Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques

Keivan Karimizadeh () and Jaeeung Yi ()
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Keivan Karimizadeh: Ajou University
Jaeeung Yi: Ajou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 13, No 14, 5235-5254

Abstract: 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.

Keywords: Climate change; Saghez watershed; CMIP6; SSPs; SWAT and machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03603-z

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