Abstract
Different sources of uncertainty exist in climate change impacts projection. This study aims to propose a framework to deal with the various sources of uncertainties involved in hydro-climate projections of Zayandeh-Rud River Basin with area of 26,917 km2 in Central Iran. The Bayesian model averaging (BMA) was here used through two distinct approaches for weighting the hydrologic outputs (App. I) as well as the global climate models (GCMs) (App. II) based on their abilities to simulate the baseline period. The results showed that different GCMs have different abilities in estimating the hydro-climatic variables and the application of uncertainty analysis is necessary for climate change studies. Application of the BMA can significantly reduce the errors in historical runoff prediction. Although App. I showed a better performance of generating the stream flow time series during the baseline period, the App. II approach has an acceptable ability in different months. The findings of flow duration curves under both approaches revealed that App. II is more appropriate to deal with uncertainty of hydro-climate projection especially in arid and semi-arid regions.
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Notes
Intergovernmental Panel on Climate Change
Long Ashton Research Station Weather Generator
Expectation-maximization
Root mean square error
Simple model averaging
Multiple linear regression
References
Abbaspour KC, Faramarzi M, Ghasemi SS, Yang H (2009) Assessing the impact of climate change on water resources in Iran. Water Resour Res 45:1–16. https://doi.org/10.1029/2008WR007615
Aguilera R, Gershunov A, Benmarhnia T (2019) Atmospheric rivers impact California’s coastal water quality via extreme precipitation. Sci Total Environ 671:488–494. https://doi.org/10.1016/j.scitotenv.2019.03.318
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 50:717–733. https://doi.org/10.1007/s00382-017-3639-4
Ahmadzadeh HR, Wayayoke M, Massah AR, Amiri E, Abdullah A, Daneshian J, The CBS (2018) Impacts of climate change on soybean production under different treatments of field experiments considering the uncertainty of general circulation models. Agr Water Manage 205:63–71. https://doi.org/10.1016/j.agwat.2018.04.023
Ajami NK, Duan Q, Sorooshian S (2007) An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resour Res 43:1–19. https://doi.org/10.1029/2005WR004745
Banihabib ME, Hasani K, Massah AR (2016) A framework for the assessment of reservoir operation adaption to climate change in an arid region. J Glob Warming 9:286–305. https://doi.org/10.1504/IJGW.2016.075446
Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–309. https://doi.org/10.1038/nature04141
Bastola S, Murphy C, Sweeney J (2011) The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv Water Resour 34:562–576. https://doi.org/10.1016/j.advwatres.2011.01.008
Bhat KS, Haran M, Terando A, Keller K (2011) Climate projections using Bayesian model averaging and space–time dependence. J Agric Biol Envir S 16:606–628. https://doi.org/10.1007/s13253-011-0069-3
Bohn TJ, Sonessa MY, Lettenmaier DP (2010) Seasonal hydrologic forecasting: Do multimodel ensemble averages always yield improvements in forecast skill? J Hydrometeorol 11(6):1358–1372. https://doi.org/10.1175/2010JHM1267.1
Box GE, Cox DR (1964) An analysis of transformations. J Roy Stat Soc B Met 26:211–243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Brouyere S, Dassargues V (2004) Spatially distributed, physically-based modelling for simulating the impacts of climate change on groundwater reserves. Proceeding of British Hydrol Soc 9:1–15
Calzadilla A, Rehdanz K, Betts R, Falloon P, Wiltshire A, Tol RS (2013) Climate change impacts on global agriculture. Clim Chang 120:357–374. https://doi.org/10.1007/s10584-013-0822-4
Croke B, Jakeman A (2008) Use of the IHACRES rainfall-runoff model in arid and semi arid regions. In: Hydrological modelling in arid and semi-arid areas, pp 41–48
Daccache A, Weatherhead E, Stalham M, Knox J (2011) Impacts of climate change on irrigated potato production in a humid climate. Agric For Meteorol 151:1641–1653. https://doi.org/10.1016/j.agrformet.2011.06.018
Dore MH (2005) Climate change and changes in global precipitation patterns: what do we know? Environ Int 31:1167–1181. https://doi.org/10.1016/j.envint.2005.03.004
Duan Q, Ajami NK, Gao X, Sorooshian S (2007) Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 30:1371–1386. https://doi.org/10.1016/j.advwatres.2006.11.014
Feng S, Hao Z, Zhang X, Hao F (2019) Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Sci Total Environ 689:1228–1234. https://doi.org/10.1016/j.scitotenv.2019.06.373
Fennessey N, Vogel RM (1990) Regional flow-duration curves for ungauged sites in Massachusetts. J Water Res Pl 116:530–549. https://doi.org/10.1061/(ASCE)0733-9496(1990)116:4(530)
