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Estimating the potential evapotranspiration of Bulgaria using a high-resolution regional climate model

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Abstract

Observations made in situ by land evaporators are a useful data source for monitoring potential evapotranspiration (PET). The National Institute of Meteorology and Hydrology of Bulgaria operates the conventional small evaporators, which are sensitive to the site’s environment, local, and geographic factors. Furthermore, there are just a few sources of reliable long-term observational data available across the region. Therefore, developing a regional map or predicting the PET at a specific location both can benefit from using regional climate models. This problem was addressed by simulating the PET using the Hargreaves-Samani (HS) approach with the regional climate model (RegCM4). The RegCM4 with 25-km grid spacing for the area of Bulgaria from 1988 to 2010 was downscaled using ERA-Interim reanalysis. The original HS formula and its calibrated version were evaluated with respect to the Climate Research Unit (CRU). Results indicated that adjusting the radiation coefficient is far more efficient than adjusting the temperature coefficient. In addition, the calibrated HS formula, as compared to the default HS formula, consistently performs better at predicting the PET with respect to the CRU, as shown by a significantly smaller bias and a more accurate representation of the climatological yearly cycle. As a result, the RegCM4 can be used as one of the potential alternatives to create a regional map of PET of Bulgaria utilizing the calibrated version of the HS equation, both in the historical and under different future scenarios.

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Code availability

The RegCM4 model code is available at https://github.com/ictp-esp/RegCM/.

References

  • Abtew W, Obeysekera J, Iricanin N (2011) Pan evaporation and potential evapotranspiration trends in South Florida. Hydrol Process 25:958–969. https://doi.org/10.1002/hyp.7887

    Article  Google Scholar 

  • Allen RG, Smith M, Pereira LS, Perrier A (1994) An update for the calculation of reference evapotranspiration. ICID Bull 43:35–92

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Food and Agricultural Organization of the United Nations (FAO) Report 56. Rome. 300p.

  • Almorox J, Grieser J (2016) Calibration of the Hargreaves-Samani method for the calculation of reference evapotranspiration in different Köppen climate classes. Hydrol Res 47(2):521–531. https://doi.org/10.2166/nh.2015.091

    Article  Google Scholar 

  • Anwar SA (2021) On the contribution of dynamic leaf area index in simulating the African climate using a regional climate model (RegCM4). Theor Appl Climatol 143:119–129. https://doi.org/10.1007/s00704-020-03414-x

    Article  Google Scholar 

  • Anwar SA, Diallo I (2021a) The influence of two land-surface hydrology schemes on the terrestrial carbon cycle of Africa: a regional climate model study. Int J Climatol 41(Suppl. 1):E1202–E1216. https://doi.org/10.1002/joc.6762

    Article  Google Scholar 

  • Anwar SA, Diallo I (2021b) On the role of a coupled vegetation-runoff system in simulating the tropical African climate: a regional climate model sensitivity study. Theor Appl Climatol 145:313–325. https://doi.org/10.1007/s00704-02-03627-8

    Article  Google Scholar 

  • Anwar SA, Diallo I (2021c) A RCM investigation of the influence of vegetation status and runoff scheme on the summer Gross Primary Production of Tropical Africa. Theor Appl Climatol 145(3):1407–1420. https://doi.org/10.1007/s00704-021-03667-0

    Article  Google Scholar 

  • Anwar SA, Diallo I (2022) Modelling the Tropical African Climate using a state-of-the-art coupled regional climate-vegetation model. Clim Dyn 58:97–113. https://doi.org/10.1007/s00382-02-05892-9

    Article  Google Scholar 

  • Anwar SA, Mamadou O, Diallo I, Sylla MB (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 5:883–897. https://doi.org/10.1007/s41748-021-00252-3

    Article  Google Scholar 

  • Anwar SA, Salah Z, Khald W, Zakey AS (2022) Projecting the potential evapotranspiration of egypt using a high-resolution regional climate model (RegCM4). Environ Sci Proc 19(1):43. https://doi.org/10.3390/ecas2022-12841

