Validation of TRMM Precipitation Radar satellite data over Indonesian region

Research has been conducted to validate monthly and seasonal rain rates derived from the Tropical Rainfall Measuring Mission Precipitation Radar (PR) using rain gauge data analysis from 2004 to 2008. The study area employed 20 gauges across Indonesia to monitor three Indonesian regional rainfall types. The relationship of PR and rain gauge data statistical analysis included the linear correlation coefficient, the mean bias error (MBE), and the root mean square error (RMSE). Data validation was conducted with point-by-point analysis and spatial average analysis. The general results of point-by-point analysis indicated satellite data values of medium correlation, while values of MBE and RMSE tended to indicate underestimations with high square errors. The spatial average analysis indicated the PR data values are lower than gauge values of monsoonal and semi-monsoonal type rainfall, while anti-monsoonal type rainfall was overestimated. The validation analysis showed very good correlation with the gauge data of monsoonal type rainfall, high correlation for anti-monsoonal type rainfall, but medium correlation for semi-monsoonal type rainfall. In general, the statistical error level of monthly seasonal monsoonal type conditions is more stable compared to other rainfall types. Unstable correlations were observed in months of high rainfall for semi-monsoonal and anti-monsoonal type rainfall.     

A shear‐dilation‐based model for evaluation of hydraulically stimulated naturally fractured reservoirs

The role of shear dilation as a mechanism of enhancing fluid flow permeability in naturally fractured reservoirs was mainly recognized in the context of hot dry rock (HDR) geothermal reservoir stimulation. Simplified models based on shear slippage only were developed and their applications to evaluate HDR geothermal reservoir stimulation were reported. Research attention is recently focused to adjust this stimulation mechanism for naturally fractured oil and gas reservoirs which reserve vast resources worldwide. This paper develops the overall framework and basic formulations of this stimulation model for oil and gas reservoirs. Major computational modules include: natural fracture simulation, response analysis of stimulated fractures, average permeability estimation for the stimulated reservoir and prediction of an average flow direction. Natural fractures are simulated stochastically by implementing ‘fractal dimension’ concept. Natural fracture propagation and shear displacements are formulated by following computationally efficient approximate approaches interrelating in situ stresses, natural fracture parameters and stimulation pressure developed by fluid injection inside fractures. The average permeability of the stimulated reservoir is formulated as a function of discretized gridblock permeabilities by applying cubic law of fluid flow. The average reservoir elongation, or the flow direction, is expressed as a function of reservoir aspect ratio induced by directional permeability contributions. The natural fracture simulation module is verified by comparing its results with observed microseismic clouds in actual naturally fractured reservoirs. Permeability enhancement and reservoir growth are characterized with respect to stimulation pressure, in situ stresses and natural fracture density applying the model to two example reservoirs. Copyright © 2002 John Wiley & Sons, Ltd.     

Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City,Bangladesh

Natural Hazards - Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is...     

Storm protection service of the Sundarbans mangrove forest,Bangladesh

Natural Hazards - Storm protection service of mangrove is often undervalued. This paper empirically assessed the damage avoided by the Sundarbans mangrove forest in 15 villages of southwest coastal...     

Detecting biophysical properties of a semi-arid grassland and distinguishing burned from unburned areas with hyperspectral reflectance

This paper examines the utility of hyperspectral remote sensing to detect fresh and dry biomass, water content and plant area index of burned and unburned grassland in southern California. Contrary to many previously published reports, the normalized difference vegetation index (NDVI, a vegetation greenness index) was not a good indicator of any of these important biophysical properties in either the burned or unburned area, especially after regeneration. Rather, the water band index (WBI, an index of water status in vegetation) showed better promise of estimating these biophysical properties in this semi-arid ecosystem. Despite the post-regeneration similarities in visual and harvested values of these two areas, we found that the full range of hyperspectral reflectance in ‘visible to infrared’ (400–1000 nm) wavelengths when used in a cluster analysis can readily differentiate the burned and unburned areas. This demonstrates the utility of hyperspectral remote-sensing in mapping subtle features that may not be detectable from conventional remote-sensing indices (e.g. NDVI) alone.     

Potential ecological risk assessment of heavy metal contamination in sediment and water body around Dhaka export processing zone,Bangladesh

Sediments and surface water contamination by the industrial effluents containing heavy metals is the most detrimental environmental impact. Therefore, the present work attempts to determine the status of eight heavy metal distribution in sediments and water samples, and their ecological risks’ assessment in the studied area. The distribution pattern of heavy metals in the water and sediment follows the sequences: Zn > Cu > Pb > Cr > Mn > Ni > As > Cd and Mn > Zn > Cr > Pb > Cu > Ni > As > Cd, respectively. Gross water pollution is observed at different sampling points of Dhalai Beel and Bangshi River. The comparison of sedimentary mean metal concentrations with several environmental contamination monitoring parameters, viz, threshold effect level (TEL), probable effect level (PEL), and severe effect lever (SEL) indicates that the metal levels are less than PEL except Cr. Moreover, the level of contamination degree (C _d) and modified degree of contamination (mC _d) indicates ‘low’ and ‘nil to low’ degree of contamination, respectively. Pollution load indices (PLI) of the studied area are lower than unity, indicates no pollution. Furthermore, a toxic-response factor is applied to assess the potential ecological risk of these heavy metals into the water body. The results of this study exhibit a low potential ecological risk of heavy metals. The Pearson’s correlation and cluster analysis are also performed to assess the heavy metal interactions in water and sediment samples.     

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd.     

Design rainfall estimation in Australia: a case study using L moments and Generalized Least Squares Regression

Design rainfall is an important input to rainfall runoff models and is used for many other water resources planning and design applications. The estimation of design rainfall is generally done by applying a regional frequency analysis technique that uses data from a large number of rainfall stations in the region. This paper presents a regional rainfall frequency analysis technique that uses an L moments based index method coupled with Generalized Least Squares Regression (GLSR). The particular advantages of the GLSR method are that it accounts for the differences in record lengths across various sites in the region and inter-station correlation in deriving regional prediction equations. The proposed method has been applied to a data set consisting of 203 rainfall stations across Australia. It has been found that the proposed method can be applied successfully in deriving reasonably accurate design rainfall estimates from 1 to 72 h durations. It has also been found that the proposed method provides quite consistent estimates where a third order polynomial is adequate in smoothing the intensity–frequency–duration (IFD) curves. The method can readily be extended to a larger data set of Australia and other countries to derive generalized IFD data.