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Journals / Journal of Agricultural Sciences / 2026 / Volume: 32 Issue: 3

Spatial Assessment of Future Drought Risk in Agricultural Lands: A CMIP6-Based Analysis

Ahmet Şekeroğlu(AMASYA UNIVERSITY, FACULTY OF ARCHITECTURE, DEPARTMENT OF CITY AND REGIONAL PLANNING)0000-0003-0764-4944
Rukiye Gizem Öztaş Karlı(BARTIN ÜNİVERSİTESİ, MÜHENDİSLİK, MİMARLIK VE TASARIM FAKÜLTESİ, PEYZAJ MİMARLIĞI BÖLÜMÜ, PEYZAJ PLANLAMA VE TASARIM ANABİLİM DALI)0000-0003-0999-418X
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Publication Date
2026-07-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that under the end of the century SSP5-8.5 scenario, approximately 83-90% of the region may become semi-arid.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The acceleration of climate change is intensifying drought severity.
📊
Key Findings & Data:Increasing drought conditions pose significant risks to agricultural lands and food security.
💡
Conclusion & Impact:The findings indicate that under the end of the century SSP5-8.5 scenario, approximately 83-90% of the region may become semi-arid.
🔬Design:Modeling & Simulation
📍Scope:2025-2040 Verileri
⚙️Variables:Climate scenarios · Drought · Aridity index

Abstract

The acceleration of climate change is intensifying drought severity. Increasing drought conditions pose significant risks to agricultural lands and food security. The aim of this study is to determine the spatial exposure rate of agricultural land classes to drought conditions based on climate scenarios and different projection periods. In this study, a spatial analysis based on the SSP2-4.5 and SSP5-8.5 scenarios was conducted using the HadGEM3-GC3.1, MIROC6, and MPI-ESM1-2-HR climate models. Assessments covering Türkiye’s Central Anatolia Region were conducted within a geographic information system (GIS)-based spatial modeling framework and encompass projections for the near future (2025-2040) and the end of the century (2081-2100). The findings indicate that under the end of the century SSP5-8.5 scenario, approximately 83-90% of the region may become semi-arid. Model results suggest that, by end of the century, the spatial exposure rate of the most productive agricultural lands, classified as prime agricultural lands and specialty crop lands, may reach a minimum of 76% under the SSP2 4.5 scenario and exceed 90% under the SSP5-8.5 scenario. This situation reveals a substantial risk to agricultural production and food security in the region. The coefficient of variation values support that the multi model results produce similar outcomes in terms of spatial distribution and consistency. These findings highlight the urgent need to rapidly develop drought-focused spatial strategies to ensure the sustainability of agricultural production in Central Anatolia Region. In this context, the scalable and transferable agricultural land class-drought oriented spatial exposure rate approach proposed in this study can be considered an important decision-support tool for policymakers.

