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Evaluating the effect of the uncertainty of CMIP6 models on extreme flows of the Caspian Hyrcanian forest watersheds using the BMA method

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Abstract

This paper investigates the effects of climate change on the hydrological and meteorological parameters of the Navrood Watershed (a Caspian Hyrcanian forest watershed) in the north of Iran. Outputs of seven CMIP6 GCMs (Tmin, Tmax, and precipitation) under two scenarios, SSP2-4.5 and SSP5-8.5, were utilized. This study considered the historical period (1994–2014), the near future (2025–2049), the middle future (2050–2074), and the far future (2075–2099). The EDCDFm and MBA methods were used for bias correction of the outputs of GCMs and combining GCMs, respectively. The LARS-WG model was used for statistical downscaling. the runoff was calculated by the IHACRES model. Based on the results, the annual Tmin under SSP2-4.5 and SSP5-8.5 will increase as much as 1.04 °C and 1.25 °C in the near future, 1.55 °C and 2.48 °C in the middle future, and 2.09 °C and 4.11 °C in the far future, respectively. The annual Tmax under SSP2-4.5 and SSP5-8.5 will increase as much as 1.59 °C and 1.38 °C in the near future, 1.98 °C and 3.02 °C in the middle future, and 2.58 °C and 4.94 °C in the far future, respectively. The annual precipitation (PCP) under SSP2-4.5 and SSP5-8.5 will increase as much as 6.81% and 7.11% in the near future, 6.15% and 4.43% in the middle future, and 8.63% and 6.58% in the far future, respectively. Finally, the annual runoff under SSP2-4.5 and SSP5-8.5 will increase as much as 16.3% and 15.4% in the near future, 14.8% and 10.6% in the middle future, and 19.2% and 15.1% in the far future, respectively.

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References

  • Adib A, Kisi O, Khoramgah S, Gafouri HR, Liaghat A, Lotfirad M, Moayyeri N (2021a) A new approach for suspended sediment load calculation based on generated flow discharge considering climate change. Water Supply. https://doi.org/10.2166/ws.2021.069

    Article  Google Scholar 

  • Adib A, Lotfirad M, Haghighi A (2019) Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds (Case study: the Navrood watershed in Iran). J Mt Sci 16(3):529–541. https://doi.org/10.1007/s11629-018-5010-6

    Article  Google Scholar 

  • Adib A, Mirsalari SB, Ashrafi SM (2021) Prediction of meteorological and hydrological phenomena in different climatic scenarios in the Karkheh watershed (southwest of Iran). Scientia Iranica 27(4):1814–1825. https://doi.org/10.24200/sci.2018.50953.1934

    Article  Google Scholar 

  • Ahmadalipour A, Moradkhani H, Rana A (2018) Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Clim Dyn 50(1):717–733

    Article  Google Scholar 

  • Ahmadalipour A, Rana A, Moradkhani H, Sharma A (2017) Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis. Theoret Appl Climatol 128(1–2):71–87. https://doi.org/10.1007/s00704-015-1695-4

    Article  Google Scholar 

  • Ahmadi H, Fallah GG, Baaghideh M, Amiri ME (2018) Investigating the effects of climate change on the pattern of heat accumulation in apple trees cultivation areas in Iran during the future period. J Spatial Anal Environ Hazarts 5(2):35–54. https://doi.org/10.29252/jsaeh.5.2.35

    Article  Google Scholar 

  • Alinezhad A, Gohari A, Eslamian S, Saberi Z (2021) A probabilistic Bayesian framework to deal with the uncertainty in hydro-climate projection of Zayandeh-Rud River Basin. Theoret Appl Climatol 144(3):847–860

    Article  Google Scholar 

  • Araji HA, Wayayok A, Bavani AM, Amiri E, Abdullah AF, Daneshian J, Teh CBS (2018) Impacts of climate change on soybean production under different treatments of field experiments considering the uncertainty of general circulation models. Agric Water Manag 205:63–71

    Article  Google Scholar 

  • Ashofteh P-S, Haddad OB, Mariño MA (2015) Risk analysis of water demand for agricultural crops under climate change. J Hydrol Eng 20(4):04014060. https://doi.org/10.1061/(asce)he.1943-5584.0001053

    Article  Google Scholar 

  • Ashrafi SM, Gholami H, Najafi MR (2020) Uncertainties in runoff projection and hydrological drought assessment over Gharesu basin under CMIP5 RCP scenarios. J Water Clim Change 11(1S):145–163. https://doi.org/10.2166/wcc.2020.088

    Article  Google Scholar 

  • Dore MHI (2005) Climate change and changes in global precipitation patterns: what do we know? Environ Int 31(8):1167–1181

    Article  Google Scholar 

  • Doulabian S, Golian S, Toosi AS, Murphy C (2021) Evaluating the effects of climate change on precipitation and temperature for iran using rcp scenarios. J Water Clim Change 12(1):166–184. https://doi.org/10.2166/wcc.2020.114