Furrer R, Knutti R, Sain S, Nychka D, Meehl G (2007) Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophys Res Lett 34:1–4. https://doi.org/10.1029/2006GL027754
Giorgi F, Raffaele F, Coppola E (2019) The response of precipitation characteristics to global warming from climate projections. Earth Syst Dynam 10:73–89. https://doi.org/10.5194/esd-10-73-2019
Gohari A, Eslamian S, Abedi-Koupaei J, Bavani AM, Wang D, Madani K (2013a) Climate change impacts on crop production in Iran’s Zayandeh-Rud River Basin. Sci Total Environ 442:405–419. https://doi.org/10.1016/j.scitotenv.2012.10.029
Gohari A, Eslamian S, Mirchi A, Abedi-Koupaei J, Bavani AM, Madani K (2013b) Water transfer as a solution to water shortage: a fix that can backfire. J Hydrol 491:23–39
Gohari A, Bozorgi A, Madani K, Elledge J, Berndtsson R (2014) Adaptation of surface water supply to climate change in Central Iran. J Water Clim Change 5:391–407. https://doi.org/10.2166/wcc.2014.189
Gohari A, Zareian MJ, Eslamian S (2015) A multi-model framework for climate change impact assessment. In: Handbook of climate change adaptation, pp 1–16
Gohari A, Mirchi A, Madani K (2017) System dynamics evaluation of climate change adaptation strategies for water resources management in central Iran. Water Resour Manag 31:1413–1434
Goodarzi E, Dastorani M, Massah Bavani A, Talebi A (2015) Evaluation of the change-factor and LARS-WG methods of downscaling for simulation of climatic variables in the future (case study: Herat Azam Watershed, Yazd - Iran). Ecopersia 3:833–846
Greene AM, Goddard L, Lall U (2006) Probabilistic multimodel regional temperature change projections. J Clim 19:4326–4343. https://doi.org/10.1175/JCLI3864.1
Guo R, Lin Z, Mo X, Yang C (2010) Responses of crop yield and water use efficiency to climate change in the North China Plain. Agr Water Manage 97:1185–1194. https://doi.org/10.1016/j.agwat.2009.07.006
Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699. https://doi.org/10.1175/JCLI3990.1
Huo W, Li Z, Wang J, Yao C, Zhang K, Huang Y (2019) Multiple hydrological models comparison and an improved Bayesian model averaging approach for ensemble prediction over semi-humid regions. Stoch Env Res Risk A 33:217–238. https://doi.org/10.1007/s00477-018-1600-7
Iizumi T, Yokozawa M, Nishimori M (2009) Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: application of a Bayesian approach. Agric For Meteorol 149:333–348. https://doi.org/10.1016/j.agrformet.2008.08.015
IPCC (2007) Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge
IPCC (2013) Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge
Jakeman AJ, Hornberger GM (1993) How much complexity is warranted in a rainfall-runoff model? Water Resour Res 29:2637–2649. https://doi.org/10.1029/93WR00877
Kao SC, Govindaraju RS (2010) A copula-based joint deficit index for droughts. J Hydrol 380(1-2):121–134. https://doi.org/10.1016/j.jhydrol.2009.10.029
Knutti R, Stocker TF, Joos F, Plattner GK (2003) Probabilistic climate change projections using neural networks. Clim Dyn 21:257–272. https://doi.org/10.1007/s00382-003-0345-1
Kouhestani S, Eslamian SS, Abedi-Koupai J, Besalatpour AA (2016) Projection of climate change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin, Iran. Glob Planet Chang 144:158–170. https://doi.org/10.1016/j.gloplacha.2016.07.013
Lee J-Y, Wang B (2014) Future change of global monsoon in the CMIP5. Clim Dyn 42:101–119. https://doi.org/10.1007/s00382-012-1564-0
Li Z, Huang G, Wang X, Han J, Fan Y (2016) Impacts of future climate change on river discharge based on hydrological inference: a case study of the Grand River Watershed in Ontario, Canada. Sci Total Environ 548:198–210. https://doi.org/10.1016/j.scitotenv.2016.01.002
Lopez A, Tebaldi C, New M, Stainforth D, Allen M, Kettleborough J (2006) Two approaches to quantifying uncertainty in global temperature changes. J Clim 19:4785–4796. https://doi.org/10.1175/JCLI3895.1
Madadgar S, Moradkhani H (2014) Improved Bayesian multimodeling: integration of copulas and Bayesian model averaging. Water Resour Res 50:9586–9603. https://doi.org/10.1002/2014WR015965
Madani K, Mariño MA (2009) System dynamics analysis for managing Iran’s Zayandeh-Rud river basin. Water Resour Manag 23:2163–2187. https://doi.org/10.1007/s11269-008-9376-z
Menzel L, Bürger G (2002) Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). J Hydrol 267:53–64. https://doi.org/10.1016/S0022-1694(02)00139-7
Moss RH, Schneider SH (2000) Uncertainties in the IPCC TAR: recommendations to lead authors for more consistent assessment and reporting. Guidance papers on the cross cutting issues of the third assessment report of the IPCC. World Meteorological Organization, Geneva, pp 33–51
Mozayyan M, Akhoond Ali AM, Massah Bavani AR, Radmanesh F, Gohari A (2017) Impacts of climate change on low flows at Tang Panj Sezar Subbasin, Southwest of Iran. J Hydrol Eng 22:05017021. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001578
