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14 results for “SPEI”
Long-term climate indices (SPEI and scPDSI) derived from monthly meteorology data collected at USHCN stations in the northern Chihuahuan Desert of the United States, 1911-2021
Drought indices — Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI) —where derived from 9 United States Historical Climate Network (USHCN) stations on the Chihuahuan Desert in North America for this dataset. USHCN is a subset of the NOAA Cooperative Observer Program (COOP) Network, which consists of selected sites based on spatial coverages and completeness of data. Monthly precipitation depths, minimum, maximum and mean temperature were pulled from the dataset. These drought indices were derived using the SPEI package and scPDSI packages in R. Potential evapotranspiration was also calculated in R using the Thornthwaite method. All 9 sites are within the bounds of the Chihuahuan Desert in the state of New Mexico, with a single site (EL PASO) in the state of Texas.
Derived SPEI and vapor pressure deficit for 15 NPP study sites on the Jornada Basin, 2013-ongoing
Standardized Precipitation Evapotranspiration Index (SPEI) and minimum, maximum, and average vapor pressure deficit (VPD) were calculated from meteorological data (temperature, precipitation, and relative humidity) from the 15 net primary production (NPP) study locations on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC) lands in southern New Mexico, U.S.A.
Monthly SPEI dataset at 3-, 6-, and 12-month time scales in the Amazon River Basin
<p>SPEI datasets derived from the Global SPEI database webpage and developed by Vicente-Serrano et al. (2010). This product is disseminated by the Spanish National Research Council (CSIC) for the period from January 1901 to December 2018 (118 years, version 2.6). The SPEI datasets at 3-, 6-, and 12-month time scales are gridded over a 0.50-degree grid on the Amazon River Basin. A total of 1416 GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format, one per month, are provided.</p>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Indonesia, monthly Standardized Precipitation-Evapotranspiration Index (SPEI) blend 1960 - 2021
<p>IDN_CLI_SPEI_blend_0p042_1961_2021 is currently the only comprehensive high resolution Indonesia gridded historical dataset of SPEI blend and available for public.</p> <p>The SPEI - https://spei.csic.es/ is an extension of the widely used SPI. The SPEI is designed to take into account both precipitation and potential evapotranspiration (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.</p> <p>The IDN_CLI_SPEI_blend_0p042_1960_2021 is derived using precipitation and potential evapotranspiration from TerraClimate data - https://www.climatologylab.org/terraclimate.html, it has 0.042 degree gridded resolution, a monthly and available from 1958 to 2021. The calibration period is January 1961 to December 2020. The starting date of the dataset is 1960 in order to provide common information across the different SPEI time-scales.</p> <p>The SPEI blend integrate several SPEI scales into a single product, combine 3-, 6-, 9-, 12- and 24-month SPEI to estimate the overall dry/wet condition. </p> <p>The SPEI processed using climate_indices, an open source Python library providing reference implementations of commonly used climate indices. https://pypi.org/project/climate-indices/</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Daily SPEI dataset in China from 1980 to 2100
<p>The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs' simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO<sub>2</sub> concentration under high emission scenarios in the mid- and late- future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.</p>
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
Daily SPEI dataset in China from 1980 to 2100
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SPEI-GD: The first global multi-scale daily SPEI dataset for 1983-2020