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Cite This Article

Şekeroğlu, A., Karlı, R. G. Ö. (2026). Spatial Assessment of Future Drought Risk in Agricultural Lands: A CMIP6-Based Analysis. Journal of Agricultural Sciences, Volume: 32 Issue: 3. https://doi.org/10.15832/ankutbd.1842184

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References (99)

  1. Abu Arra A, Birpınar M E, Gazioğlu Ş A & Şişman E (2024). Critical drought characteristics: A new concept based on dynamic time period scenarios. Atmosphere 15(7): 768. https://doi.org/10.3390/atmos15070768
  2. Afshar M H, Bulut B, Duzenli E, Amjad M & Yilmaz M T (2022). Global spatiotemporal consistency between meteorological and soil moisture drought indices. Agricultural and Forest Meteorology 316: 108848. https://doi.org/10.1016/j.agrformet.2022.108848
  3. Andrade C, Contente J & Santos J A (2021). Climate change projections of aridity conditions in the Iberian Peninsula. Water 13(15): 2035. https://doi.org/10.3390/w13152035
  4. Andrews M B, Ridley J K, Wood R A, Andrews T, Blockley E W, Booth B, Burke E, Dittus A J, Florek P, Gray L J, Haddad S, Hardiman S C, Hermanson L, Hodson D, Hogan E, Jones G S, Knight J R, Kuhlbrodt T, Misios S & Sutton R T (2020). Historical simulations with HADGEM3‐GC3.1 for CMIP6. Journal of Advances in Modeling Earth Systems 12(6):e2019MS001995. https://doi.org/10.1029/2019MS001995
  5. Arabameri A, Chandra Pal S, Santosh M, Chakrabortty R, Roy P & Moayedi H (2022). Drought risk assessment: Integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques. Geocarto International 37(21): 6087-6115. https://doi.org/10.1080/10106049.2021.1926558
  6. Assamnew A D, Gobie B G & Habtemicheal B A (2025). Evaluating the accuracy of gridded precipitation datasets and CMIP6 GCM ensemble means against gauge observations in South Wollo, Ethiopia. Scientific Reports 15(1): 34197.https://doi.org/10.1038/s41598-025-15567-8
  7. Asseng S, Ewert F, Martre P, Rötter R P, Lobell D B, Cammarano D & Zhu Y (2015). Rising temperatures reduce global wheat production. Nature Climate Change 5(2): 143-147. https://doi.org/10.1038/nclimate2470
  8. Ayugi B, Zhihong J, Zhu H, Ngoma H, Babaousmail H, Rizwan K & Dike V (2021). Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa. International Journal of Climatology 41(15): 6474-6496. https://doi.org/10.1002/joc.7207
  9. Bakanoğulları F, Bahar E, Kıvrak C & Gür M (2022). Assessment of Meteorological and Agricultural Drought Analysis in Kırklareli province. Tekirdağ Ziraat Fakültesi Dergisi 19(4): 756-768. https://doi.org/10.33462/jotaf.1060752
  10. Bannayan M, Sanjani S, Alizadeh A, Lotfabadi S S & Mohamadian A (2010). Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research 118(2): 105-114. https://doi.org/10.1016/j.fcr.2010.04.011
  11. Baykal T, Taylan D & Terzi Ö (2023). Assessment of potential future meteorological drought for Isparta Province. Doğal Afetler ve Çevre Dergisi, 9(1): 90-100. https://doi.org/10.21324/dacd.1165500 (In Turkish)
  12. Beck H E, Zimmermann N E, McVicar T R, Vergopolan N, Berg A & Wood E F (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5(1): 1-12. https://doi.org/10.1038/sdata.2018.214
  13. Beguería S, Vicente‐Serrano S M, Reig F & Latorre B (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology 34: 3001-3023. https://doi.org/10.1002/joc.3887.
  14. Benli M & Özdemir R (2025). Balancing climate sensitivity and resilience: Environmental impacts on selected crop yields in Türkiye. Journal of Agricultural Sciences 31(3): 670-689. https://doi.org/10.15832/ankutbd.1583278
  15. Bharambe K P, Shimizu Y, Kantoush S A, Sumi T & Saber M (2023). Impacts of climate change on drought and its consequences on the agricultural crop under worst-case scenario over the Godavari River Basin, India. Climate Services 32: 100415. https://doi.org/10.1016/j.cliser.2023.100415
  16. Bocchiola D, Nana E & Soncini A (2013). Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agric. Water Manag. 116: 50-61 https://doi.org/10.1016/j.agwat.2012.10.009
