Flood Response System (FRS) is a network-enabled solution developed using open-source software. The system has query based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. FRS effectively facilitates the management of post-disaster activities caused due to flood, like displaying spatial maps of area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of damage. The inputs to FRS are provided using two components: (1) a semi-automated application developed indigenously, to delineate inundated areas for Near-Real Time Flood Monitoring using Active Microwave Remote Sensing data and (2) a two-dimensional (2D) hydrodynamic river model generated outputs for water depth and velocity in flooded areas for an embankment breach scenario. The 2D Hydrodynamic model, CCHE2D (Center for Computational Hydroscience and Engineering Two-Dimensional model), was used to simulate an area of 600 km² in the flood-prone zone of the Brahmaputra basin. The resultant inundated area from the model was found to be 85% accurate when validated with post-flood optical satellite data.

The Indian subcontinent is annually affected by floods that cause profound irreversible damage to crops and livelihoods. With increased incidences of floods and their related catastrophes, the design, development, and deployment of an Early Warning System for Flood Prediction (EWS-FP) for the river basins of India is needed, along with timely dissemination of flood-related information for mitigation of disaster impacts. Accurately drafted and disseminated early warnings/advisories may significantly reduce economic losses incurred due to floods. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. HPC, remote sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. The model is open-source, supports geographic file formats, and is capable of simulating rainfall run-off, river routing, and tidal forcing, simultaneously. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta, 9225 sq km) with actual and predicted discharge, rainfall, and tide data. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time.

Timely detection of landslide precursors is essential for life-saving early warnings, yet remains challenging due to the subtle, non-linear nature of pre-failure ground motion and the computational intensity of processing SAR time series. To address this, we present an operational automated InSAR framework, co-developed under India’s National Supercomputing Mission and the India-EU GANANA HPC collaboration, that processes multi-temporal SAR data optimized on HPC infrastructure (AIRAWAT) to enable long-term satellite-based monitoring large areas for landslide hazard assessment and early warning.The system ingests Sentinel-1 SLC, IW data and ancillary geospatial layers (DEM and historical landslide inventories). Using GMTSAR-automated workflows, it generates displacement time series and LOS velocity maps across large, landslide-prone regions. These outputs are analysed to identify accelerated displacement trends for known landslides. Threshold values are identified based on the movement signatures, key precursors to slope failure, days to weeks before catastrophic events.Critically, the entire pipeline, from SAR data ingestion to risk classification, is optimized for low-latency execution on HPC, enabling updates within 24–48 hours of new satellite acquisitions. Outputs are translated into a dynamic risk alert system (Green–Red) and delivered via an interactive dashboard with API access, designed for integration into national disaster response workflows.Currently piloted in the Himalayas and Western Ghats, this framework demonstrates a scalable, HPC-driven paradigm for time-critical geo-hazard monitoring directly supporting rapid situational awareness and proactive evacuation decisions. The architecture is extensible to other InSAR-monitored hazards (e.g., subsidence, volcanic unrest).The framework was tested with the well-documented Nepal Earthquake (7.8 M) on 25 April 2015, which triggered more than 47,200 co-seismic landslides. The displacement and coherence time-series were plotted at the crown points and centroids of the landslide polygon. The time-series plots show prominent trends towards the event date. Significant peak was observed in the displacement derived from 08 February 2015 and 21 April 2015 (Sentinel-1 Ascending) interferogram, which may be used as an early warning precursor.