    Article  Google Scholar 

  • Čadro S, Uzunović M, Žurovec J, Žurovec O (2017) Validation and calibration of various reference evapotranspiration alternative methods under the climate conditions of Bosnia and Herzegovina. Int Soil Water Conserv Res 5:309–324. https://doi.org/10.1016/j.iswcr.2017.07.002

    Article  Google Scholar 

  • Clough SA, Shephard MW, Mlawer EJ, Delamere JS (2005) Atmospheric radiative transfer modeling: a summary of the AER codes, Short Communication. J Quant Spectrosc Radiat Transf 91:233–244. https://doi.org/10.1016/j.jqsrt.2004.05.058

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. QJR Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828

  • Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR Community Climate Model (No. NCAR/TN-387+STR). United States of America: University Corporation for Atmospheric Research. https://doi.org/10.5065/D67W6959.

  • Dimitriadou S, Nikolakopoulos KG (2021) Reference evapotranspiration (ETo) methods implemented as ArcMap models with remote-sensed and ground-based inputs, examined along with MODIS ET, for Peloponnese. Greece ISPRS Int J Geo-Inf 10:390. https://doi.org/10.3390/ijgi10060390

    Article  Google Scholar 

  • Dimitriadou S, Nikolakopoulos KG (2022) Multiple linear regression models with limited data for the prediction of reference evapotranspiration of the Peloponnese. Greece Hydrol 9:124. https://doi.org/10.3390/hydrology9070124

    Article  Google Scholar 

  • Droogers P, Allen RG (2002) Estimating reference evapotranspiration under inaccurate data conditions. Irrig Drain Syst 16:33–45. https://doi.org/10.1023/A:1015508322413

    Article  Google Scholar 

  • Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48(21):2313–2335. https://doi.org/10.1175/1520-0469(1991)048%3C2313:ASFRCC%3E2.0.CO;2

    Article  Google Scholar 

  • Erfanian A, Wang G, Yu M, Anyah R (2016) Multi-model ensemble simulations of present and future climates over West Africa: impacts of vegetation dynamics. J Adv Model Earth Syst 8:1411–1431. https://doi.org/10.1002/2016ms000660

    Article  Google Scholar 

  • Er-Raki S, Chehbouni A, Khabba S, Simonneaux V, Jarlan L, Ouldbba A, Rodriguez JC, Allen R (2010) Assessment of reference evapotranspiration methods in semi-arid regions: can weather forecast data be used as alternate of ground meteorological parameters? J Arid Environ 74(12):1587–1596. https://doi.org/10.1016/j.jaridenv.2010.07.002

    Article  Google Scholar 

  • Ferreira LB, da Cunha FF, Fernandes EI (2022) Exploring machine learning and multi-task learning to estimate meteorological data and reference evapotranspiration across Brazil. Agric. Water Manag. 259:107281. https://doi.org/10.1016/j.agwat.2021.107281

    Article  Google Scholar 

  • Fu G, Charles SP, Yu J (2009) A critical overview of pan evaporation trends over the last 50 years. Clim Change 97:193. https://doi.org/10.1007/s10584-009-9579-1

    Article  Google Scholar 

  • Gentilucci M, Bufalini M, Materazzi M, Barbieri M, Aringoli D, Farabollini P, Pambianchi G  (2021) Calculation of potential evapotranspiration and calibration of the Hargreaves equation using geostatistical methods over the last 10 years in Central Italy. Geosci 11:348. https://doi.org/10.3390/geosciences11080348

  • Giorgi F, Huang Y, Nishizawa K, Fu C (1999) A seasonal cycle simulation over eastern Asia and its sensitivity to radiative transfer and surface processes. J Geophys Res 104:6403–6423. https://doi.org/10.1029/1998JD200052

