Investigation of seasonal droughts and related large-scale atmospheric dynamics over the Potwar Plateau of Pakistan

As a result of climate change and unsustainable land use management in the recent past, droughts have become one of the most devastating climatic hazards whose impacts may prolong from months to years. This study presents analysis of droughts for two major cropping seasons, i.e., Kharif (May–September) and Rabi (October–April), over the Potwar Plateau of Pakistan. The analysis is performed using various datasets viz. observational, reanalysis, and Regional Climate Models (RCMs), for the past (1981–2010) and future (2011–2100) time periods. The following two methods for the identification of dry and wet years, also referred to as drought and wetness, are applied: (1) the percentile rank approach and (2) the drought indices, Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). Future projections of droughts are investigated using RCM (RegCM4.4 and RCA4) outputs from CORDEX South Asia domain under two Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5. Generally, the indices show non-significant decreasing trends of drought severity in the recent past for all cases; however, significant increasing trends are observed for annual (0.006) and Kharif (0.007) cases under RCP4.5 scenario. The analysis of large-scale atmospheric dynamics suggests the significant role of low-level geopotential height anomalies over Tibetan Plateau (northwest of Pakistan) during Kharif (Rabi) season in controlling drought occurrence by transporting moisture from the Bay of Bengal (Arabian Sea). Moreover, composites of vertically integrated moisture transport, moisture flux convergence/divergence, and precipitable water anomalies show their marked contribution in maintaining the drought/wetness conditions over the Potwar region.     

Rapid sunspot motion during a major solar flare

A major solar flare on 15 November, 1991 produced a striking perturbation in the position and shape of the sunspot related most closely to the flare. We have studied these perturbations by use of the aspect-sensor images from the Soft X-ray Telescope on board YOHKOH, and with ground-based data from the Mees Solar Observatory. The perturbation occurred during the impulsive phase of the flare, with a total displacement on the order of 1 arc sec. The apparent velocity of approximately 2 km s^–1 exceeds that typically reported for sunspot proper motions even in flare events. We estimate that the magnetic energy involved in displacing the sunspot amounted to less than 4 × 10³⁰ ergs, comparable to the radiant energy from the perturbed region. Examination of the Mees Observatory data shows that the spot continued moving at lower speed for a half-hour after the impulsive phase. The spot perturbation appears to have been a result of the coronal restructuring and flare energy release, rather than its cause.     

Earth Observation Scientific Workflows in a Distributed Computing Environment

Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package ( http://code.google.com/p/eo4vistrails/ ). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC.     

The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh

Natural Hazards - The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial...     

Bringing globalization to the countryside: Special Economic Zones in India

Special Economic Zones (SEZs) are important vectors of neoliberal globalization in India. Despite facing widespread resistance against the proposed land acquisition for these zones, they are still being promoted across the country. We argue that the wealth redistribution to the country's elites and the fractured resistance movements enable neoliberalism and its practices to grow in the countryside. Using a private sector SEZ in Gurgaon as a case study, this article explores how special economic zoning, as a neoliberal policy, has been implicated in the spatialized production of poverty. We also show that the main actors who promote neoliberalism in India (the state and the large‐scale urban private sector) have found a seemingly unlikely ally in rural India in the form of farmers with large landholdings, rural elites who are willing to let go of their land under certain conditions. The data for the article was collected in India in 2009–10.     

Investigation of Arsenotrophic Microbiome in Arsenic‐Affected Bangladesh Groundwater

