Investigation into the Effects of Climatic Change on Temperature, Rainfall, and Runoff of the Doroudzan Catchment, Iran, Using the Ensemble Approach of CMIP3 Climate Models

• Published in: Advances in meteorology 2019-01, Vol.2019 (2019), p.1-16
• Main Authors: Najafizadeh, Mohammad Mahdi, Najarchi, Mohsen, Beydokhti, Nasser Taleb, Sayadi, Abolghasem
• Format: Article
• Language: English
• Subjects: Air pollution; Analysis; Annual rainfall; Annual rainfall data; Aquatic resources; Catchment area; Catchments; Civil engineering; Climate change; Climate effects; Climate models; Decision making; Emissions; Emissions control; Environmental impact; General circulation models; Greenhouse effect; Greenhouse gases; Hydrology; Intercomparison; Investigations; Iran; Management; Maximum temperatures; Minimum temperatures; Neural networks; Rain; Rain and rainfall; Rainfall; Resource management; Rivers; Runoff; Simulation; Statistical methods; Surface water; Temperature; Temperature effects; Uncertainty; Water; Water resources; Water resources planning; Watersheds
• ISSN: 1687-9309; 1687-9317
• DOI: 10.1155/2019/6357912

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in three periods including 2011–2030, 2046–2065, and 2080–2099 were downscaled and studied using 15 Coupled Model Intercomparison Project, Phase 3 (CMIP3) climatic models, under three scenarios of greenhouse gas emissions A2, B1, and A1B, from the database of the LARS-WG model. The difference in the amount of changes in temperature and rainfall in these three periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15-model scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next three periods, under the second approach and three emission scenarios, were produced using the feedforwad neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature in all three periods under the three scenarios. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, the average annual rainfall in all these three periods under the climatic ensemble model, and three emission scenarios showed a reduction in the average annual rainfall in the three periods. The maximum amount of reduction was in 2080–2099 (101 mm) under the scenario B1. Besides, a reduction occurred in the average runoff of the catchment under three ensemble models and the emission scenario in all three periods, as compared to the average of the long-term observational values in most years.