Published May 12, 2025 | Version 1

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060

  • 1. Center for Mountain Futures, Kunming Institute of Botany Chinese Academy of Sciences, Kunming, Yunnan, China
  • 2. CIFOR-ICRAF China Program, World Agroforestry Centre, Kunming, Yunnan, China
  • 3. National Biodiversity Future Center - NBFC, Palermo, Sicily, Italy

Description

The "Future_Global_AI_PET Database" provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021–2041; 2041–2060) and two historical (1960–1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. An overview of the methodological approach, geospatial implementation and a technical evaluation of the results is provided. Historical results were compared for technical validation with weather station data ( PET: r 2 = 0 .72; AI: r 2 = 0.91) and the CRU_TS v 4.04 dataset ( PET: r 2 = 0 .67; AI: r 2 = 0 .80). Within the context of projected significant change in the near- and medium-term, the "Future_Global_AI_PET Database" provides a set of data projections and tools available for a variety of scientific and practical applications, illustrating trends and magnitude of predicted climatic and eco-hydrological impacts on terrestrial ecosystems. The Future_Global_AI_PET Database is archived in the ScienceDB repository and available online at: https://doi.org/10.57760/sciencedb.nbsdc.00086

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References

  • Allen RG, Pereira LS, Raes D (1998). Crop evapotranspiration—guidelines for computing crop water requirements. FAO irrigation and drainage paper 56.
  • Anwar SA, Mamadou O, Diallo I (2021). On the influence of vegetation cover changes and vegetation-runoff systems on the simulated summer Potential Evapotranspiration of tropical Africa using RegCM4. Earth Syst Environ. doi:10.1007/s41748-021-00252-3
  • Arora VK (2002). The use of the aridity index to assess climate change effect on annual runoff. J Hydrol. doi:10.1016/S0022-1694(02)00101-4
  • Aschonitis VG, Papamichail D, Demertzi K (2017). High-resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation. Earth Syst Sci Data. doi:10.5194/essd-9-615-2017
  • Basarin B, Lukić T, Matzarakis A (2020). Review of biometeorology of heatwaves and warm extremes in Europe. Atmosphere. doi:10.3390/atmos11121276
  • Bobrowski M, Weidinger J, Schickhoff U (2021). Is new always better? Frontiers in global climate datasets for modeling treeline species in the Himalayas. Atmosphere. doi:10.3390/atmos12050543
  • Choisnel E, de Villele O, Lacroze F (1992). Une approche uniformisee du calcul de l'evapotranspiration potentialle pour l'ensemble des pays de la communaute europeenne.
  • Derdous O, Tachi SE, Bouguerra H (2020). Spatial distribution and evaluation of aridity indices in northern Algeria. Arid Land Res Manag. doi:10.1080/15324982.2020.1796841
  • Doorebos J, Pruitt WO (1977). Guidelines for predicting crop water requirements. Irrigation and Drainage Paper 24,.
  • Droogers P, Allen RG (2002). Estimating reference evapotranspiration under inaccurate data conditions. Irrigation Drainage Syst. doi:10.1023/A:1015508322413
  • Eyring V, Bony S, Meehl GA (2016). Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev. doi:10.5194/gmd-9-1937-2016
  • (2001). World-wide agroclimatic data of FAO (FAOCLIM).
  • Fick SE, Hijmans RJ (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. doi:10.1002/joc.5086
  • Forster PM, Smith CJ, Walsh T (2023). Indicators of global climate change 2022: annual update of large-scale indicators of the state of the climate system and the human influence. Earth Syst Sci Data. doi:10.5194/essd-15-2295-2023
  • Girvetz EH, Zganjar C (2014). Dissecting indices of aridity for assessing the impacts of global climate change. Clim Change. doi:10.1007/s10584-014-1218-9
  • Gordon LJ, Steffen W, Jönsson BF (2005). Human modification of global water vapor flows from the land surface. Proc Natl Acad Sci U S A. doi:10.1073/pnas.0500208102
  • Greve P, Roderick ML, Ukkola AM (2019). The Aridity Index under global warming. Environ Res Lett. doi:10.1088/1748-9326/ab5046
  • Hadria R, Benabdelouhab T, Lionboui H (2021). Comparative assessment of different reference evapotranspiration models towards a fit calibration for arid and semi-arid areas. J Arid Environ. doi:10.1016/j.jaridenv.2020.104318
  • Hansen JE, Sato M, Simons L (2023). Global warming in the pipeline. Oxf Open Clim Chang. doi:10.1093/oxfclm/kgad008
  • Hargreaves GH (1994a). Defining and using reference evapotranspiration. J Irrig Drain Eng. doi:10.1061/(ASCE)0733-9437(1994)120:6(1132)
  • Hargreaves GH (1994b). Defining and using reference evapotranspiration. J Irrig Drain Eng. doi:10.1061/(ASCE)0733-9437(1994)120:6(1132)
  • Hargreaves GL, Hargreaves GH, Riley JP (1985). Irrigation water requirements for Senegal River Basin. J Irrig Drain Eng. doi:10.1061/(ASCE)0733-9437(1985)111:3(265)
  • Hargreaves GH, Samani ZA (1985). Reference crop evapotranspiration from temperature. Appl Eng Agric. doi:10.13031/2013.26773
  • Harris I, Osborn TJ, Jones P (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data. doi:10.1038/s41597-020-0453-3
  • Hijmans RJ, Cameron SE, Parra JL (2005). Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. doi:10.1002/joc.1276
