has significantly lower sensitivity to error in climatic inputs (Hargreaves and Allen, 2003). The currently available downscaled CMIP6 projections (available from WorldClim) do provide fewer climate variables idoneous for implementation of temperature-based evapotranspiration methods, such as the Hargreaves method. Hargreaves (1985, 1994) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of the atmosphere) to calculate ET0, as shown below: PET = 0.023 * RA * (Tmean + 17.8) * TD0.5where RA is extraterrestrial radiation at the top of the atmosphere, TD is the difference between mean maximum temperatures and mean minimum temperatures (Tmax - Tmin), and Tmean is equal to Tmax + Tmin divided by 2. The Hargreaves equation has been implemented globally on a per grid cell basis at 30 arc seconds resolution (~ 1km2 at the equator), in ArcGIS (v11.1) using Python v3.2 (see code availability section) to estimate PET/AI globally using future projections provided by the CMIP6 collaboration. The data to parametrize the equation were obtained from the Worldclim (worldclim.org) online data repository, which provides bias-corrected downscaled monthly values of minimum temperature, maximum temperature, and precipitation for 25 CMIP6 Earth System Models (ESMs), across four Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. PET/AI was estimated for two historical periods, WorldClim 1.4 (1960-1990) and WorldClim 2.1 (1970-2000), representing on average a decades change, by applying the Hargreaves methodology described above. Similarly, PET/AI was estimated for two future time periods, namely 2021-2040 and 2041-2060, for each of the 25 models across their respective four SSP scenarios (126, 245, 370,585). Aridity Index Aridity is often expressed as an Aridity Index, comprised of the ratio of precipitation over PET, and signifying the amount of precipitation available in relation to atmospheric water demand and quantifying the water (from rainfall) availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture. The AI for the averaged time periods has been calculated on a per grid cell basis, as: AI = MA_Prec/MA_PETwhere: AI = Aridity Index MA_Prec = Mean Annual Precipitation MA_PET = Mean Annual Reference EvapotranspirationUsing the mean annual precipitation (MA_Prec) values obtained from the CMIP6 climate projections, while ET0 datasets estimated on a monthly average basis by the method described above were aggregated to mean annual values (MA_PET). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.Multi-Model Averaged EnsemblesBased upon the distribution of the various scenarios along a gradient of their projected temperature and precipitation estimates for the each of the four SSP and two future time period, three multi-model ensembles, each articulated by their four respective SSPs, were identified. The three parameters of monthly minimum temperature, monthly maximum temperature and monthly precipitation for ESM’s included within each of these ensemble categories were averaged for each of their respective SSPs. These averaged parameters were then used to calculate the PET/AI as per the above methodology.Code Availablity:The algorithm and code in Python used to calculate PET and AI is available on Figshare at this link below:https://figshare.com/articles/software/Global_Future_PET_AI_Algorithm_Code_Python_-_Calculate_PET_AI/24978666DATA FORMATPET datasets are available as monthly averages (12 datasets, i.e. one dataset for each month, averaged over the specified time period) or as an annual average (1 dataset) for the specified time period. Aridity Index grid layers are available as one grid layer representing the annual average over the specified period. The following nomenclature is used to describe the dataset: Zipped Files - Directory Names refer to: Model_SSP_Time-PeriodFor example: ACCESS-CM2_126_2021-2040.zip Model: ACCESS-CM2 / SSP:126 / Time-Period: 2021-2040Prefix of Files (TIFFs) is either:pet_ for PET layers aridity_index for Aridity Index (no suffix)Suffix For PET Files is either:1, 2, ... 12 Month of the yearyr Yearly averagesd Standard DeviationExamples:pet_02.tif is the PET average for the month of February.pet_yr.tif is the PET annual average.’pet_sd.tif is the standard deviation of the annual PETaridity_index.tif is the annual aridity index. The PET values are defined as total mm of PET per month or per year. The Aridity Index values are unit-less.The geospatial dataset is in geographic coordinates; datum and spheroid are WGS84; spatial units are decimal degrees. The spatial resolution is 30 arc-seconds or 0.008333 degrees. Arc degrees and seconds are angular distances, and conversion to linear units (like km) varies with latitude, as below:The Future-PET and Future-Aridity Index data layers have been processed and finalized for distribution online as GEO-TIFFs. These datasets have been zipped (.zip) into monthly series or individual annual layers, by each combination of climate model/scenarios, and are available for online access. Data Storage HierarchyThe database is organized for storage into a hierarchy of directories (see ReadMe.doc):( Individual zipped files are generally about 1 GB or less) Associated Peer Reviewed Journal Article:Zomer RJ, Xu J, Spano D and Trabucco A. 2024. CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. Open Research Europe 4:157 https://doi.org/10.12688/openreseurope.18110.1For further info, please refer to these earlier paper describing the database and methodology:Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409.Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80.Trabucco, A.; Zomer, R. J.; Bossio, D. A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation through Afforestation / Reforestation: A global analysis of hydrologic

