The Standardized Precipitation Evapotranspiration Index (SPEI) is computed similarly to the SPI. The main difference is that SPI assesses precipitation variance, while SPEI also considers demand from evapotranspiration which is subtracted from any precipitation accumulation prior to assessment. Unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.

This dataset provides data at the county level for the contiguous United States. It includes monthly Standardized Precipitation Evapotranspiration Index (SPEI) data from 1895-2016 provided by the Cooperative Institute for Climate and Satellites - North Carolina. Please refer to the metadata attachment for more information.

These data are used by the CDC's National Environmental Public Health Tracking Network to generate drought measures. Learn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

Problems or Questions? Email trackingsupport@cdc.gov.

Drought projections on seasonal to annual time scales are presented for Canada over the twenty-first century, based on the Standardized Precipitation Evapotranspiration Index (SPEI). Results make use of bias-corrected temperature and precipitation projections from 29 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and include three different forcing scenarios (RCP2.6, RCP4.5 and RCP8.5). Large differences in projected drought changes are observed among different regions. On the annual time scale, southwestern Canada and the Prairies may experience an increase in drying under a warmer climate. On the other hand, coastal regions, including northern Canada, the northwest Pacific coast and the Atlantic region, show a small increase in wetness. Winter and spring SPEI results depict an increase in wetting, reflecting the projected country-wide winter and spring precipitation increases under climate change. For the most part, autumn and summer show increases in drying. The largest relative changes in both summer drying and winter wetting were found over northern regions, but the offsetting seasonal effects typically balance out to yield various degrees of wetting on the annual scale for this region. The projected drought responses are relatively modest in the low forcing scenario (RCP2.6) for most Canadian regions. In addition, even for regions most affected, a marked increase in surface water deficit might not occur until the second half of this century. Inter-model variation (a crude measure of projection uncertainty) typically increases with forcing intensity and lead time, and is generally greater in northern and western Canada.

Persistent, large-area droughts are among Canada’s costliest natural disasters, having major impacts on sectors such as agriculture, industry, forestry, recreation, human health and society, and ecosystems. This dataset provides 50 km gridded, 12-month Standardized Precipitation Evapotranspiration Index (SPEI) values across land regions of Canada. Over southern areas of the country (south of 60°N) data are from 1900-2011 while in northern areas, the time period is shorter (approximately 1950-2011). The SPEI is a commonly used drought index, which evaluates the deviation of moisture deficit calculated as the difference between precipitation and potential evapotranspiration, the latter determined by temperature. It can be calculated on a variety of temporal scales (e.g., 1, 3, 12, 24 months). The values are standardized with negative SPEI representing drier than normal conditions and positive values corresponding to wetter than normal conditions. The SPEI are calculated using precipitation and temperature input from the Canadian gridded (CANGRD) dataset. For each grid point, the data consist of consecutive monthly values that represent the SPEI values for the previous 12 months. At present, these data have been used to characterize historical drought and excessive wet periods over the Oldman and Swift Current Creek watersheds in the southern Prairies, and the Athabasca River Basin in north-central Alberta. Supplemental Information References: Vicente-Serrano S.M., Begueria S., Lopez-Moreno J.I. 2010. A multiscalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate 23: 1696-1718. Bonsal, B.R. and C. Cuell. 2017. Hydro-climatic variability and extremes over the Athabasca river basin: Historical trends and projected future occurrence. Canadian Water Resources Journal, 42, 315-335, doi:10.1080/07011784.2017.1328288. Bonsal, B.R., C. Cuell, E. Wheaton, D.J. Sauchyn, and E. Barrow. 2017. An assessment of historical and projected future hydro-climatic variability and extremes over southern watersheds in the Canadian Prairies, International Journal of Climatology, doi:10.1002/joc.4967.

The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year].

This SPEI is calculated using R's SPEI package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with AWAP's monthly rainfall and ALWB's potential evapotranspiration.