    Article  Google Scholar 

  • Esmaeili-Gisavandani H, Farajpanah H, Adib A, Kisi O, Riyahi MM, Lotfirad M, Salehpoor J (2022) Evaluating ability of three types of discrete wavelet transforms for improving performance of different ML models in estimation of daily-suspended sediment load. Arab J Geosci 15(1):1–13. https://doi.org/10.1007/s12517-021-09282-7

    Article  Google Scholar 

  • Esmaeili-Gisavandani H, Lotfirad M, Sofla MSD, Ashrafzadeh A (2021) Improving the performance of rainfall-runoff models using the gene expression programming approach. J Water Clim Change 12(7):3308–3329. https://doi.org/10.2166/wcc.2021.064

    Article  Google Scholar 

  • Fallah-Ghalhari G, Shakeri F, Dadashi-Roudbari A (2019) Impacts of climate changes on the maximum and minimum temperature in Iran. Theoret Appl Climatol 138(3):1539–1562

    Article  Google Scholar 

  • Farajpanah H, Lotfirad M, Adib A, Gisavandani HE, Kisi Ö, Riyahi MM, Salehpoor J (2020) Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge. Water Sci Technol Water Supply 20(8):3156–3171. https://doi.org/10.2166/ws.2020.211

    Article  Google Scholar 

  • Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19(21):5686–5699

    Article  Google Scholar 

  • Jakeman AJ, Hornberger GM (1993) How much complexity is warranted in a rainfall-runoff model? Water Resour Res 29(8):2637–2649. https://doi.org/10.1029/93WR00877

    Article  Google Scholar 

  • Javanshiri Z, Fathi M, Mohammadi SA (2021) Comparison of the BMA and EMOS statistical methods for probabilistic quantitative precipitation forecasting. Meteorol Appl 28(1):e1974

    Article  Google Scholar 

  • Jungclaus J, Bittner M, Wieners K-H, Wachsmann F, Schupfner M, Legutke S, Giorgetta M, Reick C, Gayler V, Haak H, de Vrese P, Raddatz T, Esch M, Mauritsen T, von Storch J-S, Behrens J, Brovkin V, Claussen M, Crueger T, Roeckner E (2019) MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6594

  • Lerat J, Thyer M, McInerney D, Kavetski D, Woldemeskel F, Pickett-Heaps C, Shin D, Feikema P (2020) A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data. J Hydrol 591:125129

    Article  Google Scholar 

  • Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res Atmos 115(D10):4410

    Article  Google Scholar 

  • Lotfirad M, Adib A, Haghighi A (2018) Estimation of daily runoff using of the semi- conceptual rainfall-runoff IHACRES model in the Navrood watershed (a watershed in the Gilan province. Iran J Ecohydrol 5(2):449–460. https://doi.org/10.22059/IJE.2017.234237.614 (In Persian with English abstract)

    Article  Google Scholar 

  • Lotfirad M, Adib A, Salehpoor J, Ashrafzadeh A, Kisi O (2021) Simulation of the impact of climate change on runoff and drought in an arid and semiarid basin (the Hablehroud, Iran). Appl Water Sci 11(10):168. https://doi.org/10.1007/s13201-021-01494-2

    Article  Google Scholar 

  • Lotfirad M, Salehpoor LJ, Ashrafzadeh A (2019) Using the IHACRES model to investigate the impacts of changing climate on streamflow in a semi-arid basin in north-central Iran. J Hydraul Struct 5(1):27–41. https://doi.org/10.22055/jhs.2019.27816.1090

    Article  Google Scholar 

  • Lovato T, Peano D, Butenschön M (2021) CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13195

  • Madadgar S, Moradkhani H (2014) Improved Bayesian multimodeling: integration of copulas and Bayesian model averaging. Water Resour Res 50(12):9586–9603

    Article  Google Scholar 

  • Maghsood FF, Moradi H, Bavani ARM, Panahi M, Berndtsson R, Hashemi H (2019) Climate change impact on flood frequency and source area in northern Iran under CMIP5 scenarios. Water (Switzerland) 11(2):1–22. https://doi.org/10.3390/w11020273

    Article  Google Scholar 

  • Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26(6):2137–2143

    Article  Google Scholar 

  • Mohammadi B (2021) A review on the applications of machine learning for runoff modeling. Sustain Water Resour Manag 7(6):1–11

    Article  Google Scholar 

  • Mohammadi B, Moazenzadeh R, Christian K, Duan Z (2021) Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environ Sci Pollut Res 28(46):65752–65768

    Article  Google Scholar 

  • Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25(18):2814–2826

    Article  Google Scholar 

  • Najafi MR, Moradkhani H (2015) Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J Hydrol 525:352–361

