Observed and Predicted Precipitation Changes in Pakistan Using Ground Observations, Satellite Data and Model Projections with Special Focus on Winter and Pre-Monsoon Precipitation

Abstract

This study utilizes ground, satellite and model data to investigate the observed and future precipitation changes in Pakistan. Pakistan Meteorological Department’s (PMD) monthly precipitation data set along with Tropical Rainfall Measuring Mission (TRMM) monthly dataset TRMM_3B43 (0.25˚x0.25˚ resolution) have been used to evaluate rainfall trends over the climatic zones of Pakistan through Man-Kendall test and Sen’s slope estimator for the time period 1978-2018. Community Climate System Model (CCSM4) projections have been employed to explore the projected changes in precipitation till 2099. Furthermore, TRMM and CCSM4 projections have been correlated and validated using Root Mean Square Error (RMSE) and Mean Bias Error (MBE). There is a good correlation between TRMM and PMD ground observation at all stations of the country for all seasons, with correlation coefficient values ranging from 0.89 (November) to 0.97 (July and August). The study shows a decreasing trend in winter precipitation in all zones of the country with a significant decrease over western mountains i.e. zone C of the country. During 2008-2018, a sharp decrease in winter precipitation is observed as compared to the baseline value of 1978-2007 in all climatic zones. There seems to be a shift in precipitation from winter towards pre-monsoon season as pre-monsoon precipitation in last 11 years increased in all zones except Zone C. Coherently, there is a decrease in area affected by winter precipitation and an increase in area for pre-monsoon precipitation. Future precipitation estimates from CCSM4 model for RCP 4.5 and RCP 8.5 over-estimate precipitation in most parts of the country for the first 9 observed years (2010-2018) and predict a rise in precipitation by 2099 which is more pronounced in the northern and western Pakistan while a decrease is predicted for the plains of the country, which might have negative consequences for agriculture.