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

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 Inter...

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Vydané v:	Water resources management Ročník 37; číslo 13; s. 5235 - 5254
Hlavní autori:	Karimizadeh, Keivan, Yi, Jaeeung
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.10.2023 Springer Nature B.V
Predmet:	Artificial neural networks Atmospheric Sciences Civil Engineering Climate change Climate effects Earth and Environmental Science Earth Sciences Environment Environmental impact Geotechnical Engineering & Applied Earth Sciences Humidity Hydrogeology Hydrologic models Hydrology/Water Resources Intercomparison Iran Machine learning Neural networks Precipitation prediction Predictions Relative humidity Runoff Scaling Socioeconomic aspects socioeconomics Soil and Water Assessment Tool model Soil water Surface runoff temperature Water resources Watersheds Weather forecasting Wind speed Iran Climate change CMIP6 SSPs SWAT and machine learning Saghez watershed
ISSN:	0920-4741, 1573-1650
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Shrnutí:	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 R 2 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0920-4741 1573-1650
DOI:	10.1007/s11269-023-03603-z