<p>The global daily SPEI dataset (SEPI-GD) at 0.25° spatial resolution form 1982 to 2021. This depository includes the five files of the daily SPEI data with five time scales (5, 30, 90, 180, and 360 days). The calculation based on ERA5's precipitation and Singer's potential evapotranspiration. All data are geographic latitude-longitude projection and NetCDF format. See paper for detailed explanation: Liu X, Yu S, Yang Z, et al. The first global multi-timescale daily SPEI dataset from 1982 to 2021[J]. Scientific Data, 2024, 11(1): 223.</p>
SPEI and SSI time-series datasets for the Indus Basin of Pakistan
<p>The file contains 2 datasets namely:<br> 1-Standard Precipitation and Evapotranspiration Index (SPEI) for four catchments (Chenab,Jhelum, Indus and Kabul) of the Indus Basin<br> 2-Standard Streamflow Index (SSI) for four catchments of the Indus Basin.</p> <p>SPEI:<br> The Standard Precipitation and Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010a) is the indicator used <br> for quantification and monitoring of meteorological droughts.<br> SPEI is calculated for the four catchments (Chenab,Jhelum, Indus and Kabul) at each catchment level using gridded climate data from the CRU data set (Harris et al., 2020). <br> For catchment level SPEI calculations, gridded precipitation and Potential Evapotranspiration values extracted from CRU dataset are averaged over the catchment. A map(study_area) delineating the four catchments is provided.</p> <p>SSI:<br> The Standard Streamflow Index (SSI) (Nalbantis and Tsakiris, 2009; Modarres, 2007) is used (also called the Streamflow<br> Drought Index and the Streamflow Runoff Index) to quantify and analyze hydrological droughts.SSIs are computed at catchment outlets as shown in the attached map (study_area). Streamflow station names are Marala(for Chenab),Mangla (Jhelum), Tarbela(for Indus) and Nowshera(for Kabul).</p> <p><br> Units: Both the datasets are dimensionless </p> <p><br> References:</p> <p>Harris, I. C. and Jones, P. D.: CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018), https://doi.org/10.5285/10d3e3640f004c578403419aac167d82, university of East Anglia Climatic Research Unit; Centre for Environmental Data Analysis, 2020.</p> <p>Modarres, R.: Streamflow drought time series forecasting, Stochastic Environmental Research and Risk Assessment, 21, 223–233, https://doi.org/10.1007/s00477-006-0058-1, 2007.</p> <p>Nalbantis, I. and Tsakiris, G.: Assessment of hydrological drought revisited, Water Resources Management, 23, 881–897,<br> https://doi.org/10.1007/s11269-008-9305-1, 2009.</p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to GlobalWarming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, https://doi.org/10.1175/2009JCLI2909.1, 2010a.</p>
Mean annual water yield versus SPEI value, geodetector results and land cover type corresponding water yield
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Global SPEI over the last millennium calculated from monthly climate variables of isotope-enabled climate model simulations
<p>This data is Standardized precipitation evapotranspiration index (SPEI) for different time scales from 851 to 2000. The time scale is partially omitted due to the upload capacity, but it is from 1 month to 48 months at maximum.</p> <p>[Structure]</p> <p>Spatial resolution: 1.9(Treated the earth as a 94x192 grid)</p> <p>Time resolution: 1 month(1150 year = 13800 month)</p> <p>This data is one-dimensional. When using, please slice to (13800,94,192) using python, etc.</p> <p>The method for obtaining the grid for the survey area is as follows.</p> <p>north latitude: (94/180)*(90+lat)</p> <p>south latitude: (94/180)*(90-lat)</p> <p>east longitude: (192/360)*longitude</p> <p>west longitude: (192/360)*(360-longitude)</p> <p> </p> <p>This SPEI is calculated using the SPEI package in R. The settings for this package are as follows.</p> <p>Setting</p> <p>kernel: type = rectangular, shift =0</p> <p>distribution: log-Logistic</p> <p>fit: ub-pwm</p> <p> </p> <p>The data used for this SPEI calculation are climate data reconstructed by data assimilation using isotope ratios. Please refer to the following page for details of the data.</p> <p>Details</p> <p>[Title]</p> <p>Data assimilation products by using multiple climate model simulations and different combinations of proxies</p> <p>[url]</p> <p>https://zenodo.org/record/5760209#.ZAs2qxXP1D9</p>
Daily SPEI-90 Days Values – Elevation Below 1500 m a.s.l., 1950–2023
