Cited by 2,128Hydrology and Drought AnalysisClimate variability and modelsPlant Water Relations and Carbon Dynamics

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring

Beguería, Santiago, Vicente-Serrano, Sergio M., Reig, Fergus, Latorre, Borja·2014·International Journal of Climatology

Open this paper in CoChat — free account

Abstract

48 págs., 25 figs. Available online 21 December 2013. The definitive version is available at: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0088

Integrity signals

Registry

Crossref · 10.1002/joc.3887

Cites

0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Increasing drought under global warming in observations and models
Dai, A. (2013) · Nature Climate Change · doi:10.1038/nclimate1633 · Cited by 4,762
Understanding plant responses to drought—from genes to the whole plant.
Chaves, M. M., Maroco, J. P., & Pereira, J. S. (2003) · Functional Plant Biology · doi:10.1071/fp02076 · Cited by 3,845
Little change in global drought over the past 60 years
Sheffield, J., Wood, E. F., & Roderick, M. L. (2012) · Nature · doi:10.1038/nature11575 · Cited by 1,978 · cited 4×
“Based on the PDSI, Dai (2011, 2013) and Van Der Schrier et al. (2011) reported that the specific formulation of the ET0 equation had little effect on the magnitude of global drought, but Sheffield et al. (2012) used the same index and found substantial differences based on different data.”
BackgroundMethodInfluential
The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., & García-Herrera, R. (2011) · Science · doi:10.1126/science.1201224 · Cited by 1,656

