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Sand and dust storm events are frequent natural hazards in the Kingdom of Saudi Arabia. Sand and dust storm monitoring is therefore essential to mitigate their environmental-related issues. Satellite remote sensing has been successfully used for sand and dust storm monitoring in various parts of the world. In the current endeavor, we are applying the Global Dust Detection Index (GDDI) on Moderate Resolution Imaging Spectroradiometer (MODIS) data onboard Terra satellite to monitor sand and dust storm activities over the Kingdom of Saudi Arabia. In the current study, fourteen sand and dust storm events are analyzed between the years 2000 to 2017. The GDDI based results are validated by using MODIS combined Dark Target (DT) and Deep Blue (DB) Aerosol Optical Depth (AOD) product, Meteosat satellite images, ground-based meteorological stations data, and AOD data from AERONET (Aerosol Robotic Network) stations in the study area. Also, GDDI based results are analyzed by determining algorithm accuracy, Probability Of Correct positive Detection (POCD), and Probability Of False positive Detection (POFD). Results of the study show that GDDI can successfully identify sand and dust storm events with various threshold values over the Kingdom of Saudi Arabia. It is envisaged that the outcome of this study would be very beneficial to understand sand and dust storm characteristics in the study region. [ABSTRACT FROM AUTHOR]

Copyright of Journal of King Abdulaziz University: Meteorology, Environment & Arid Land Agriculture Sciences is the property of King Abdulaziz University, Scientific Publishing Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sand and dust storm (SDS) events are closely associated with meteorological parameters. This study aims to analyze SDS events in relation to temperature, precipitation, and wind speed. In addition, since El Nino–Southern Oscillation Index (ENSO) cycle has a great impact on precipitation and temperature, the relationship between SDS events and ENSO cycles are also investigated. For this purpose, data for 30 years (1985–2014) from relevant organizations are retrieved and analyzed. Results of the study show that SDS, precipitation, and wind speed have a decreasing trend while temperature has an increasing trend in the KSA during the study period. The results of the study also reveal that SDS events have the highest correlation with temperature and lowest correlation with precipitation. Furthermore, the relationship between SDS events and ENSO shows that El Nino was dominant during the first two decades of the study period while La Nina prevailed in the last decade of the study period. It is envisaged that the outcome of the current endeavor will be very useful for aerosol-related research both at regional and at a global scale. [ABSTRACT FROM AUTHOR]

The impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002-2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region. [ABSTRACT FROM AUTHOR]