  17. Bonham-Carter GF (1994). Geographical information systems for geoscientists: Modeling with GIS. Pergamon, New York. Bozoglu M, Başer U, Eroglu N A & Topuz B K (2019). Impacts of climate change on Turkish agriculture. Journal of International Environmental Application and Science 14(3): 97-103
  18. Charalampopoulos I, Droulia F & Tsiros I X (2023). Projecting bioclimatic change over the south-eastern European agricultural and natural areas via ultrahigh-resolution analysis of the de Martonne Index. Atmosphere 14(5): 858. https://doi.org/10.3390/atmos14050858Cook B
  19. I, Mankin J S & Anchukaitis K J (2018). Climate change and drought: From past to future. Current Climate Change Reports 4(2): 164 179. https://doi.org/10.1007/s40641-018-0093-2
  20. Çamalan G & Çetin S (2022). Drought projections for Türkiye, 97-137. Çevre ve Meteoroloji içinde. (Ed. V. M. Çoşkun, H. Doğan & H. Eroğlu). Sonçağ Yayıncılık. (In Turkish)
  21. Çamalan G, Oğuz K & Akıl S (2023). Investigation of the effect of global warming on drought conditions in the Central Anatolia Region. Disaster Science and Engineering 9(1): 35-55 (In Turkish)
  22. Çelik M Ö & Yakar M (2024). Investigation of Mersin’s drought-threat through different drought indexes. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 24(1): 71-84. https://doi.org/10.35414/akufemubid.1331753 (In Turkish)
  23. Çelik M Ö, Orhan O & Kurt M A (2025). Predicting climate change impacts on sub-tropical fruit suitability using maxent: A regional study from southern Türkiye. Sustainability 17(12): 5487. https://doi.org/10.3390/su17125487
  24. Çetin B & Kumanlioglu A (2023). Meteorological and hydrological drought analysis of Medar Basin. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 25(73): 167-180 (In Turkish)
  25. De Martonne E (1926). Aerisme, et Índices d’aridite. Comptes Rendus de L’Academy of Science 182: 1395-1398 Demiral E & Bahçekapılı C Z Z (2025). Climate vulnerability in agriculture: a comparative analysis for developed and developing countries. Doğuş Üniversitesi Dergisi 26(2): 187-211 (In Turkish)
  26. Deniz A, Toros H & Incecik S (2011). Spatial variations of climate indices in Turkey. International Journal of Climatology 31(3): 394-403. https://doi.org/10.1002/joc.2081
  27. Dietz K J, Zörb C & Geilfus C M (2021). Drought and crop yield. Plant Biology 23(6): 881-893
  28. Dinç N, Aydinşakir K, Işık M & Büyüktaş D (2016). Drought analysis of Antalya province by standardized precipitation index (SPI). Derim, 33(2): 279-298. (In Turkish)
  29. Emberger L (1930). La v´eg´etation de la r´egion m´editerran´eenne: essai d’une classification des groupements v´eg´etaux. Rev. G´en. Bot. 42: 705-772
  30. Erinç S (1965). An attempt on precipitation efficiency and a new index. Istanbul University Institute Release. Baha Press: Istanbul, Turkey.
  31. Eyring V, Bony S, Meehl G A, Senior C A, Stevens B, Stouffer R J & Taylor K E (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9(5): 1937-1958 https://doi.org/10.5194/gmd-9-1937-2016, 2016.
  32. Feng S & Fu Q (2013). Expansion of global drylands under a warming climate. Atmospheric Chemistry and Physics: 13(19), 10081-10094. https://doi.org/10.5194/acp-13-10081-2013
  33. Fick S E & Hijmans R J (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology: 37(12): 4302-4315. https://doi.org/10.1002/joc.5086
  34. Gao F, Zhang S, Yu R, Zhao Y, Chen Y & Zhang Y (2023). Agricultural drought risk assessment based on a comprehensive model using geospatial techniques in Songnen Plain, China. Land 12(6): 1184. https://doi.org/10.3390/land12061184 Gebremedhin M A, Kahsay G H & Fanta H G (2018). Assessment of spatial distribution of aridity indices in Raya valley, northern Ethiopia. Applied Water Science 8(8): 217 https://doi.org/10.1007/s13201-018-0868-6
  35. Ghazi B, Salehi H, Przybylak R & Pospieszyńska A (2025). Assessment of drought conditions under climate change scenarios in Central Europe (Poland) using the https://doi.org/10.1016/j.cliser.2025.100591 standardized precipitation index (SPI). Climate Services 39: 100591.
