The objective of this study is to reveal the spatial and temporal variations of surface water quality in this part of the River Nile with respect to heavy metals pioneerution. Seventeen parameters in total were monitored at seven sites on a monthly basis from October 2013 to September 2014. The dataset was treated using the tools of univariate and multivariate statistical analyses. Cluster analysis showed three different groups of similarity between the sampling sites reflecting the variability in physicochemical characteristics and pollution levels of the study area. Six PCs factors were identified as responsible for the data structure explaining 91 % of the total variance. These were eutrophication factor (23.2 %), physicochemical factor (20.6 %), nutrients (16.3 %) and three additional factors, affected by alkalinity and heavy metals, recorded variance less than 15 % each. Also, the heavy metals pollution index (HPI) revealed that most of the calculated values were below the critical index limit of 100. However, two higher values (124.89 and 133.11) were calculated at sites V and VI during summer due to the temperature and increased run-off in the river system.
Global Positioning System (GPS) technology is ideally suited for inshore and offshore positioning because of its high accuracy and the short observation time required for a position fix. Precise point positioning (PPP) is a technique used for position computation with a high accuracy using a single GNSS receiver. It relies on highly accurate satellite position and clock data that can be acquired from different sources such as the International GNSS Service (IGS). PPP precision varies based on positioning technique (static or kinematic), observations type (single or dual frequency) and the duration of observations among other factors. PPP offers comparable accuracy to differential GPS with safe in cost and time. For many years, PPP users depended on GPS (American system) which considered the solely reliable system. GLONASS's contribution in PPP techniques was limited due to fail in maintaining full constellation. Yet, GLONASS limited observations could be integrated into GPS-based PPP to improve availability and precision. As GLONASS reached its full constellation early 2013, there is a wide interest in PPP systems based on GLONASS only and independent of GPS. This paper investigates the performance of kinematic PPP solution for the hydrographic applications in the Nile river (Aswan, Egypt) based on GPS, GLONASS and GPS/GLONASS constellations. The study investigates also the effect of using two different observation types; single-frequency and dual frequency observations from the tested constellations.
Limited to fourth percent or less of the country’s total land area, Egypt’s agricultural landscape is threatened by the repercussions of climate change, desertification, soil depletion, and looming water scarcity. Outside of the Nile river valley and scattered fertile pockets in the desert oases, the vast majority of land is desert: rocky, parched and unable to support conventional farming. According to Egyptian National Action Program 2005 (ENAP), Egypt covers an area of about one million km2 ~ 100 million hectares, out of which about of 76.5 thousands km2 ~ 7.6% of the total area are inhabited, and the remaining (92.4%) area is desert. Desertification is a very complex process governed by several variables which influence each other. It is thus not possible to conclude for the general picture from a single factor alone. This process has a high rate in arid and hyper-arid countries such as Egypt. The main objective of this research was to evaluation the present-day climate-induced desertification in El-Dakhla Oasis, so in this study, the newest method for evaluating and mapping of desertification was used. The mathematic method was carried out by European Commission (EC), (MEditerranean Desertification And Land Use) at the MEDALUS project and booked as ESAs in 1999 integrated with remote sensing and GIS. All indices of the model were revised before using, and regarding to the region condition these indices were defined as key indices which were: Temperature, precipitation, wind, albedo, ground water and soil benchmark, and each benchmark has some sub-layers getting from their geometric mean. Based on the MEDALUS model, each sub-benchmark was quantified according to its quality and given a weighting of between 1.0 and 2.0. All benchmarks should be reinvestigated and adjusted to local conditions. Ultimately, desertification severity was classified in four level including low, moderate, Severe and high Severe. ArcGIS 10 was used to analysis and prepares the layers of quality maps using the geometric mean to integrate the individual sub-indicator maps. In turn the geometric mean of six quality maps was used to generate a single desertification status map. Remote sensing data have great potential to improve models mapping spatial variability of temperature and precipitation since they are available as time worldwide, and have high spatial resolution. The HYDRA visualization software was used to measure the present surface albedo from MODIS product (MOD43C1). Results showed that 60% of the area is classified as Severe, 14 % as moderate and 12%, 16% as low and none affected by desertification respectively. In addition the climatic variations including rainfall, temperature, sunlight, wind indicators were the most important factors affecting desertification process in El-Dakhla Oasis.
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