Flagged first, then by citations

Show all 0 references

Research works printed but not linked: 74

Abiodun, 2013, Potential impacts of afforestation on climate change and extreme events in Nigeria, Clim. Dyn., 41, 277, 10.1007/s00382-012-1523-9 · DOI
Adams, 2009, Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought, Proc. Natl. Acad. Sci. U. S. A., 106, 7063, 10.1073/pnas.0901438106 · DOI
Allen, 1998, Crop Evapotranspiration: Guidelines for Computing Crop Requirements, Irrigation and Drainage Paper 56
Allen, 2011, The potential to reconstruct broadscale climate indices associated with southeast Australian droughts from Athrotaxis species, Tasmania, Clim. Dyn., 37, 1799, 10.1007/s00382-011-1011-7 · DOI
Anderegg, 2012, The roles of hydraulic and carbon stress in a widespread climate-induced forest die-off, Proc. Natl. Acad. Sci. U. S. A., 109, 233, 10.1073/pnas.1107891109 · DOI
Barbeta, 2013, Dampening effects of long-term experimental drought on growth and mortality rates of a Holm oak forest, Global Change Biol., 19, 3133, 10.1111/gcb.12269 · DOI
Beguería, 2010, A multi-scalar global drought data set: the SPEIbase: a new gridded product for the analysis of drought variability and impacts, Bull. Am. Meteorol. Soc., 91, 1351, 10.1175/2010BAMS2988.1 · DOI
Breshears, 2005, Regional vegetation die-off in response to global-change-type drought, Proc. Natl. Acad. Sci. U. S. A., 102, 15144, 10.1073/pnas.0505734102 · DOI
Cai, 2008, Evidence of impacts from rising temperature on inflows to the Murray-Darling Basin, Geophys. Res. Lett., 35, L07701, 10.1029/2008GL033390 · DOI
Camarero, 2012, Los precedentes y la respuesta de los árboles a sequías extremas revelan los procesos involucrados en el decaimiento de bosques mediterráneos de coníferas, Ecosistemas, 21, 22, 10.7818/ECOS.2012.21-3.04 · DOI
Cavin, 2013, Extreme drought alters competitive dominance within and between tree species in a mixed forest stand, Funct. Ecol., 27, 1424, 10.1111/1365-2435.12126 · DOI
Ciais, 2005, Europe-wide reduction in primary productivity caused by the heat and drought in 2003, Nature, 437, 529, 10.1038/nature03972 · DOI
Compo, 2011, The twentieth century reanalysis project, Quart. J. Roy. Meteorol. Soc., 137, 1, 10.1002/qj.776 · DOI · Paper page
Dai, 2011, Characteristics and trends in various forms of the Palmer drought severity index during 1900-2008, J. Geophys. Res., 116, D12115, 10.1029/2010JD015541 · DOI
Deng, 2011, Recent global CO2 flux inferred from atmospheric CO2 observations and its regional analyses, Biogeosci. Discuss., 38, 3497, 10.5194/bgd-8-3497-2011 · DOI
Donohue, 2010, Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate, J. Hydrol., 386, 186, 10.1016/j.jhydrol.2010.03.020 · DOI
Drew, 2013, Wood properties in a long-lived conifer reveal strong climate signals where ring-width series do not, Tree Physiol., 33, 37, 10.1093/treephys/tps111 · DOI
Droogers, 2002, Estimating reference evapotranspiration under inaccurate data conditions, Irrig. Drain. Syst., 16, 33, 10.1023/A:1015508322413 · DOI
Eagleman, 1976, Visualization of the Climate
Fan, 2008, A global monthly land surface air temperature analysis for 1948-present, J. Geophys. Res., 113, D01103, 10.1029/2007JD008470 · DOI
Garcia-Herrera, 2010, A review of the European summer heat wave of 2003, Crit. Rev. Environ. Sci. Technol., 40, 267, 10.1080/10643380802238137 · DOI
Guttman, 1998, Comparing the Palmer Drought Index and the Standardized Precipitation Index, J. Am. Water Resour. Assoc., 34, 113, 10.1111/j.1752-1688.1998.tb05964.x · DOI
Hargreaves, 1985, Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1, 96, 10.13031/2013.26773 · DOI
Hargreaves, 2003, History and evaluation of Hargreaves evapotranspiration equation, J. Irrigat. Drain. Eng., 129, 53, 10.1061/(ASCE)0733-9437(2003)129:1(53) · DOI
Hayes, 2011, The Lincoln declaration on drought indices: universal meteorological drought index recommended, Bull. Am. Meteorol. Soc., 92, 485, 10.1175/2010BAMS3103.1 · DOI
Heim R Brewer M 2012 Earth Int 16 1 28 · DOI
Hosking, 1990, L-Moments: analysis and estimation of distributions using linear combinations of order statistics, J. R. Stat. Soc. B, 52, 105, 10.1111/j.2517-6161.1990.tb01775.x · DOI
1990, Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practices No. 70, 360
Joetzjer, 2012, Evaluation of drought indices at interannual to climate change timescales: a case study over the Amazon and Mississippi river basins, Hydrol. Earth Syst. Sci. Discuss., 9, 13231, 10.5194/hessd-9-13231-2012 · DOI
Konovalov, 2011, Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region, Atmos. Chem. Phys., 11, 10031, 10.5194/acp-11-10031-2011 · DOI
Lévesque, 2013, Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch, Glob. Chang. Biol., 19, 3184, 10.1111/gcb.12268 · DOI
Li, 2012, Study on drought trend in south China based on standardized precipitation evapotranspiration index, J. Nat. Disasters, 21, 84
Lobell, 2011, Climate trends and global crop production since 1980, Science, 29, 616, 10.1126/science.1204531 · DOI
Lobo, 2006, Stratified analysis of satellite imagery of SW Europe during summer 2003: the differential response of vegetation classes to increased water deficit, Hydrol. Earth Syst. Sci., 10, 151, 10.5194/hess-10-151-2006 · DOI
Lorenzo-Lacruz, 2010, The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain), J. Hydrol., 386, 13, 10.1016/j.jhydrol.2010.01.001 · DOI
Martin-Benito, 2013, Influence of drought on tree rings and tracheid features of Pinus nigra and Pinus sylvestris in a mesic Mediterranean forest, Eur. J. For. Res., 132, 33, 10.1007/s10342-012-0652-3 · DOI
McDowell, 2008, Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?, New Phytol., 178, 719, 10.1111/j.1469-8137.2008.02436.x · DOI
McEvoy DJ Huntington JL Abatzoglou J Edwards L 2012 http://dx.doi.org/10.1175/2012EI000447.1 · DOI
McKee, 1993, Proceedings of Eighth Conference on Applied Climatology, 179
Nkemdirim, 1999, Comparison between the droughts of the 1930s and the 1980s in the southern praires of Canada, J. Clim., 12, 2434, 10.1175/1520-0442(1999)012<2434:CBTDOT>2.0.CO;2 · DOI
Palmer WC 1965 · DOI
Paulo, 2012, Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal, Nat. Hazards Earth Syst. Sci., 12, 1481, 10.5194/nhess-12-1481-2012 · DOI
Potop, 2011, Evolution of drought severity and its impact on corn in the Republic of Moldova, Theor. Appl. Climatol., 105, 469, 10.1007/s00704-011-0403-2 · DOI
Potop, 2012, Drought at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic, Agric Forest Meteorol, 156, 121, 10.1016/j.agrformet.2012.01.002 · DOI
Quiring, 2009, Developing objective operational definitions for monitoring drought, J. Appl. Meteorol. Climatol., 48, 1217, 10.1175/2009JAMC2088.1 · DOI
Rao, 2000, Flood Frequency Analysis
Seneviratne, 2012, Swiss prealpine Rietholzbach research catchment and lysimeter: 32 year time series and 2003 drought event, Water Resour. Res., 48, W06526, 10.1029/2011WR011749 · DOI
Sohn, 2013, Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble, Geophys. Res. Lett., 40, 579, 10.1002/grl.50133 · DOI
Soo-Jin, 2013, Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble, Geophys. Res. Lett.
Spinoni, 2013, An overview of drought events in the Carpathian Region in 1961-2010, Adv. Sci. Res., 10, 21, 10.5194/asr-10-21-2013 · DOI
Stephenson, 1990, Climatic control of vegetation distribution: the role of the water balance, Am. Nat., 135, 649, 10.1086/285067 · DOI · Paper page
Stephenson, 1998, Actual evapotranspiration and deficit: Biologically meaningful correlates of vegetation distribution across spatial scales, J. Biogeogr., 25, 855, 10.1046/j.1365-2699.1998.00233.x · DOI · Paper page
Svoboda, 2002, The drought monitor, Bull. Am. Meteorol. Soc., 83, 1181, 10.1175/1520-0477(2002)083<1181:TDM>2.3.CO;2 · DOI
Teuling, 2013, Evapotranspiration amplifies European summer drought, Geophys. Res. Lett., 40, 2071, 10.1002/grl.50495 · DOI
Thornthwaite, 1948, An approach toward a rational classification of climate, Geogr. Rev., 38, 55, 10.2307/210739 · DOI
Toromani, 2011, Growth responses to climate and drought in silver fir (Abies alba) along an altitudinal gradient in southern Kosovo, Can. J. For. Res., 41, 1795, 10.1139/x11-096 · DOI
Schrier, 2011, The sensitivity of the PDSI to the Thornthwaite and Penman-Monteith parameterizations for potential evapotranspiration, J. Geophys. Res., 116, D03106
Vicente-Serrano, 2006, Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula, Water Resour. Manag., 20, 37, 10.1007/s11269-006-2974-8 · DOI
Vicente-Serrano, 2010a, A multi-scalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index - SPEI, J. Clim., 23, 1696, 10.1175/2009JCLI2909.1 · DOI
Vicente-Serrano, 2010b, A new global 0.5° gridded dataset (1901-2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index, J. Hydrometeorol., 11, 1033, 10.1175/2010JHM1224.1 · DOI
Vicente-Serrano, 2010c, Aridification determines changes in leaf activity in Pinus halepensis forests under semiarid Mediterranean climate conditions, Agric. For. Meteorol., 150, 614, 10.1016/j.agrformet.2010.02.002 · DOI
Vicente-Serrano, 2011a, Comment on “Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI) during 1900-2008” by A Dai, J. Geophys. Res., 116, D19112, 10.1029/2011JD016410 · DOI
Vicente-Serrano, 2011b, A multi-scalar global evaluation of the impact of ENSO on droughts, J. Geophys. Res., 116, D20109, 10.1029/2011JD016039 · DOI
Vicente-Serrano, 2012a, Performance of drought indices for ecological, agricultural and hydrological applications, Earth Int., 16, 1, 10.1175/2012EI000434.1 · DOI
Vicente-Serrano, 2012b, Dryness is accelerating degradation of vulnerable shrublands in semiarid Mediterranean environments, Ecol. Monogr., 82, 407, 10.1890/11-2164.1 · DOI
Vicente-Serrano, 2013a, The response of vegetation to drought time-scales across global land biomes, Proc. Natl. Acad. Sci. U. S. A., 110, 52, 10.1073/pnas.1207068110 · DOI
Vicente-Serrano, 2013b, Temporal evolution of surface humidity in Spain: recent trends and possible physical mechanisms, Clim. Dyn.
Walter, 2004, Increasing evapotranspiration from the conterminous United States, J. Hydrometeorol., 5, 405, 10.1175/1525-7541(2004)005<0405:IEFTCU>2.0.CO;2 · DOI
Walter, 2000, Proceedings National Irrigation Symposium, 209
Wei-Guang, 2012, Standardized precipitation evapotranspiration index shows drought trends in China, Chin. J. Eco-Agri., 20, 643, 10.3724/SP.J.1011.2012.00643 · DOI
Wolf JF Abatzoglou J 2011 47th Annual Water Resources Conference Albuquerque NM http://awra.org/annual2011/doc/pres/S11-Wolf.pdf
Wu, 2007, Appropriate application of the standardized precipitation index in arid locations and dry seasons, Int. J. Climatol., 27, 65, 10.1002/joc.1371 · DOI
Xu, 2001, Evaluation and generalization of temperature based methods for calculating evaporation, Hydrol. Process., 15, 305, 10.1002/hyp.119 · DOI
Yu, 2013, Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951-2010?, Int. J. Climatol.