SPEI package: https://cran.r-project.org/web/packages/SPEI/index.html

AWAP website: http://www.csiro.au/awap/

Australian Landscape Water Balance website: http://www.bom.gov.au/water/landscape/

The data presented contain on-site and modeled PRISM (Parameter-elevation Regressions on Independent Slopes Model) meteorological data, as well as five drought indices parameterized with these two sources of meteorology. Data are provided for three sites, from October 1933 through September 2020, on the western slope of the southern Sierra Nevada, California and identified by the closest geographical name place: Marble Fork Kaweah River (Lodgepole, CA, Sequoia National Park), Stony Creek (Stony Creek Campground, Sequoia National Monument), and Huckleberry Meadow (near Crescent Meadow, Sequoia National Park). The meteorological data include daily precipitation sum and mean air temperature. Monthly PET (potential evapotranspiration) derived from the Thornthwaite equation, and annual hydrological year (October 1 through September 30) SPEI (Standardized Precipitation and Evapotranspiration Index), AI (Aridity Index), PDSI (Palmer Drought Severity Index), PHDI (Palmer Hydrological Drought Index), and scPDSI (self-calibrating PDSI) are also given as parameterized from on-site and modeled meteorological data.Drought indices (DIs) are relied upon for monitoring and evaluating drought severity relevant to ecological systems, and down-stream agricultural, industrial, and domestic users. The application of these data was to evaluate the performance of five commonly used DIs (SPEI, AI, PDSI, scPDSI, PHDI) in a complex, upland landscape in the Sierra Nevada in a separate analysis (Kim et al. 2022). Correlation coefficients between different parameterizations of DIs (on-site vs. modeled meteorology) on the one hand, and streamflow and remotely-sensed stand normalized difference vegetation index (NDVI) on the other hand were used as relative measures of DI performance. A model was developed to predict tree basal area increment (BAI) within each of three stands using on-site or modeled meteorological parameterizations of DIs, along with biological co-factors (tree vigor, tree to tree competition). The significance and proportion of variance explained by on-site or modeled meteorological parameterizations of DI within the context of the co-factors were also used to evaluate DI performance.For more information about this study and these data, see Kim et al. (2022).

These data were published on 02/24/2022. On 10/24/2024, minor metadata updates were made.

These are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale.
These data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880.

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.

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.

To measure drought events, this dataset uses the Standardised Precipitation-Evapotranspiration Index (SPEI), which incorporates temperature and precipitation. We report on drought frequency, duration, severity, and intensity at three different time scales, short-term (3 months), medium-term (6 months) and long-term (12 months). These different time scales are approximately equivalent to meteorological, agricultural, and hydrological drought, respectively. We report the trends for 30 sites across Aotearoa New Zealand monitored by NIWA (National Institute for Water and Atmospheric Research) from 1972 to 2022.

Variables: site: Site the NIWA climate stations represent. time_scale: The number of months of drought drought_type: The drought the SPEI values represent given at 3, 6, and 12 months (meterological, agricultural and hydrological respectively. lat: Approx. lattitude location of NIWA climate stations to represent a site. lon: Approx. longitude location of NIWA climate stations to represent a site. trend_type: Duration is the number of months a drought event lasts. Average SPEI is the annual average SPEI value. Severity is the sum of SPEI values per drought event. Intensity is severity/duration. Peak month is the lowest SPEI value recorded per drought event. Frequency is the numbers of months between each drought event. p_value: Probability of obtaining test results at least as extreme as the result actually observed. z: Z statistic after correcting for autocorrelated data method: The type of trend test undertaken. Note that for methane a linear model with a quadratic term is used. For the Mann Kendall test we used a modified Mann Kendall test for autocorrelated data modifiedmk::mmkh() n: Number of data points included in trend calculation. note: Linear model analysis notes s, var_s, tau: Mann-Kendall test statistics. alternative: Alternative hypothesis trend_likelihood: Likelihood of trend direction adapted from IPCC criteria. period_start, period_end: The period the trend represents.

Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, which cover ~45% of Earth’s land surface and face increasingly drier and more variable climates. Here, we analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland habitats in the southwestern USA. We captured two time series: 1995-2006 and 2004-2013 that coincide with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of ~5% of animals captured. During the first time series (PDO wet phase), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (dry phase), climate best explained rodent abundance for 60% of taxa. Temporal dynamics in rodent diversity and abundance differed spatially among habitats and sites, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Both habitat type and phase of the PDO modulated which species were winners or losers under increasing drought and amplified interannual variability in drought. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among habitats for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995 - 2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major phase of the PDO.

