Abstract
Formulation of sustainable development plans in the water sector requires reliable estimates of future hydrological conditions. The general circulation models (GCM) are usually used in the prediction of runoff in future periods but the predictions have large uncertainty. This study aimed to propose an approach for reducing the uncertainty of the results when using GCM models. To that end, the IHACRES hydrological model was first calibrated for the baseline period (1981–2005) in the Gharesu basin, Iran. The runoff corresponding to the GCM outputs of temperature and precipitation was then calculated in the historical period using the IHACRES. Twelve top GCMs suitable to the case study for the estimation of runoff were selected by the TOPSIS algorithm. The selected twelve GCMs were combined by the runoff hybrid approach (RHM), as an ensemble model, to reduce the uncertainty of hydrological modeling. The RHM model is formed by weighted integration of the calculated runoff based on the temperature and precipitation achieved from the selected GCMs. Results showed that the mean coefficient of variation (CV) of RHM was 0.76 and the uncertainty of runoff estimation by ensemble modeling was less than that of any single GCM. The RHM model under the RCP4.5 and RCP8.5 scenarios was used to predict runoff in the near future period P1 (2006–2030), mid-future period P2 (2031–2055), and far future period P3 (2056–2080). The annual runoff prediction for the Gharesu basin under scenario RCP4.5 showed an increase of about 1% in the near future period (P1), a decrease of -2.4% in the period P2, and a decrease of -10% in the period P3 relative to the baseline period. Runoff decreased by -7.8, -6.9, and − 1.9%, respectively, in periods P1, P2, and P3 under scenario RCP8.5. The results picture that the studied sub-basin of Karkheh will face more heavy rains and floods in the winter and will be drier than the past. That is an important alarm for water managers to adapte their management strategies.
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Acknowledgements
The authors gratefully acknowledge support by the Shahid Chamran University of Ahvaz (SCU) through the grant SCU.C1400.31254.
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Hooman Gholami has gathered and downloaded the basic data, and developed and executed hybrid models under the supervision of Seyed Mohammad Ashrafi. Morteza Lotfirad has developed and executed AHP and post processing models under the supervision of Seyed Mohammad Ashrafi. Seyed mohammad Ashrafi has provided the basic idea of the research and supervised the research Also, wrote the main text of the paper. Moreover, Dr. Ashrafi obtained the funds from Shahid Chamran University of Ahvaz to perform the research. Seyed Mostafa Biazar has edited the text and some presentation materials. Professor Vijay P. Singh has edited the paper comprehensively, and supervised the research direction. All authors have read and agreed to the published version of the paper.
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Gholami, H., Lotfirad, M., Ashrafi, S.M. et al. Multi-GCM ensemble model for reduction of uncertainty in runoff projections. Stoch Environ Res Risk Assess 37, 953–964 (2023). https://doi.org/10.1007/s00477-022-02311-1
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DOI: https://doi.org/10.1007/s00477-022-02311-1