Climate change projection using statistical downscaling model over southern coastal Iran

Area of Science:

• Climate Science
• Environmental Science
• Water Resource Management

Background:

• Iran faces significant climate change vulnerability, particularly in its southern coastal regions, with shifting precipitation and temperature patterns.
• Urgent adaptive management of water resources and energy supply is crucial to address future climate change challenges.

Purpose of the Study:

• To evaluate alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) in Iran's southern coastal regions (Hormozgan province).
• To utilize climate change scenarios from Coupled Model Inter-comparison Project phases 5 (CMIP5) and 6 (CMIP6) for future climate predictions.

Main Methods:

• Employed the Statistical Downscaling Model (SDSM) with National Centre for Environmental Prediction (NCEP) predictors.
• Incorporated CMIP5 (RCPs 2.6, 4.5, 8.5) and CMIP6 (SSPs 1, 2, 5) climate change scenarios for early, middle, and late 21st century periods.

Main Results:

• CMIP5 models demonstrated superior performance over CMIP6 in simulating Tmean and rrr24.
• A significant increase in Tmean was projected across all scenarios and time periods, with notable rises in the middle and late 21st century.
• Precipitation showed significant fluctuations, with CMIP5 RCP8.5 indicating the highest temperature increase (+1.22°C) and rainfall increase (+150.2 mm) in specific locations and periods.

Conclusions:

• Hormozgan province's coastal areas will experience rising temperatures and changing rainfall patterns, posing risks to water, energy, agriculture, and infrastructure.
• Findings support the development of local mitigation and adaptation policies, including water harvesting, watershed management, and improved irrigation systems.
• Further research is recommended to assess the precise impacts on water resources, ecosystems, and infrastructure for effective management.