Najafi MR, Moradkhani H (2015) Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J Hydrol 525:352–361. https://doi.org/10.1016/j.jhydrol.2015.03.045
Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. https://doi.org/10.1002/hyp.8043
Özdoğan M (2011) Modeling the impacts of climate change on wheat yields in Northwestern Turkey. Agric Ecosyst Environ 141:1–12. https://doi.org/10.1016/j.agee.2011.02.001
Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Chang 14:53–67. https://doi.org/10.1016/j.gloenvcha.2003.10.008
Pumo D, Arnone E, Francipane A, Caracciolo D, Noto LV (2017) Potential implications of climate change and urbanization on watershed hydrology. J Hydrol 554:80–99. https://doi.org/10.1016/j.jhydrol.2017.09.002
Qu B, Zhang X, Pappenberger F, Zhang T, Fang Y (2017) Multi-model grand ensemble hydrologic forecasting in the Fu river basin using Bayesian model averaging. Water 9:74. https://doi.org/10.3390/w9020074
Raftery AE (1993) Bayesian model selection in structural equation models. Sage Focus Editions 154:163–163
Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133:1155–1174. https://doi.org/10.1175/MWR2906.1
Sarhadi A, Burn DH, Yang G, Ghodsi A (2017) Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques. Clim Dyn 48:1329–1351. https://doi.org/10.1007/s00382-016-3145-0
Semenov MA (2007) Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric For Meteorol 144:127–138. https://doi.org/10.1016/j.agrformet.2007.02.003
Semenov M, Barrow E (2002) A stochastic weather generator for use in climate impact studies: user manual. Harpenden, Hertfordshire
Sloughter JML, Raftery AE, Gneiting T, Fraley C (2007) Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon Weather Rev 135:3209–3220. https://doi.org/10.1175/MWR3441.1
Soleimani A, Hosseini SM, Bavani ARM, Jafari M, Francaviglia R (2017) Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran). Sci Total Environ 599:1646–1657. https://doi.org/10.1016/j.scitotenv.2017.05.077
Tao F, Zhang Z (2010) Adaptation of maize production to climate change in North China Plain: quantify the relative contributions of adaptation options. Eur J Agron 33:103–116. https://doi.org/10.1016/j.eja.2010.04.002
Teixeira, E. I., Fischer, G., Van Velthuizen, H., Walter, C., Ewert, F., (2013) Global hot-spots of heat stress on agricultural crops due to climate change. Agric For Meteorol 170: 206-215. https://doi.org/10.1016/j.agrformet.2011.09.002
Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Masui T (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. https://doi.org/10.1007/s10584-011-0148-z
Woldesenbet TA, Elagib NA, Ribbe L, Heinrich J (2018) Catchment response to climate and land use changes in the Upper Blue Nile sub-basins, Ethiopia. Sci Total Environ 644:193–206. https://doi.org/10.1016/j.scitotenv.2018.06.198
Xiong L, Shamseldin AY, O’connor KM (2001) A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system. J Hydrol 245(1-4):196–217. https://doi.org/10.1016/S0022-1694(01)00349-3
Xu CY, Gong L, Jiang T, Chen D, Singh V (2006) Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J Hydrol 327:81–93. https://doi.org/10.1016/j.jhydrol.2005.11.029
Zareian MJ, Eslamian S, Safavi HR (2015) A modified regionalization weighting approach for climate change impact assessment at watershed scale. Theor Appl Climatol 122:497–516. https://doi.org/10.1007/s00704-014-1307-8
Zhang Q, Xu CY, Tao H, Jiang T, Chen YD (2010) Climate changes and their impacts on water resources in the arid regions: a case study of the Tarim River basin, China. Stoch Env Res Risk A 24:349–358. https://doi.org/10.1007/s00477-009-0324-0
Acknowledgements
We acknowledge the Iran Meteorological Organization for providing the historical data and World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. The authors thank the anonymous reviewers for their valuable comments and suggestions.
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All authors collaborated in the research presented in this publication by making the following contributions: research conceptualization, Alireza Gohari (A.G.), Ali Alinezhad (A.A.), Saeid Eslamian (S.E.), and Zahra Saberi (Z.S.); methodology, A.G., A.A., and Z.S.; formal analysis, A.A.; writing—original draft preparation, A.A.; writing—review and editing, A.G. and S.E.; supervision, A.G. and S.E.
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Alinezhad, A., Gohari, A., Eslamian, S. et al. A probabilistic Bayesian framework to deal with the uncertainty in hydro-climate projection of Zayandeh-Rud River Basin. Theor Appl Climatol 144, 847–860 (2021). https://doi.org/10.1007/s00704-021-03575-3
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DOI: https://doi.org/10.1007/s00704-021-03575-3