Sorted by citations

Items 801–900 of 1,004

Multi-model assessment of historical and future drought characteristics (1990–2100) in the constantine highlands, Algeria: a comparative analysis of SPI and SPEI indices
Slimani, F., Daifallah, T., Ziouch, O., Tanda, M., Todaro, V., Dali, N., Tayeb, K. S., & Boukhallat, K. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06640-x
“At these aggregation periods, the relative ranking of dry/wet periods is less sensitive to the absolute magnitude of PET than to its interannual variability (Beguería et al. 2014).”
Influential
Meteorological drought characteristics in the South Central region of Vietnam based on SPI and SPEI indices in the period 1984–2023
Nguyen-Quoc, B., Nguyen-Minh, T., Vu-Thanh, H., & Pham-Thanh, H. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06672-3
Drought-Index Forecasting in the Yellow River Basin: Evaluating Index Timescales, Lead Times, and Machine Learning Models
Cheng, F., Han, J., Fan, Y., Han, X., Yang, Z., Juan, C., & Mei, Q. (2026) · Water · doi:10.3390/w18202582
Agricultural drought hazard assessment from the perspective of the impact of continuous drought on actual crop transpiration
Cui, Y., Jia, S., Jin, J.-L., Zhou, Y.-L., Xiong, X., & Zhang, Y.-L. (2026) · Geomatics, Natural Hazards & Risk · doi:10.1080/19475705.2026.2746263
Temporal drought characteristics in semi-arid northern Tanzania based on multi-scale SPI and SPEI (1994–2024)
Lyimo, U. E., Mabhuye, E. B., & Pauline, N. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06661-6
“…PET method, although widely applied in data-scarce environments because of its relatively low data requirements, may underestimate atmospheric evaporative demand under conditions of high radiation and low humidity typical of semi-arid climates (Beguería et al. 2014; Vicente-Serrano et al. 2010).”
Spatio-temporal Characterization of Reference Evapotranspiration in Supaul District, Bihar, India, using the Fao-56 Penman–monteith Method
Kumar, V., & Puja, P. (2026) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2026.v08i05.89744
Using AI-powered modelling for drought forecasting in the Democratic People’s Republic of Korea
Ham, Y., Ri, H.-S., Jong, S.-I., Om, K., Sonu, K.-B., & Paek, U.-S. (2026) · Australian Journal of Agricultural Research · doi:10.1071/cp26003
Drought Trends in Türkiye Under Global Warming Conditions
Sekendur, F., & Ünlükara, A. (2026) · Tarım Bilimleri Dergisi · doi:10.15832/ankutbd.1779288
Hydroclimatic variability and drought characterisation in Northern and North-Central Togo, 1981–2025
Pekeyi, M. G., Okhimamhe, A., Yahaya, T. I., & Sule, I. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06621-0
“SPEI (Vicente-Serrano et al. 2010 ; Beguería et al. 2014) was developed specifically to address this limitation by incorporating potential evapotranspiration into the water balance, making it sensitive to temperature-driven changes in atmospheric water demand that SPI cannot capture; recent work in…”
Wood type influences tree responses to recurring hot and dry summer tree water deficits across European forests
Nikinmaa, L., Stegehuis, A., Lindner, M., Zweifel, R., Teuling, A., von Arx, G., Meerbeek, K. V., Salomón, R. L., Bär, A., Bellan, M., et al. (2026) · European Journal of Forest Research · doi:10.1007/s10342-026-01956-9
Hot drought reshapes the relationship between tree species diversity and forest resilience
Zhang, Z.-F., Wang, P., Wu, X.-C., Zhang, W., Li, Y., Guo, R.-J., Wang, X.-N., & Meng, Z.-K. (2026) · Environmental Research Letters · doi:10.1088/1748-9326/aeb1df
“10, which provides SPEI data at 0.5 ◦ resolution for 1901–2023 [36].”
Influential
A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects
Saba, F., Ahmadian, N., & Dubovyk, O. (2026) · GIScience & Remote Sensing · doi:10.1080/15481603.2026.2738981
Tropical belt expansion and its impacts on climate variability in the Mediterranean from 1980 to 2022
Darrag, M., Jin, S.-G., Calabia, A., Samy, A., Radevski, I., & Radwan, A. M. (2026) · Atmospheric Chemistry and Physics · doi:10.5194/acp-26-14031-2026
Drought regime shifts and changing drought characteristics in Jharkhand, India: A SPEI based analysis from 1951 to 2025
Raha, S., & Deb, S. (2026) · Turkish Journal of Remote Sensing · doi:10.51489/tuzal.1979467
Spatiotemporal characteristics of vegetation responses to compound drought and heatwave events in Central Asia
Ding, M.-F., Zhang, R.-P., Guo, J., Li, L., Niu, F.-F., Li, Y.-S., & Gao, J.-L. (2026) · GIScience & Remote Sensing · doi:10.1080/15481603.2026.2741443
Hydroclimatic variability is associated with gastrointestinal parasite dynamics in black-horned capuchins (Sapajus nigritus cucullatus) in the Atlantic Forest of Argentina
Illia, G., Kowalewski, M. M., Contreras, F., Gillespie, T. R., DeMatteo, K., & Oklander, L. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1959428
“The Standardized Precipitation Evapotranspiration Index (SPEI) is a multiscalar drought index that integrates information on both precipitation and atmospheric evaporative demand (27–29).”
Influential
A refined reservoir evaporation loss estimation approach through improved wind function expression accuracy
Pan, C.-H., Luo, Z., Wang, L.-C., Niu, Z.-G., Wei, Z.-S., & Wang, R.-J. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136090
Research on drought prediction models by multiple machine learning methods in a semi-arid region
Zhang, Y.-F., Hao, Y., Tian, J.-Y., Du, K., & Tian, M.-H. (2026) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2026.103799
Biome-dependent forest GPP responses to compound drought and heat from 2001 to 2023
Liu, J.-P., Ren, Y.-Y., Hu, Q.-F., Chen, S.-K., & Li, M.-Z. (2026) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2026.111408
Spatial heterogeneity analysis of climate driving factors for spatiotemporal changes in potential evapotranspiration based on Multi‐Scale Geographically Weighted Regression and Geographical Detector
Ma, Y.-L., Niu, Z.-R., & Sun, D.-Y. (2026) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2026.103927
Modified daily standardized precipitation evapotranspiration index based on influence attenuation of water surplus and deficit
Pei, W., Fang, T.-L.-B., Liu, S.-T., Hao, L., Liu, H., Li, T.-X., & Fu, Q. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110746
