Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia

The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.     

The best fitting distribution of annual maximum rainfall in Peninsular Malaysia based on methods of L-moment and LQ-moment

This paper discusses patterns of annual and monthly precipitation variability at seven weather stations in east central Europe (1851–2007). Precipitation patterns were compared to three simple regional indices of atmospheric circulation, i.e., western circulation, southern circulation and the cyclonicity (C) index and a relationship between precipitation and the North Atlantic Oscillation index was identified. Correlations of the monthly records and multiple regression, using a principal components’ analysis, helped determine the statistical significance of the dependence of precipitation on the circulation indices. The Mann–Kendall test revealed no trend to change in any of the precipitation series, but a certain spatial regularity could be discerned in the phase of the annual periodic component. A common feature of the variability in central European annual precipitation is the dry period identified in the 1980s and the first half of the 1990s. In the northern part of the region, above-average precipitation was noted from the 1960s through to the mid-1970s as a result of the frequent prevalence of depressions. South of the divide, the wettest period was recorded at the turn of 1930s/1940s. After a number of very wet years in the last decade of the twentieth century and the beginning of the twenty-first century, precipitation began to fall at all of the region’s weather stations. The C index is the strongest circulation-linked factor influencing precipitation in central Europe and it accounts for more than 40% of the variance in spatially averaged wintertime precipitation.     

A Simplified Semi-Automatic Technique for Highway Extraction from High-Resolution Airborne LiDAR Data and Orthophotos

Information on highways is an essential input for various geospatial applications, including car navigation, forensic analysis on highway geometries, and intelligent transportation systems. Semi-automatic and automatic extractions of highways are critical for the regular updating of municipal databases and for highway maintenance. This study presents a semi-automatic data processing approach for extracting highways from high-resolution airborne LiDAR height information and aerial orthophotos. The method was developed based on two data sets. Experimental results for the first testing site showed that the accuracy of the proposed method for highway extraction was 74.50 % for completeness and 73.13 % for correctness. Meanwhile, the completeness and correctness for the second testing site were 71.20 and 70.72 %, respectively. The proposed method was compared with an object-based approach on a different data set. The accuracy for highway extraction of the object-based approach was 64.29 % for completeness and 63.11 % for correctness, whereas that of the proposed method was 67.14 % for completeness and 65.08 % for correctness. This research aims to promote semi-automatic highway extraction from LiDAR data and orthophotos by proposing a new approach and a multistep post-processing technique. The proposed method provides an accurate final output that is valuable for a wide range of geospatial applications.     

Update earthquake risk assessment in Cairo,Egypt

The Cairo earthquake (12 October 1992; m _b  = 5.8) is still and after 25 years one of the most painful events and is dug into the Egyptians memory. This is not due to the strength of the earthquake but due to the accompanied losses and damages (561 dead; 10,000 injured and 3000 families lost their homes). Nowadays, the most frequent and important question that should rise is “what if this earthquake is repeated today.” In this study, we simulate the same size earthquake (12 October 1992) ground motion shaking and the consequent social-economic impacts in terms of losses and damages. Seismic hazard, earthquake catalogs, soil types, demographics, and building inventories were integrated into HAZUS-MH to produce a sound earthquake risk assessment for Cairo including economic and social losses. Generally, the earthquake risk assessment clearly indicates that “the losses and damages may be increased twice or three times” in Cairo compared to the 1992 earthquake. The earthquake risk profile reveals that five districts (Al-Sahel, El Basateen, Dar El-Salam, Gharb, and Madinat Nasr sharq) lie in high seismic risks, and three districts (Manshiyat Naser, El-Waily, and Wassat (center)) are in low seismic risk level. Moreover, the building damage estimations reflect that Gharb is the highest vulnerable district. The analysis shows that the Cairo urban area faces high risk. Deteriorating buildings and infrastructure make the city particularly vulnerable to earthquake risks. For instance, more than 90 % of the estimated buildings damages are concentrated within the most densely populated (El Basateen, Dar El-Salam, Gharb, and Madinat Nasr Gharb) districts. Moreover, about 75 % of casualties are in the same districts. Actually, an earthquake risk assessment for Cairo represents a crucial application of the HAZUS earthquake loss estimation model for risk management. Finally, for mitigation, risk reduction, and to improve the seismic performance of structures and assure life safety and collapse prevention in future earthquakes, a five-step road map has been purposed.     

Consistency of precipitation products over the Arabian Peninsula and interactions with soil moisture and water storage

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R² = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region.     

