Precipitation concentration is an important component of climate, and an unbalanced distribution of precipitation can yield excess or scarcity of water resources, which in turn can influence plant growth, flood risk, and water resource use. The precipitation concentration index (PCI) is a well-known indicator for the measurement of temporal precipitation in a short or long area. The purpose of this study was to analyze precipitation concentration rates in different regions of Bangladesh using the precipitation concentration index (PCI) and the inverse distance weighting method. In this study, the rainfall data from 30 meteorological observatory stations across Bangladesh were collected for the period 1980 to 2011. We defined periods of varying lengths (i.e., annual, supraseasonal, seasonal, and three- and two-month rainfall concentrations) and compared their PCI values. The results showed that precipitation concentrations were mostly irregular when rainfall was concentrated within two to four months of the year. Higher PCI values were mainly identified in the eastern region and have strong seasonal influences, whereas lower PCI values were mostly observed in the northern region. The analyses of periodic variation and precipitation in Bangladesh generally follow through the SW–NE direction due to the summer monsoon, while during the winter monsoon, they follow the N–S direction where JAS and JFM showed higher and lower PCI values. We observed variations in PCI among different regions using the Kruskal–Wallis test of the mean PCI on a decadal scale (1980–1989, 1990–1999, and 2000–2011). The result showed that significant changes in the precipitation occurred during the period of 1980–2011. At a two-month scale, significant changes were identified during transition periods where PCI values were lower from 2000 to 2011 than those in the earlier decades.

We describe the potential of high-resolution remote sensing imagery in the geostatistical mapping of sediment grain size distribution in order to supplement sparsely sampled ground observations. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsampled locations. Multiple regression and generalized additive models are applied to compute local mean values. From a case study of Baramarae beach, Korea, all imagery bands showed a reasonable linear relationship with grain size values in phi units, having a correlation coefficient of more than -0.80. Accounting for the IKONOS imagery via simple kriging with local means could reflect detailed surface characteristics with less smoothing effects. Cross validation results showed that the mean square errors from simple kriging with local means via the generalized additive model provided a relative improvement of about 60% over univariate multi-Gaussian kriging and a superior predictive capability when compared with simple kriging with local means via the traditional multiple regression model.

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This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.

This article deals with the problem of estimating two shape parameters and the reliability function of Burr type XII distribution, on the basis of a general progressively type II censored sample using Bayesian viewpoints. However, the maximum likelihood and Bayes estimators do not exist in the explicit forms for the parameters. An approximation form due to Lindley (1980) is used for obtaining Bayes estimates under the squared error loss and linex loss functions. The root mean square errors of the estimates are computed. Comparisons are made between Bayes and the maximum likelihood estimates using Monte Carlo simulation study.