Applying the AOGCM-AR5 models to the assessments of land suitability for walnut cultivation in response to climate change: A case study of Iran.

Due to higher temperatures and lower water availability, climate change is likely to have a major impact on walnut production in the near future. Climate change will alter the land suitability for walnut cultivation around the world, especially in arid and semi-arid regions like Iran. Here, land suitability for the cultivation of walnut (Juglans regia L.) in Iran was determined using the GIS for present and future conditions (2020-2049) with an approach to climate change. Accordingly, data from 375 synoptic stations throughout Iran were gathered for climatic factors including average, minimum and maximum temperatures, relative humidity and chilling requirement. Also, ASTER sensors (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and their data provided this research with cells that make a precision of 150 m (5 s), and the data were used for gauging geological parameters such as altitude and land slope. The electrical conductivity (EC) of soil and water were informed by the data bank of the Iranian Water Resources Management. The results of temperature simulations for the future (2020-2049) were analyzed by 21 AOGCM-AR5 models under the RCP4.5 emission scenario. In the first phase of evaluations, the maps of land suitability were constructed for present conditions by considering a network of the above-mentioned parameters. By combining these layers of information, the final map of land suitability was illustrated for walnut cultivation. In the second phase, the NEX-GDDP was used in order to determine land suitability for the future (2020-2049). The results showed that Iran currently has 582844 km2 of land suitable for walnut cultivation. However, the future will see less suitable lands: the current area will be reduced by 6.19%, from 582844 km2 to 546710 km2. In general, the northern, northwestern and western margins of Iran are currently suitable for walnut cultivation. By approximation, these lands will also be major areas for prospective cultivations of walnut in the future (2020-2049), even though their current stretch will be reduced.

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran).

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R2=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34-21.83tCha-1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.