An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

Depending on the speed, the wind has beneficial potential in the pollination of plants, in the generation of energy, in the maintenance of temperature, but when it exceeds a certain level it becomes dangerous and destructive, and can cause damage to buildings, plantations and navigation. The theory of extreme values plays a key role in modeling events associated with very small probabilities or rare events. Probabilistic models based on this theory aim to predict, from a set of maximum values of an environmental process recorded in a relatively short period of time, the maximum values expected in a longer period of time, which for the specific case of wind, is of great utility, for example, in the choice of the cultivar to be sown. This work consisted in adjusting the generalized distribution of extreme values (GVE) to the maximum monthly wind speed data recorded over a period of 54 years (1961 to 1983 and 1989 to 2015) in Uruguaiana, State of Rio Grande do Sul. The adjustment to the data was evaluated through the Kolmogorov-Smirnov test. The generalized distribution of extreme values with their parameters estimated by the maximum likelihood method presented a satisfactory adjustment to the data.