The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET0) equation.

Aridity Index represent the ratio between precipitation and ET0, thus rainfall over vegetation water demand (aggregated on annual basis). Under this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions. The Aridity Index values reported within the Global-AI geodataset have been multiplied by a factor of 10,000 to derive and distribute the data as integers (with 4 decimal accuracy). This multiplier has been used to increase the precision of the variable values without using decimals. The Readme File is provided with a detailed description of the dataset files, and the following article for a description of the methodology and a technical validation.The Global-AI_PET_v3 datasets are provided for non-commercial use in standard GeoTiff format, at 30 arc seconds or ~ 1km at the equator.

Grid of estimated aridity with a spatial resolution of 10 arc minutes. This dataset represents average yearly precipitation divided by average yearly potential evapotranspiration, an aridity index defined by the United Nations Environmental Programme (UNEP). The classification of the aridity index is: - Classification Aridity Index Global land area - Hyperarid AI < 0.05 - 7.5% of the global land area - Arid 0.05 < AI < 0.20 - 12.1% of the global land area - Semi-arid 0.20 < AI < 0.50 - 17.7% of the global land area - Dry subhumid 0.50 < AI < 0.65 - 9.9% of the global land area

A figure created for "Roots of Resilience: Using Trees to Mitigate Rising Heat in Arid, Frontline Communities,” a TNC Tackle Climate Change report published in 2024 by Rob McDonald, et al.

Global aridity index from 1970 to 2100 per land grid cell. Calculated as annual precipitation / annual potential evaporation. The IMAGE-team would appreciate to be involved in projects using the data. SSP scenarios are documented in: Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm, https://doi.org/10.1016/j.gloenvcha.2016.05.008

The aridity index also known as the dryness index is the ratio of potential evapotranspiration to precipitation. The aridity index indicates water deficiency. The aridity index is used to classify locations as humid or dry. The evaporation ratio (evaporation index) on the other hand indicates the availability of water in watersheds. The evaporation index is inversely proportional to water availability. For long periods renewable water resources availability is residual precipitation after evaporation loss is deducted. These two ratios provide very useful information about water availability. Understating the powerful potential of the aridity index and evaporation ratio, this app is developed on the Google Earth Engine using NLDAS-2 and MODIS products to map temporal variability of the Aridity Index and Evaporation ratio over CONUS. The app can be found at https://cartoscience.users.earthengine.app/view/aridity-index.

1) This data is the aridity index data calculated based on the latest simulation results of 22 cmip6 coupled global climate models; 2) The calculation formula is p / PET (ratio of precipitation to potential evapotranspiration), and the calculation of pet is based on PM formula; 3) The monthly data of the Great Lakes region of Central Asia from January 1900 to December 2100, including ssp2-4.5 and ssp5-8.5, with a resolution of 1 degree * 1 degree; 4) The data can be used to analyze the distribution and evolution of dry and wet pattern in the Great Lakes region of Central Asia under medium and high emission scenarios in the future. The data has been converted into 3-mongth running means.

