Evaluation of climate change performance using two hydrological models with different structures

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Sadegh Valeh ¹ , Baharak Motamedvaziri ² , Hadi Kiadaliri ³ , Hasan Ahmadi ⁴

1 - Ph.D. Student, Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant Professor, Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Associate professor, Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Professor of Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran.

Received: 2020-12-13 Accepted : 2021-07-15 Published : 2021-10-23

Keywords: ;ANN, ;SWAT, Amameh watershed, ;Climate change,

Abstract :

One of the major challenges affecting the natural ecosystems and various aspects of human life is climate change. The effects of global warming on the hydrology and water cycle in nature are very serious, and the quantitative recognition of these effects creates more readiness to deal with its consequences. In the present study, the black box model (artificial neural network) and the semi-distributed model (SWAT) were selected and examined according to error and uncertainty. This paper employed the large-scale model (CanESM2) under scenarios RCP2.6 and RCP8.5 to investigate the effect of climate change. The results of climate change in this study showed that runoff (3-59%) and temperature (1.53-6.93 °C) have an increasing trend. In particular, this increasing trend is further exhibited by extreme values and severe floods. Also, the amount of runoff will increase by 7-11% in the upcoming period. For this reason, it is necessary to pay attention in urban studies to the increase of flood occurrence in the future.

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