Claudia Fernanda Almeida Teixeira-Gandra

BARTLETT-LEWIS OF RECTANGULAR PULSE MODIFIED MODEL: ESTIMATE OF PARAMETERS FOR SIMULATION OF PRECIPITATION IN SUB-HOURLY DURATION

The Bartlett-Lewis Rectangular Pulse Modified (BLPRM) model simulates the precipitous slide in the hourly and sub-hourly and has six parameters for each of the twelve months of the year. This study aimed to evaluate the behavior of precipitation series in the duration of 15 min, obtained by simulation using the model BLPRM in situations: (a) where the parameters are estimated from a combination of statistics, creating five different sets; (b) suitability of the model to generate rain. To adjust the parameters were used rain gauge records of Pelotas/RS/Brazil, which statistics were estimated - mean, variance, covariance, autocorrelation coefficient of lag 1, the proportion of dry days in the period considered. The results showed that the parameters related to the time of onset of precipitation (λ) and intensities (μx) were the most stable and the most unstable were ν parameter, related to rain duration. The BLPRM model adequately represented the mean, variance, and proportion of the dry period of the series of precipitation lasting 15 min and, the time dependence of the heights of rain, represented autocorrelation coefficient of the first retardation was statistically less simulated series suitability for the duration of 15 min.

Phosphorus adsorption in lowlands of Santa Catarina cultivated with rice and its relation with soil properties

Soils have different capacities to retain phosphorus (P). In seasonal flooded soils iron (Fe) oxides and hydroxides of poor crystallinity, increase with time, and could be the most important constituent in P adsorption, during the time the soils remain unflooded. The present paper had the objective to determine the maximum adsorption capacity of P (MACP), collected from five selected soils in lowland rice areas in the Santa Catarina state, to search for soil individual parameters that best correlates with maximum adsorption capacity of P (MACP). The rates of P used to determine the MACP were calculated based on the concentration of indigenous P in the soil. The sorption of P and the P in solution data were then fitted to the Langmuir sorption isotherm equations. The results obtained for the parameters ´a´ and ´b´ (binding energy and MACP, respectively) were correlated with selected soil parameters. The Langmuir equations adequately described P adsorption in the soils. The maximum sorption of P varied widely, ranging from 0.419 to 1,450 mg g-¹, where the organic soil (OXs) presented very high MACP, which could lead to a less availability of the element to the plants. The coefficients of correlation showed a significant association between MACP and the following soil parameters: P indigenous, Fe oxides extracted with ammonium oxalate at pH 3.0 (Feo3) and pH 6.0 (Feo6), as well as the ratios Feo3/Fed and Feo6/Fed, suggersting that these chemical parameters can be utilized to characterize the P adsorption in these soils.

Prediction of rainfall from geographical coordinates of the state of Rio Grande do Sul

Based on the fact that the altitude, latitude and longitude influence climate characteristics of a region, the aim of the present work was to verify the existence of information gain when the statistical procedure of multiple linear regression is applied for the prediction of total precipitates annual, monthly and seasonal, for some municipalities in the state of Rio Grande do Sul were used daily rainfall data from 26 weather stations. Therefore, besides other 7, used for validation of the proposed linear models. Data were obtained from the Agência Nacional de Águas (ANA), which belong to seven mesoregions the state of Rio Grande do Sul. After the formation of the series, precipitation values were adjusted from linear models using linear regression Multiple, in which the dependent variable was the precipitation and the independent variables, the geographic coordinates latitude and longitude, and altitude. The prediction of the values of annual, seasonal and monthly precipitation, depending on the geographical coordinates and altitute, can be performed from the multiple linear regression. Of the three variables, the latitude seems to be that most influences the estimation of precipitation.