Propagation of drought to groundwater in natural aquifers of Southern Spain: a multi–case study analysis
Serrano-Acebedo, P., & Limones, N. (2026) · Natural Hazards Research · doi:10.1016/j.nhres.2026.10.004
Responses of stable vegetation to drought accumulation timescales and time lags in the Weihe River Basin
Hu, X.-X., & Shi, Y.-Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1974112
Climate trends (1960–2020) in the Rif mountains: assessing changes in Cedar and Abies forest ecosystems
Boutahar, A., Bakali, I. E., Kadiri, M., Lamrani, Z., & Merzouki, A. (2026) · Euro-Mediterranean Journal for Environmental Integration · doi:10.1007/s41207-026-01287-6
Climatic zoning of Türkiye under drought stress: a cluster analysis approach
Akşan, G. N. (2026) · Hydrological Sciences Journal · doi:10.1080/02626667.2026.2734279
Exploring the agricultural productivity channel linking drought and heatwave shocks to food insecurity and human mobility in rural Ethiopia
Bovienzo, D., Cottier, F., Pal, J., Dasgupta, S., de Sherbinin, A., & Mysiak, J. (2026) · Environmental Research: Climate · doi:10.1088/2752-5295/aea9bd
“The standardized precipitation evapotranspiration index (SPEI) (Vicente-Serrano et al 2010, Beguería et al 2014) is used to capture drought severity by integrating precipitation, temperature, and atmospheric moisture demand, and it is calculated at multiple accumulation periods (3-, 6, and 12 month…”
Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022
Nagamoto, E., Ombadi, M., Ciulla, F., Willard, J., Carroll, R., & Varadharajan, C. (2026) · Communications Earth & Environment · doi:10.1038/s43247-026-03890-5
Global drought shows no detectable recent acceleration under climate warming
Xu, J.-M., Zhang, X., McColl, K., Berg, A., Zhou, S., Yang, J., Dong, Z.-Y., Luo, Y.-Y., & Fan, Y.-C. (2026) · Communications Earth & Environment · doi:10.1038/s43247-026-03954-6
Physics-Guided Neural Networks for Physically Consistent SPEI: A Bias Correction Framework for Drought Monitoring over Southern Africa
Tsidu, G. M. (2026) · Climate · doi:10.3390/cli14090190
Spatiotemporal Characterization of Drought and Flood Extremes Across Assam Using Multi-Scale Standardized Precipitation Index (1901–2024)
Bothra, N., & Deka, S. (2026) · Current World Environment · doi:10.12944/cwe.21.2.19
Drought Variability and Cycles in Smallholder Farming Systems of the Sudano-Sahelian Region of Burkina Faso and Ghana
Tefera, M. L. (2026) · Meteorology · doi:10.3390/meteorology5030029
Hydroclimatic memory and scaling effects on drought persistence and complexity across river basins in Türkiye
Çelik, M. A. (2026) · Acta Geophysica · doi:10.1007/s11600-026-01999-x
“…shift by explicitly incorporating potential evapotranspiration alongside precipitation, making SPEI particularly well-suited to warming climates where temperature-driven moisture loss increasingly amplifies precipitation-based drought signals (Beguería et al. 2013; Tirivarombo et al. 2018).”
Assessment and Zoning of Agricultural Drought Disaster Risk in Henan Province, China
Yang, S.-L., Jiang, N., Zheng, E.-N., Li, Y.-X., & Yu, C.-Z. (2026) · Sustainability · doi:10.3390/su18179205
Characterization of meteorological drought variability over the Tigris–Euphrates Basin, Türkiye, using SPI and SPEI
Terzi, T., & Üçüncü, O. (2026) · Acta Geophysica · doi:10.1007/s11600-026-01997-z
“However, a key limitation of SPI is that it relies exclusively on precipitation and does not account for atmospheric evaporative demand (Beguería et al. 2013; Ngoc et al. 2026).”
Extreme Hydrological Events and Lake-Wetland Landscape Dynamics: Toward Adaptive Management in Large Freshwater Systems-Insights from Poyang Lake, China
Yuan, L., Liu, X.-G., Zeng, J.-F., & Song, Z.-M. (2026) · Land · doi:10.3390/land15091620
Daily SPEI-based real-time hazard assessment for winter wheat drought: A multi-scale framework with remote sensing validation
Gele, T., Bao, Y., Guga, S., Sudu, B., Wei, S.-C., Zhang, J.-Q., Tong, Z.-J., & Liu, X.-P. (2026) · European Journal of Agronomy · doi:10.1016/j.eja.2026.128233
Age-dependent divergence in stem versus canopy resilience to extreme wetness in a permafrost-affected boreal forest
Li, Y., Xia, A.-Q., Zou, H., Baxter, R., & Sun, Q.-Q. (2026) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2026.111332
Climate shocks, attitudes towards intimate partner violence, and the protective role of climate-resilience projects
Guimbeau, A., Ji, X. J., & Menon, N. (2026) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2026.107713 · Cited by 1
PhyTTA: Physics-Informed Test-Time Adaptation of Foundation Models for Regional Drought Prediction
Gao, W.-T., Li, J.-Y., Liu, L., Le, T. D., Liu, J.-X., Chen, Y., & Zhao, Y.-C. (2026) · Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2026/795
“SPEI reflects the climatic water balance: precipitation contributes to water supply, while temperature affects evaporative demand [Vicente-Serrano et al. , 2010; Beguería et al. , 2014].”
Comparative Analysis of Drought Indices for Assessing Drought Conditions in Agricultural Areas of Northern Eurasia
Emelina, S. В., & Khan, V. (2026) · Russian Meteorology and Hydrology · doi:10.3103/s1068373926130078
“However, approaches to assessing drought conditions are constantly being upgraded, and the Standardized Precipitation Evapotranspiration Index (SPEI) has become very popular [8].”
SPEIChanges: A framework for assessing climate-change-driven shifts in drought frequency using the Standardized Precipitation Evapotranspiration Index
Blain, G., Sobierajski, G. D. R., & Martins, L. L. (2026) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2026.107158
Predictive Boundaries in Tropical Rice Agro-Climatology: Machine-Learning Assessment of Soil Moisture and ERA5-Land Low-Vegetation LAI
Rehman, H., Mazlan, N., Tufail, M., Bux, I., & Saleem, R. (2026) · GEOMATICA · doi:10.1016/j.geomat.2026.100130
Multi-frequency microwave remote sensing reveals stratified forest responses to diverse drought stresses
Zhang, X., Zhang, X., Zhang, F., Gu, X.-H., Ma, H.-L., Gulakhmadov, A., Panda, R. K., Kganyago, M., Costa, V., Nam, W.-H., et al. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105545
Probabilistic associations between meteorological drought and compound moisture sources-transport anomalies in the middle and lower Yangtze River
