J. Negreiros

Identification of Inhomogeneities in Precipitation Time Series Using Stochastic Simulation

Accurate quantification of observed precipitation variability is required for a number of purposes. However, high quality data seldom exist because in reality many types of non-climatic factors can cause time series discontinuities which may hide the true climatic signal and patterns, and thus potentially bias the conclusions of climate and hydrological studies. We propose the direct sequential simulation (DSS) approach for inhomogeneities detection in precipitation time series. Local probability density functions, calculated at known monitoring stations locations, by using spatial and temporal neighbourhood observations, are used for detection and classification of inhomogeneities. This stochastic approach was applied to four precipitation series using data from 62 surrounding stations located in the southern region of Portugal (1980–2001). Among other tests, three well established statistical tests were also applied: the Standard normal homogeneity test (SNHT) for a single break, the Buishand range test and the Pettit test. The inhomogeneities detection methodology is detailed, and the results from the testing procedures are compared and discussed.

A New Two-Step Robust Surface Matching Approach for Three-Dimensional Georeferencing of Historical Digital Elevation Models

In this letter, a new approach based on a two-step (coarse and fine) automatic surface matching for registering two overlapping multidate digital elevation models (DEMs) is proposed to avoid the costly and time-consuming ground-control-point acquisition. The proposed methodology was tested to georeference a historical grid DEM obtained from a photogrammetric flight taken in 1977 and located at a heavily developed coastal area of Almería (southeast Spain). The reference DEM consisted of a newer DEM produced by the Andalusia Regional Government from a photogrammetric flight taken in 2001. The results obtained from this work may be deemed as very promising, showing high efficiency and accuracy for historical-DEM georeferencing. The vertical accuracy of the finally coregistered DEM was computed over a recent LiDAR-derived DEM (validation data set) which presented relatively unaltered areas, yielding a standard deviation that is fairly similar to the estimated uncertainty of the reference DEM.

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

Abstract A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Morans I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Morans I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).

The Local Confidence Uncertainty Plume Of SAKWeb

The main goal of this research paper is to introduce a new uncertainty tool based on the Moran I correlogram, rescaled OK variance and local variance in a Web environment. It is hoped that this implementation will be used by users with problems to layout risk analysis environmental maps and plumes assessment. Spatial analysis, Moran I and other uncertainty measures are also reviewed.

Risk assessment: the global confidence uncertainty plume of SAKWeb ©

SAKWeb