Petrography and geochemistry of manganiferous rocks in the Mankwadzi area in the southern Kibi‐Winneba metavolcanic belt,Ghana: Implications for genesis of Mn formations in the Birimian of West Africa

Manganiferous rocks in the Mankwadzi area in the southernmost portion of the Kibi‐Winneba metavolcanic belt, one of several Mn occurrences in the Paleoproterozoic Birimian of Ghana, are hosted in hornblende schist and amphibolite. These rocks are, in places, intruded by hornblende dyke. In outcrop, the manganiferous rocks appear to be conformable with the host schist and amphibolite, are macroscopically dark, fine‐grained and structurally massive to distinctly banded. Observed alternating light and dark occasionally macro‐folded bands suggest post‐depositional deformation of both light and dark bands. Microscopic observations revealed that the light bands are dominantly Si‐rich and the dark bands mainly of opaque minerals. Whole rock analyses of the manganiferous rocks show high contents of MnO (16.75–27.4 wt%) suggesting that the opaque minerals are likely rich in Mn. The analyzed rock samples show moderate to strong enrichments in light rare earth elements compared to heavy rare earth elements. Whereas the manganiferous rocks show perceptibly negative Eu anomaly, host hornblende schist and hornblende dyke do not. Eu anomaly in amphibolite samples is, however, uncertain as the three samples analyzed gave positive, negative and no Eu anomalies. Based on the field characteristics, microscopic and geochemical features, we suggest that the Mn occurrence in the Mankwadzi area originated via sedimentary deposition and was later modified by metamorphism, hydrothermal and/or supergene processes similar to manganiferous occurrences at Nsuta and Tambao in the Birimian of West Africa.     

Inversion-based deblending using migration operators

In this paper, we compare the denoising- and inversion-based deblending methods using Stolt migration operators. We use Stolt operator as a kernel to efficiently compute apex-shifted hyperbolic Radon transform. Sparsity promoting transforms, such as Radon transform, can focus seismic data into a sparse model to separate signals, remove noise or interpolate missing traces. Therefore, Radon transforms are a suitable tool for either the denoising- or the inversion-based deblending methods. The denoising-based deblending treats blending interferences as random noise by sorting the data into new gathers, such as common receiver gather. In these gathers, blending interferences exhibit random structures due to the randomization of the source firing times. Alternatively, the inversion-based deblending treats blending interferences as a signal, and the transform models this signal by incorporating the blending operator to formulate an inversion problem. We compare both methods using a robust inversion algorithm with sparse regularization. Results of synthetic and field data examples show that the inversion-based deblending can produce more accurate signal separation for highly blended data.     

Variability of water exchanges through the Strait of Hormuz

Ocean Dynamics - The variability of the water mass exchange between the Arabian Gulf and the Indian Ocean is investigated using a high-resolution (1/36°) ocean model. We focus on the period...     

Performance and environmental impact of a turbojet engine fueled by blends of biodiesels

Depletion of conventional fuels, concerns about environmental pollution and the tightening of exhaust emission legislations are the main reasons for increasing research on alternative fuels produced from agricultural feedstock. In this study, biodiesel fuels produced from cotton and corn vegetable oils are investigated as renewable fuels for a gas turbine engine for aviation. The biodiesel fuels are defined as cotton methyl ester (CTME) and corn methyl ester. The performance characteristics and exhaust emissions of the gas turbine engine are investigated when the engine fueled with three blends of 10%(B10), 20%(B20) and 50%(B50) of biodiesel/JetA-1 by volume. The biodiesel fuels were produced using transesterification process and characterized according to ASTM biodiesel specifications. Chemical and physical properties show a real potential of using biodiesel blends as an alternative for JetA-1. The measured engine performance parameters and exhaust emissions are compared with that of pure JetA-1 over a range of throttle setting. The gas turbine engine used in this study is equipped with pressure, flow, temperature, thrust and speed sensors that connected to data acquisition system and control unit in addition to exhaust gas analyzer. The experimental results show that biodiesel fuels can be used up to blend of 50% with JetA-1 in gas turbine engines with slight enhancement in engine performance and significant improvements in exhaust emissions. The engine static thrust is increased with 2% for B50 at lower and medium engine speeds and decreased with 11% at high engine speed compared to conventional JetA-1 fuel. The thrust-specific fuel consumption for biodiesel blends is lower than that for regular JetA-1 fuel. The gas turbine engine efficiency is increased for biodiesel blends by 14% compared to JetA-1, and this is reported for CTME B50. For oxygen concentration in exhaust gases emissions, the higher the biodiesel blend, the higher the O₂ concentration in the exhaust compared with JetA-1 fuel. The O₂ level increased by 6% for biodiesel blend of B50 compared to JetA-1 fuel. The emissions of CO and HC emissions decreased by 5 and 37%, respectively, compared with conventional JetA-1. Additionally, the biodiesel blends achieve a higher CO₂ and NO_x emissions with 11 and 27%, respectively, compared to JetA-1. The sulfur dioxide SO₂ decreased by 75% compared to the regular JetA-1 fuel.