Zheng, H.-L., Jiang, T.-L., Qu, Y.-P., Wang, Q.-F., & Zhang, Y.-C. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105551
Phenophase-specific responses of winter wheat to drought stress: insights from four decades of observations in Shandong, China
Ran, Y.-Y., Wu, M.-Q., Ren, L.-N., Wu, H., Zhang, L.-N., Tang, Y.-J., Liu, L.-X., Li, W., Chen, G., Lv, J.-Y., et al. (2026) · International journal of biometeorology · doi:10.1007/s00484-026-03316-z
“Drought conditions were quantified using the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates both precipitation supply and atmospheric evaporative demand (Beguería et al. 2014).”
Mapping Groundwater‐Dependent Ecosystems for Contiguous China With Selected Hydroclimatic Windows
Li, H.-X., Pan, Y., & Guan, H. (2026) · Water Resources Research · doi:10.1029/2025wr042737
“SPEI is based on precipitation and temperature data, with the capacity to include the effects of temperature variability on drought assessment (Beguería et al., 2014).”
Effects of Climatic Factors and Disturbance on Plant Above‐Ground Biomass and Species Diversity of Chinese Grasslands: A Meta‐Analysis
Huang, W., Yin, Y., Wang, W., Liu, F., Zhao, Y., & Li, J. (2026) · Ecology and Evolution · doi:10.1002/ece3.74320
“In this study, we used the random forest model to identify the main factors affecting AGB and species richness because this model could accommodate nonlinear relationships between dependent and independent variables and multicollinearity among predictors (Beguería et al. 2014; Gill et al. 2017).”
Long‐Range Dependence, Multifractality, and Trends of Drought Across Multiple Accumulation Periods in Türkiye Derived From SPEI
Çelik, M. A. (2026) · Atmospheric Science Letters · doi:10.1002/asl2.70055
Quantifying nonlinear vegetation loss thresholds in response to cumulative and lagged drought effects in East Africa.
Zhao, Y.-F., Wang, L.-H., Yang, Q., Huang, J.-L., Mapfumo, P., Nyakudya, E., Du, Y., Yan, X., & Ling, F. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130948
Divergent Response of Maize ( Zea mays L.) Growth to Two Consecutive Winter–Spring Droughts and the Underlying Mechanisms in Southwest China
Yang, J.-M., Xue, X., Ma, T.-J., Cen, Y.-Q., Luo, Z.-X., & Chen, Y.-Z. (2026) · Journal of Agronomy and Crop Science · doi:10.1111/jac.70248
Greater Coupling Between Tree Radial Growth and Canopy Activity Is Associated With Weaker Drought Resistance in Drier Closed Forests
Song, W.-Q., Luo, Y.-P., Wang, Z.-G., Li, Z.-S., Wang, X.-C., & Wu, X.-C. (2026) · Global Change Biology · doi:10.1111/gcb.71105
Spatiotemporal evolution and concentration of global meteorological drought burden from a three-dimensional object perspective
Wang, H., Wang, F.-Q., Xu, C.-Y., Zhao, H., Huang, S.-Z., Yang, H., Zhang, H.-L., Cui, L.-B., & Du, Y.-T. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136473
Spatio-temporal patterns of waterborne pesticides in relation to long-term hydrological variability: Insights from a multi-country integrated analysis
Panday, D. P., & Kumar, M. (2026) · Groundwater for Sustainable Development · doi:10.1016/j.gsd.2026.101685
CHIRPS ‐Based Multi‐Index Assessment of Precipitation Variability and Drought Dynamics in Sulaymaniyah Province, Kurdistan Region of Iraq (1981–2023)
Kareem, H. H., Khayyat, H. A., Gaznayee, H. A. A., Alqrinawi, F., Aziz, T. A., Zaki, S. H., Mohammad, R. K., Hakzi, K. K., & Crespi, M. (2026) · Meteorological Applications · doi:10.1002/met.70265
Drastic Biodiversity Transition as a Function of Rainfall Patterns: From Coastal Drylands to Amazonian Rainforests
Zarzosa, P. S., Arana, A., Durán, J., Villar, R., Bastias, C. C., Jentsch, A., Wolff, P., Herraiz, A. D., Muñoz, D. U., Cruz, G., et al. (2026) · Journal of Biogeography · doi:10.1111/jbi.70346
Predicting Sandstorms and Drought Using Artificial Intelligence Techniques Based on Climate Data: A Case Study of Jifarah Plain, Libya
Altarhouni, L. A. M. (2026) · Tobruk University Journal of Engineering Sciences · doi:10.64516/tujes.v7i2.81
“The SPEI values were computed using the R package 'SPEI' (Beguería et al., 2014), with potential evapotranspiration estimated via the Thornthwaite equation.”
Evaluating SPEI Accumulation Timescales Against ESA CCI Surface Soil Moisture Drought in Saudi Arabia
Alnahit, A. (2026) · Atmosphere · doi:10.3390/atmos17090853
Delayed and Heterogeneous Effects of Flood Exposure on Hemorrhagic Fever with Renal Syndrome Risk: A Nationwide Spatiotemporal and Quasi-Experimental Study
Cheng, C., Zuo, H., Zhang, Y.-Q., Cai, J., Lu, L., & Li, X.-J. (2026) · Environmental Health Perspectives · doi:10.1021/ehp.6c00861
Impact of Hydrometeorological and Climatic Pressures on Greek Rivers: Implementation into the Water Framework Directive 2000/60
Mentzafou, A., Dimitriou, E., Παπαδόπουλος, Α., & Katsafados, P. (2026) · Hydrology · doi:10.3390/hydrology13090232
Soil moisture-drought linkages in the Brahmaputra Valley: a multi-index approach to understanding increasing droughts in recent decades
Chakraborty, A., Pathak, B., Singh, B. B., & Bhuyan, P. K. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06525-z
“…has increased the frequency of standardised way to assess precipitation anomalies, it can be used to compare the severity of drought across geographical areas with varied climates, whereas the SPEI incorporates data on precipitation and potential evapotranspiration (PET) (Beguería et al. 2014).”
Influential
Impacts of droughts on biomass burning emissions, air quality, and public health in the Amazon
Ng, L. T. H., Mao, J., Liu, X., Zhai, S., Fawcett, D., Sitch, S. A., Aragão, L. E. O. C., & Tai, A. P. K. (2026) · Atmospheric chemistry and physics · doi:10.5194/acp-26-11835-2026
A Framework for Evaluating Meteorological Drought Indices as Indicators of Low Flows: A Case Study of the Biała Lądecka River Catchment Area
Jakubczyk, T., & Leśny, J. (2026) · Water · doi:10.3390/w18162047
“The SPEI [7,11] includes evapotranspiration in addition to daily precipitation totals for the analyzed period.”
A dynamic eco-hydrological drought framework integrating soil moisture memory, human pressure, and vegetation–atmosphere coupling under a non-stationary climate
Zarei, A. R. (2026) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2026.182207