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

In two Costa Rican and three Honduran sites that vary in rainfall and soil properties, we used natural isotopes, a soil water balance model and climate-based drought indices to study shifts in water use with ontogeny from seedlings to mature tropical live oak (Quercus oleoides) trees. Water use patterns help to explain persistence of this broadly distributed species in Mesoamerica and to evaluate likely threats of on-going climate changes. At the end of dry seasons, soil d18O profiles can be described by one-phase exponential decay curves. Minimum values reflect geographic origins of the last significant rain event, and curvature is inversely related to the canopy closure, demonstrating its role in controlling topsoil evaporation. Partitioning of soil water sources for transpiration was analyzed with a mixing model. In the Costa Rican sites, in a relatively dry year, saplings and mature trees took up water from the upper soil. In a relatively wet year in the Honduran sites, we observed deeper water extraction. In all sites, soil storage dampens extreme variation in water availability. The size-dependence of water uptake with larger stems exploiting deeper layers is translated into variation in bulk leaf d13C-based WUE with the exception of mature trees. From 1932 -2015, drought severity was evaluated with the Standardized Precipitation Evapotranspiration Index (SPEI) concurrently with simulations of the soil water balance model. Drought occurrence increased, regardless of the time period averaged across 6, 12 or 24 months. All ontogenetic stages in all populations experienced frequent water limitation. We found evidence for linear trends toward aridification with increases of return periods of drought for October SPEI-24 declining from 42 to 6 yr in Costa Rica and from 21 to 7 yr in Honduras and recent occurrence of multiyear droughts from 2013 to 2016. October SPEI-12 and SPEI-24 were significantly related to the Oceanic Niño Indices demonstrating that local inter-annual variations in drought severity in Mesoamerica are modulated by large-scale climate forces. Drought severity in the near-term future depends on the extent to which the Pacific will adopt a more La Niña-like vs. a more El Niño-like state under on-going climatic changes. Methods The methods have been detailed both in the paper and in supporting information

The files contain cover and biomass data under ambient and nutrient addition (by NPK; up to 15 years) conditions at 55 NutNet sites (https://nutnet.org/), geolocations and growing seasons, and precipitation and potential evapotranspiration (1901 - 2021) at these 55 sites. Precipitation and potential evapotranspiration are used to calculate the standardized precipitation–evapotranspiration index (SPEI), these data were downloaded from https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.06/.

This contains layers categorizing stream favorability using PROSPER data to determine wet/dry year scenarios instead of the Standardized Precipitation Evapotranspiration Index (SPEI). All of the layers use a web Mercator projection.Due to the format of the "StreamSuitability_WebMercat" and "HUC10_WebMercat" data layers, we recommend viewing them using the RAEVEN dashboard. These data layers are meant to be filtered to display one combination of Climate Condition and Water Temperature Threshold. Without filtering, there are multiple entries for each area or line segment. These layers contain values based on categorizing stream favorability. Briefly, the underlying data use predictions of streamflow permanence and August stream temperature. The flow status was estimated by the PROSPER (Jaeger et al. 2019) Streamflow Permanence Probability (SPP), which is the likelihood that a given reach flows year-round. Stream temperature was extracted from the NorWeST (Isaak et al. 2017) estimate of mean August stream temperature for the given year. For further details on categorizing stream favorability, please view the RAEVEN Streamflow and Stream Temperature Limiting Factor Assessment Dashboard.The "StreamSuitability_WebMercat" layer categorizes stream reach favorability for each HUC 10 within the two-digit HUC 17 region, the Pacific Northwest. Please note that the stream reaches within a HUC 10 have been dissolved by favorability category to allow for quicker rendering in the RAEVEN dashboard. There is a value for each combination of climate condition and water temperature threshold, in a long data format suitable for filtering. Possible stream reach categories that appear in this analysis are No Data, Limited by flow, Limited by temperature, Limited by both flow and temperature, and None - not limited. No Data occurs when either flow status or stream temperature data are missing from the record. Limited by flow indicates that flow status is estimated to be dry for the evaluated time period and reach. Limited by temperature indicates that temperature status is unfavorable (e.g., exceeds either 16°C or 18°C). Limited by both flow and temperature indicates that the given reach has both a flow status of dry and a temperature status of unfavorable. None - not limited indicates that the given reach has an estimated flow status of flowing and a temperature status of favorable.The "HUC10_WebMercat" layer was created to display the proportion of favorable streams according to HUC 10 within the two-digit HUC 17 region, the Pacific Northwest. There is a value for each combination of climate condition and water temperature threshold, in a long data format suitable for filtering. The proportion of favorable streams is defined as the proportion of total stream lengths within a HUC 10 for which flow and temperature conditions are not estimated to be limiting for the given year, as determined by a limiting factor of ‘None – not limited’. See the "StreamSuitability_WebMercat" layer for stream reach limiting categories. The "Outline_HUC10_Merc" layer simply consists of the HUC 10 boundaries for HUC 10 watersheds within the two-digit HUC 17 region, the Pacific Northwest. This layer was included for display purposes in the RAEVEN dashboard.