Arsenotrophic bacteria contribute to the nutrient cycling in arsenic (As) affected groundwater. This study employed a culture‐independent and ‐dependent investigation of arsenotrophic microbiomes in As affected groundwater samples collected from Madhabpur, Sonatengra, and Union Porishod in Singair Upazila, Manikganj, Bangladesh. Total As contents, detected by Atomic Absorption Spectrophotometry (AAS) of the samples, were 47 µg/L (Madhabpur, SNGW‐1), 53 µg/L (Sonatengra, SNGW‐2), and 12 µg/L (Union porishod, SNGW‐3), whereas the control well (SNGW‐4; depths >150 m) showed As content of 6 µg/L. Denaturing Gradient Gel Electrophoresis (DGGE) analysis of the amplified 16S rRNA gene from As‐affected groundwater samples revealed the dominance of aerobic bacteria Pseudomonas within heterogeneous bacterial populations. DGGE of heterotrophic enrichments supplemented with arsenite [As (III)] for 4 weeks showed the dominance of Chryseobacterium, Flavobacterium, and Aquabacterium, whereas the dominant genera in that of autotrophic enrichments were Aeromonas, Acinetobacter, and Pseudomonas. Cultured bacteria retrieved from both autotrophic and heterotrophic enrichments were distinguished into nine genotypes belonging to Chryseobacterium, Acinetobacter, Escherichia, Pseudomonas, Stenotrophomonas, Janibacter, Staphylococcus, and Bacillus. They exhibited varying range of As(III) tolerance from 4 to 27 mM. As(III) transformation potential was confirmed within the isolates with oxidation rate as high as 0.143 mM/h for Pseudomonas sp. Sn 28. The arsenotrophic microbiome specifies their potential role in groundwater As‐cycling and their genetic information provide the scientific basis for As‐bioremediation.     

新疆库尔勒市道路积尘重金属污染及健康风险评价

为探究绿洲城市道路积尘重金属污染风险,在新疆库尔勒市采集54个代表性道路积尘样品,分析其中Hg、Cd、As、Pb、Cr和Cu等6种元素含量,基于GIS技术与地学统计法,采用污染负荷指数法和US EPA健康风险评价模型,对道路积尘中重金属污染及潜在健康风险进行评价。结果表明：库尔勒市道路积尘中Hg、Cd、As、Pb和Cu等元素含量的平均值均小于土壤环境质量—建设用地土壤污染风险管控标准（GB 36600—2018）中的筛选值,但Cr元素含量平均值为相应筛选值的9.90倍。污染评价结果表明,研究区道路积尘中Cr呈现重度污染,Hg、Cd、As、Pb和Cu呈无污染。道路积尘中重金属元素的污染负荷指数介于0.0142~0.0522,平均值为0.0266,处于无污染水平。从道路积尘重金属污染空间分布格局来看,库尔勒市东北部和北部区域出现污染高值区。健康风险评估结果表明,经手-口摄入途径是库尔勒市道路积尘重金属日均暴露量及健康风险的主要途径,儿童受到的健康风险高于成人。库尔勒市道路积尘中Hg、Cd、As、Pb、Cr与Cu等元素的非致癌风险及致癌风险处于安全范围内,As对非致癌风险的贡献最大,Cr对致癌风险的贡献最大。     

Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.     

Comparison and Hydrochemical Characterization of Groundwater Resources in the Arabian Peninsula: A Case Study of Al-Baha and Al-Qassim in Saudi Arabia

Water Resources - The objective of this study was to compare and assess the quality and study the hydrochemistry of groundwater in the Al-Baha and Al-Qassim areas, representing the Arabian Shield...     

Analytical derivation of water retention for random monodisperse granular media

The mechanics of water retention in unsaturated granular media is of critical importance to a broad range of disciplines including soil science, geotechnical engineering, hydrology and agriculture. Fundamental to water retention is the relationship between degree of saturation and suction, referred to as the water retention curve (WRC). The majority of WRC models are empirically based and seldom incorporate physically meaningful parameters. This study presents an analytical model for the WRC that considers separate contributions from fully filled pores and partially filled pores containing liquid bridges. A recently established unique k-gamma pore volume distribution function for randomly assembled monodisperse granular materials is adopted to determine the contributions of fully filled pores. Calculation of the contribution of residual pore water retained in partially filled pores is undertaken by representing pores as individual cells shaped as platonic shapes of various sizes and determining the volume of all liquid bridges suspended between particles within the pore cells. Weighting factors for the various cell types are obtained from the pore volume distribution to determine the relative contribution of different pore cell geometries to the total residual pore water. The combined model accurately describes experimental data for monodisperse spherical glass beads for both wetting and drying, even though the underlying assumptions do not reflect exactly the complex, interconnected and highly irregular geometry of the pore space. A single parameter provides the lateral shift between the wetting and drying curves. The results of this study provide a geometric understanding of the mechanisms of water retention in granular media.