Forty years of Landsat and Sentinel-2 retrospective analysis reveal a species-differentiated dieback signal in the Atlas forests of Béni Mellal-Khénifra (Morocco)
Laidi, Y., Bouchti, N. E., Abbas, Y., Benhssaine, K., & El-Mderssa, M. (2026) · Ecological Engineering & Environmental Technology · doi:10.12912/27197050/226093
“…BFAST family (Verbesselt et al., 2010, 2012), the PELT change-point algorithm (Killick et al., 2012; Truong et al., 2020) and the multi-scale SPEI (Beguería et al., 2014; Vicente-Serrano et al., 2010) together provide a mature analytical toolkit that has been validated at biome scale but not…”
Spatial patterns of climate-related stress in the Algerian Steppe based on long- term temperature and precipitation trends (1951–2022)
Oubadi, M. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06493-4
Multi-basin assessment of terrestrial water storage changes and drought-lake interactions in Türkiye using GRACE/GRACE-FO, GLDAS, and SPEI (2002–2025) with future projections to 2050 under SSP2-4.5
Çelik, M. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06470-x
“SPEI is more effective than other indices, especially PDSI, in identifying hydrological drought and distinguishing drought types (Beguería et al. 2014).”
Decadal Fluctuations in Migratory Influx of Loxostege sticticalis (Lepidoptera: Crambidae) into Northern Xinjiang Are Regulated by Continental-Scale Outbreak Waves of This Species
Liu, H.-M., Zhang, Y., Wang, H.-Q., Li, D.-M., Ji, R., & Chen, X. (2026) · Journal of entomological science · doi:10.18474/jes25-54
Spatiotemporal Variations and Spatially Associated Factors of Drought in Hebei Province Based on Multi-Source Consistency Evaluation of Remote Sensing Drought Indices
Zhao, S., Wang, K.-Y., Zhang, X., Shang, G., Liu, C.-Y., Cui, T.-Y., & Ren, J. (2026) · Land · doi:10.3390/land15081406
From Global Hydroclimatic Signals to Local Water-Resources Adaptation: A Critical Review of Detection, Attribution, and Scale-Dependent Evidence
Kourgialas, N. (2026) · Climate · doi:10.3390/cli14080158
“The original SPEI formulation and subsequent methodological work also show that results depend on the method used to estimate atmospheric evaporative demand and to fit the underlying probability distribution [63,64].”
A XGBoost-based composite drought index combining multi-source remote sensing data for drought monitoring in China
Huang, X., Li, X.-H., Song, Y., Lv, Z., Xu, L.-G., & Zhang, D. (2026) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2026.103623
Deep soil water dynamics and water productivity indicate partial buffering of yield losses across land-use systems during drought
Wu, M., Roscher, C., Schädler, M., Tarkka, M. T., Vetterlein, D., & Schlüter, S. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110536
A data-based method to identify anthropogenic impacts on hydrological drought evolution: A case study for the Grande River basin, São Francisco River, Brazil
Lima, C. F. S., Silveira, C., Costa, A. C., & Ribeiro, G. (2026) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2026.103676
Assessing structural uncertainty in water–human systems: A multi-model ensemble approach for agricultural water management
González-López, H., Essenfelder, A., Sapino, F., Rivas-Tabares, D., Bodoque, J., & Pérez-Blanco, C. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110582
Differential responses of AGPP to meteorological, hydrological, and atmospheric droughts across four eco-geographical zones in the Jinsha River Basin, China
Gao, F., Pan, J.-Y., Wang, J., Wu, F.-Z., Deng, Y.-C., Ma, J., Chen, A., & He, S.-L. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110602
Multi-decadal drought variability and regime dynamics in Darfur, Sudan (1981–2025)
Ahmed, A., & Czimber, K. (2026) · Journal of Arid Environments · doi:10.1016/j.jaridenv.2026.105708
Assessment of drought hazard change across agro-climatic regions of India under 2 °C, 3 °C and 4 °C warming levels based on CMIP6 projections
Sreelakshmi, P., & Sawant, S. (2026) · Geoenvironmental Disasters · doi:10.1186/s40677-026-00402-7
“It considers precipitation variability as well as increased atmospheric evaporative demand through potential evapotranspiration (PET) under warming climates (Vicente-Serrano et al. 2010; Beguería et al. 2014).”
Intensifying dry-to-wet extremes across Europe: long-term evolution and contrasting impacts on vegetation dynamics
Chen, H., Tuo, Y., Boers, N., & Disse, M. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136153
Spatio-temporal assessment of Atlas cedar decline and vegetation-drought dynamics: Belezma National Park (Algeria), 1985–2025
Boukhalfa, S., & Bougherara, A. (2026) · Ecological Engineering & Environmental Technology · doi:10.12912/27197050/225418
“…the Palmer Drought Severity Index, and a growing-season Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al., 2010; Beguería et al., 2014), and tested the SPEI–vegetation relationship at the annual scale following the rationale that vegetation responds to drought…”
Feature Selection and Data Decomposition-Driven Drought Forecasting: Designing Hybrid Deep Learning Models
Parveen, A., Azhar, M., Gulistan, M., Ali, M., & Farooque, A. (2026) · Water resources management · doi:10.1007/s11269-026-04866-y
Cadmium and lead concentrations in the dorsal muscles of spotted knifefish (Chitala ornata Gray) from Laguna de Bay, Philippines.
Concepcion, R., Domingo, J. P. S., Orario, H., Pacheco, P. R. D. C., Solis, K., & Alfonso, N. (2026) · Journal of Trace Elements in Medicine and Biology · doi:10.1016/j.jtemb.2026.127941
Recovery time patterns of vegetation productivity following anomalies preceding abrupt declines
Wei, M., Li, H., Wan, X., Zhu, L., Meng, W., & Feng, J. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130667
A GRACE/‐FO‐Based Framework for Improved Reproduction of Water‐Balance‐Constrained Evapotranspiration Across Hydroclimatic Regimes
Li, X.-L., Kusche, J., Jin, T., Zhong, Y., & Gutknecht, B. (2026) · Earth and Space Science · doi:10.1029/2026ea005082
“Beyond hydrology, ET also support applications in climate analysis, drought monitoring, and land‐atmosphere coupling studies (Beguería et al., 2014; Tabari & Hossein-zadeh Talaee, 2014; Zhou et al., 2023).”
Thresholds of facilitation and competition—Climate modulates the growth–density relationship in Quercus robur