  36. Grose M R, Narsey S, Delage F P, Dowdy A J, Bador M, Boschat G & Power S (2020). Insights from CMIP6 for Australia's future climate. Earth's Future, 8(5), e2019EF001469. https://doi.org/10.1029/2019EF001469
  37. Gumus B, Oruc S, Yucel I & Yilmaz M T (2023). Impacts of climate change on extreme climate indices in Türkiye driven by high-resolution downscaled CMIP6 climate models. Sustainability, 15(9): 7202 https://doi.org/10.3390/su15097202
  38. Hausfather Z (2019) CMIP6: the next generation of climate models explained. Carbon Brief. (Web page: https://www.carbonbrief.org/cmip6 the-next-generation-of-climate-models-explained) (Data accessed: October 2025).
  39. Huang J, Ji M, Xie Y, Wang S, He Y & Ran J (2016). Global semi-arid climate change over last 60 years. Climate Dynamics 46(3): 1131-1150 https://doi.org/10.1007/s00382-015-2636-8
  40. IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Eds. T F, Stocker, D. Qin, G K, Plattner, M. Tignor, S K Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex & P M Midgley). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp
  41. IPCC (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Eds. V R, Barros, C B Field, D J Dokken, M D
  42. Mastrandrea, K J Mach, T E Bilir, M Chatterjee, K L Ebi, Y O Estrada, R C Genova, B Girma, E S Kissel, A N Levy, S. MacCracken, P R Mastrandrea & L L White). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 688 pp
  43. IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Eds. H O, Pörtner, D C Roberts, M Tignor, E S Poloczanska, K Mintenbeck, A Alegría, M Craig, S Langsdorf, S Löschke, V Möller, A Okem, B Rama). Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp
  44. IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Eds. V P Masson-Delmotte, A Zhai, S L Pirani, C Connors, S Péan, N Berger, Y Caud, L Chen, M I Goldfarb, M Gomis, K Huang, E Leitzell, J B R Lonnoy, T K Matthews, T Maycock, O Waterfield, R Yelekçi & B. Zhou).
  45. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Islam A R M T, Islam H T, Shahid S, Khatun M K, Ali M M, Rahman M S & Almoajel A M (2021a). Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. Journal of Environmental Management, 289, 112505. https://doi.org/10.1016/j.jenvman.2021.112505
  46. Islam A R M T, Salam R, Yeasmin N, Kamruzzaman M, Shahid S, Fattah M A & Techato K (2021b). Spatiotemporal distribution of drought and its possible associations with ENSO indices in Bangladesh. Arabian Journal of Geosciences 14(23): 2681. https://doi.org/10.1007/s12517-021-08849-8
  47. Islam H T, Islam A R M T, Abdullah-Al-Mahbub M, Shahid S, Tasnuva A, Kamruzzaman M & Ibrahim S M (2021c). Spatiotemporal changes and modulations of extreme climatic indices in monsoon-dominated climate region linkage with large-scale atmospheric oscillation. Atmospheric Research, 264: 105840 https://doi.org/10.1016/j.atmosres.2021.105840
  48. Jerin J N, Islam H M, Islam A R M T, Shahid S, Hu Z, Badhan M A & Elbeltagi A (2021). Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh. Theoretical & Applied Climatology, 144: 793-808 https://doi.org/10.1007/s00704-021-03566-4
  49. Kartal V, Topçu E & Kılıç Z (2025). Projected intensification of drought in the Kızılırmak Basin, Türkiye, under CMIP6 climate scenarios: comparative evaluation of multi-model and multi-index approaches. Meteorology and Atmospheric Physics, 137(6): 52 https://doi.org/10.1007/s00703-025-01097-5
  50. Knutti R & Sedláček J (2013). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3(4): 369 373 https://doi.org/10.1038/nclimate1716
  51. Leng G & Hall J (2019). Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future. Science of the Total Environment, 654: 811-821https://doi.org/10.1016/j.scitotenv.2018.10.434
  52. Lewin A, Murali G, Rachmilevitch S & Roll U (2024). Global evaluation of current and future threats to drylands and their vertebrate biodiversity. Nature Ecology & Evolution 8(8): 1448-1458https://doi.org/10.1038/s41559-024-02450-4
  53. Li M, Chu R, Islam A R M T, Jiang Y & Shen S (2020). Attribution analysis of long-term trends of aridity index in the Huai River basin, eastern China. Sustainability, 12(5): 1743 https://doi.org/10.3390/su12051743
  54. Lickley M & Solomon S (2018). Drivers, timing and some impacts of global aridity change. Environmental Research Letters 13(10): 104010.
