Plausible Precipitation Trends over the Large River Basins of Pakistan in Twenty First Century

Inter alia, inter-annual and spatial variability of climate, particularly rainfall, shall trigger frequent floods and droughts in Pakistan. Subsequently, a higher proportion of the country's population will be exposed to water-related challenges. This study analyzes and projects the long-term spatio-temporal changes in precipitation using the data from 2005 to 2099 across two large river basins of Pakistan. The plausible precipitation data to detect the projected trends seems inevitable to study the future water resources in the region. For, policy decisions taken in the wake of such studies can be instrumental in mitigating climate change impacts and shape water management strategies. Outputs of the Coupled Model Intercomparison Project 5 (CMIP5) climate models for the two forcing scenarios of RCP 4.5 and RCP 8.5 have been used for the synthesis of projected precipitation data. The projected precipitation data have been synthesized in three steps (1) dividing the area in different climate zones based on the similar precipitation statistics (2) selection of climate models in each climate zone in a way to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity in a baseline period of 1971-2004 and the projected period of 2005-2099 and (3) combining the selected model's data in mean and median combinations. The future precipitation trends were detected and quantified, for the set of four scenarios. The spatial distribution of the precipitation trends was mapped for better understanding. All the scenarios produced consistent increasing or decreasing trends. Significant declining trends were projected in the warm wet season at 0.05% significance level and the increasing trends were projected in cold dry, cold wet and warm dry seasons. Framework developed to project climate change trends during the study can be replicated for any other area. The study therefore can be of interest for researchers working on climate impact modeling.