Ambs, D., Caicoya, A. T., Peters, R. L., Biber, P., Schmied, G., Hilmers, T., Uhl, E., Tomelleri, E., Steinacker, L., & Pretzsch, H. (2026) · Journal of Ecology · doi:10.1111/1365-2745.70426
“To quantify the dynamic climatic conditions and water availability during our survey period, we used the Standardised Precipitation Evapotranspiration Index (SPEI; Beguería et al., 2014 ).”
Drought Analysis Using Machine Learning and Deep Learning on Climatic and Remote Sensing Data
Mallik, R., Behera, D. K., & Mohanty, S. (2026) · 2026 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11717291
“[9] 3) Normalized Difference Vegetation Index: The Normalized Difference Vegetation Index (NDVI) is derived from satellite-based red and near-infrared reflectance and serves as a proxy for vegetation health and density.”
Differences in the Climate Responses of Radial Growth and Water Use Efficiency in Larix sibirica Under Drought Stress
Huang, X.-M., Xu, X.-B., Che, J., Zeng, G., Lv, Y., Qian, J.-R., & Ye, M. (2026) · Forests · doi:10.3390/f17080889
Temperature-induced shift in 21st-century Amazon droughts
Albuquerque, R., dos Santos, D. D., Miranda, V., Peres, L., Trigo, R., Nunes, A., Liberato, M., Gouveia, C., & Libonati, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-62139-5
“…a fixed surface resistance of 70 s m −1 , and an albedo of 0.23) rather than ecosystem-specific parameters, this approach effectively isolates purely atmospheric forcing from the complex biophysical dynamics of the AB 79 , allowing for a standardized spatial comparison across different regions 92 .”
Influential
Spatial Assessment of Future Drought Risk in Agricultural Lands: A CMIP6-Based Analysis
Şekeroğlu, A., & Karlı, R. G. Ö. (2026) · Tarım Bilimleri Dergisi · doi:10.15832/ankutbd.1842184
Assessment of drought dynamics and vegetation response across Moroccan climatic zones using SPEI-12 and MODIS NDVI (2010–2024): trends, frequencies, and correlations
Mourjane, M. (2026) · Acta Geophysica · doi:10.1007/s11600-026-01968-4
A Decomposition-Based Hybrid Prophet–LSTM Framework for SPEI-12 Drought Forecasting in Kano State, Nigeria
Olaleye, O. S., Faloye, O. T., Adeyeri, O. E., Akintola, O. A., Ogunrinde, A. T., Adabembe, B. A., Babalola, T. E., & Akinremi, J. O. (2026) · AgriEngineering · doi:10.3390/agriengineering8080307 · Cited by 1
SPEI-Based Drought Regionalization and Cluster-Specific Machine Learning Forecasting in the U.S. Black Belt
Nahidi, N., Zech, W. C., Kamali, M. I., & Nazari, R. (2026) · Pure and Applied Geophysics · doi:10.1007/s00024-026-04045-5 · Cited by 1
“Since standardized drought indices such as SPEI exhibit substantial interannual variability and oscillatory behavior around zero (Beguerı´a et al., 2014; Spinoni et al., 2013), regression-based goodness-of-ﬁt metrics such as R 2 may remain relatively low even when gradual long-term trends are…”
Future Climate Change and Extremes in the Wami Basin, Tanzania: A CMIP6 Multi-Model Assessment Across Rainfall Regimes
Simon, C., Tariq, A., Msigwa, A., Mwaijengo, G., & Komakech, H. (2026) · Earth Systems and Environment · doi:10.1007/s41748-026-01327-9
“…temperature-based approaches such as the Thornthwaite method, the Hargreaves formulation has lower data requirements and has been reported to provide reasonable agreement with FAO PM estimates in semi-arid environments (Beguería et al. 2014; Droogers and Allen 2002; Harg-reaves and Allen 2003).”
Multiscale and seasonal meteorological drought variability in China’s Lancang River Basin: Relative roles of precipitation and potential evapotranspiration
Lei, J.-S., Zhang, H.-L., Ji, X.-J., Liang, H.-L., Zhu, X., Yu, H., & Wang, L. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06437-y
Influence of solar radiation on hydroclimatic variability in Bangladesh: evidence from trend analysis and variable autoregressive modelling
Upama, S. A., & Islam, A. R. M. T. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06426-1
“An SPEI of 0 signifies a number that corresponds to the 50th percentile of the cumulative probability of D, based on a Log-logistic distribution (Beguería et al. 2014).”
SPEI-Based Drought Frequency, Intensity, and Duration from CMIP6 Models Under SSP1-2.6 and SSP5-8.5 Across Tropical–Subtropical Monsoon Asia
Liu, M., Wu, W.-X., Zhang, Z., Ren, X., Wang, K., Cheng, J., Yang, B., & Geng, J. (2026) · Water · doi:10.3390/w18141745
Characterization of the impacts of climate change on meteorological and hydrological drought in the koliba-corubal watershed of Guinea and Guinea-Bissau region
Walù, C. E., & Faye, C. (2026) · Water Practice & Technology · doi:10.2166/wpt.2026.383
Enhanced seasonal soil moisture forecasts by integrating APCC multi-model ensemble predictions into a land surface model framework
Choi, C., Kim, M.-S., Yoon, J.-H., Kim, H., & Jeong, J.-H. (2026) · Environmental Research Letters · doi:10.1088/1748-9326/ae8c54
Spatiotemporal dynamics of historical and future drought conditions over the Arabian Peninsula from ERA5 and CMIP6 data
Siddiqui, M. H., Assiri, M. E., Saeed, S., Ali, M. A., Rafei, M. E., Bahamdan, S., Alqahtani, A., Alhaidari, A., Alsalhi, A., Alzahrani, O., et al. (2026) · Proceedings of the Indian Academy of Sciences, Earth and Planetary Sciences · doi:10.1007/s12040-026-02890-5
“SPEI was introduced to address these limitations by incorporating both precipitation and potential evapotranspiration (PET) into a standardised water-balance framework (Vicente-Serrano et al. 2010; Beguer (cid:2) ıa et al. 2013).”
Assessing projected changes in meteorological drought severity and frequency under future climate scenarios: Insights from CMIP5 Models in Eastern Tigray, Northern Ethiopia
Rubangakene, D., Lamaro, G. P., Girma, A., & Zenebe, A. (2026) · PLOS Climate · doi:10.1371/journal.pclm.0000944
Climate extremes in the Eastern Himalayas: Zone-specific adaptation strategies and policy design for climate-resilient rainfed agriculture.
Choudhury, B. U., Mandal, S., Narzari, R., Zafar, M., & Bal, S. K. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130430
Season-specific drought hazard assessment in monsoon Bangladesh using an EDI-based composite index (1981–2018)
Kamruzzaman, M., Hasnat, J., & Bhuiyan, N. I. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06418-1