  55. Mauritsen T, Bader J, Becker T, Behrens J, Bittner M, Brokopf R, Brovkin V, Claussen M, Crueger T, Esch M, Fast I, Fiedler S, Fläschner D, Gayler V, Giorgetta M, Goll D S, Haak H, Hagemann S, Hedemann C & Roeckner E (2019). Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11(4): 998 1038https://doi.org/10.1029/2018MS001400
  56. Meehl G A, Senior C A, Eyring V, Flato G, Lamarque J F, Stouffer R J & Schlund M (2020). Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981. Mishra A K & Singh V P (2011). Drought modeling-A review. Journal of Hydrology 403(1-2): 157 175.https://doi.org/10.1016/j.jhydrol.2011.03.049
  57. Miyamoto B C & Hackmann C L (2025). Effects of drought on crop yields and agricultural revenue in Southern Brazil. Discover Agriculture 3(1): 246 https://doi.org/10.1007/s44279-025-00425-y
  58. Mollema P, Antonellini M, Gabbianelli G, Laghi M, Marconi V & Minchio A (2012). Climate and water budget change of a Mediterranean coastal watershed, Ravenna. Italy. Environmental Earth Sciences, 65(1): 257-276. https://doi.org/10.1007/s12665-011-1088-7
  59. Moral F J, Rebollo F J, Paniagua L L, García-Martín A & Honorio F (2016). Spatial distribution and comparison of aridity indices in Extremadura, southwestern Spain. Theoretical and Applied Climatology, 126(3): 801-814 https://doi.org/10.1007/s00704-015-1615-7
  60. Moral F J, Aguirado C, Alberdi V, Paniagua L L, García-Martín, A & Rebollo F J (2023). Future Scenarios for Aridity under Conditions of Global Climate Change in Extremadura, Southwestern Spain. Land, 12(3): 536. https://doi.org/10.3390/land12030536
  61. Narsey S Y, Brown J R, Colman R A, Delage F, Power S B, Moise A F & Zhang H (2020). Climate change projections for the Australian monsoon from CMIP6 models. Geophysical Research Letters, 47(13), e2019GL086816. https://doi.org/10.1029/2019GL086816
  62. O’Neill B C, Tebaldi C, van Vuuren D P, Eyring V, Friedlingstein P, Hurtt G & Sanderson B M (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9: 3461-3482
  63. Oğuz K, Çamalan G & Pekin M A (2022). Drought analysis and impact of drought on crop yield in Nevşehir City. Nevşehir Bilim ve Teknoloji Dergisi 11(2): 16-30
  64. Önol B & Ünal Y S (2014) Assessment of climate change simulations over climate zones of Turkey. Regional Environmental Change 14(5): 1921-1935
  65. Öztürk A & Çolak M S (2024). A quantitative and qualitative assessment of Turkey's water resources potential. Journal of Agricultural Sciences 30(1): 1-34
  66. Pour S H, Abd Wahab A K & Shahid S (2020). Spatiotemporal changes in aridity and the shift of drylands in Iran. Atmospheric Research, 233: 104704. https://doi.org/10.1016/j.atmosres.2019.104704
  67. Quan C, Han S, Utescher T, Zhang C & Liu Y S C (2013). Validation of temperature-precipitation based aridity index: Paleoclimatic implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 386: 86-95. https://doi.org/10.1016/j.palaeo.2013.05.008
  68. Roberts M J, Baker A, Blockley E W, Calvert D, Coward A, Hewitt H T, Jackson L C, Kuhlbrodt T, Mathiot P, Roberts C D, Schiemann R, Seddon J, Vannière B & Vidale P L (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geoscientific Model Development, 12(12): 4999-5028. https://doi.org/10.5194/gmd-12-4999-2019
  69. Rotbeei F, Nuri Balov M, Safari M J S & Vaheddoost B (2025). A CMIP6-based drought assessment over Küçük Menderes Basin, Türkiye. Theoretical and Applied Climatology, 156(6): 331. https://doi.org/10.1007/s00704-025-05552-6