<table> <tbody> <tr> <td> <p><strong>Science Case Name</strong> </p> </td> <td> <p>Multi-Hazards in the Downstream Area of the Adige River Basin. </p> </td> </tr> <tr> <td> <p><strong>Dataset Name/Title</strong> </p> </td> <td> <p>Daily SPEI-90 Days Values – Elevation Below 1500 m a.s.l., 1950–2023</p> </td> </tr> <tr> <td> <p><strong>Dataset Description</strong> </p> </td> <td> <p>SPEI (Standardized Precipitation Evapotranspiration Index) data calculated using the E-OBS daily gridded data with a spatial resolution of 0.1° for the period from 1950 to 2023. The dataset describes the anomalies of the balance between precipitation and potential evapotranspiration with respect to the average conditions (Vicente-Serrano et al., 2010), considering both temperature and precipitation as inputs. </p> </td> </tr> <tr> <td> <p><strong>Key Methodologies</strong> </p> </td> <td> <p>Daily SPEI is here calculated at a time scale of 90 days (3 months). The SPEI calculation is based on the ‘SPEI’ R package by Beguería and Vicente-Serrano (2023) (https://github.com/sbegueria/SPEI) with the potential evapotranspiration derived from Hargreaves-Samani formulation. Based on E-OBS orography, SPEI values for altitudes above 1500 m a.s.l. are excluded from the current analysis to focus on low and mid-elevation areas only.</p> </td> </tr> <tr> <td> <p><strong>Temporal Domain</strong> </p> </td> <td> <p>1950–2023</p> </td> </tr> <tr> <td> <p><strong>Spatial Domain</strong> </p> </td> <td> <p>The dataset is provided over the [7.10, 44.10, 15.30, 49.10] spatial domain (min longitude, min latitude, max longitude, max latitude in WGS84, EPSG:4326).</p> </td> </tr> <tr> <td> <p><strong>Key Variables/Indicators</strong> </p> </td> <td> <p>Standardized Precipitation Evapotranspiration Index 90-days (SPEI-90 days)</p> </td> </tr> <tr> <td> <p><strong>Data Format</strong> </p> </td> <td> <p>netCDF </p> </td> </tr> <tr> <td> <p><strong>Source Data</strong> </p> </td> <td> <p>E-OBS dataset (v29.0e)</p> </td> </tr> <tr> <td> <p><strong>Accessibility</strong> </p> </td> <td> <p>https://doi.org/10.5281/zenodo.13778103</p> </td> </tr> <tr> <td> <p><strong>Stakeholder Relevance</strong> </p> </td> <td> <p>SPEI represents a valuable insight for researchers working on drought related issues, technical experts from public agencies and practitioners, as it is a comprehensive index able to identify drought hazard (dry event), while also considering temperature in the computation. Moreover, it can foster the use of Earth Observation when coupled with event identification methods by providing a robust meteorological-based event identification. This can be then further refined by the use of higher spatial resolution EO data capable of capturing subtle spatial patterns (e.g., soil moisture as influenced by droughts).</p> </td> </tr> <tr> <td> <p><strong>Limitations/Assumptions</strong> </p> </td> <td> <p>Excludes areas above 1500 m a.s.l. </p> </td> </tr> <tr> <td> <p><strong>Additional Outputs/Information</strong> </p> </td> <td> <p>DBSCAN 3D Clusters of SPEI-90 Days Values – Italian NUTS3 (ITH31, 32, 34, 35, 36, 37), 1950–2023 (https://zenodo.org/records/13785996) </p> <p>The dataset access is currently restricted due to pending related publication.</p> </td> </tr> <tr> <td> <p><strong>Contact Information</strong></p> </td> <td> <p>Crespi, Alice (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Lemus i Cánovas, Marc (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy, CRETUS, Non-linear Physics Group, Universidade de Santiago de Compostela, Galicia, Spain)- Data curator<br>Maines, Elena (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data curator<br>Masina, Marinella (CMCC Foundation - Euro-Mediterranean Center on Climate Change)- Data curator<br>Maraschini, Margherita (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator<br>Ferrario, Davide Mauro (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator<br>Furlanetto, Jacopo (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager<br>Pittore, Massimiliano (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Terzi, Stefano (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Torresan, Silvia (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager</p> </td> </tr> </tbody> </table>
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