A probabilistic Bayesian framework to deal with the uncertainty in hydro-climate projection of Zayandeh-Rud River Basin

Alinezhad, Ali; Gohari, Alireza; Eslamian, Saeid; Saberi, Zahra

doi:10.1007/s00704-021-03575-3

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…Therefore, it can be concluded that the omission of some models and the use of a limited number of them cannot provide a reliable estimate of runoff and climatic variables in the future. This result is consistent with the statements (Alinezhad et al, 2021;Ashrafi et al, 2020;Lotfirad et al, 2021). Therefore, in this study, the BMA method was used to maintain the capacity of all models and reduce the uncertainty caused by using a single model (Najafi et al, 2011).…”

Section: Discussionsupporting

confidence: 84%

“…Climate change will not have the same effects across the globe, with drier areas becoming drier and wetter areas becoming wetter (Held & Soden, 2006). The phenomenon of global warming has caused changes in the hydrological cycle (Alinezhad et al, 2021). Therefore, studying the effect of climate change on the basin's runoff is of particular importance.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the effects of CMIP6 model uncertainty on extreme flows of the Caspian Hyrcanian forest watersheds by BMA method

Lotfirad

Adib

Riyahi

et al. 2022

Preprint

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This paper investigates the climate change effects on the hydrological and meteorological parameters of the Navrood Basin (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 to 2014), the near future (2025 to 2049), the middle future (2050 to 2074), and the far future (2075 to 2099). The EDCDFm and MBA methods were used for bias correction of the outputs of GCMs and combine GCMs, respectively. The LARS-WG model was used for statistical downscaling. The IHACRES model calculated the runoff. The annual Tmin under SSP2-4.5 and SSP5-8.5 will increase 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 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 6.81% and 7.11% in the near future, 6.15% and 4.43% in the middle future, 8.63% and 6.58% in the far future, respectively. Finally, the annual runoff under SSP2-4.5 and SSP5-8.5 will increase 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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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Evaluating the effects of CMIP6 model uncertainty on extreme flows of the Caspian Hyrcanian forest watersheds by BMA method

Lotfirad

Adib

Riyahi

et al. 2022

Preprint

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“…In recent years, the Bayesian model averaging (BMA) algorithm has been extensively applied in hydroclimate ensemble projections (Raftery et al 2005). For example, Alinezhad et al (2021) introduced the BMA method for weighting the hydrologic results and the GCMs based on their capabilities of simulating the historical period; Strazzo et al (2019) applied the BMA approach to yield optimal forecasts of temperature and precipitation on the basis of pre-developed bridging and calibration models; Ma et al (2018) proposed a framework for integrating multiple satellite precipitation data through the dynamic BMA method.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model averaging of the RegCM temperature projections: a Canadian case study

Song

Huang

Wang

et al. 2021

Journal of Water and Climate Change

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The choices of physical schemes coupled in the regional climate model (RegCM), the input general circulation model (GCM) results, and the emission scenarios may cause considerable uncertainties in future temperature projections. Therefore, the ensemble approach, which can be used to reflect these uncertainties, is highly desired. In this study, the probabilistic projections for future temperature are generated at 88 Canadian climate stations based on the developed RegCM ensemble and obtained Bayesian model averaging (BMA) weights. The BMA weights indicate that the RegCM coupled with the holtslag PBL scheme driven by the HadGEM can provide relatively reliable temperature projections at most climate stations. It is also suggested that the BMA approach is effective in simulating temperature over middle and eastern Canada through taking the advantage of each ensemble member. However, the effectiveness of the BMA method is limited when all the models in the ensemble cannot simulate the temperature robustly. The projected results demonstrate that the temperature will increase continuously in the future, while the temperature increase under RCP8.5 will be significantly larger than that under RCP4.5.

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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

Lotfirad

Adib

Riyahi

et al. 2022

Stoch Environ Res Risk Assess

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A probabilistic Bayesian framework to deal with the uncertainty in hydro-climate projection of Zayandeh-Rud River Basin

Cited by 9 publications

References 58 publications

Evaluating the effects of CMIP6 model uncertainty on extreme flows of the Caspian Hyrcanian forest watersheds by BMA method

Evaluating the effects of CMIP6 model uncertainty on extreme flows of the Caspian Hyrcanian forest watersheds by BMA method

Bayesian model averaging of the RegCM temperature projections: a Canadian case study

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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