Abstract
Potential evapotranspiration (PET) serves as a proxy for estimating actual evapotranspiration (AET) in hydrological model simulations and constitutes an input for drought analyses. Nonetheless, it is elusive how the inclusion of different PET models in a modeling chain, which encapsulates multiple general circulation models (GCMs) operating under varying emission scenarios, can affect drought projections. In this study, utilizing four GCMs, two representative concentration pathways (RCPs), and eleven widely used PET models, ensemble projections of the frequency of droughts in the Gordes, a semi-arid watershed located in Western Turkey, were derived for the period 2021–2050. The standardized precipitation evapotranspiration index (SPEI) was used to characterize meteorological drought, while the standardized runoff index (SRI) was preferred for inspecting hydrological drought. Using an analysis of variance decomposition, the contribution of each stage of the modeling chain to both meteorological and hydrological drought uncertainty was quantified. Results show that PET models expectedly produced large disparities in projected changes in the evapotranspiration regime. Even so, only the 25% uncertainty contribution of PET models to severe meteorological drought frequency can be deemed notable. Yet, their contribution to uncertainties in mild and moderate meteorological drought frequencies is rather marginal (≤ 5%) compared to what GCMs overwhelmingly do. It is also worth noting that SPEI and SRI respond differently to sources of uncertainty and that SRI suggests drought frequencies of significantly lower amounts compared to those of SPEI, possibly because the temperature dependence of AET, which SRI considers synthetically, is much less than that of PET.
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References
Aadhar S, Mishra V (2020) Increased drought risk in South Asia under warming climate: implications of uncertainty in potential evapotranspiration estimates. J Hydrometeorol 21(12):2979–2996. https://doi.org/10.1175/JHM-D-19-0224.1
Ahmadalipour A, Moradkhani H, Demirel MC (2017) A comparative assessment of projected meteorological and hydrological droughts: elucidating the role of temperature. J Hydrol 553:785–797. https://doi.org/10.1016/j.jhydrol.2017.08.047
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome 300(9):D05109
Araujo DS, Marra F, Merow C, Nikolopoulos EI (2022) Today’s 100 year droughts in Australia may become the norm by the end of the century. Environ Res Lett 17(4):044034. https://doi.org/10.1088/1748-9326/ac58ac
Bae DH, Jung IW, Lettenmaier DP (2011) Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Basin, Korea. J Hydrol 401(1–2):90–105. https://doi.org/10.1016/j.jhydrol.2011.02.012
Bai P, Liu X, Yang T, Li F, Liang K, Hu S et al (2016) Assessment of the influences of different potential evapotranspiration inputs on the performance of monthly hydrological models under different climatic conditions. J Hydrometeorol 17(8):2259–2274. https://doi.org/10.1175/JHM-D-15-0202.1
Burke EJ, Brown SJ (2008) Evaluating uncertainties in the projection of future drought. J Hydrometeorol 9(2):292–299. https://doi.org/10.1175/2007JHM929.1
Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J Climate 28(17):6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1
Cook BI, Smerdon JE, Seager R, Coats S (2014) Global warming and 21st century drying. Climate Dynamics 43:2607–2627. https://doi.org/10.1007/s00382-014-2075-y
Diasso U, Abiodun BJ (2017) Drought modes in West Africa and how well CORDEX RCMs simulate them. Theoret Appl Climatol 128:223–240. https://doi.org/10.1007/s00704-015-1705-6
Duan K, Mei Y (2014) Comparison of meteorological, hydrological and agricultural drought responses to climate change and uncertainty assessment. Water Resour Manag 28:5039–5054. https://doi.org/10.1007/s11269-014-0789-6
Gupta V, Jain MK (2018) Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. J Hydrol 567:489–509. https://doi.org/10.1016/j.jhydrol.2018.10.012
