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Assessment of the Impact of Climate Change on Runoff on the Kan Watershed in the Future | ||
| پژوهش های آبخیزداری | ||
| Article 6, Volume 33, Issue 2 - Serial Number 127, July 2020, Pages 66-81 PDF (1.04 M) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmej.2019.127163.1247 | ||
| Authors | ||
| Mehdi Ahmadi1; Baharak Motamedvaziri* 1; Hassan Ahmadi2; Abolfazl Moeini3; Gholam Reza Zehtabian2 | ||
| 1Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran | ||
| 3Department of Forest, Range and Watershed Management, Faculty Natural Resources and Environmental, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| Abstract | ||
| Climate change is among the most important challenges affecting the natural ecosystems and various aspects of the human life. The global warming imposes serious impacts on the hydrology and water cycle in the nature, and quantitative evaluation of such impacts provides further preparedness for confronting their anticipated consequences. The so-called statistical downscaling model (SDSM) was used to forecast the trends of precipitation and temperature during the 2006 – 2100 period based on the CanESM2 large ensembles. The impact of climate change on hydrologic conditions on the Kan Watershed was evaluated using the SWAT and ANN models. The results indicated that an increase in precipitation and temperature are probable in the forecasted future period (2006 – 2100). In general, it can be stipulated that the temperature will rise by 0.8 – 5.6℃ and the precipitation will increase by 4 – 55%. Given its structure, the ANN exhibited a superior performance over the SWAT. The results of the runoff studies indicated that for the forecasted future period (2006 – 2100), the ANN model predicts 2% and 4% decrease under the RCP2.6 and RCP8.5 scenarios, respectively, and a 25% increase under the RCP4.5 scenario. However, the SWAT model forecasted 42%, 43%, and 49% increase under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. A 49% increase in the runoff to 200 m3/s will not only add to the suspended sediment load of the Kan River, but also will bury the Emamzadeh Davood, Rendan, Kiga, Sangan, Suleghan, and Keshar villages under sediment, but also will cause extensive financial and life damages. | ||
| Keywords | ||
| Artificial neural network; precipitation- runoff simulation; RCP scenario; SDSM statistical model; SWAT hydrological model | ||
| References | ||
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