Overview of the data set used for the YIELD and SOC meta-analyses, with columns for the study*country*site combination index (id), study (reference), aridity index class (AI class)*, time span of the study’s experiment (time span), number of yield data (nYield), number of SOC data associated with C and N rates (nSOC C input and nSOC N input, respectively) (*) The site’s aridity index was extracted from the CGIAR-CSI Global-Aridity and Global-PET Database [51].

Context

I made this dataset while performing Integrated Valuation of Ecosystem Services and Tradeoffss (InVEST) models of wetlands in India.

Content

This dataset is a collection of Geographic Information System (GIS) data sourced from various public domains. It includes shapefiles, image raster files, etc which can are primarily developed with the aim of using with GIS software such as ArcGIS Pro, QGIS, etc. Most of the datasets are global in nature with some, like the OpenStreetMap data pertaining to India only. The data is as described below:

This nc file contains F-IDF values at streamflow gage sites over the CONUS.

The header of this netcdf file is shown below:

netcdf flashiness_dataset { dimensions: stnid = 6987 ; duration = 6 ; frequency = 6 ; variables: double flashiness(stnid, duration, frequency) ; flashiness:description = "It is calculated with the slope of a given time window (cfs/15min*len(window)), divided by the drainage area (sqkm) and convert to a standardized unit" ; flashiness:units = "mm/h^2" ; flashiness:_FillValue = NaN ; area (sqkm)" ; string stnid(stnid) ; int64 frequency(frequency) ; int64 duration(duration) ; double lon(stnid) ; lon:_FillValue = NaN ; double lat(stnid) ; lat:_FillValue = NaN ; double area(stnid) ; area:_FillValue = NaN ; area:units = "sqkm" ; double data_length(stnid) ; data_length:_FillValue = NaN ; data_length:units = "years" ; data_length:description = "years of available data from USGS 15-min observation" ; double dor_pc_pva(stnid) ; dor_pc_pva:_FillValue = NaN ; dor_pc_pva:units = "percent" ; dor_pc_pva:long_name = "degree of regulation" ; dor_pc_pva:description = "degree of regulation retrieved from hydrobasin V10 level 12" ; double slp_dg_uav(stnid) ; slp_dg_uav:_FillValue = NaN ; slp_dg_uav:units = "degree" ; slp_dg_uav:long_name = "Terrain slope" ; slp_dg_uav:description = "Terrain slope of total watershed upstream of a pour point" ; double sgr_dk_sav(stnid) ; sgr_dk_sav:_FillValue = NaN ; sgr_dk_sav:units = "degree" ; sgr_dk_sav:long_name = "Stream gradient" ; sgr_dk_sav:description = "the stream gradient was calculated as the ratio between the elevation drop within the river reach (i.e. the difference between min. and max. elevation along the reach) and the length of the reach." ; double tmp_dc_uyr(stnid) ; tmp_dc_uyr:_FillValue = NaN ; tmp_dc_uyr:units = "degree celsius" ; tmp_dc_uyr:long_name = "Annual mean air temperature" ; tmp_dc_uyr:description = "Annual mean air temperature retrieved from WorldClim, station-based monitoring network" ; double