Precipitation Variability in Connection with Various Potential Determinants
Climate change due to global warming has given rise to many challenges, especially the variation in precipitation trends which are resulting in the extended possibility of more droughts and floods. Global Climate Risk Index (GCRI) has reported Pakistan as the 5th most effected and vulnerable country in the world which is an alarming situation. Baluchistan, the most aected province of Pakistan, is selected, as the study area for this doctoral research. Baluchistan is under drought warning by Pakistan Meteorological Department (PMD) and faced many severe droughts in the past few decades. According to the reports, 62 % of the people of Baluchistan are deprived of safe drinking water and more than 58 % of its land is uncultivated due to water scarcity. As a result, the water crisis in Baluchistan should be tackled on a war footing.
Mann-Kendall (MK) statistical test is used to identify the monthly signicant precipitation trends in thirteen meteorological stations located in four regions of Baluchistan. Theil and Sens slope method (TS) is used to compute the magnitude of the trends. Partial Mann-Kendall (PMK) test is performed to study the trend variations in precipitation in the presence of climatic indices as covariates. Variability patterns of precipitation are identied through Principal Component Analysis (PCA) and their corresponding time series (PCs) are also made. Empirical Orthogonal Analysis (EOF) is performed on Sea Surface Temperature (SST), Sea Level Pressure (SLP), and Zonal Wind Surface (ZW-S) to determine the dominated teleconnections. Correlation analysis is also performed between time series of precipitation and climate indices, SST anomalies, atmospheric circulations such as SLP, Geopotential Heights (500 hpa), and Zonal winds (surface) to observe their relationship and in fluence on precipitation. Modeling, and validation are done using both Multilinear Regression (MLR) and Principal Component Regression (PCR) and prediction has been done for the next ve years.
Baluchistan receives its greater portion of rainfall in the winter and spring months. Variation in trends, i.e. increase/decrease in precipitation are observed in months of January/June when the time series data is analyzed through Mann-Kendall test.
The change in precipitation, trends under the influence of climatic indices are determined through PMK for January and June in Region-1. EQWIN, ENSO-MEI, and EMI-MODOKI show moderate to strong influence on precipitation. Model using MLR technique identied the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), EQWIN, and EMI-MODKOI as the potential determinants causing variability in the precipitation of Baluchistan. The second model using the PCR technique is also developed to address the multicollinearity among variables. Apart from other variables that are identied as potential determinants, PCR also shows that EQWIN is a signicant potential determinant having a strong positive impact on precipitation variability in Baluchistan. Hence, endorsing the novelty of this doctoral research. PCR model produces more authentic, accurate, and statistically reliable results as compared to the MLR model. Other studies using both PCR and MLR techniques also conrm that PCR is better than MLR. Therefore, based on the current ndings along with previous studies, it can be concluded that the PCR technique is better than MLR. And PCR is recommended for further study as well as for detailed analysis.