Mapping hotspots of underground water quality based on the variation of chemical concentration in Amman,Zarqa and Balqa regions,Jordan

This study uses geographic information systems to map the hotspots level changes in underground water quality based on pH, nitrate and conductivity in Amman, Zarqa and Balqa regions, Jordan using data from 57 wells. The chemical concentration for each parameter was obtained from the Water Authority of Jordan. The data were tested according to ISO/IEC 17025 standards by the Jordan Inorganic Laboratory of the Laboratories and Quality Department of Water Authority for 2004 and 2010 and were processed using Getis-Ord Gi statistics. The study indicated that there are periodic fluctuations for the values of selected chemical elements of groundwater that are localized, creating contamination hotspots within the study regions. The pH, nitrate and conductivity all indicated specific hotspots of higher than average values in 2004 and 2010 in the Zarqa region. The evidence indicates that there are two major localized areas of high value for all parameters centered in the western Zarqa region, an area dominated by urban infrastructure and irrigated lands. These findings have implications for the management of aquifer water quality through the identification of high-value hotspots. Because the aquifers in the regions are connected, understanding the hotspots with high pH, nitrate and conductivity values may enable improvements in the water quality across the aquifer systems in the regions.     

Simulation of the lately injected dykes in an Iranian porphyry copper deposit using the plurigaussian model

Ore deposits are usually composed of rock units or facies with different grade distributions and complex spatial structures. Being able to simulate the spatial layout of these facies are essential to have a comprehensive mining plan and an accurate resources and reserves evaluation. Modelers are faced with a set of challenges when creating the facies model such as: reproducing the facies proportions and spatial continuity as well as the topological contacts between facies, capturing post depositional overprinting, and honoring the data obtained from drill holes. Plurigaussian simulation (PGS) is a geostatistical approach that allows covering these challenges. This study addresses the application of PGS to Sungun porphyry copper deposit (Iran), in order to simulate the layout of three facies: mineralized porphyry and skarn and non-mineralized dykes. The aim of this study is to construct numerical models in which the dyke structures reflect the evolution observed in the geology.     

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R ² value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.     

Drought risk assessment using remote sensing and GIS techniques

Beginning with a discussion of drought definitions, this review paper attempts to provide a review of fundamental concepts of drought, classification of droughts, drought indices, and the role of remote sensing and geographic information systems for drought evaluation. Owing to the rise in water demand and looming climate change, recent years have witnessed much focus on global drought scenarios. As a natural hazard, drought is best characterized by multiple climatological and hydrological parameters. An understanding of the relationships between these two sets of parameters is necessary to develop measures for mitigating the impacts of droughts. Droughts are recognized as an environmental disaster and have attracted the attention of environmentalists, ecologists, hydrologists, meteorologists, geologists, and agricultural scientists. Temperatures; high winds; low relative humidity; and timing and characteristics of rains, including distribution of rainy days during crop growing seasons, intensity, and duration of rain, and onset and termination, play a significant role in the occurrence of droughts. In contrast to aridity, which is a permanent feature of climate and is restricted to low rainfall areas, a drought is a temporary aberration. Often, there is confusion between a heat wave and a drought, and the distinction is emphasized between heat wave and drought, noting that a typical time scale associated with a heat wave is on the order of a week, while a drought may persist for months or even years. The combination of a heat wave and a drought has dire socio-economic consequences. Drought risk is a product of a region’s exposure to the natural hazard and its vulnerability to extended periods of water shortage. If nations and regions are to make progress in reducing the serious consequences of drought, they must improve their understanding of the hazard and the factors that influence vulnerability. It is critical for drought-prone regions to better understand their drought climatology (i.e., the probability of drought at different levels of intensity and duration) and establish comprehensive and integrated drought information system that incorporates climate, soil, and water supply factors such as precipitation, temperature, soil moisture, snow pack, reservoir and lake levels, ground water levels, and stream flow. All drought-prone nations should develop national drought policies and preparedness plans that place emphasis on risk management rather than following the traditional approach of crisis management, where the emphasis is on reactive, emergency response measures. Crisis management decreases self-reliance and increases dependence on government and donors.     

Structural interpretation and hydrocarbon potential of Balkassar oil field,eastern Potwar,Pakistan, using seismic 2D data and petrophysical analysis

The Balkassar oil field is situated in the eastern Potwar sub-basin, lies on the southern flank of Soan syncline in Himalayan collisional regime. The area represents Indo-Pak and Eurasian blocks of Precambrian to recent time. Thrusting and folding of Himalayan, Indo-Pak plate movement and Salt Range uplift form the structural trap in Balkassar sub-surface (Balkassar anticline). On the basis of information from eleven seismic 2D lines and wells data six reflectors well data, four faults were identified and marked. The structural trend is northeast southwest. Interpretation of seismic 2D data reveals that the study area has undergone intense deformation as a consequence of development of thrusts and backthrusts.The Balkassar anticline is bounded by two thrust faults one from southeast and the other from northwest. Time and depth contour models shows that anticline limbs at north-western side are steep as compared to south-eastern limbs. Seismic interpretation indicates the presence of well-developed anticline bounded by three faults in the cover sequence and one fault in basement and thus the structure may act as a trap for hydrocarbons. The petrophysical analysis of Balkassar-OXY-1 well shows about 83.1% hydrocarbons saturation in the reservoir rocks, hence this study suggest that Balkassar Oilfield has potential to produce hydrocarbons.     

Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland,Iran

One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.     

Phytoplankton and bacterial community structures and their interaction during red-tide phenomena

Phytoplankton and bacteria diversity were studied before, during and after red tide phenomena during spring season 2015 in the Eastern Harbour (E.H.) of Alexandria, Egypt. Fifty five species of phytoplankton were identified and represented different distinct classes “Bacillariophyceae; Dinophyceae, Chlorophyceae, Cyanophyceae and Eugelenophyceae”. Also, Diatom formed the most dominant group. The average number of the phytoplankton density varied from 4.8 × 10⁴ to 1.1 × 10⁶ cell l^-1 during the study period and Skeletonema costatum was the agent causing the red tide. The existence percentages of bacteria ranged from 2.6 to 17.9% on all media tested. The bacterial isolates on the nutrient agar medium represented the highest existence with a total percentage of 43.6%, followed by MSA medium (25.7%), while the lowest percentage was for the AA medium at 7.8%. However, twelve isolates were selected as representative for bacterial community during study interval. Based on the morphological, biochemical, physiological and enzymatic characteristics, the bacterial strains were described. Depending on the 16S rDNA gene sequence, three common antagonists were aligned as: Vibrio toranzoniae strain Vb 10.8, Ruegeria pelagia strain NBRC 102038 and Psychrobacter adeliensis strain DSM 15333. The interaction between these bacteria and S. costatum was studied. The growth of S. costatum was significantly lower whenever each bacterium was present as compared to axenic culture. More specifically, 30% (v/v) of the all tested bacteria showed the strongest algicidal activities, as all S. costatum cells were killed after two days. 10% of R. pelagia and P. adeliensis also showed significant algicidal activities within six days.     

Assessment of sea water inundation along Daboo creek area in Indus Delta Region,Pakistan

Indus Deltaic Region (IDR) in Pakistan is an erosion vulnerable coast due to the high deep water wave energy. Livelihood of millions of people depends on the fisheries and mangrove forests in IDR. IDR consists of many creeks where Daboo is a major creek located at southeast of the largest city of Pakistan, Karachi. Unfortunately, there has been no detailed study to analyze the damages of sea water intrusion at a large temporal and spatial scale. Therefore, this study is designed to estimate the effects of sea water inundation based on changing sea water surface salinity and sea surface temperature (SST). Sea surface salinity and SST data from two different surveys in Daboo creek during 1986 and 2010 are analyzed to estimate the damages and extent of sea water intrusion. Mean salinity has increased 33.33% whereas mean SST decreased 13.79% from 1987 to 2010. Spatio-temporal analysis of creek area using LANDSAT 5 Thematic mapper (TM) data for the years 1987 and 2010 shows significant amount of erosion at macro scale. Creek area has increased approximately 9.93% (260.86 m² per year) which is roughly equal to 60 extensive sized shrimp farms. Further Land Use Land Cover (LULC) analyses for years 2001 and 2014 using LANDSAT 7 Enhanced Thematic Mapper Plus (ETM+) has indicated 42.3% decrease in cultivated land. Wet mud flats have spread out at the inner mouth of creek with enormous increase of 123.3%. Significant sea water intrusion has increased the area of barren land by 37.9%. This also resulted in overall decrease of 6.7% in area covered by mangroves. Therefore, this study recorded a significant evidence of sea water intrusion in IDR that has caused serious damages to community living in the area, economical losses. Additionally, it has also changed the environment by reducing creek biological productivity as reported by earlier studies over other regions of the world.     

Effect of remote forcings on the winter precipitation of central southwest Asia part 1: observations

Summary We investigate the effects of the North Atlantic Oscillation (NAO) and the El Nino Southern Oscillation (ENSO) on winter precipitation in Central Southwest Asia (CSWA) using an analysis of available observed climate data. The analysis is based on correlations, composites and Singular Value Decomposition (SVD) performed using the gridded dataset of the Climatic Research Unit (CRU) and station data for the region. We find that both the NAO and ENSO affect climate over the region. In particular a positive precipitation anomaly is typically found in correspondence of the positive NAO phase and warm ENSO phase over a sub-region encompassing northern Pakistan, Afghanistan, Tajikistan and southern Uzbekistan. This conclusion is supported by a consistency across the different analysis methods and observation datasets employed in our study. A physical mechanism for such effect is proposed, by which western disturbances are intensified over the region as they encounter a low pressure trough, which is a dominant feature during positive NAO and warm ENSO conditions. Our results give encouraging indications towards the development of statistically-based prediction tools for winter precipitation over the CSWA region.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.