Abstract
In this study, two machine learning (ML) models, named multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to downscale outputs of three general circulation models using CMIP6. According to the literature, it is the first time that MGGP has been used for downscaling purposes. The historical measurements of daily temperature observed at Dogonbadan station, Kohgiluyeh and Boyer-Ahmad Province, Iran, were divided into training (1985–2006) and test (2006–2015) parts. The results indicate that MGGP performed slightly better than ANN in terms of three criteria considered for the test data. The ML-based downscaling models were also used for forecasting daily temperature for 30 years (2030–2060) in the future for two scenarios (SPP1 and SPP5). The results of the Mann–Kendall’s test and Sen’s slope estimator for estimating temperatures in future overall indicate an increasing trend compared to the observed data. The future estimations demonstrate that the significant increase of the minimum daily temperature may yield cooler summers, whereas the small reduction of the maximum daily temperature may result in warmer winters in the future in the study area under investigation. Finally, pros and cons of MGGP as a downscaling model were presented for further applications in impact assessments of climate change.
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References
Ahmed K, Shahid S, Haroon SB, Xiao-jun W (2015) Multilayer perceptron neural network for downscaling rainfall in arid region: a case study of Baluchistan, Pakistan. J Earth Syst Sci 124:1325–1341
Akurut M, Willems P, Niwagaba CB (2014) Potential impacts of climate change on precipitation over Lake ictoria, East Africa, in the 21st Century. Water 2114(6):2634–2659
Almazroui M, Saeed S, Saeed F, Islam MN, Ismail M (2020) Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Syst Environ 4(2):297–320
Beecham S, Rashid M, Chowdhury RK (2014) Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a Generalized Linear Model. Int J Climatol 34:3654–3670
Chen H, Xu CY, Guo S (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434:36–45
Coulibaly P (2004) Downscaling daily extreme temperatures with genetic programming. Geophys Res Lett 31:L16203
Duan K, Mei Y (2014) A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China. Theoret Appl Climatol 116(3–4):707–719
Estoque RC, Ooba M, Togawa T, Hijioka Y (2020) Projected land-use changes pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180
Eyring VS, Bony GA, Meehl CA et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Ghosh S, Mujumdar P (2008) Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv Water Resour 31:132–146
Gidden MJ, Riahi K, Smith SJ, Fujimori S, Luderer G, Kriegler E, Takahashi K (2019a) Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific Model Development. https://doi.org/10.5194/gmd-12-1443-2019
Gidden M, Riahi K, Smith S, Fujimori S, Luderer G, Kriegler E, Calvin K (2019) Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci Model Dev Discuss 12:1443–1475
Goly A, Teegavarapu RSV, Mondal A (2014) Development and evaluation of statistical downscaling models for monthly precipitation. Earth Interact 18:1–28
Goodarzi MR, Fatehifar A, Moradi A (2020) Predicting future flood frequency under climate change using Copula function. Water Environ J 34:710–727
Goodarzi MR, Mohtar RH, Piryaei R, Fatehifar A, Niazkar M (2022) Urban WEF nexus: an approach for the use of internal resources under climate change. Hydrology 9(10):176. https://doi.org/10.3390/hydrology9100176
Hao Z, Aghakouchak A, Phillips TJ (2013) Changes in concurrent monthly precipitation and temperature extremes. Environ Res Lett 8:1–7
Hashmi MZ, Shamseldin AY, Melville BW (2011) Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP). Environ Modell Software 26:1639–1646
Hirca T, Eryılmaz Türkkan G, Niazkar M (2022) Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin Turkey. Theoretical Appl Climatol 147(1):651–667. https://doi.org/10.1007/s00704-021-03837-0
IPCC (2018) GlobalWarming of 1.58C.V.Masson-Delmotteetal.,Eds., Cambridge University Press, 630 pp., https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Full_Report_Low_Res.pdf
Kumar YP, Maheswaran R, Agarwal A, Sivakumar B (2021) Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models. J Hydrol 599:126373
Mendez M, Maathuis B, Hein-Griggs D, Alvarado-Gamboa LF (2020) Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water 12(2):482
Niazkar M (2020) Assessment of artificial intelligence models for calculating optimum properties of lined channels. J Hydroinf 22(5):1410–1423. https://doi.org/10.2166/hydro.2020.050
Niazkar M (2021) (2021) Optimum design of straight circular channels incorporating constant and variable roughness scenarios: assessment of machine learning models. Math Prob Eng 2021:1–21. https://doi.org/10.1155/2021/9984934 (Article ID 9984934)
Niazkar M (2021) Zakwan M (2021) Assessment of artificial intelligence models for developing single-value and loop rating curves. Complexity 2021:1–21. https://doi.org/10.1155/2021/6627011 (Article ID 6627011)
Nourani V, Razzaghzadeh Z, Baghanam AH, Molajou A (2019) ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theoret Appl Climatol 137(3):1729–1746
