This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.

The aim of this study was to evaluate the statistical properties of a daily rainfall time series recorded in Piracicaba river basin, state of Sao Paulo, Brazil in the period 1917-2016. We apply Multifractal Detrended Fluctuation Analysis (MF-DFA) on deseasonalized data and calculate generalized scaling exponents, h(q). We find that h(q) is a decreasing function of q, indicating multifractal behavior of precipitation dynamics. We also perform calculations on two subseries corresponding to the periods before and after 1970, where the second period is characterised by a significant increase of positive trend in rainfall intensity. We find no significant change in behaviour between these two subseries and the entire series, indicating that the dynamics of the underlying process did not change. Our results agree with previous studies on multifractal properties of rainfall time series in other regions of the world supporting the hypotheses of the existence of multiplicative cascade processes in the atmosphere.

We study the daily precipitation in the municipality of Campina Grande, estimating the parameters of Gamma, Log-Normal, and Weibull distributions. To evaluate the parameter estimators, we compared the Particle Swarm Optimization (PSO) versus Maximum Likelihood Estimation (MLE) to analyze and understand the behaviour of the daily precipitation in Campina Grande. In most cases, our results show evidence that the PSO algorithm is an efficient and robust technique. Notwithstanding, the algorithm also presents an efficient parameter estimation due to its fast convergence.