  • Holland HD (1978). The ocean-atmosphere system: the chemistry of the atmosphere and oceans.
  • (2019). Summary for policymakers.
  • (2021). Summary for policymakers. doi:10.1017/9781009157896.001
  • (2022). Climate change 2022: mitigation of climate change - working group III contribution to the WGIII sixth assessment report of the intergovernmental panel on climate change.
  • Itenfisu D, Elliott RL, Allen RG (2003). Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort. J Irrig Drain Eng. doi:10.1061/(ASCE)0733-9437(2003)129:6(440)
  • Jensen ME, Allen RG, (eds.) (2016). Evaporation, evapotranspiration, and irrigation water requirements: task committee on revision of manual 70, second edition. doi:10.1061/9780784414057
  • Jensen DT, Hargreaves GH, Temesgen B (1997). Computation of ETo under nonideal conditions. J Irrig Drain Eng. doi:10.1061/(ASCE)0733-9437(1997)123:5(394)
  • McMahon TA, Finlayson BL, Peel MC (2016). Historical developments of models for estimating evaporation using standard meteorological data. WIREs Water. doi:10.1002/wat2.1172
  • Muñoz G, Greiser J (2006). CLIMWAT 2.0 for CROPWAT.
  • Nastos PT, Politi N, Kapsomenakis J (2013). Spatial and temporal variability of the Aridity Index in Greece. Atmos Res. doi:10.1016/j.atmosres.2011.06.017
  • Navarro A, Lee G, Martín R (2024). Uncertainties in measuring precipitation hinders precise evaluation of loss of diversity in biomes and ecotones. npj Clim Atmos Sci. doi:10.1038/s41612-024-00581-w
  • Pandey PK, Nyori T, Pandey V (2017). Estimation of reference evapotranspiration using data driven techniques under limited data conditions. Model Earth Syst Environ. doi:10.1007/s40808-017-0367-z
  • Pimentel R, Arheimer B, Crochemore L (2023). Which Potential Evapotranspiration formula to use in hydrological modeling world-wide?. Water Resour Res. doi:10.1029/2022WR033447
  • Richards M (2015). PyETo. Github.
  • Šimůnek J, van Genuchten MTh, Šejna M (2005). The HYDRUS-1D software package for simulating the movement of water, heat, and multiple solutes in variably saturated media.
  • Srdić S, Srđević Z, Stričević R (2023). Assessment of empirical methods for estimating reference evapotranspiration in different climatic zones of Bosnia and Herzegovina. Water. doi:10.3390/w15173065
  • Sutapa IW, Saparuddin , Wicana S (2020). Sensitivity of methods for estimating Potential Evapotranspiration to climate change. Iop Conf Ser Earth Environ Sci. doi:10.1088/1755-1315/437/1/012039
  • Tegos A, Malamos N, Efstratiadis A (2017). Parametric modelling of Potential Evapotranspiration: a global survey. Water. doi:10.3390/w9100795
  • Tegos A, Stefanidis S, Cody J (2023). On the sensitivity of standardized-precipitation-evapotranspiration and aridity indexes using alternative Potential Evapotranspiration models. Hydrology. doi:10.3390/hydrology10030064
  • Thornthwaite CW (1948). An approach toward a rational classification of climate. Geogr Rev. doi:10.2307/210739
  • Trabucco A, Zomer RJ (2008). Global Aridity Index and PET database v1 (Global_AI_PET_v1).
  • Trabucco A, Zomer RJ (2019). Global Aridity Index and Potential Evapotranspiration (ET0) climate database v2 (Global_AI_PET_v2). doi:10.6084/m9.figshare.7504448.v3
  • Trabucco A, Zomer RJ, Bossio DA (2008). Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric Ecosyst Environ. doi:10.1016/j.agee.2008.01.015
  • (1997). World Atlas of Desertification.
  • Valipour M, Bateni SM, Sefidkouhi MAG (2020). Complexity of forces driving trend of reference evapotranspiration and signals of climate change. Atmosphere. doi:10.3390/atmos11101081
  • Vicente-Serrano SM, Beguería S, López-Moreno JI (2010). A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J Clim. doi:10.1175/2009JCLI2909.1
  • Walter IA, Allen RG, Elliott R (2001). ASCE's standardized reference evapotranspiration equation. Watershed Management and Operations Management 2000. doi:10.1061/40499(2000)126
  • Wang W, Xing W, Shao Q (2015). How large are uncertainties in future projection of reference evapotranspiration through different approaches?. J Hydrol. doi:10.1016/j.jhydrol.2015.03.033
  • Zhou J, Jiang T, Wang Y (2020). Spatiotemporal variations of Aridity Index over the belt and road region under the 1.5°C and 2.0°C warming scenarios. J Geogr Sci. doi:10.1007/s11442-020-1713-z
  • Zomer RJ, Trabucco A, Bossio D (2008). Climate change mitigation: a spatial analysis of global land suitability for Clean Development Mechanism Afforestation and Reforestation. Agric Ecosyst Environ. doi:10.1016/j.agee.2008.01.014
  • Zomer R, Trabucco A, Straaten OV (2006). Carbon, land and water: a global analysis of the hydrologic dimensions of climate change mitigation through afforestation/reforestation. doi:10.3910/2009.101
  • Zomer RJ, Trabucco A (2024). Future global Aridity Index and PET database (CIMP_ 6).
  • Zomer RJ, Xu J, Spano D (2024). Extended Data: CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021–2060.
  • Zomer RJ, Xu J, Trabucco A (Dataset, 2022). Version 3 of the global Aridity Index and Potential Evapotranspiration database. Sci Data. doi:10.1038/s41597-022-01493-1
  • Zotarelli L, Dukes MD, Romero CC (2018). Step by step calculation of the penman-monteith evapotranspiration (FAO-56 Method).