Appraisal of spatial and temporal behavior in monsoon precipitation series of Punjab-Pakistan using hierarchical Bayesian Models

Precipitation shows that structured variation across space and time and the pattern of this variation is changing round the world due to global warming. These changes affect the water resources and hydrological cycle. A spatio-temporal dataset of precipitation records together with data on temperature, wind speed and humidity is used to analyze changes in spatiotemporal pattern of precipitation across 17 major meteorological stations of Punjab-Pakistan for monsoon period during 2004–2012. A distinguishable trend in precipitation structure along spatial and temporal domains is noticed. We consider a model-based bayesian spatio-temporal analysis using dynamic linear model and gaussian process model. The meteorological variables (covariates) temperature, wind speed and humidity show varying effect on the precipitation at different locations and time points. Additionally, we construct the spatial and temporal boxplots of all meteorological stations and time points to observe the distribution of precipitation and covariates at micro level. Further to this, the interval plots also reveal the spatially varying and temporally dynamic pattern of covariates. The prediction results through predictive contour plots show high and low precipitation zones in the study area and are helpful for policy makers to identify the homogeneous climate.