  70. Sansom P G, Stephenson D B & Bracegirdle T J (2021). On Constraining Projections of Future Climate Using Observations and Simulations From Multiple Climate Models. Journal of the American Statistical Association, 116(534): 546-557. https://doi.org/10.1080/01621459.2020.1851696
  71. Selçuk S F, Çirci Selçuk B & Sarıoğlu Cebeci M (2024). Projections of meteorological drought events in the upper Kızılırmak basin under climate change scenarios. Theoretical and Applied Climatology, 155(6): 5629-5646. https://doi.org/10.1007/s00704-024-04952-4
  72. Sen B, Topcu S, Türkeş M, Sen B & Warner J F (2012). Projecting climate change, drought conditions and crop productivity in Turkey. Climate Research, 52: 175-191
  73. Seneviratne S I & Hauser M (2020). Regional climate sensitivity of climate extremes in CMIP6 versus CMIP5 multimodel ensembles. Earth's Future, 8(9), e2019EF001474.
  74. Spinoni J, Vogt J V. Naumann G, Barbosa P & Dosio A (2018). Will drought events become more frequent and severe in Europe? International Journal of Climatology, 38(4), 1718-1736. https://doi.org/10.1002/joc.5291
  75. Stagge J H, Kohn I, Tallaksen L M & Stahl K (2015). Modeling drought impact occurrence based on meteorological drought indices in Europe. Journal of Hydrology, 530, 37-50. https://doi.org/10.1016/j.jhydrol.2015.09.039
  76. Straffelini E & Tarolli P (2023). Climate change-induced aridity is affecting agriculture in Northeast Italy. Agricultural Systems, 208, 103647. https://doi.org/10.1016/j.agsy.2023.103647
  77. Şener E & Davraz A (2025). Future drought projections with CMIP6 in Eğirdir Lake Basin (Türkiye). Natural Hazards, 121(16), 19297-19327. https://doi.org/10.1007/s11069-025-07573-w
  78. Talukder A, Shaid S, Hwang S, Alam E, Islam K & Kamruzzaman M (2025). Optimizing the multi-model ensemble of CMIP6 GCMs for climate simulation over Bangladesh. Scientific Reports, 15(1): 11343. https://doi.org/10.1038/s41598-025-96446-0
  79. Tao S, Xu Y, Liu K, Pan J & Gou S (2011). Research progress in agricultural vulnerability to climate change. Advances in Climate Change Research, 2(4): 203-210. https://doi.org/10.3724/SP.J.1248.2011.00203
  80. Tatebe H, Ogura T, Nitta T, Komuro Y, Ogochi K, Takemura T, Sudo K, Sekiguchi M, Abe M, Saito F, Chikira M, Watanabe S, Mori M, Hirota N, Kawatani Y, Mochizuki T, Yoshimura K, Takata K, O’ishi R & Kimoto M (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12(7): 2727-2765. https://doi.org/10.5194/gmd-12-2727-2019
  81. Tegegne G, Kim Y & Lee J (2019). Spatiotemporal reliability ensemble averaging of multimodel simulations. Geophysical Research Letters, 46(21): 12321-12330. https://doi.org/10.1029/2019GL083053
  82. Trenberth K E, Fasullo J T & Shepherd T G (2015). Attribution of climate extreme events. Nature Climate Change, 5(8), 725-730. https://doi.org/10.1038/nclimate2657
  83. Tuğrul T & Hınıs M A (2022). Drought Analysis at Apa Dam. Karamanoğlu Mehmetbey Üniversitesi Mühendislik ve Doğa Bilimleri Dergisi, 4(2): 161-178. (In Turkish)
  84. Türkeș M, Yozgatlıgil C, Batmaz İ, İyigün C, Kartal Koç E & Fahmi F M (2016). Has the climate been changing in Turkey? Regional climate change signals based on a comparative statistical analysis of two consecutive time periods, 1950-1980 and 1981-2010. Climate Research, 70(1), 77-93.