Jiao Y, Yuan X (2019) More severe hydrological drought events emerge at different warming levels over the Wudinghe watershed in Northern China. Hydrol Earth Syst Sci 23(1):621–635. https://doi.org/10.5194/hess-23-621-2019
Katipoğlu OM (2023) Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye. Environ Sci Pollut Res 30:44043–44066. https://doi.org/10.1007/s11356-023-25369-y
Kingston DG, Todd MC, Taylor RG, Thompson JR, Arnell NW (2009) Uncertainty in the estimation of potential evapotranspiration under climate change. Geophys Res Lett 36(20). https://doi.org/10.1029/2009GL040267
Koedyk LP, Kingston DG (2016) Potential evapotranspiration method influence on climate change impacts on river flow: a mid-latitude case study. Hydrol Res 47(5):951–963. https://doi.org/10.2166/nh.2016.152
Kumanlioglu AA (2023) A new approach for characterization of meteorological and hydrological droughts: cumulative standardized drought index (CSDI). Phys Chem Earth Parts A/B/C 131:103420. https://doi.org/10.1016/j.pce.2023.103420
Lai C, Chen X, Zhong R, Wang Z (2022) Implication of climate variable selections on the uncertainty of reference crop evapotranspiration projections propagated from climate variables projections under climate change. Agric Water Manag 259:107273. https://doi.org/10.1016/j.agwat.2021.107273
Lee MH, Im ES, Bae DH (2019) A comparative assessment of climate change impacts on drought over Korea based on multiple climate projections and multiple drought indices. Climate Dynam 53:389–404. https://doi.org/10.1007/s00382-018-4588-2
Lee S, Qi J, McCarty GW, Yeo IY, Zhang X, Moglen GE et al (2021) Uncertainty assessment of multi-parameter, multi-GCM, and multi-RCP simulations for streamflow and non-floodplain wetland (NFW) water storage. J Hydrol 600:126564. https://doi.org/10.1016/j.jhydrol.2021.126564
Lemaitre-Basset T, Oudin L, Thirel G, Collet L (2022) Unraveling the contribution of potential evaporation formulation to uncertainty under climate change. Hydrol Earth Syst Sci 26(8):2147–2159. https://doi.org/10.5194/hess-26-2147-2022
Lu J, Carbone GJ, Grego JM (2019) Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Scientific Rep 9(1):1–12. https://doi.org/10.1038/s41598-019-41196-z
Mendicino G, Senatore A, Versace P (2008) A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a Mediterranean climate. J Hydrol 357(3–4):282–302. https://doi.org/10.1016/j.jhydrol.2008.05.005
Moriasi DN, Arnold JG, van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885-900. https://doi.org/10.13031/2013.23153
Nahar J, Johnson F, Sharma A (2017) Assessing the extent of non-stationary biases in GCMs. J Hydrol 549:148–162. https://doi.org/10.1016/j.jhydrol.2017.03.045
Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23:881–897. https://doi.org/10.1007/s11269-008-9305-1
Nashwan MS, Shahid S (2020) A novel framework for selecting general circulation models based on the spatial patterns of climate. Int J Climatol 40(10):4422–4443. https://doi.org/10.1002/joc.6465
Nguvava M, Abiodun BJ, Otieno F (2019) Projecting drought characteristics over East African basins at specific global warming levels. Atmospheric Res 228:41–54. https://doi.org/10.1016/j.atmosres.2019.05.008
Okkan U, Kirdemir U (2020) Towards a hybrid algorithm for the robust calibration of rainfall–runoff models. J Hydroinform 22(4):876–899. https://doi.org/10.2166/hydro.2020.016
Okkan U, Kiymaz H (2020) Questioning of empirically derived and locally calibrated potential evapotranspiration equations for a lumped water balance model. Water Supply 20(3):1141–1156. https://doi.org/10.2166/wcc.2019.292
Okkan U, Fistikoglu O, Ersoy ZB, Noori AT (2023) Investigating adaptive hedging policies for reservoir operation under climate change impacts. J Hydrol 619:129286. https://doi.org/10.1016/j.jhydrol.2023.129286
Orlowsky B, Seneviratne SI (2013) Elusive drought: uncertainty in observed trends and short-and long-term CMIP5 projections. Hydrol Earth Syst Sci 17(5):1765–1781. https://doi.org/10.5194/hess-17-1765-2013
Oudin L, Hervieu F, Michel C, Perrin C, Andréassian V, Anctil F et al (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