    Article  Google Scholar 

  • Petroselli A, Wałkega A, Młyński D, Radecki-Pawlik A, Cupak A, Hathaway J (2022) Rainfall-runoff modeling: a modification of the EBA4SUB framework for ungauged and highly impervious urban catchments. J Hydrol 606:127371

    Article  Google Scholar 

  • Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133(5):1155–1174

    Article  Google Scholar 

  • Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35(4):397–414

    Article  Google Scholar 

  • Shin Y, Shin Y, Hong J, Kim M-K, Byun Y-H, Boo K-O, Chung I-U, Park D-SR, Park J-S (2021) Future projections and uncertainty assessment of precipitation extremes in the Korean Peninsula from the CMIP6 ensemble with a statistical framework. Atmosphere 12(1):97

    Article  Google Scholar 

  • Shokouhi M, Nejad SHS, Aval MB (2018) Evaluation of simulated precipitation and temperature from CMIP5 climate models in regional climate change studies (case study: Major rainfed wheat-production areas in Iran). J Water Soil 32:Pe1013–Pe1027

    Google Scholar 

  • Silakhori E, Dahmardeh GMR, Meshram SG, Alvandi E (2022) To assess the impacts of climate change on runoff in Golestan Province, Iran. Nat Hazards 5:1–20

    Google Scholar 

  • Song Z, Qiao F, Bao Y, Shu Q, Song Y, Yang X (2019) FIO-QLNM FIO-ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9199

  • Stocker TF, Qin D, Plattner G-K, Alexander LV, Allen SK, Bindoff NL, Bréon F-M, Church JA, Cubasch U, Emori S (2013) Technical summary. In: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, pp 33–115. Cambridge University Press

  • Tan ML, Liang J, Samat N, Chan NW, Haywood JM, Hodges K (2021) Hydrological extremes and responses to climate change in the Kelantan River Basin, Malaysia, Based on the CMIP6 HighResMIP experiments. Water 13(11):1472

    Article  Google Scholar 

  • Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9(1):1–11. https://doi.org/10.1038/s41598-018-38071-8

    Article  CAS  Google Scholar 

  • Vetter T, Reinhardt J, Flörke M, Van Griensven A, Hattermann F, Huang S, Koch H, Pechlivanidis IG, Plötner S, Seidou O et al (2017) Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Clim Change 141(3):419–433

    Article  CAS  Google Scholar 

  • Volodin E, Mortikov E, Gritsun A, Lykossov V, Galin V, Diansky N, Gusev A, Kostrykin S, Iakovlev N, Shestakova A, Emelina S (2019) INM INM-CM4-8 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.5069

  • Wen K, Gao B, Li M (2021) Quantifying the impact of future climate change on runoff in the Amur River Basin using a distributed hydrological model and CMIP6 GCM projections. Atmosphere 12(12):1560

    Article  Google Scholar 

  • Wu T, Chu M, Dong M, Fang Y, Jie W, Li J, Li W, Liu Q, Shi X, Xin X, Yan J, Zhang F, Zhang J, Zhang L, Zhang Y (2018) BCC BCC-CSM2MR model output prepared for CMIP6 CMIP esm-hist. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2901

  • Yoo C, Jung K-S, Kim T-W (2005) Rainfall frequency analysis using a mixed Gamma distribution: evaluation of the global warming effect on daily rainfall. Hydrol Process Int J 19(19):3851–3861

    Article  Google Scholar 

  • Yukimoto S, Koshiro T, Kawai H, Oshima N, Yoshida K, Urakawa S, Tsujino H, Deushi M, Tanaka T, Hosaka M, Yoshimura H, Shindo E, Mizuta R, Ishii M, Obata A, Adachi Y (2019) MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6842

  • Zamani R, Berndtsson R (2019) Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method. Theoret Appl Climatol 137(1–2):533–543. https://doi.org/10.1007/s00704-018-2616-0

    Article  Google Scholar 

  • Zareian MJ, Eslamian S, Safavi HR (2015) A modified regionalization weighting approach for climate change impact assessment at watershed scale. Theoret Appl Climatol 122(3–4):497–516. https://doi.org/10.1007/s00704-014-1307-8

    Article  Google Scholar 

  • Zarrin A, Dadashi-Roudbari A (2021) Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoret Appl Climatol 144(1):643–660

    Article  Google Scholar 

  • Zhang S, Li Z, Lin X, Zhang C (2019) Assessment of climate change and associated vegetation cover change on watershed-scale runoff and sediment yield. Water 11(7):1373

    Article  Google Scholar 

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Lotfirad, M., Adib, A., Riyahi, M.M. et al. Evaluating the effect of the uncertainty of CMIP6 models on extreme flows of the Caspian Hyrcanian forest watersheds using the BMA method. Stoch Environ Res Risk Assess 37, 491–505 (2023). https://doi.org/10.1007/s00477-022-02269-0

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