During and shortly after a disaster data about the hazard and its consequences are scarce and not readily available. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in case of floods. The use case in the city of Dresden for the June 2013 flood shows that social media may help to bridge the information gap when traditional data sources are lacking or are sparse.

In order to support as critical input to air quality forecasting task during Commonwealth Games (CWG) – 2010 in mega city Delhi, we have developed a high resolution emission inventory of major atmospheric pollutants. For the same, inventories of ozone precursors like NOx and CO are developed over a domain of 70 km × 65 km with a grid of 1.67 km ×1.67 km resolution covering Delhi and surrounding region using Geographical Information System (GIS) technique for base year 2010.

A remote sensing and GIS based landslide susceptibility zonation (LSZ) of the Tehri reservoir rim region has been presented here. Landslide causal factors such
as land use/land cover, photo-lineaments, landslide incidences, drainage, slope, aspect, relative relief, topographic wetness index and stream power index were
derived from remote sensing data. Ancillary data included published geological map, soil map and topographic map. Correlation between factor classes and

This paper presents the application of a new methodology for coastal multi-hazard assessment & management under a changing global climate on the state of Karnataka, India. The recently published methodology termed the Coastal Hazard Wheel (CHW) is designed for local, regional and national hazard screening in areas with limited data availability, and covers the hazards of ecosystem disruption, gradual inundation, salt water intrusion, erosion and flooding.

Coastal managers face the task of assessing and managing flood risk. This requires knowledge of the area of land, the number of people, properties and other infrastructure
potentially affected by floods. Such analyses are usually static; i.e. they only consider a snapshot of the current situation. This misses the opportunity to learn about the role
of key drivers of historical changes in flood risk, such as development and population rise in the coastal flood plain and sea-level rise.

A pressing problem facing coastal decision makers is the conversion of "high level" but plausible climate change assessments into an effective basis for climate change adaptation at the local scale. Here, we describe a web-based, geospatial decision-support tool (DST) that provides an assessment of the potential flood risk for populated coastal lowlands arising from future sea-level rise, coastal storms and high river flows.