    Article  Google Scholar 

  • Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla MB, Bi X, Elguindi N, Diro GT, Nair V, Giuliani G, Turuncoglu UU, Cozzini S, Güttler I, O’Brien TA, Tawfik AB, Shalaby A, Zakey AS, Steiner AL, Stordal F, Sloan LC, Brankovic C  (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29. https://doi.org/10.3354/cr01018

    Article  Google Scholar 

  • Hargreaves GL, Allen RG (2003) History and evaluation of Hargreaves evapotranspiration equation. J Irrigat Drain Eng 129(1):53–63. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:1(53)

    Article  Google Scholar 

  • Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7:109. https://doi.org/10.1038/s41597-020-0453-3

  • Hegerl GC, Black E, Allan RP, Ingram WJ, Polson D, Trenberth KE, Chadwick RS, Arkin PA, Sarojini BB, Becker A, Dai A, Durack PJ, Easterling D, Fowler HJ, Kendon EJ, Huffman GJ, Liu C, Marsh R, New M, Osborn TJ, Skliris N, Stott PA, Vidale PL, Wijffels SE, Wilcox LJ, Willett KM, Zhang X (2015) Challenges in quantifying changes in the global water cycle. Bull Am Meteorol Soc 96(7):1097–1115. https://doi.org/10.1175/BAMS-D-13-00212.1

  • Hersbach H, Bell B, Berrisford P, Hirahara S, Horanyi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Holm E, Janiskova M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut JN (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/10.1002/qj.3803

  • Holtslag AAM, Boville BA (1993) Local versus nonlocal boundary layer diffusion in a global model. J Clim 6:1825–1842. https://doi.org/10.1175/1520-0442(1993)006%3c1825:LVNBLD%3e2.0.CO;2

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: Synthesis report – summary for policy makers. IPCC Secretariat, Geneva, Switzerland, p 22

    Google Scholar 

  • Kazandjiev V, Georgieva V, Malasheva, Atanassov D (2022) Evapotranspiration and drought in different agricultural zones of Bulgaria. In: Saifullah M, Tardio G, Mickovski SB (eds) Challenges in Agro-Climate and Ecosystem. IntechOpen. https://doi.org/10.5772/intechopen.102391.

  • Klein Tank AMG, Wijngaard JB, Können GP, Böhm R, Demarée G, Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C, Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass A, Bukantis A, Aberfeld R, van Engelen AFV, Forland E, Mietus M, Coelho F, Mares C, Razuvaev V, Nieplova E, Cegnar T, Antonio López J, Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O, Alexander LV, Petrovic P (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European climate. Intern J Climatol 22:1441–1453. https://doi.org/10.1002/joc.773

  • Koprivšek M, Vihar A, Petan S (2021) Application of the Hargreaves method for calculating the potential evapotranspiration in the hydrological modelling. Acta Hydrotech 34(61):77–92. https://doi.org/10.15292/acta.hydro.2021.06

    Article  Google Scholar 

  • Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen-Geiger climate classification updated. Meteorol Z 15:259–263. https://doi.org/10.1127/0941-2948/2006/0130

    Article  Google Scholar 

  • Marinova T, Malcheva K, Bocheva L, Trifonova L (2017) Climate profile of Bulgaria in the period 1988–2016 and brief climatic assessment of 2017. Bulg J Meteorol Hydrol 22(3–4):2–15. http://meteorology.meteo.bg/global-change/files/2017/BJMH_2017_vol_22_3-4/BJMH_v22_issue_3-4_tmarinova_climate.pdf.

