Projected Rainfall Variability Based on PRECIS Regional Model
Abstract
The present study indicates the potential projected variation of decadal mean rainfall over Kohistan region of Sindh Province, Pakistan. Precipitation variability is a crucial climatic factor that affects human health and their settlements. In this study, the precipitation variability associated with climate change in Kohistan region, Sindh, Pakistan is simulated using the PRECIS regional climate modeling system. The study analyses the precipitation variability in the future for two spells (2021-2050 and 20712099) with respect to the past (1961-1990) climate under the baseline ECHAM5 dataset for A1B Scenario at a resolution of 25x25 km. Based on this analyses, the precipitation scarcity is projected for 2021-2050 and 2071-2099 decades. The projected results showed a serious precipitation variation and shortfall of 12.60, 53.98, and 48.19% during 2031-2040,2041-2050 and 2081-2090 decades respectively as compared to baseline (1961-1990). The analyzed situation would be harmful to the water resources and agricultural production in the region during the shortfall, which imposes the adverse effect on the recharge of groundwater and quality. That might cause of long drought spell in the region. While during the 20212030 decade shown slight influence on the potential of hill torrents and groundwater recharge. However, the results reveal for the period of 2071-2080 and 2091-2099, the extreme floods with 60.50 and 70.50% are projected as compared to baseline 1961-1990. The increasing trend of precipitation indicates additional recharge of fresh groundwater and quality, with increasing level of aquifers, subsequently more agricultural production would be expected with alternate employment opportunities in the water sector. The projected results, indicating the decadal scenarios of the drought and wet spells in the region by the precipitation variation, which may impact on the hill torrents, groundwater and agricultural production, and employment opportunities. These quantitative projections should enable policymakers and stakeholders to plan for future measures.