Abstract
Climate change is the most important problem of the earth in the current century. In this study, the effects of climate change on precipitation, temperature, wind speed, relative humidity and surface runoff in Saghez watershed in Iran investigated. The main methods were using the Coupled Model Intercomparison Project phase 6 (CMIP6), the Soil and Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN) model under the Shared Socio-economic Pathway scenarios (SSPs) using the Linear Scaling Bias Correction (LSBC) for the future period (2021–2050) compared to the base period (1985–2014). Additionally, MAE, MSE, RMSE and R2 indices used for model calibration and validation. The average projected precipitation was forecasted to decrease by 6.1%. In terms of the temperature, 1.4 Cº, and 1.6 Cº increases were predicted for minimum and maximum temperatures, respectively. Prediction of surface runoff using the SWAT model also illustrated that based on SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios, runoff will decrease in the future period, which based on three mentioned scenarios is equals to 17.5%, 23.7% and 26.3% decrease, respectively. Furthermore, the assessment using the artificial neural network (ANN) also showed that the parameters of precipitation in the previous two days, wind speed and maximum relative humidity have the greatest effect on the watershed runoff. These findings may be helpful to reduce the impacts of climate change, and make the suitable long-term plans for management of the watersheds and water resources in the region.
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The data used in this paper have been prepared through the Meteorological Organization and Water Resources Research Center of Iran and Earth System Grid Federation (ESGF) from this link: https://esgf-node.llnl.gov/search/cmip6/
Code Availability
In this paper, the codes in MATLAB 2018 software used to re-gridding the models.
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Acknowledgements
We would like to acknowledge the Meteorological Organization, Water Resources Research Center of Iran and Earth System Grid Federation (ESGF) for providing data.
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Keivan Karimizadeh: Conceived of the presented idea and developed the theory, performed the computations, Concepts, Design, Definition of intellectual content, literature search, experimental studies, data acquisition, data analysis, Statistical analysis, manuscript preparation, and manuscript editing. Jaeeung Yi: Encouraged and developed the theoretical formalism, verified the analytical methods, literature search, and manuscript editing. All authors discussed the results and contributed to the final manuscript.
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Karimizadeh, K., Yi, J. Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques. Water Resour Manage 37, 5235–5254 (2023). https://doi.org/10.1007/s11269-023-03603-z
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DOI: https://doi.org/10.1007/s11269-023-03603-z