Reservoir Quality Assessment of the Upper Permian Chhidru Formation,Salt and Surghar Ranges,Pakistan

The Late Permian succession of the Upper Indus Basin in northeastern Pakistan is represented by the carbonate-dominated Zaluch Group, which consists of the Amb, Wargal and Chhidru formations, which accumulated on the southwestern shelf of the Paleo-Tethys Ocean, north of the hydrocarbon-producing Permian strata of the Arabian Peninsula. The reservoir properties of the mixed clastic-carbonate Chhidru Formation (CFm) are evaluated based on petrography, using scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX) and x-ray diffraction (XRD) techniques. The diagenetic features are recognized, ranging from marine (isopachous fibrous calcite, micrite), through meteoric (blocky calcite-I, neomorphism and dissolution) to burial (poikilotopic cement, blocky calcite-II-III, fractures, fracture-filling, and stylolites). Major porosity types include fracture and moldic, while inter- and intra-particle porosities also exist. Observed visual porosity ranges from 1.5%–7.14% with an average of 5.15%. The sandstone facies (CMF-4) has the highest average porosity of 10.7%, whereas the siliciclastic grainstone microfacies (CMF-3) shows an average porosity of 5.3%. The siliciclastic mudstone microfacies (CMF-1) and siliciclastic wacke-packestone microfacies (CMF-2) show the lowest porosities of 4.8% and 5.0%, respectively. Diagenetic processes like cementation, neomorphism, stylolitization and compaction have reduced the primary porosities; however, processes of dissolution and fracturing have produced secondary porosity. On average, the CFm in the Nammal Gorge, Salt Range shows promise and at Gula Khel Gorge, Trans-Indus, the lowest porosity.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

Characterizing urban area dynamics in historic city of Kurukshetra,India, using remote sensing and spatial metric tools

Abstract

Developing countries like India are an urbanization hotspot with many upcoming towns and cities. Growth in small and medium sized towns and cities have been unnoticed and growing without appropriate urban planning. Utilizing the available medium resolution satellite data and geospatial platforms, the growth dynamics of Kurukshetra city was analysed over a period of 24 years. The study employed a combination of change detection technique and spatial metrics (six each of class and landscape levels) analysis to delineate the growth track of the city and its environs. A significant increase in urban built up (dense 237%; open 1038%) is seen majorly at the cost of open area (70%) and tree clad (58%). Phases of city’s aggregation and diffusion are observed using class and landscape level spatial metrics. Understanding and monitoring of land use changes in and around city limits using integrated spatial tools provide better decision making capability.     

Deciphering heavy metal contamination zones in soils of a granitic terrain of southern India using factor analysis and GIS

Soil contamination by heavy metals has been a major concern for last few decades due to increase in urbanization and industrialization. The main objective of this research was to identify the heavy metal contaminated zones in the study area. Twenty five soil samples collected throughout the agriculture, residential and industrial areas were analysed by X-ray Fluorescence Spectrometer (XRF) for trace metals and major oxides. These metals can affect the quality of soil and infiltrate through the soil, thereby causing groundwater pollution. Based on the chemical analysis of major oxides (SiO_2, Al₂O_3, ?Fe₂O_3, MnO, MgO, CaO, Na₂O, K₂O, TiO₂, and P₂O₅) and their distribution; it is observed that these soils are predominantly siliceous type with slight enrichment of alumina component in the study area. Correlation matrix (CM) and factor analysis (FA) is employed to the heavy metal variables, viz., Ba, Cr, Cu, Ni, Pb, Rb, Sr, V, Y, Zn and Zr of the soil to determine the dominant factors contributing to the soil contamination in the area. In the analysis, five factors emerged as significant contributors to the soil quality. The total contribution of these five factors is about 90%. The contribution of the first factor is about 45% and has significant positive loadings of Co, Cr, Cu, Ni and Zn. The contribution of second factor is 22% and has significant positive loadings of Rb, Sr and Y. The contribution of third, fourth and fifth factors is 10, 8 and 5% and show positive loadings for lead, molybdenum and barium respectively to the soil contamination. The spatial variation maps deciphering different zones of heavy metal concentration in the soil were generated in a GIS (geographic information system) based environment using ArcGIS 9.3.1. The results reveal that heavy metal contamination in the area is mainly due to anthropogenic activities.     

A Periglacial Palaeoenvionment in the Upper Carboniferous–Lower Permian Tobra Formation of the Salt Range, Pakistan

The Upper Carboniferous—Lower Permian(Upper Pennsylvanian-Asselian) Tobra Formation is exposed in the Salt and Trans Indus ranges of Pakistan.The formation exhibits an alluvial plain(alluvial fan-piedmont alluvial plain) facies association in the Salt Range and Khisor Range.In addition,a stream flow facies association is restricted to the eastern Salt Range.The alluvial plain facies association is comprised of clast-supported massive conglomerate(Gmc),diamictite(Dm)facies,and massive sandstone(Sm) Hthofacies whereas the stream flow-dominated alluvial plain facies association includes fine-grained sandstone and siltstone(Fss),fining upwards pebbly sandstone(Sf),and massive mudstone(Fm) Hthofacies.The lack of glacial signatures(particularly glacial grooves and striatums) in the deposits in the Tobra Formation,which are,in contrast,present in their timeequivalent and palaeogeographically nearby strata of the Arabian peninsula,e.g.the AI Khlata Formation of Oman and Unayzah B member of the Saudi Arabia,suggests a pro-to periglacial,i.e.glaciofluvial depositional setting for the Tobra Formation.The sedimentology of the Tobra Formation attests that the Salt Range,Pakistan,occupied a palaeogeographic position just beyond the maximum glacial extent during Upper Pennsylvanian-Asselian time.     

Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain,northwest of Iran

The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca²⁺, Mg²⁺, Na⁺ and K⁺, HCO₃ ^?, SO₄ ^2?, Cl^?, F^? and NO₃ ^?) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO₃ and mixed Ca–Na–HCO₃. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

Computer-based Model for Flood Evacuation Emergency Planning

A computerized simulation model for capturing human behavior during flood emergency evacuation is developed using a system dynamics approach. It simulates the acceptance of evacuation orders by the residents of the area under threat; number of families in the process of evacuation; and time required for all evacuees to reach safety. The model is conceptualized around the flooding conditions (physical and management) and the main set of social and mental factors that determine human behavior before and during the flood evacuation. The number of families under the flood threat, population in the process of evacuation, inundation of refuge routes, flood conditions (precipitation, river elevation, etc.), and different flood warnings and evacuation orders related variables are among the large set of variables included in the model. They are linked to the concern that leads to the danger recognition, which triggers evacuation decisions that determine the number of people being evacuated. The main purpose of the model is to assess the effectiveness of different flood emergency management procedures. Each procedure consists of the choice of flood warning method, warning consistency, timing of evacuation order, coherence of the community, upstream flooding conditions, and set of weights assigned to different warning distribution methods. Model use and effectiveness are tested through the evaluation of the effectiveness of different flood evacuation emergency options in the Red River Basin, Canada.