Morphological classification pertaining to validate the climatology and category of thunderstorms over Kolkata,India

As the world's highest and largest plateau, the Qinghai–Xizang Plateau has experienced a greater warming than the Northern Hemisphere and global averages. This warming has been reported to exhibit an elevation-dependent pattern. However, the finding involved plenty of uncertainties caused by the spatially limited datasets and complex topography. Here, we explored an approach integrating satellite-derived LST data and ground records to generate a spatially continuous air temperature dataset for the plateau grasslands from 2003 to 2012, and then examined influences of elevation/topography on temperature change trends. The derived temperature dataset was validated to be closely correlated with field-station records. Based on the derived spatially continuous temperature datasets, we found an opposite change trend of annually average temperature between Qinghai and Xizang Province. The contrasted trend was obvious in daytime and more so in summer season. By analyzing the temperature trend in relation to elevation, we found an enhanced temperature change trend in higher elevation than in lower elevation for autumn nights and winter temperatures, while the temperature change trends for other seasons were more evident in lower elevation areas. The varying temperature change trends as regulated by elevation implies that temperate grasslands have experienced a more rapid temperature change than alpine grasslands during the past decade.     

Uncertainty of soil erosion modelling using open source high resolution and aggregated DEMs

Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km². The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.     

Future Scenarios of Environmental Vulnerability Mapping Using Grey Analytic Hierarchy Process

Any sustainable resource utilization plan requires evaluation of the present and future environmental impact. The present research focuses on future scenario generation of environmental vulnerability zones based on grey analytic hierarchy process (grey-AHP). Grey-AHP combines the advantages of grey clustering method and the classical analytic hierarchy process (AHP). Environmental vulnerability index (EVI) considers twenty-five natural, environmental and anthropogenic parameters, e.g. soil, geology, aspect, elevation, slope, rainfall, maximum and minimum temperature, normalized difference vegetation index, drainage density, groundwater recharge, groundwater level, groundwater potential, water yield, evapotranspiration, land use/land cover, soil moisture, sediment yield, water stress, water quality, storage capacity, land suitability, population density, road density and normalized difference built-up index. Nine futuristic parameters were used for EVI calculation from the Dynamic Conversion of Land-Use and its Effects, Model for Interdisciplinary Research on Climate 5 and Soil and Water Assessment Tool. The resulting maps were classified into three classes: “high”, “moderate” and “low”. The result shows that the upstream portion of the river basin comes under the high vulnerability zone for the years 2010 and 2030, 2050. The effectiveness of zonation approach was between “better” and “common” classes. Sensitivity analysis was performed for EVI. Field-based soil moisture point data were utilized for validation purpose. The resulting maps provide a guideline for planning of detailed hydrogeological studies.