pre_mm_uyr(stnid) ; pre_mm_uyr:_FillValue = NaN ; pre_mm_uyr:units = "mm" ; pre_mm_uyr:long_name = "Annual mean precipitation" ; pre_mm_uyr:description = "Annual mean precipitation retrieved from WorldClim, station-based monitoring network and interpolated by the thin-plate smoothing spline algorithm" ; double pet_mm_uyr(stnid) ; pet_mm_uyr:_FillValue = NaN ; pet_mm_uyr:units = "mm" ; pet_mm_uyr:long_name = "Annual mean potential evaporation" ; pet_mm_uyr:description = "Annual mean PET based on termperature inputs from WorldClim and a simple temperature-based transfer model" ; double aet_mm_uyr(stnid) ; aet_mm_uyr:_FillValue = NaN ; aet_mm_uyr:units = "mm" ; aet_mm_uyr:long_name = "Annual mean actural evaporation" ; aet_mm_uyr:description = "Annual mean AET based on the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ; double ari_ix_uav(stnid) ; ari_ix_uav:_FillValue = NaN ; ari_ix_uav:units = "" ; ari_ix_uav:long_name = "Global aridity index" ; ari_ix_uav:description = "The Global Aridity Index (Global-Aridity) is modeled using data from WorldClim as input parameters" ; double cmi_ix_uyr(stnid) ; cmi_ix_uyr:_FillValue = NaN ; cmi_ix_uyr:units = "" ; cmi_ix_uyr:long_name = "Global climate moisture index" ; cmi_ix_uyr:description = "The Climate Moisture Index (CMI) was derived from the annual precipitation (P) and potential evapotranspiration (PET) datasets as provided by the WorldClim v1.4" ; double snw_pc_uyr(stnid) ; snw_pc_uyr:_FillValue = NaN ; snw_pc_uyr:units = "%" ; snw_pc_uyr:long_name = "snow cover extent" ; snw_pc_uyr:description = "data obtained from The MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1)" ; double cly_pc_uav(stnid) ; cly_pc_uav:_FillValue = NaN ; cly_pc_uav:units = "%" ; cly_pc_uav:long_name = "clay fraction in soils" ; cly_pc_uav:description = "Data obtained from SoilGrids1km" ; double slt_pc_uav(stnid) ; slt_pc_uav:_FillValue = NaN ; slt_pc_uav:units = "%" ; slt_pc_uav:long_name = "silt fraction in soils" ; slt_pc_uav:description = "Data obtained from SoilGrids1km" ; double snd_pc_uav(stnid) ; snd_pc_uav:_FillValue = NaN ; snd_pc_uav:units = "%" ; snd_pc_uav:long_name = "sand fraction in soils" ; snd_pc_uav:description = "Data obtained from SoilGrids1km" ; double swc_pc_uyr(stnid) ; swc_pc_uyr:_FillValue = NaN ; swc_pc_uyr:units = "%" ; swc_pc_uyr:long_name = "Soil water content" ; swc_pc_uyr:description = "Soil water content is provided as part of the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ; double kar_pc_use(stnid) ; kar_pc_use:_FillValue = NaN ; kar_pc_use:units = "%" ; kar_pc_use:long_name = "Karst area extent" ; kar_pc_use:description = "The World Map of Carbonate Rock Outcrops represents an upper limit of the area of exposed karst terrain." ; double ero_kh_uav(stnid) ; ero_kh_uav:_FillValue = NaN ; ero_kh_uav:units = "kg/hectare per year" ; ero_kh_uav:long_name = "Soil erosion" ; string ero_kh_uav:description = "GloSEM erosion estimates were produced with a high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data" ; double