Assessment of the Impact of Climate Change on Runoff on the Kan Watershed in the Future

Document Type : Research

Authors

1 Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran

3 Department 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

References

Aalinejad MH,  Dinpashoh Y, Jahanbakhsh ASL S. 2016. Impact of Climate Change on Runoff from Snowmelt by Taking into Account the Uncertainty of GCM Models (Case Study: Shahrchay Basin in Urmia). European Online Journal of Natural and Social Sciences. 5(1): 200–211.
Abbaspour K C, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang, Kløve B. 2015. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology. 524(1): 733–752.
Ahmadi M, Ghasemieh H, Ghermezcheshmeh B. 2014 A. Assessment of statistical downscaling  methods in Downscaling AO-GCMS model data as an input of hydrological models, 2nd National   Conference on Water Crisis, Shahrekord. 2(1): 203-213. (In Persian).
Ahmadi M. 2014 B. Analyzing impact of climate change on annual discharge in Qorantalar Watershed. MSc Thesis, Shahrekord University. 145 pp. (In Persian).
Ahmadi M, Moeini A, Ahmadi, H, Motamedvaziri B, Zehtabiyan, GR. 2019. Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (Case study: Kan watershed, Iran). Physics and Chemistry of the Earth. 111(1): 65–77.
Al-mukhtar M. 2016. Modelling the root zone soil moisture using artificial neural networks , a case study. Environmental Earth Sciences. 75(1): 1115–1124.
Ashrafvaghefi S, Mousavi SJ, Abbaspour KC, SrinivasanR, Yang H. 2014. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. hydrological processes. 28(4): 2018–2032.
Carcano EC, Bartolini P, Muselli M, Piroddi L. 2008. Jordan recurrent neural network versus IHACRES in modelling daily streamflows. Journal of Hydrology. 362(3–4): 291–307.
Forests, Range and Watershed Management Organization. 2019. Detailed-executive study plan of watershed management of Kan district of Tehran. The Report in Natural Recourses and Watershed Management of Tehran province.  124 p. (In Persian).
Ghermezcheshmeh B, Rasuli, A, Rezaei-banafsheh, M, Massah A, Khorshiddoost  A. 2014. Investigation impact of morpho-climatic parameters on accuracy of SDSM model. Journal of Watershed Engineering and Management. 6(2): 155–164. (In Persian).
Goodarzi M. 2011. Evaluation and assessment of impacts of climate variability on surface water resources, Case stydy: Karkheh olia sub- basin. phD Thesis. 134 p. (In Persian).
Hajimohamadi M, Azizian A, Ghermezcheshmeh B. 2018. Evaluation of the impact of climate change runoff in Kan Watershed. Watershed Engineering and Management. 10(2): 144–156. (In Persian).
Hassan Z, Shamsudin S, Harun S, Malek MA, Hamidon N. 2015. Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia. Environmental Earth Sciences. 74(1): 463–477.
Jimeno-Saez P, Senent-Aparicio J, Perez-Sanchez J, Pulido-Velazquez D. 2018. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water. 10(2): 189–192.
Mahmood R, Babel MS. 2013. Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theoretical and Applied Climatology. 113(1–2): 27–44.
Mirdashtvan  M,  Najafinejad A, Malekian A, Sadoddin A. 2018. Downscaling the contribution to uncertainty in climate change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran. Meteorological Applications. 25 (3): 441–422.
Nazari-Sharabian M, Karakouzian M, Ahmad S. 2019. Water quality modeling of Mahabad Dam watershed–reservoir system under climate change conditions, using SWAT and system dynamics. Water. 11(2): 380–394.
Ning J, Gao Z, Lu Q. 2015. Runoff simulation using a modified SWAT model with spatially continuous HRUs. Environmental Earth Sciences. 74(7): 5898–5905.
Noori N, Kalin L. 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology. 533(1): 141–151.
Rahimi j, Malekian A, Khalili A. 2018. Climate change impacts in Iran: assessing our current knowledge. Theoretical and Applied Climatology. 135(1–2): 545–564.
Tejaswini V, Sathian K. 2018. Calibration and Validation of Swat Model for Kunthipuzha Basin Using SUFI-2 Algorithm. Int. J. Curr. Microbiol. App. Sci. 7(1): 2162–2172.
Wilby RL, Dawson CW, Barrow EM. 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software. 17(2): 145–157.
Wilby RL, Dawson CW. 2013. The statistical downscaling model: insights from one decade of application. International Journal of Climatology. 33(7):1707–1719.
Zehtabian GR, Salajegheh A, Malekian A, Boroomand N, Azareh A. 2016. Evaluation and comparison of performance of SDSM and CLIMGEN models in simulation of climatic variables in Qazvin plain. Desert. 21(2) 155–164.

Files

History

  • Receive Date: 21 July 2019
  • Revise Date: 02 September 2019
  • Accept Date: 17 December 2019

Share

How to cite

Statistics