This study attempts to use the geographic information system (GIS) technique to map and understand the tectonics and crustal structures of Pakistan. Maps of surficial tectonic features and seismological parameters including Moho depth,Pnvelocity andPgvelocity are complied. Based on the seismological data-set of the country the earthquake hazard map of Pakistan is also presented by applying regression technique on seismological, geological and topographical parameters. A case study of 8 October 2005 earthquake is used to validate the hazard map. It is envisaged that the developed GIS database would help policy-makers and scientists in natural hazard evaluation, seismic risk assessment and understanding of earthquake occurrences in Pakistan. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimation of snow cover characteristics (snow grain size, snow contamination, snow depth and liquid water content) from satellite data are important components for many hydrological models used for the water resource management. This research aimed to use Landsat ETM+ (Enhanced Thematic Mapper Plus) data to estimate the snow pack characteristics in northern Pakistan. The Normalized Difference Snow Index (NDSI), Snow Contamination Index (SCI) and Snow Grain Size Index (SGI) are applied to estimate the snow cover characteristics in northern Pakistan for the first time. Qualitative maps are generated to show the snow cover distribution, snow contamination concentration and snow grain size distribution over snow cover area. Our results show that NDSI, SCI and SGI can be effectively used to identify snow area, contaminated snow and ageing snow. Furthermore, the results of the current study indicate that in the HKH region 99.8% of the snow is least contaminated whereas 94.50% of the area has fine snow grain size. As no such attempt in the past has been made in northern Pakistan, it is envisaged that the results of this study will be helpful in planning remote sensing data for the water resource management and in characterizing the snow cover for the climate change applications in the region. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Snow-covered areas (SCAs) are the fundamental source of water for the hydrological cycle for some region. Accurate measurements of river discharge from snowmelt can help manage much needed water required for hydropower generation and irrigation purposes. This study aims to apply the snowmelt runoff model (SRM) in the Upper Indus basin by the Astore River in northern Pakistan for the years 2000 to 2006. The Shuttle Radar Topographic Mission (SRTM) data are used to generate the Digital Elevation Model (DEM) of the region. Various variables (snow cover depletion curves (SCDCs), temperature and precipitation) and parameters (degree-day factor, recession coefficient, runoff coefficients, time lag, critical temperature and temperature lapse rate) are used as input in the SRM. However, snow cover data are direct and an important input to the SRM. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the SCA. Normalized difference snow index (NDSI) algorithm is applied for snow cover mapping and to differentiate snow from other land features. Nash-Sutcliffe coefficient of determination ( R2) and volume difference ( DV) are used for quality assessment of the SRM. The results of the current research show that for the study years (2000-2006), the average value of R2 is 0·87 and average volume difference DV is 1·18%. The correlation coefficient between measured and computed runoff is 0·95. The results of the study further show that a high level of accuracy can be achieved during the snowmelt season. The simulation results endorse that the SRM in conjunction with MODIS snow cover product is very useful for water resource management in the Astore River and can be used for runoff forecasts in the Indus River basin in northern Pakistan. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Soil erosion is the most important reason of sedimentation load of water reservoirs in the world. In Pakistan, Mangla dam is one of the most important water reservoirs used for the production of electricity and for the supply of water for irrigation purposes. However, the capacity of Mangla dam reservoir has reduced by more than 20% since its construction. This study highlights the impact of rainfall on soil erosion and consequently on sedimentation deposition in Mangla dam reservoir. Sedimentation, annual rainfall, and normal rainfall data of 39 years were used in this study. Shuttle Radar Topographic Mission data were used to calculate the total drainage area of the Mangla watershed region. The sedimentation data of Mangla reservoir from 1967 to 2005 were retrieved from Water and Power Development Authority in Pakistan. The meteorological observatories in the Mangla watershed region are identified. Annual rainfall data from 1967 to 2005 for the meteorological observatories in the Mangla watershed regions were retrieved from Pakistan Meteorological Department (PMD). In addition, normal rainfall data for the years 1949 to 1978 and for the years 1979 to 2008 were also retrieved from PMD. The impact of annual rainfall is observed on sedimentation load in Mangla dam. The correlation coefficient between annual rainfall and sedimentation load is 0.94. This study shows that with an increase in rainfall, the soil erosion of the area increases which subsequently is responsible for the increase in the rate of sedimentation load in Mangla dam. This study further demonstrates that better soil management can reduce the sedimentation load in the Mangla reservoir. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the excavated dental material of the late Miocene hyaenids, Percrocuta carnifex and Adcrocuta eximia latro, from the Siwaliks of Pakistan. It cites the well exposed left mandibular ramus with p3-m1 of Percrocuta carnifex that was discovered at the Nagri Formation near village Nagri Sethi in Pakistan. The abbreviations used in the study are also cited, including P1 for first upper premolar, AMNH for American Museum of Natural History, and p2 for second lower premoral.