  • Mehboob MS, Kim Y, Lee J, Um MJ, Erfanian A, Wang G (2020) Projection of vegetation impacts on future droughts over West Africa using a coupled RegCM-CLM-CN-DV. Clim Change 163:653–668. https://doi.org/10.1007/s10584-020-02879-z

    Article  Google Scholar 

  • Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712. https://doi.org/10.1002/joc.1181

    Article  Google Scholar 

  • Murat C, Hatice C, Tefaruk H, Kisi O (2017) Modifying Hargreaves-Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey. Hydrol Res 48(2):480–497. https://doi.org/10.2166/nh.2016.217

    Article  Google Scholar 

  • New M, Hulme M, Jones PD (2000) Representing twentieth century space-time climate variability. Part 2: development of 1901–96 monthly grids of terrestrial surface climate. J Clim 13:2217–2238. https://doi.org/10.1175/1520-0442(2000)013%3C2217:RTCSTC%3E2.0.CO;2

    Article  Google Scholar 

  • Oleson KW, Niu G, Yang ZL, Lawrence DM, Thornton PE, Lawrence PJ, Stöckli R, Dickinson RE, Bonan GB, Levis S, Dai A, Qian T  (2008) Improvements to the community land model and their impact on the hydrologic cycle. J Geophys Res 113:G01021. https://doi.org/10.1029/2007JD000563

    Article  Google Scholar 

  • Oleson KW, Lawrence DM, Bonan GB, Drewniak B, Huang M, Koven CD, Levis S, Li F, Riley WJ, Subin ZM, Swenson S, Thornton PE, Bozbiyik A, Fisher R, Heald CL, Kluzek E, Lamarque JF, Lawrence PJ, Leung LR, Lipscomb W, Muszala SP, Ricciuto DM, Sacks WJ, Sun Y, Tang J, Yang ZL (2013) Technical description of version 4.5 of the Community Land Model (CLM). National Center for Atmospheric Research, Boulder. NCAR technical note NCAR/TN-503+STR. https://doi.org/10.5065/D6RR1W7M

  • Oudin L, Hervieu F, Michel C, Perrin C, Andréassian V, Anctil F, Loumagne C (2005) Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. J Hydrol 303(1–4):290–306. https://doi.org/10.1016/j.jhydrol.2004.08.026

    Article  Google Scholar 

  • Popova Z, Kercheva M, Pereira LS (2006) Validation of the FAO methodology for computing ETo with limited data. Application to south Bulgaria. Irrig and Drain 55:201–215. https://doi.org/10.1002/ird.228

    Article  Google Scholar 

  • Potop V, Boroneant C (2014) Assessment of potential evapotranspiration at Chisinau station. In: Rožnovský J, Litschmann T (eds) Mendel and Bioclimatology, Conference proceedings from the Mendel and Bioclimatology International Conference, 3–5 September 2014. Czech Republic, Brno, pp 343–354

    Google Scholar 

  • Qian JH, Robertson AW, Moron V (2010) Interactions among ENSO, the monsoons, and diurnal cycle in rainfall variability over Java, Indonesia. J Atmos Sci 67:3509–3524. https://doi.org/10.1175/2010JAS3348.1

    Article  Google Scholar 

  • Rolle M, Tamea S, Claps P (2021) ERA5-based global assessment of irrigation requirement and validation. PLoS ONE 16(4):e0250979. https://doi.org/10.1371/journal.pone.0250979

    Article  Google Scholar 

  • Singer M, Asfaw D, Rosolem R, Cuthbert MO, Miralles DG, MacLeod D, Michaelides K (2021) Hourly potential evapotranspiration (hPET) at 0.1degs grid resolution for the global land surface from 1981-present. Sci Data 8:224. https://doi.org/10.1038/s41597-021-01003-9

    Article  Google Scholar 

  • Sperna Weiland FC, Tisseuil C, Dürr HH, Vrac M, Van Beek LPH (2012) Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study. Hydrol Earth Syst Sci 16:983–1000. https://doi.org/10.5194/hess-16-983-2012

    Article  Google Scholar 

  • Srivastava A, Sahoo B, Raghuwanshi NS, Singh R (2017) Evaluation of variable-infiltration capacity model and MODIS-terra satellite-derived grid-scale evapotranspiration estimates in a River Basin with Tropical Monsoon-Type climatology. J Irrig Drain Eng 143(8):04017028. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199