  85. Türkeş M (2012). Observed and Projected Climate Change, Drought and Desertification in Turkey. Ankara Üniversitesi Çevre Bilimleri Dergisi 4(2): 1-32. (In Turkish)
  86. Türkeş M, Turp M T, An N, Ozturk T & Kurnaz M L (2020) Impacts of climate change on precipitation climatology and variability in Turkey, 467-491. In: Water resources of Turkey, (Eds. N. Harmancioglu & D. Altinbilek), Berlin: Springer International Publishing.
  87. Türkiye İstatistik Kurumu (TÜİK). (2025). Agricultural production areas, 2024. (Web page: https://biruni.tuik.gov.tr/medas/?locale=tr) (Data accessed: November 2025). aridity indices:
  88. Ullah S, You Q, Sachindra D A, Nowosad M, Ullah W, Bhatti A S & Ali A (2022). Spatiotemporal changes in global aridity in terms of multiple An assessment based on the CRU data. Atmospheric Research, 268, 105998. https://doi.org/10.1016/j.atmosres.2021.105998
  89. UNEP (1992). World Atlas of Desertification. Edward Arnold, London, United Kingdom. Xu Y, Gao X & Giorgi F (2010). Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections. Climate Research, 41, 61-81. https://doi.org/10.3354/cr00835 on farmer
  90. Wellens J, Raes D, Traore F, Denis A, Djaby B & Tychon B (2013). Performance assessment of the FAO AquaCrop model for irrigated cabbage plots in https://doi.org/10.1016/j.agwat.2013.05.012 a semi-arid environment. Agricultural Water Management, 127: 40-47.
  91. Wen J, Tagliabue G, Rossini M, Fava F P, Panigada C, Merbold L & Sun Y (2025). Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF). Biogeosciences 22(8): 2049-2067. https://doi.org/10.5194/bg 22-2049-2025
  92. Whelen T & Siqueira P (2018). Coefficient of variation for use in crop area classification across multiple climates. International Journal of Applied Earth Observation and Geoinformation, 67, 114-122. https://doi.org/10.1016/j.jag.2017.12.014
  93. Wilhite D A & Glantz M H (1985). Understanding: the drought phenomenon: the role of definitions. Water International 10(3): 111-120. https://doi.org/10.1080/02508068508686328
  94. Yavaşlı D D & Erlat E (2023). Climate model projections of aridity patterns in Türkiye: A comprehensive analysis using CMIP6 models and three aridity indices. International Journal of Climatology 43(13): 6207-6224. https://doi.org/10.1002/joc.8201
  95. Yeşilköy S & Şaylan L (2022). Spatial and temporal drought projections of northwestern Turkey. Theoretical and Applied Climatology, 149(1 2): 1-14. https://doi.org/10.1007/s00704-022-04029-0
  96. Zambakas J (1992). General Climatology; Department of Geology, National & Kapodistrian University of Athens: Athens, Greece.
  97. Zhang C, Yang Z, Zhao H, Sun Z, Di L, Bindlish R & Yueh S H (2022). Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over US Cropland. International Journal of Applied Earth Observation and Geoinformation 112, 102902. https://doi.org/10.1016/j.jag.2022.102902
  98. Zhou J, Jiang T, Wang Y, Su B, Tao H, Qin J & Zhai J (2020). Spatiotemporal variations of aridity index over the Belt and Road region under the 1.5 C and 2.0 C warming scenarios. Journal of Geographical Sciences 30(1): 37-52. https://doi.org/10.1007/s11442-020-1713-z
  99. Dinç N, Aydinşakir K, Işık M & Büyüktaş D (2016). Drought analysis of Antalya province by standardized precipitation index (SPI). Derim, 33(2): 279-298. (In Turkish)