pop_ct_usu(stnid) ; pop_ct_usu:_FillValue = NaN ; pop_ct_usu:units = "count" ; pop_ct_usu:long_name = "Population count" ; pop_ct_usu:description = "The Gridded Population of the World (GPW) database." ; double urb_pc_use(stnid) ; urb_pc_use:_FillValue = NaN ; urb_pc_use:units = "%" ; urb_pc_use:long_name = "Urban extent" ; urb_pc_use:description = "The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time" ; double rdd_mk_uav(stnid) ; rdd_mk_uav:_FillValue = NaN ; rdd_mk_uav:units = "meters per km^2" ; rdd_mk_uav:long_name = "Road density" ; rdd_mk_uav:description = "The Global Roads Inventory Project (GRIP) dataset" ; double dis_m3_pyr(stnid) ; dis_m3_pyr:_FillValue = NaN ; dis_m3_pyr:units = "cms" ; dis_m3_pyr:long_name = "Natural discharge" ; dis_m3_pyr:description = "Simulated discharge by WaterGAP" ; double run_mm_syr(stnid) ; run_mm_syr:_FillValue = NaN ; run_mm_syr:units = "mm" ; run_mm_syr:long_name = "Land surface runoff" ; run_mm_syr:description = "Simulated land surface runoff by WaterGAP" ; double inu_pc_umx(stnid) ; inu_pc_umx:_FillValue = NaN ; inu_pc_umx:units = "%" ; inu_pc_umx:long_name = "Annual maximum inundation extent " ; inu_pc_umx:description = "GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds" ; double ria_ha_usu(stnid) ; ria_ha_usu:_FillValue = NaN ; ria_ha_usu:units = "hectares" ; ria_ha_usu:long_name = "River area" ; ria_ha_usu:description = "River area was calculated using the the HydroSHEDS database at 15 arc-second resolution. It is based on a rating curve" ; double riv_tc_usu(stnid) ; riv_tc_usu:_FillValue = NaN ; riv_tc_usu:units = "1000 m^3" ; riv_tc_usu:long_name = "River volume" ; riv_tc_usu:description = "River volume was calculated using the the HydroSHEDS database at 15 arc-second resolution." ; double gwt_cm_sav(stnid) ; gwt_cm_sav:_FillValue = NaN ; gwt_cm_sav:units = "cm" ; gwt_cm_sav:long_name = "Groundwater table depth" ; gwt_cm_sav:description = "Fan et al. (2013) compiled global observations of water table depth from government archives and literature" ; double ele_mt_uav(stnid) ; ele_mt_uav:_FillValue = NaN ; ele_mt_uav:units = "m" ; ele_mt_uav:long_name = "Elevation" ; ele_mt_uav:description = "Elevation above mean sea level based on EarthEnv-DEM90" ; double glc_pc_u01(stnid) ; glc_pc_u01:_FillValue = NaN ; glc_pc_u01:units = "%" ; glc_pc_u01:long_name = "Land cover area extent" ; glc_pc_u01:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ; double glc_pc_u02(stnid) ; glc_pc_u02:_FillValue = NaN ; glc_pc_u02:units = "%" ; glc_pc_u02:long_name = "Land cover area extent" ; glc_pc_u02:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ; double glc_pc_u03(stnid) ; glc_pc_u03:_FillValue = NaN ; glc_pc_u03:units = "%" ; glc_pc_u03:long_name = "Land cover area extent" ; glc_pc_u03:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ; double glc_pc_u04(stnid) ; glc_pc_u04:_FillValue = NaN ; glc_pc_u04:units = "%" ; glc_pc_u04:long_name = "Land cover area extent" ; glc_pc_u04:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;