Authors: Beguería, S., Vicente-Serrano, S. M., Reig, F., & Latorre, B.
Affiliations: Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas (EEAD-CSIC); Zaragoza Spain
Consejo Superior de Investigaciones Científicas (ES)
ORCID: Vicente-Serrano, Sergio M.
Reig, Fergus
Latorre, Borja
Venue: International Journal of Climatology
Publisher: Wiley
Published: 2013-12-21
Volume: 34
Issue: 10
Pages: 3001-3023
ISSN: 0899-8418 (ISSN-L)
DOI: 10.1002/joc.3887
OpenAlex: W2165772416
Cited by: 2,128
References: 0
Citations by year: 2026: 300 · 2025: 284 · 2024: 290 · 2023: 246 · 2022: 252 · 2021: 195 · 2020: 174 · 2019: 135 · 2018: 104 · 2017: 61 · 2016: 48 · 2015: 32 · 2014: 5
FWCI: 21.61
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 619 d)
Locations: International Journal of Climatology (publishedVersion)
DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
Funders: European Commission (LIFE MEDACC)
Spanish Commission of Science and Technology (CGL2011-27574-CO2-02)
Spanish Commission of Science and Technology and FEDER (CGL2011-24185, CGL2011-27536)
Topics: Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
Keywords: Standardized Precipitation Evapotranspiration Index, drought monitoring, evapotranspiration modeling, parameter fitting, drought indices, climate data tools

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Metadata

Record

About these checks