Correlating soil porosity and respective geological unit in Paraíba do sul Valley, Brazil: a geostatistical methodology proposal

This manuscript aims proposing a methodology for correlating soil porosity to the respective geological units using geostatistical analysis techniques, including interpolation data by kriging. The site studied was in Lorena municipality, Paraíba do Sul Valley, southeastern Brazil. Specifically all studies were carried out within an area of 12 km2 located at Santa Edwirges farm. The database comprehended 41 soil samples taken at different geological and geomorphologic units at three different depths: surface, 50 cm and 100 cm depth. The geostatistical analyses results were correlated to a geological mapping specifically elaborated for the site. This mapping accounts for two different geological formations and a geological contact characterized by a shearing zone. The results indicate the existence of a significant relationship between the soil porosity and the respective geological units. The studies revealed that the residual soils from weathered granitic rocks tend to have higher porosities than the residual soils from weathered biotite gneiss rocks, while the soil porosity within the shearing zone is relatively un-sensitive to the respective geological formation. The spatial patterns observed were efficient to evaluate the relationship between the soil porosity, geology unit and the and geomorphology showing a good potential for correlating with others soil properties such as hydraulic conductivity, soil water retention curves and erosion potentials.

Spatial intra-annual variability of precipitation based on geostatistics. a case study for the Paraiba do Sul Basin, southeastern Brazil

The emphasis in this research is to evaluate the spatial distribution of the precipitation using a geostatistics approach. Seasonal time scales records considering DJF, MAM, JJA e SON periods performed the analysis. Procedures to evaluate the variogram selection and to produce kriging maps were performed in a GIS environment (ArcGIS®). The results showed that kriging method was very suitable to detect both large changes in the whole area as those local small and subtle changes. Kriging demonstrated be a powerful statistical interpolation method that might be very useful in regions with great complexity in climatology and geomorphology.

Suscetibilidade magnética e análise de incertezas em mapeamento de solos

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Caracterização da distribuição física dos dolomitos do membro Búzios no Campo de Badejo - Bacia de Campos

In the region of Badejo oil Ffeld (Campos Basin) lies an Lower Albian dolomitic layer that shows reservoir rock and seal conditions, among others and, it is occasionally karstified. This work applies geostatistical techniques of ordinary and indicatior kriging, in an attempt to determine a method that assists the analysis of different scenarios offered for petroleum drilling from a quantitative point of view this fact is justified, because there are different visions and strategies that would be adopted under different dolomite conditions (stable - low porosity and high density; reservoir - high porosity and medium density; instable - high porosity and low dentisty). The main objective is a methodological introduction that has not been tested in dolomites before aiming to characterize the distribution of the three conditions described above by ordinary and indicatior kriging, which was based on the obtained data from the dolomite layer identification through well logs interpretation and correlation, followed by seismic interpretation. In addition, it was generated structural contour maps, based on 2D and 3D seismic data interpretation, and then, seismic attributes maps were calculated, in order to transform them into pseudo-density maps, i.e., maps that correlate the density values with the attribute values. As primary results, structural contour maps and seismic attributes were obtained and ordinary and indicatior kriging maps were done, on which it is possible to interpret the distribution of the main reservoir and risk probability for drilling of exploration wells obtaining trends N35E and N10W direction for areas of stability or reservoir levels, while the central part of the map presents a higher risk for loss of drilling fluid. The cut-off values levels were based on the values of first and third quartiles of cumulative histogram (instable and stable zones, respectively), as well as the reservoir level was set as the interval...

Spatial distribution of runoff depth from a watershed located in a cuesta relief area of São Paulo State, Brazil