This dataset contains 2001-2020 burned areas and climate variables for three regions with Mediterranean climates: South America from 31-46 degrees South, including Chile and the forested Andean region of Argentina; the western United States from 33-49 degrees North from the coast extending to the eastern extent of forest, and the Iberian Peninsula, including all of Spain and Portugal.

Burned areas are polygon shapefiles for all regions except Chile, for which the burn area is represented in a point shapefile. The data sources for the fire shapefiles are: Chile: unpublished, originally from Corporación Nacional Forestal (CONAF) and compiled by Miranda Argentina: unpublished, compiled by Diego Mohr-Bell and others at Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP) North America: NIFC 2023 Iberian Peninsula: EFFIS 2022

All of the fire shapefiles are contained within the zip folder fire_areas, and the individual regions are ch_fire (Chile), ar_fire (Argentina), na_fire (North America), ib_fire (Iberian Peninsula). The attributes of the shapefiles are the year and the fire area in square kilometers. For Chile, the fire start dates were documented. If the fire started in June-December, the year assigned is advanced by 1 from the original year. This is because the summer fire season straddles the calendar year boundary, and the fire year is assigned based on the year with most of the summer season. For Argentina, the end dates of the fire were available, so these end dates were used to assign the fire year.

Annual summaries of fire area and climate variables are provided in the fire_ann_all.csv file. The columns in this file are: year wetdryzone: dry if mean annual aridity index <1; wet if mean annual aridity index >1 cont: location, either Iberian Peninsula, North America, or South America area_km2: total burned area in square km AIann: annual aridity index calculated as total precipitation over total potential evapotranspiration AIjas: summer aridity index for July-September in northern hemisphere; January-March, southern hemisphere vpd: mean annual vapor pressure deficit (kPa) vpd_jas: mean summer vapor pressure deficit (kPa) def: mean annual climatic water deficit (mm) def_jas: summer climatic water deficit (mm)

Climate data were obtained from TerraClimate (Abatzoglou et al. 2018).

Mean annual summaries of fire and climate data by aridity index zone are provided in the file AI_bins_meanannual.csv. AI zones/bins are in increments of 0.2. Columns are: cont: location, either Iberian Peninsula, North America, or South America AI_max: maximum AI for the AI zone. For example, if the value is 0.2, the zone is from AI=0 to AI=0.2 zone_area: total area of the climate zone in square kilometers forest_area: total area forested in 2000 in square kilometers (Potapov et al. 2022) fire_area: total area burned from 2001-2020 in square kilometers (same sources as fire shapefiles) frac_forest: fraction of climate zone area that is forested frac_fire: fraction of the climate zone area that was burned mean_patch_area: mean size of forest patches identified within each AI zone in square kilometers fwi: mean fire weather index from the European Center for Medium-range Weather Forecasts (Vitolo et al. 2020)

Original data sources: Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. (2018), Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 170191.

European Forest Fire Information System EFFIS (2022). Burnt area mapped using Sentinel-2/MODIS images. Accessed September, 2022.

NIFC 2023. Interagency Fire Perimeter History https://data-nifc.opendata.arcgis.com/search?tags=Category%2Chistoric_wildlandfire_opendata, downloaded 3/25/23.

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing, 3.

Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.

Thornthwaite's formula is globally an optimum candidate for large scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has the lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (former FAO-56), which is the most data demanding method and is commonly used as benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature and Er data of the period 1950-2000 at 30 arc-sec resolution (~1 km at equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er/Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep <45 mm month-1 because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe, California-USA and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0 mm m-1 for monthly and from 388.8 to 174.8 mm y-1 for annual step estimations compared to Ep using as benchmark the values of Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations and the results showed that the correction of Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63% to 76% for the case of Thornthwaite classification, and from 76% to 93% for the case of UNEP classification. […]