Paredes P, Pereira LS (2019) Computing FAO56 reference grass evapotranspiration PM-ETo from temperature with focus on solar radiation. Agric Water Manag 215:86–102. https://doi.org/10.1016/j.agwat.2018.12.014
Peng S, Ding Y, Wen Z, Chen Y, Cao Y, Ren J (2017) Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric Forest Meteorol 233:183–194. https://doi.org/10.1016/j.agrformet.2016.11.129
Prudhomme C, Williamson J (2013) Derivation of RCM-driven potential evapotranspiration for hydrological climate change impact analysis in Great Britain: a comparison of methods and associated uncertainty in future projections. Hydrol Earth Syst Sci 17:1365–1377. https://doi.org/10.5194/hess-17-1365-2013
Reyniers N, Osborn TJ, Addor N, Darch G (2023) Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index. Hydrol Earth Syst Sci 27(5):1151–1171. https://doi.org/10.5194/hess-27-1151-2023
Rhee J, Cho J (2016) Future changes in drought characteristics: regional analysis for South Korea under CMIP5 projections. J Hydrometeorol 17(1):437–451. https://doi.org/10.1175/JHM-D-15-0027.1
Samavati A, Babamiri O, Rezai Y, Heidarimozaffar M (2023) Investigating the effects of climate change on future hydrological drought in mountainous basins using SWAT model based on CMIP5 model. Stochastic Environ Res Risk Assess 37(3):849–875. https://doi.org/10.1007/s00477-022-02319-7
Seiller G, Anctil F (2016) How do potential evapotranspiration formulas influence hydrological projections? Hydrol Sci J 61(12):2249–2266. https://doi.org/10.1080/02626667.2015.1100302
Sheffield J, Wood EF, Roderick ML (2012) Little change in global drought over the past 60 years. Nature 491:435–438. https://doi.org/10.1038/nature11575
Shi L, Feng P, Wang B, Li Liu D, Yu Q (2020) Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models. J Hydrol 590:125394. https://doi.org/10.1016/j.jhydrol.2020.125394
Shi L, Feng P, Wang B, Li Liu D, Cleverly J, Fang Q et al (2020) Projecting potential evapotranspiration change and quantifying its uncertainty under future climate scenarios: a case study in southeastern Australia. J Hydrol 584:124756. https://doi.org/10.1016/j.jhydrol.2020.124756
Shi L, Feng P, Wang B, Li Liu D, Zhang H, Liu J et al (2022) Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections. J Hydrol 612:128042. https://doi.org/10.1016/j.jhydrol.2022.128042
Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35(2). https://doi.org/10.1029/2007GL032487
Stagge JH, Tallaksen LM, Gudmundsson L, Van Loon AF, Stahl K (2015) Candidate distributions for climatological drought indices (SPI and SPEI). Int J Climatol 35(13):4027–4040. https://doi.org/10.1002/joc.4267
Sun F, Mejia A, Zeng P, Che Y (2019) Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin. Sci Total Environ 696:134076. https://doi.org/10.1016/j.scitotenv.2019.134076
Thompson JR, Green AJ, Kingston DG (2014) Potential evapotranspiration-related uncertainty in climate change impacts on river flow: an assessment for the Mekong River basin. J Hydrol 510:259–279. https://doi.org/10.1016/j.jhydrol.2013.12.010
Thompson JR, Laizé CLR, Green AJ, Acreman MC, Kingston DG (2014) Climate change uncertainty in environmental flows for the Mekong River. Hydrol Sci J 59(3–4):935–954. https://doi.org/10.1080/02626667.2013.842074
Tomas‐Burguera M, Vicente‐Serrano SM, Peña‐Angulo D, Domínguez‐Castro F, Noguera I, El Kenawy A (2020) Global characterization of the varying responses of the standardized precipitation evapotranspiration index to atmospheric evaporative demand. J Geophys Res: Atmospheres 125(17):e2020JD033017. https://doi.org/10.1029/2020JD033017
Touma D, Ashfaq M, Nayak MA, Kao SC, Diffenbaugh NS (2015) A multi-model and multi-index evaluation of drought characteristics in the 21st century. J Hydrol 526:196–207. https://doi.org/10.1016/j.jhydrol.2014.12.011
Trambauer P, Maskey S, Werner M, Pappenberger F, Van Beek LPH, Uhlenbrook S (2014) Identification and simulation of space–time variability of past hydrological drought events in the Limpopo River basin, southern Africa. Hydrol Earth Syst Sci 18(8):2925–2942. https://doi.org/10.5194/hess-18-2925-2014