Okkan U, Inan G (2015) Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios. Int J Climatol 35(11):3274–3295
Partal T, Kahya E (2006) Trend analysis in Turkish precipitation data. Hydrol Process: Int J 20(9):2011–2026
Riahi K, Van Vuuren DP, Kriegler E, Edmonds J, O’neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ Change 42:153–168
Sachindra DA, Huang F, Barton AF, Perera BJC (2013) Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows. Int J Climatol 33:1087–1106
Sachindra DA, Huang F, Barton AF, Perera BJC (2014) Multi-model ensemble approach for statistically downscaling general circulation model outputs to precipitation. Q J Roy Meteor Soc 140:1161–1178
Sachindra DA, Ng AWM, Muthukumaran S, Perera BJC (2016) Impact of climate change on urban heat island effect and extreme temperatures: a case-study. Q J Roy Meteor Soc 142:172–186
Sachindra DA, Kanae S (2019) Machine learning for downscaling: the use of parallel multiple populations in genetic programming. Stoch Env Res Risk Assess 33(8):1497–1533
Sachindra DA, Ahmed K, Rashid MM, Sehgal V, Shahid S, Perera BJC (2019) Pros and cons of using wavelets in conjunction with genetic programming and generalised linear models in statistical downscaling of precipitation. Theoret Appl Climatol 138(1):617–638
Sachindra DA, Ahmed K, Rashid MM, Shahid S, Perera BJC (2018a) Statistical downscaling of precipitation using machine learning techniques. Atmos Res 212:240–258
Sachindra DA, Ahmed K, Shahid S, Perera BJC (2018b) Cautionary note on the use of genetic programming in statistical downscaling. Int J Climatol 38(8):3449–3465
Salmi T, Maatta A, Anttila P, Ruoho-Airola T, Amnell T (2002) Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen’s slope estimates—the excel template application MAKESENS. Ilmanlaadun julkaisuja Publikationer om luftkvalitet Publications on air quality, No. 31
Serrano A, Mateos VL, Garcia JA (1999) Trend analysis of monthly precipitation over the iberian peninsula for the period 1921–1995. Phys Chem Earth Part B 24(1):85–90. https://doi.org/10.1016/S1464-1909(98)00016-1
Swart Neil C, Cole Jason NS, Kharin Viatcheslav V et al (2019) The Canadian earth system model version 5 (CanESM5. 0.3). Geosci Model Dev 12(11):4823–4873
Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3–4):621–640
Vu MT, Aribarg T, Supratid S, Raghavan SV, Liong SY (2016) Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok? Theoret Appl Climatol 126(3–4):453–467
Wu T, Lu Y, Fang Y et al (2019) The Beijing Climate Center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci Model Dev 12:1573–1600
Wu Tongwen, Chu Min, Dong Min et al (2018). BCC BCC-CSM2MR model output prepared for CMIP6 CMIP piControl. Version YYYYMMDD[1]. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.3016
Yue Y, Yan D, Yue Q, Ji G, Wang Z (2021) Future changes in precipitation and temperature over the Yangtze River Basin in China based on CMIP6 GCMs. Atmos Res 264:105828. https://www.sciencedirect.com/science/article/pii/S0169809521003847
Yukimoto S, Kawai H, Koshiro T, Oshima N, Yoshida K, Urakawa S et al (2019) The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2. 0: description and basic evaluation of the physical component. J Meteorol Soc Jpn Ser II. https://doi.org/10.2151/jmsj.2019-051
Zakwan M, Niazkar M (2022) Innovative triangular trend analysis of monthly precipitation at Shiraz Station, Iran. In: Current Directions in Water ScarcityResearch, vol. 7. Elsevier, New York, pp589-598
Zakwan M, Niazkar M (2021) A comparative analysis of data-driven empirical and artificial intelligence models for estimating infiltration rates. Complexity 2021:1–13. https://doi.org/10.1155/2021/9945218 (Article ID 9945218)
Zerenner T, Venema V, Friederichs P, Simmer C (2018) Downscaling daily station precipitation amounts using deterministic and stochastic regression models generated by multi-objective genetic programming. In: EGU General Assembly Conference Abstracts, p. 15007. https://ui.adsabs.harvard.edu/abs/2018EGUGA..2015007Z/abstract
Zerenner T, Venema V, Friederichs P, Simmer C (2021) Multi-objective downscaling of precipitation time series by genetic programming. Int J Climatol 41(14):6162–6182. https://doi.org/10.1002/joc.7172
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The authors would like to thank IPCC, the international community, and local data providers. Indeed, this study could not be done without their support in providing us with the data required.
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MN: conceptualization; investigation; methodology; formal analysis; validation; writing—original draft; review and editing; resources; journal format preparation. MG: conceptualization; investigation; supervision; resources; data curation; writing—review and editing; project administration. AF: conceptualization; methodology; data curation; formal analysis; writing—original draft; writing—review and editing; validation. MJA: formal analysis. All authors read and approved the final manuscript.
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Niazkar, M., Goodarzi, M.R., Fatehifar, A. et al. Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios. Theor Appl Climatol 151, 153–168 (2023). https://doi.org/10.1007/s00704-022-04274-3
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DOI: https://doi.org/10.1007/s00704-022-04274-3