Fungus-growing termites (subfamily Macrotermitinae) have a distinct geographical distribution, found only in the old-world tropics. Where present, they are considered to be major contributors and regulators of decomposition, with consumption rates often greater than other termite groups. This study sought to understand the relative roles of termite distribution (specifically the presence or absence of fungus-growing termites) and climatic variables (mean annual temperature, mean annual precipitation and mean annual aridity) on global patterns in deadwood decay. To answer this question, we added new salient data to an existing dataset on global wood decay by Zanne et al. (2022) available at https://doi.org/10.6084/m9.figshare.19920416.v1. We filtered the data to only include sites where termites were present and thus analysed a dataset containing 102 sites across 16 countries. We found that termite-driven decay of deadwood increased with aridity but was higher in sites with fungus-growing termites than sites without fungus-growing termites. Our results also showed that the relative role of fungus-growing termites increased with aridity, as rates of wood-discovery by termites increased with aridity but only in sites where fungus-growing termites were present. Our findings indicate that the inclusion of biogeographical differences in termite distribution could potentially alter global estimates of deadwood turnover. This repository contains new datasets on termite-driven deadwood decay of Pinus radiata wood blocks and code used for all data analyses and production of figures. Methods All experimental data collected on the decay of wood blocks in the datasets 'new_global_wood_decay.csv' and 'pine_shade.csv' followed a standard protocol outlined by Cheesman et al., (2018), https://doi.org/10.1111/aec.12561. Data in 'new_global_wood_decay.csv' was collected from 140 sites across 20 countries by different researchers. A complete description of how data was collected in this dataset is provided in Zanne et al., (2022), https://www.science.org/doi/10.1126/science.abo3856. All researchers followed the same method except for one difference: data from the original Zanne et al., (2022) dataset covered wood blocks with 70% green shade cloth to reduce solar radiation degradation of mesh bags while new data did not use green shade cloth. Environmental parameters for each site were extracted from global databases, mean annual temperature (MAT) and mean annual precipitation (MAP) was extracted from WorldClim, https://doi.org/10.1002/joc.5086, and mean annual aridity (MAA) from the Global-Aridity Index by Zomer et al. (2022), https://doi.org/10.1038/s41597-022-01493-1. Data in 'pine_shade.csv' was used to analyse if the inclusion of shade cloth had any affect on decay rates of wood blocks. In the dataset 'pine_shade.csv' the decay of Pinus radiata wood blocks is measured in a rainforest site (named DRO) and savanna site (named PNW) in Queensland, Australia, following the methods described in Wijas et al., (2024), https://doi.org/10.1111/1365-2435.14494, but with the inclusion of a 70% green shade cloth. The sites in 'pine_shade.csv' are the same sites named ‘wet rainforest’ and ‘dry savanna’ in Wijas et al. (2024) and site descriptions are also provided in Clement et al., (2021), https://doi.org/10.3389/fevo.2021.657444. For analyses, we processed the 'new_global_wood_decay' dataset to only include sites where termites were known to be present (102 of the 140 sites). The presence of fungus-growing termites (subfamily Macrotermitinae) was assigned to sites if sites were within the geographical distribution of fungus-growing termites, i.e. if they were in Afrotropical, Oriental, or Malagasy realms. Additionally, we checked for the presence of fugus-growing termites by reviewing published termite transect surveys at the same sites and through personal communication with researchers based at sites. Termite decomposition of deadwood was considered a two step process: first we looked at termite discovery of deadwood, wood blocks were considered discovered by termites if researchers had noted imported soil on wood blocks by termites, termite related damage to wood blocks or termite presence on wood blocks; second we looked at decay rates of wood blocks discovered by termites. Decay of undiscovered wood blocks was attributed primarily to microbial decay. Decay of discovered wood blocks includes microbial decay but is refered to as termite-driven decay. We compared termite discovery of deadwood and termite-driven decay rates in sites where fungus-growing termites were present and absent. We used R software to run linear regression models to compare differences in termite discovery and termite-driven decay rates with climatic variables (MAT, MAP and MAA) and with the presence or absence of fungus-growing termites.

Data used to compose the Figure 1 and the Table S1 of the paper Biogeography of Global Drylands, by Maestre et al. (2021).

How much terrestrial precipitation is used by vegetation and how much runs off, represents central issues in hydrologic science, ecology, climate change, and even geopolitics. We present a theory for the water balance to predict the fractional change in streamflow due to given fractional changes in temperature and precipitation. The theory involves a single parameter whose value is derived under the conditions of neither energy- nor water-limitations and, therefore, is not an adjustable parameter. By comparison with extensive data for precipitation elasticity εp at global scale, we find that the theory captures the key trends of the variations of the median value of εp with the aridity index AI . In contrast to a shortcoming of the classical Budyko phenomenology, namely, convergence to εp = 4 for large AI , our theory yields a value of 2 for the median value of εp for all AI > 1, in accord with the data for major river basins, as well as with the median value of summaries of global and continental data sets. Incorporating in the theory the effects of annual changes in water storage leads to the ability to predict the range of observed values of the elasticity as a function of the aridity index, or its inverse, the humidity index, as well as the run-off ratio. When changes in storage are neglected, the theory yields more accurate predictions for major river drainages than for small watersheds, particularly if the large basin spans various climate regimes and, as such, an integration over climates tends to reduce relative changes in the storage.

