Projection of lightning over South/South East Asia using CMIP5 models

Physical phenomena observed before strong earthquakes have been reported for centuries. Precursor signals, which include radon anomalies, electrical signals, water level changes and ground lights near the epicenter, can all be used for earthquake prediction. Anomalous negative signals observed by ground-based atmospheric electric field instruments under fair weather conditions constitute a novel earthquake prediction approach. In theory, the abnormal radiation of heat before an earthquake produces fair weather around the epicenter. To determine the near-epicenter weather conditions prior to an earthquake, 81 global earthquake events with magnitudes of 6 or above from 2008 to 2021 were collected. According to Harrison's fair weather criteria, in 81.48% of all statistical cases, the weather was fair 6 h before the earthquake; in 62.96% of all cases, the weather was fair 24 h before the event. Moreover, most of these cases without fair weather several hours before the earthquake were near the sea. Among the 37 inland earthquakes, 86.49% were preceded by 6 h of fair weather, and 70.27% were preceded by fair weather for 24 h. We conclude that the weather near the epicenter might be fair for several hours before a strong earthquake, especially for inland events.

     

A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya

Natural Hazards - Avalanche forecasting is carried out using physical as well as statistical models. All these models have certain limitations associated with their mathematical formulation that...     

Evaluating the effect of active ions on the early performance of soft clay solidified by modified biomass waste-rice husk ash

Acta Geotechnica - Soft clay generally cannot be directly used as subgrade material due to its poor engineering characteristics. The application of rice husk ash (RHA) for the solidification of...     

On the interaction of mesoscale eddies and a tropical cyclone in the Bay of Bengal

Natural Hazards - Oceanic eddies span over a wide range of sizes and affect the thermodynamic properties of water column. By modifying the thermal structure of the upper ocean, these eddies...     

Performance study of various spatial indexes on 3D geo-data in Geo-RDBMS

In the emerging era of information and communication technologies, geotechnology is one of the fastest growing fields. Geo-RDBMS is very important and evolving aspect for GIS, as it can manage large volume of spatial data inside RDBMS. The utilization of RDBMS for geospatial data was one of the important focuses of GIS professionals in last decades to store and manage 2D geo-data. However, the support for 3D geo-data inside RDBMS is still limited and is a challenging task for RDBMS providers. In this study, data organization and performance assessment of 3D geo-data inside RDBMS are carried out. In this process, various file-based 3D data models such as CityGML, COLLADA and KML are migrated to geo-RDBMS to bring entire 3D geo-data in common platform. Various spatial indexing techniques viz. R-Tree, B-Tree, GiST, etc. are applied on these 3D data models and best indexing techniques are studied for 3D GIS operations.     

Axi-symmetric Analysis of Geosynthetic-reinforced Granular Fill-soft Soil System with Group of Stone Columns

The paper presents a mechanical model to predict the behavior of geosynthetic-reinforced granular fill resting over soft soil improved with group of stone columns subjected to circular or axi-symmetric loading. The saturated soft soil has been idealized by spring-dashpot system. Pasternak shear layer and rough elastic membrane represent the granular fill and geosynthetic reinforcement layer, respectively. The stone columns are idealized by stiffer springs. The nonlinear behavior of granular fill and soft soil is considered. Consolidation of the soft soil due to inclusion of stone columns has also been included in the model. The results obtained by using the present model when compared with the reported results obtained from laboratory model tests shows very good agreement. The effectiveness of geosynthetic reinforcement to reduce the maximum and differential settlement and transfer the stress from soft soil to stone columns is highlighted. It is observed that the reduction of settlement and stress transfer process are greatly influenced by stiffness and spacing of the stone columns. It has been further observed that for both geosynthetic-reinforced and unreinforced cases, the maximum settlement does not change if the ratio between spacing and diameter of stone columns is greater than 4.     

Use of objective analysis to estimate winter temperature and precipitation at different stations over western Himalaya

Temperature and fresh snow are essential inputs in an avalanche forecasting model. Without these parameters, prediction of avalanche occurrence for a region would be very difficult. In the complex terrain of Himalaya, nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence. In view of this persistent problem present study estimates maximum temperature, minimum temperature, ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions. The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone. Initial step to estimate the parameters at a location, is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated. The parameters at this reference height are then spatially interpolated by using Barnes objective analysis. The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE) of the observed and estimated values of the parameters are discussed for the winters of 2007–2008.