Abstract The use of technology and planning in agricultural production is essential in Northeastern Brazil, which is the region of the country that most suffers from water shortage. For the best irrigation management, it is necessary to know the potential evapotranspiration rate for water control in order to increase productivity. There are several direct and indirect methods for estimating evapotranspiration, but the standard method recommended by the United Nations Agriculture Organization (FAO) is the Penman-Monteith (PETpm) method because it has higher accuracy than other methods. However, it is a difficult method to be used due to the need for a large number of meteorological elements. In this context, the objective of this study was to estimate potential evapotranspiration by the Penman-Monteith method in the micro-region of Baixo Parnaíba in Maranhão state using artificial neural networks. Agro-meteorological data were collected daily over 34 years, from 1984 to 2017, and these data were obtained from the NASA/POWER website. Subsequently, liquid radiation and potential evapotranspiration were calculated by the Penman-Monteith standard method (1998). To predict potential daily evapotranspiration, the Multi-Layer Perceptron (MLP) was chosen, which is a traditional Artificial Neural Network. The period that presented a higher evapotranspiration index was the same one that showed precipitation with a lower volume and higher temperatures. The artificial neural network model that best adapted to estimate PETpm was MLP 2-5-1. It is concluded that artificial neural networks estimate with accuracy and precision the Penman-Monteith daily potential evapotranspiration of the Lower Parnaiba in Maranhão, and potential evapotranspiration can be estimated by the Penman-Monteith method using neural networks with inputs of air temperatures.

Abstract

In the face of recurrent climatic shocks across many countries that negatively affect farmers income, undermine the impact of investments, IFAD has been promoting the resilience of vulnerable smallholders through investments that enhance farmers capacity to mitigate, recover and adapt to shocks and chronic stresses.

The Participatory Small-Scale Irrigation Development Programme (PASIDP) was implemented to improve the food security, family nutrition, and income of poor rural households living in drought-prone and food-deficit areas in Amhara, Oromia, Tigray, and Southern Nations, Nationalities and Peoples Region (SNNPR) in Ethiopia through a sustainable farmer-owned and -managed system of small-scale irrigated agriculture.

Amongst others, some of the PASIDP approaches to achieving the goal were to: innovatively build on indigenous knowledge; promote beneficiary participation in the selection, construction, operation, maintenance and management of irrigation schemes; and secure communal ownership through grassroots organizations such as water users' association.

At the start, food-deficit woredas (districts) under the Productive Safety Net Programme (PSNP) that are high density, drought prone and food insecure were selected to participate in the project. Then, following a participatory approach, the woreda and kebele (sub-districts) officials along with community leaders, selected the type of small-scale irrigation scheme most appropriate for the area based on the local conditions and implementation capacity of the targeted beneficiaries. Implemented from March 2008 to September 2015, the PASIDP project constructed a total of 121 irrigation schemes and benefitted about 62,000 households.

For more information, please, click on the following link: https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-participatory-small-scale-irrigation-development-programme

Geographic coverage

Four regions (Amhara, Oromia, SNNPR, and Tigray) of Ethiopia, which were selected by the Government of Ethiopia (GOE).

Analysis unit

Poor Rural Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Approximately 10 beneficiary kebeles were randomly selected per region from the 93 treated Kebeles to obtain a sufficiently representative sample of all kebeles covered by the project. In addition, 10 control kebeles were randomly sampled from non-beneficiary kebeles that had similar agro-climatic indicators, geographical landscape, and agricultural activities. After selecting the Kebeles, around 13 households were randomly selected out of the total 300 to 400 households living in each beneficiary and non-beneficiary kebeles. In total, 1,033 beneficiary and non-beneficiary households were sampled from the four regions. In summary, the beneficiaries (treatment group) resided in areas that had a functioning PASIDP irrigation scheme in place for at least one year to ensure that the benefits from irrigation to their agricultural activities could be observed. The non-beneficiaries (control group) resided instead in areas without any PASIDP-related activities, but with similar agro-climatic indicators, geographical landscape, and agricultural activities.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The high-frequency data contained detailed information on access to irrigation water supply, agricultural production and household expenditure, along with a full set of household-level data such as household demographics, social and economic characteristics, and special modules on risk management strategies, coping strategies and self-perceived shocks which were measured across four rounds. This information was used to construct a number of impact indicators and generate a wide range of household level explanatory variables to be used in the analysis. Self-reported shocks in the survey were also complemented with an objective shock measure, notably the Standardized Precipitation Evapotranspiration Index (SPEI), which was used as a covariate in the analysis. Such indicator is an extension of the widely used Standardized Precipitation Index (SPI).

Note: some variables may have missing labels. Please, refer to the questionnaire for more details.