These are the required data to do all analyses in Smith et al. 2022 in Oecologia using the assembled database across gradients. Included are the richness, evenness, and site level abiotic data. Paper abstract: We sought to understand the role that water availability (expressed as an aridity index) plays in determining regional and global patterns of richness and evenness, and in turn how these water availability-diversity relationships may result in different richness-evenness relationships at regional and global scales. We examined relationships between water availability, richness and evenness for eight grassy biomes spanning broad water availability gradients on five continents. Our study found that relationships between richness and water availability switched from positive for drier (South Africa, Tibet and USA) vs. negative for wetter (India) biomes, though were not significant for the remaining biomes. In contrast, only the India biome showed a significant relationship between water availability and evenness, which was negative. Globally, the richness-water availability relationship was hump-shaped, however, not significant for evenness. At the regional scale, a positive richness-evenness relationship was found for grassy biomes in India and Inner Mongolia, China. In contrast, this relationship was weakly concave-up globally. These results suggest that different, independent factors are determining patterns of species richness and evenness in grassy biomes, resulting in differing richness-evenness relationships at regional and global scales. As a consequence, richness and evenness may respond very differently across spatial gradients to anthropogenic changes, such as climate change.

This dataset is the original data of the paper “Predicting the spatial distribution of invasive species richness using the combination of machine learning and geostatistical algorithms” .The dataset includes forest survey data, eco-climatic data, and topographic and geomorphological data. Among them, the forest survey data comes from the US Forest Inventory and Analysis (FIA) program, which collects information on the occurrence and distribution of invasive plants in all public and private forests in the United States. The ecological and climatic data includes 31 climatic variables, extracted from the WorldClim Global Climate Data (Version 2.1, https://www.worldclim.org/data/worldclim21.html). The topographic and geomorphological data includes three variables: elevation, soil carbon content, and aridity index. Among these, the elevation data comes from WorldClim Global Climate Data, soil carbon content data is extracted from the International Soil Reference and Information Centre (ISRIC-World Soil Information, https://www.isric.org/), and the aridity index is extracted from the Global Aridity Index (http://www.cgiar-csi.org/data). The definitions of each variable are as follows: prov_ID: Eco-region code; LAT/LON: Decimal latitude/longitude; Seasonability: SD of mean annual temperature; Alt: Altitude (m); PLT_TPA/Tpha: Trees per acre/hectare; RelDen: Successional development proportion (0–1); prpfor: Forested plot proportion; plt_drybio_adj/ha: Native tree biomass (English tons/acre/hectare); native_spp: Native tree species richness; PD_all: Phylogenetic diversity of tree species; PSV_all/var: Phylogenetic variability and variance; PSR_all/var: Phylogenetic richness and variance; PSE_all/PSC_all: Phylogenetic evenness/clustering; InvSpRichness: Invasive species richness; soilcarbon: 0–20 cm soil carbon content; aridity: Precipitation/evapotranspiration ratio; BIO1–BIO19: Standard climatic metrics (e.g., temperature, precipitation); vaprmin/max/range/avg: Water vapor pressure metrics (kPa); sradmin/max/range/avg: Solar radiation metrics (KJ/m²/day); windmin/max/range/avg: Wind speed metrics (m/s).

This data is the aridity index (AI) under the rcp4.5 scenario. AI data is the ratio of precipitation to potential evapotranspiration. This data is calculated by the average of 14 models. These 14 modes are canesm2; ccsm4; cnrm-cm5; csiro-mk3-6-0; giss-e2-r; hadgem2-cc; hadgem2-es; inmcm4; ipsl-cm5a-lr; miroc5; miroc-esm-chem; miroc-esm; mpi-esm-lr; mri-cgcm3. The spatial resolution is 2 * 2 degrees, and the temporal resolution is from January 2020 to December 2099. This data set can be used to analyze the future dry and wet change scenarios in the Great Lakes region of Central Asia, as well as the dry and wet past and pattern in other regions of the world under the future scenarios.