Water has always played a vital role in human societies. In order to manage the water resources, many countries have built water reservoirs, for example, dams for sustainable development, growth and poverty reduction. Vegetation, in the watershed region of a dam, plays a very vital role on soil erosion and consequently on the sediments deposition in the water reservoir. This study intends to analyze the impact of vegetation cover on soil erosion and thereby the sediments deposition in the watershed region of a dam by using satellite remote sensing technique. For this, Mangla dam which is one of the most important water reservoirs in Pakistan and its capacity has been reduced by more than 20% since its construction, is selected as the study area. Shuttle Radar Topographic Mission (SRTM) data, onboard the space shuttle Endeavor, was used to calculate the total drainage area for the Mangla watershed. Landsat images for the years 1979, 1980, 1992, 1998 and 2002 were used to estimate the total area of vegetation in the Mangla watershed region. In order to estimate the vegetation area, two vegetation indices, that is, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were applied on Landsat MSS, TM, and ETM+ images. The comparison of sedimentation and vegetation data indicates that except for the year 1992, the load of sedimentation in Mangla dam decreases with an increase in the vegetation area in the Mangla watershed region. The maximum vegetation in the Mangla watershed region is estimated in the year 2002, and subsequently, the load of sediments in the same year in Mangla dam is minimum. The current study thus indicates that the soil erosion which is the main reason of sedimentation in water reservoir can be controlled with the help of plantation of various species of vegetation in the watershed region of the reservoir. [ABSTRACT FROM AUTHOR]

For the development of passive microwave remote sensing techniques, brightness temperature information on the medium covering the Earth's surface under different conditions is required. An emission model is a useful tool for the estimation of the brightness temperature of the medium. If the medium is a snow pack, the microwave radiation emitted will depend on the physical temperature, crystal characteristics, stratification and density of the snow. The parameters of microwave emission models available for the retrieval of snow characteristics are highly dependent on local environmental and climatological conditions. The aim of this study was to compare the empirical Chang model with the semiempirical radiative transfer model of snow developed at Helsinki University of Technology (HUT) for snow depth (SD) retrieval for UK snow packs. In the first step we used the HUT model. The root mean square error (RMSE) was used to validate the accuracy of model estimates. The snow events from different days in 1995, 1996 and 1997 were used in this study. In the second step a revised form of the Chang model, which was originally calibrated for global snow monitoring, was applied to estimate the SD. It is evident from this study that the Chang model underestimates the SD whereas the HUT model both underestimates and overestimates the SD for UK snow cover. This study also demonstrates that the application of algorithms for snow pack monitoring requires local calibration for effective and reasonable results. [ABSTRACT FROM AUTHOR]

Microwave imagery can be used successfully for mapping of snow and estimation of snow pack characteristics under almost all weather conditions. This research is a contribution to the field of space borne remote sensing of snow by means of passive microwave data imagery. The satellite data are acquired from the Special Sensor Microwave Imager (SSM/I). The SSM/I is a four frequency seven channels dual polarization (except 22 GHz which is only vertically polarized) scanning radiometer with channels located at 19, 22, 37, and 85 GHz frequencies. A radiative transfer theory based model is used to estimate the snow cover characteristics of different snow pack types in the UK. A revised form of the Chang et al. (Nord Hydrol 16:57–66, ) model is used for this purpose. The revised Chang model was calibrated for global snow monitoring and takes into account forest fractional coverage effects. Snow cover characteristics have significant effects on up-welling naturally emitted microwave radiation through the processes of forward scattering. The up-welling signal is more complex for snow covers that consist of free liquid water content. The aim of this study is to test the global snow depth model for the UK snow cover. The Chang model predicted snow depth bias results for January, February, and March 1995 are −1.26, −0.35, and −0.63 cm, respectively. Similarly, the Chang model Mean Absolute Error (MAE) for January, February, and March 1995 have values 2.88, 2.38, and 1.91 cm, respectively. These results show that the Chang model underestimates the snow depth prediction for all the case studies. The results of this study led us to the conclusion that the global snow models (Chang model) when applied for the retrieval of local snow depth estimation (UK snow cover) underestimate snow depth. [ABSTRACT FROM AUTHOR]

Percutaneous mitral valve closure is a novel technique in evolution as an alternative therapy for nonsurgical candidates with significant mitral regurgitation. Current use has been limited to patients with single prosthetic valves and discrete perivalvular defects. We describe a case of successful percutaneous repair of a mitral perivalvular leak with an Amplatzer ductal occluder in a patient with both prosthetic mitral and aortic valves. In this case, a left ventricle apical approach for access was utilized. [ABSTRACT FROM AUTHOR]