Agricultural drought periods analysis by using nonhomogeneous poisson models and regionalization of appropriate model parameters

Precipitation has a dominant role in agriculture, and regular rain pattern is usually vital to agriculture; excessive or inadequate rainfall can be harmful. In this paper, an agricultural drought index utilized study the periods analyze them with their intensities at various locations. Some nonhomogeneous Poisson models are also used calculate probability of droughts (number times occurred) time interval interest. It assumed that number event occurrences Nonhomogeneous Process (NHPP) rate function, which depends on some parameters must estimated. Two cases these functions considered: Weibull linear intensity function. The Bayesian approach Gibbs sampling under Markov Chain Monte Carlo (MCMC) algorithm estimate functions. most appropriate fitted model selected by using Deviance Information Criteria (DIC) use appropriately accumulated events interest each location. Ordinary Kriging (OK) regionalize present its spatial behavior. results based DIC indicate Power Law (PLP) performs better than NHPP model. interpolated parameter values for model, patterns, fluctuations area efficiently presented contour maps. novel straightforward assess predict un-sampled proposed framework might help other variables climate-change study, ecosystem modeling, etc. findings make decisions sustainable environmental management Pakistan.