ABSTRACT:The spatial distribution of the annual mean urban heat island (UHI) intensity was simulated applying empirical models based on datasets from urban areas of Szeged and Debrecen, using simple and easily determinable urban surface cover variables. These two cities are situated on the Alföld (Great Hungarian Plain) and have similar topographic and climatic conditions. Temperature field measurements were carried out, Landsat satellite images were evaluated, and then one-and multiple variable models were constructed using linear regression techniques. The selected multiple-parameter models were verified using independent datasets from three urban settlements. In order to obtain some impression of the mean UHI patterns in other cities with no temperature measurements available, the better model was extended to urban areas of four other cities situated in geographical environments similar to Szeged and Debrecen. The main shortcoming of typical empirical models, namely that they are often restricted to a specific location, is overcome by the obtained model since it is not entirely site but more region specific, and valid in a large and densely populated area with several settlements.
In the environmental risk assessment of oil fields, a detailed knowledge of the heterogeneity of groundwater surfaces is absolutely indispensable. Based on theoretical considerations, in order to analyse small-scale heterogeneities, we decided that the Sequential Gaussian Simulation (SGS) approach seemed to be the most appropriate one. This method gives preference to the reproduction of small-scale heterogeneities at the expense of local accuracy. To test whether this kind of heterogeneity of the groundwater level corresponds to sedimentological variability, a point bar of the River Tisza (South-Hungary) was chosen. In variograms, the longest range was derived from the large-scale sedimentological heterogeneity of the point-bar, the medium range was in accordance with the radius of the meander and its direction coincided with the depositional strike of the meander, while the shortest range corresponded to the lateral heterogeneity of the deposits where the ground water level was measured. The similarities and differences of the realizations of SGS express the uncertainty of the map representation of the ground water surface. The E-type estimates of 100 equiprobable realizations resulted in a very detailed surface. The hydraulic gradient map obtained from the E-type estimates can provide us with a better understanding of the local flow characteristics.
Abandoned channels are essential in the Quaternary floodplains, and their infill contains different paleoenvironment recorders. Grain-size distribution (GSD) is one proxy that helps characterize the alluviation and associated sedimentological processes of the abandoned channels. The classic statistical methods of the grain-size analysis provide insufficient information on the whole distribution; this necessitates a more comprehensive approach. Grain-size endmember modeling (EMM) is one approach beyond the traditional procedures that helps unmix the GSDs. This study describes the changes in the depositional process by unmixing the GSDs of a Holocene abandoned channel through parameterized EMM integrated with lithofacies, age–depth model, loss-on-ignition (LOI), and magnetic susceptibility (MS). This approach effectively enabled the quantification and characterization of up to four endmembers (EM1-4); the characteristics of grain-size endmembers imply changes in sedimentary environments since 8000 BP. EM1 is mainly clay and very fine silt, representing the fine component of the distribution corresponding to the background of quiet water sedimentation of the lacustrine phase. EM2 and EM3 are the intermediate components representing the distal overbank deposits of the flood. EM4 is dominated by coarse silt and very fine sand, representing deposition of overbank flow during the flood periods. This paper demonstrates that the parametrized grain-size EMM is reasonable in characterizing abandoned channel infill sedimentary depositional and sedimentation history.
This study was undertaken to quantify and evaluate the density and porosity characteristics of a Boda Claystone Formation (BCF) core sample using medical CT. Each voxel of the 3D CT volume was described with three variables: dry CT number, saturated CT number, and effective porosity. Disparity pore voxels were revealed using the genetic groups’ algorithm of data-mining techniques. The K-fold cross-validation algorithm has been applied to determine the number of the most stable cluster. The 3D spatial distributions of voxel-porosity by rock constituents, as well as the 3D distribution of porosity clusters by rock components, were found by Boolean function implementation. The terrigenous detrital fragments had the lowest porosity mean (0.16%) and highest coefficient variation value (1039.39%). While the Fine siltstone component had the highest porosity mean (3.39%) and lower coefficient of variation (134.99%). The difference in the variation of coefficient proportions is related to the outlier ratios in each rock component. Independently of both the rock types and the sedimentary structures, two clusters could be defined: one for the micro-porosity and one for the macro-porosity regimes. The former showed a continuous 3D spatial appearance, while the latter appeared in patches. These patches may also be connected, at least partly, to some local smectite aggregates. These clay minerals could lose their structured water content during vacuuming and swell when adsorbing water during sample saturation. In each rock type, the micro-porosity regime could be related to low-density rock fragments. The mean effective porosity of the micro-pore regime was about 0.02, which corresponds to the petrophysical core measurements. For the macro regimes, the average was 0.1.
This paper deals with a question: how many stochastic realizations of sequential Gaussian and indicator simulations should be generated to obtain a fairly stable description of the studied spatial process? The grids of E-type estimations and conditional variances were calculated from pooled sets of 100 realizations (the cardinality of the subsets increases by one in the consecutive steps). At each pooling step, a grid average was derived from the corresponding E-type grid, and the variance (calculated for all the simulated values of the pooling set) was decomposed into within-group variance (WGV) and between-group variance (BGV). The former was used as a measurement of numerical uncertainty at grid points, while the between-group variance was regarded as a tool to characterize the geologic heterogeneity between grid nodes. By plotting these three values (grid average, WGV, and BGV) against the number of pooling steps, three equidistant series could be defined. The ergodic fluctuations of the stochastic realizations may result in some "outliers" in these series. From a particular lag, beyond which no "outlier" occurs, the series can be regarded as being fully controlled by a background statistical process. The number of pooled realizations belonging to this step/lag can be regarded as the sufficient number of realizations to generate. In this paper, autoregressive integrated moving average processes were used to describe the statistical process control. The paper also studies how the sufficient number of realizations depends on grid resolutions. The method is illustrated on a computed tomography slice of a sandstone core sample.
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