Modeling impacts of climate change on aridity and crop water demand in Syria

Abstract

Rising temperature and changing rainfall patterns due to global warming would cause a shift in aridity, particularly in the dry regions of the world which may subsequently affect several sectors particularly the agricultural and water resources. The effect of climate change may severely affect the livelihood of the vast population depending on agriculture in dry regions if proper adaptation and mitigation measures are not taken. The major objective of the present study is to develop a framework for the projection of climate change using general circulation model (GCM) and assess the impacts of climate change aridity and crop water demand (CWD) in dry regions for different representative concentration pathways (RCP) scenarios. Syria, located in a predominantly arid region and one of the most vulnerable countries of the world to climate change was considered as the case study area. Considering scarcity of data, a gauge based gridded rainfall data of global precipitation climatology center (GPCC) and temperature data of climate research unit (CRU) for the period 1951-2010 were used. The temporal variations in aridity were estimated using the UNESCO aridity index while the CWD was estimated using a simple water balance model. A novel entropy-based method known as symmetrical uncertainty (SU) was used for the selection of GCMs to reduce uncertainties in climate change projections. The performance of four state-of-the-art bias correction approaches was compared for selecting the best method for reliable downscaling of climate using model output statistics (MOS) approach. A random forest (RF) based regression was used for the generation of the multi-model ensemble (MME) mean projections of climate. Results revealed an increase in aridity and crop water demand in Syria during 1981-2010 compared to 1951-1980. The temperature was found to be the dominating factor for defining aridity in semi-arid regions in the north while rainfall as the dominating factor in the arid south. Four GCMs namely HadGEM2-AO, NorESM1-M, CSIRO-Mk3.6.0, and CESM1-CAM5 were found to be the most suitable for the projection of rainfall and temperature in Syria. Performance assessment of bias correction methods revealed linear scaling (LS) as the most suitable for downscaling of both precipitation and temperature using the MOS approach. The LS downscaled GCM simulations were found to replicate the mean, variability and temporal distribution of GPCC/CRU precipitation/temperature reliably. Future projection of rainfall and temperature using MME for the period 2010 – 2100 revealed a decrease in precipitation in the range of -30 – 85.2% mostly in the coastal areas while it was projected to increase next to those areas in the range of 18 – 87.3%. The MME projected an increase in temperature in the range of 0.0 – 5.1°C over the entire country for different RCPs. All the RCPs projected a higher increase in average temperature in the east, particularly northeast and least in the western coastal region. The change in precipitation and temperature would cause an increase in aridity and CWD in Syria. The aridity and CWD were projected to increase more in the western coastal region where precipitation was projected to decrease more. Besides, those were projected to increase in most of the areas of the country used for agriculture. It is expected that the methodology proposed in this study can be used as a tool for providing the information required for climate change adaptation and mitigation planning.