Abstract
Concern about the effects of climatic change on numerous aspects of human life in general and on agricultural production in particular is growing. The utility of HadCM3 as a tool in climate change predictions in cross cultural studies is scarce. Therefore, this study sought to investigate and predict climate change induced temperature and precipitation in Iran. The calibration and validation using the HadCM3 was performed during 1961–2001, using daily temperatures and precipitation. The data on temperature and precipitation from 1961 to 1990 were used for calibration, and, for model validation, data from 1991 to 2001 were used. Moreover, in order to downscale general circulation models to station scales, SDSM version 4.2 was utilized. The least difference between observed data and simulation data during calibration and validation showed that the parameter was precisely modeled for most of the year. Simulation under the A2 scenario was performed for three time periods (2020, 2050, and 2080). According to our simulated model, precipitation showed a decreasing trend whereas temperature showed an increasing trend. The result of this research paper makes a significant contribution to climate smart agriculture in Iran. For example, rural development practitioners can devise effective policies and programs in order to reduce the vulnerability of local communities to climate change impacts. Moreover, the result of this study can be used as an optimal model for land allocation in agriculture. Moreover, a shortage of rainfall and decreased temperatures also have implications for agricultural land allocation.
Highlights
Iran is home to 77 million people [1], with an area of 1,648,000 km2
Since Kermanshah is located in a semi-arid region, it is significantly affected by climate change
The prediction of local climate variables was conducted through corrolation analysis between the predictors and predictands
Summary
Iran is home to 77 million people [1], with an area of 1,648,000 km. Iran is home to 77 million people [1], with an area of 1,648,000 km2 It has 1.1% of the global population and is located in an arid and semi-arid region with a yearly average precipitation of 250 mm [2]. About 60% of the country is mountainous and the remaining part (1/3) is deserts and arid lands. At farm level, information on climate variability can be used for planning future crop patterns and the prediction of climate change can aid farmer resilience when adapting to climate variability. Predictions of climate change induced temperature and precipitation aid rain-fed farmers to take proactive measures when selecting cultivars and planning for water resource management
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