    Article  Google Scholar 

  • Srivastava A, Sahoo B, Raghuwanshi NS, Chatterjee C (2018) Modelling the dynamics of evapotranspiration using variable infiltration capacity model and regionally calibrated Hargreaves approach. Irrig Sci 36(4):289–300. https://doi.org/10.1007/s00271-018-0583-y

    Article  Google Scholar 

  • Srivastava A, Kumari N, Maza M (2020) Hydrological response to agricultural land use heterogeneity using variable infiltration capacity model. Water Resour Manag 34(12):3779–3794. https://doi.org/10.1007/s11269-020-02630-4

    Article  Google Scholar 

  • Stan FI, Neculau G (2015) Estimation of reference evapotranspiration from pan evaporation data in Romania. Rom J Meteorol 12(1–2):65–73. http://rjm.inmh.ro/articole/vol12-1-2/RJM2015-3.pdf.

  • Stoyanova J, Georgiev C, Neytchev P, Kulishev A (2019) Spatial-temporal variability of land surface dry anomalies in climatic aspect: biogeophysical insight by meteosat observations and SVAT modeling. Atmos 10:636. https://doi.org/10.3390/atmos10100636

    Article  Google Scholar 

  • Stoyanova J, Georgiev C, Neytchev P, Kulishev A (2021) Drought and vegetation monitoring using satellite derived climate data records. In 7th SALGEE Virtual Workshop "Drought & Vegetation Monitoring: Energy–Water Cycle", https://training.eumetsat.int/pluginfile.php/45224/mod_folder/content/0/Session5_Stoyanova.pdf?forcedownload=1.

  • Wilco T, Walter WI, Peter D (2013) Climate change projections of precipitation and reference evapotranspiration for the Middle East and Northern Africa until 2050. Int J Climatol 33:3055–3072. https://doi.org/10.1002/joc.3650

    Article  Google Scholar 

  • World Meteorological Organization (2008) Guide to hydrological practices. Volume I, Hydrology – From Measurement to Hydrological Information. Sixth edition (WMO-168-Vol-I), Geneva, Switzerland.

  • World Meteorological Organization (2021) Guide to instruments and methods of observation. Volume I: Measurement of Meteorological Variables (WMO-No. 8-I), Geneva, Switzerland.

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Acknowledgements

The Egyptian Meteorological Authority (EMA) is acknowledged for providing the computational power to run the RegCM4 model. The ERA-Interim dataset can be retrieved from http://www.clima-dods.ictp.it/RegCM4/. University of East Anglia was acknowledged for providing the observed dataset of potential evapotranspiration from the web link: https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.06/cruts.2205201912.v4.06/pet/. ERA5 was retrieved from https://cds.climate.copernicus.eu/. Version 25.0e of the E-OBS product was downloaded from https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php.

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Authors and Affiliations

  1. Qobry EL-Kobba, Egyptian Meteorological Authority, P.O. Box 11784, Cairo, Egypt

    Samy A. Anwar

  2. National Institute of Meteorology and Hydrology, Tsarigradsko shose 66, Sofia, 1784, Bulgaria

    Krastina Malcheva

  3. Faculty of Science, University of Technology Sydney, Sydney, Australia

    Ankur Srivastava

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  1. Samy A. Anwar
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  3. Ankur Srivastava
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Contributions

Samy A. Anwar designed the simulation, analyzed the results, and wrote the manuscript. Krastina Malcheva provided the data analysis of 20-m2 evaporation tanks and conventional small evaporators GGI-3000. Krastina Malcheva and Ankur Srivastava participated in analyzing the results and writing the manuscript. The final version was revised by all authors.

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Correspondence to Samy A. Anwar.

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Anwar, S.A., Malcheva, K. & Srivastava, A. Estimating the potential evapotranspiration of Bulgaria using a high-resolution regional climate model. Theor Appl Climatol 152, 1175–1188 (2023). https://doi.org/10.1007/s00704-023-04438-9

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