Ukkola AM, Pitman AJ, De Kauwe MG, Abramowitz G, Herger N, Evans JP et al (2018) Evaluating CMIP5 model agreement for multiple drought metrics. J Hydrometeorol 19(6):969–988. https://doi.org/10.1175/JHM-D-17-0099.1
UNEP (1992) World Atlas of Desertification. Edward Arnold, London
Valiantzas JD (2013) Simplified forms for the standardized FAO-56 Penman-Monteith reference evapotranspiration using limited weather data. J Hydrol 505:13–23. https://doi.org/10.1016/j.jhydrol.2013.09.005
Vázquez-Patiño A, Samaniego E, Campozano L, Avilés A (2022) Effectiveness of causality-based predictor selection for statistical downscaling: a case study of rainfall in an Ecuadorian Andes basin. Theoret Appl Climatol 150(3–4):987–1013. https://doi.org/10.1007/s00704-022-04205-2
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Climate 23(7):1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Wanders N, Wada Y, Van Lanen HAJ (2015) Global hydrological droughts in the 21st century under a changing hydrological regime. Earth Syst Dynam 6(1):1–15. https://doi.org/10.5194/esd-6-1-2015
Wang L, Chen W, Zhou W (2014) Assessment of future drought in Southwest China based on CMIP5 multimodel projections. Adv Atmospheric Sci 31(5):1035–1050. https://doi.org/10.1007/s00376-014-3223-3
Wu C, Yeh PJF, Ju J, Chen YY, Xu K, Dai H et al (2021) Assessing the spatiotemporal uncertainties in future meteorological droughts from CMIP5 models, emission scenarios, and bias corrections. J Climate 34(5):1903–1922. https://doi.org/10.1175/JCLI-D-20-0411.1
Wu Y, Miao C, Fan X, Gou J, Zhang Q, Zheng H (2022) Quantifying the uncertainty sources of future climate projections and narrowing uncertainties with bias correction techniques. Earth's Future 10(11):e2022EF002963. https://doi.org/10.1029/2022EF002963
Xu K, Wu C, Zhang C, Hu BX (2021) Uncertainty assessment of drought characteristics projections in humid subtropical basins in China based on multiple CMIP5 models and different index definitions. J Hydrol 600:126502. https://doi.org/10.1016/j.jhydrol.2021.126502
Yip S, Ferro CA, Stephenson DB, Hawkins E (2011) A simple, coherent framework for partitioning uncertainty in climate predictions. J Climate 24(17):4634–4643. https://doi.org/10.1175/2011JCLI4085.1
Zhang L, Potter N, Hickel K, Zhang Y, Shao Q (2008) Water balance modeling over variable time scales based on the Budyko framework–Model development and testing. J Hydrol 360(1–4):117–131. https://doi.org/10.1016/j.jhydrol.2008.07.021
Zhao P, Lü H, Yang H, Wang W, Fu G (2019) Impacts of climate change on hydrological droughts at basin scale: a case study of the Weihe River Basin, China. Quat Int 513:37–46. https://doi.org/10.1016/j.quaint.2019.02.022
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We would like to express our sincere gratitude to two reviewers for their insightful feedback and suggestions for improvement, which assisted in enhancing the quality of this paper.
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Financial support was provided by Scientific and Technological Research Council of Turkey (Grant No.121Y037).
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Umut Okkan: Conceptualization, Supervision, Methodology, Software, Data Curation, Formal analysis, Visualization, Investigation, Writing - Original Draft, Writing – review & editing. Okan Fistikoglu: Conceptualization, Methodology, Investigation, Writing – review & editing. Zeynep Beril Ersoy: Software, Formal analysis, Data Curation, Visualization, Investigation. Writing – review & editing. Ahmad Tamim Noori: Data Curation, Formal analysis.
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Okkan, U., Fistikoglu, O., Ersoy, Z.B. et al. Analyzing the uncertainty of potential evapotranspiration models in drought projections derived for a semi-arid watershed. Theor Appl Climatol 155, 2329–2346 (2024). https://doi.org/10.1007/s00704-023-04817-2
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DOI: https://doi.org/10.1007/s00704-023-04817-2