This study was undertaken in a 1566 ha drainage basin situated in an area with cuesta relief in the state of São Paulo, Brazil. The objectives were: 1) to map the maximum potential soil water retention capacity, and 2) to simulate the depth of surface runoff in each geographical position of the area based on a typical rainfall event. The database required for the development of this research was generated in the environment of the geographical information system ArcInfo v.10.1. Undeformed soil samples were collected at 69 points. The ordinary kriging method was used in the interpolation of the values of soil density and maximum potential soil water retention capacity. The spherical model allowed for better adjustment of the semivariograms corresponding to the two soil attributes for the depth of 0 to 20 cm, while the Gaussian model enabled a better fit of the spatial behavior of the two variables for the depth of 20 to 40 cm. The simulation of the spatial distribution revealed a gradual increase in the depth of surface runoff for the rainfall event taken as example (25 mm) from the reverse to the peripheral depression of the cuesta (from west to east). There is a positive aspect observed in the gradient, since the sites of highest declivity, especially those at the front of the cuesta, are closer to the western boundary of the watershed where the lowest depths of runoff occur. This behavior, in conjunction with certain values of erodibility and depending on the land use and cover, can help mitigate the soil erosion processes in these areas.

Classificação climática de Thornthwaite e sua aplicabilidade agroclimatológica nos diferentes regimes de precipitação em Pernambuco

Knowing the annual climatic conditions is of great importance for appropriate planning in agriculture. However, the systems of climatic classification are not widely used in agricultural studies because of the wide range of scales in which they are used. A series with data from 20 years of observations from 45 climatological stations in all over the state of Pernambuco was used. The probability density function of the incomplete gamma distribution was used to evaluate the occurrence of dry, regular and rainy years. The monthly climatic water balance was estimated using the Thornthwaite and Mather method (1955), and based on those findings, the climatic classifications were performed using the Thornthwaite (1948) and Thornthwaite and Mather (1955) for each site. The method of Kriging interpolation was used for the spatialization of the results. The study classifications were very sensitive to the local reliefs, to the amount of rainfall, and to the temperatures of the regions resulting in a wide number of climatic types. The climatic classification system of Thornthwaite and Mather (1955) allowed efficient classification of climates and a clearer summary of the information provided. In so doing, it demonstrated its capability to determine agro climatic zones.

Espacialização dos sedimentos produzidos na parte inicial da bacia hidrográfica do Rio Pardo, Botucatu/SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise multivariada na diferenciaçãoentre manejos de solos cultivados com café

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de imagens em áreas cultivadas com citrus por técnicas de sensoriamento remoto e geoestatística

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Eficiência da cokrigagem na estimativa da produtividade do café conilon

The objective of this research was to estimate the productivity (PRODUT) in sc/ha of coffee conilon through the technique of cokriging, using as covariate the production of humid coffee (PROD) in kg and compare the results with estimates obtained by kriging ordinary. The study was conducted in a commercial area of conilon coffee, Coffea canephora Pierre var. conilon, located in São Mateus Municipality, Espirito Santo State. For the field work was sampled the humid coffee production in a sampling grid irregular of 18.5 ha, 87 sampling points in the total. We also determined the production of dry coffee beans and coffee benefited 12% moisture, to obtain the PRODUT variable. After exploratory data analysis, which showed the correlation between variables in the order of 0.899, was performed variogram analysis. Were adjusted theoretical variograms to PROD and PRODUT and cross variogram between two variables. Finally we estimated the value of productivity, both by ordinary kriging as per cokriging. The validation of the estimation by cokriging not shows, however, significant gains in relation to validation by ordinary kriging.

Perdas na colheita mecanizada de algodão em variabilidade espacial e em função de velocidades de colheita

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação da teoria da confiabilidade em estacas escavadas de grande diâmetro para obtenção das superfícies resistentes de projeto

In this work, the author looks forward to develop a new method capable of incorporate the concepts of the Reliability Theory and Ruin Probability in Deep Foundations, in order to do a better quantification of the uncertainties, which is intrinsic in all geotechnical projects, meanly because we don't know all the properties of the materials that we work with. Using the methodologies of Decourt Quaresma and David Cabral, resistance surfaces have been developed utilizing the data achieved from the Standard Penetration Tests performed in the field of study, in conjecture with the loads defined in the executive project of the piles. The construction of resistance surfaces shows to be a very useful tool for decision making, no matter in which phase it is current on, projecting or execution. The surfaces were developed by Kriging (using the software Surfer® 12), making it easier to visualize the geotechnical profile of the field of study. Comparing the results, the conclusion was that a high safety factor doesn't mean higher security. It is fundamental to consider the loads and resistance of the piles in the whole field, carefully choosing the project methodology responsible to define the diameter and length of the piles

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.