Introduction

Floods are major natural disasters that affect many regions around the world year after year, causing loss of lives, damaging economies and human health. More than one third of the world’s land area is flood prone, affecting about 82% of the world’s population. According to EM-DAT disaster statistics, about 3 billion people in more than 110 countries are affected by catastrophic flooding. Between 1980 and 2011, about 212,460 deaths were associated with floods worldwide. Remotely sensed information from satellites and airborne instruments can be used for estimating the extent and dynamics of flood inundation for large areas and complement in-situ observations. For example, flood monitoring and warning systems need rapid access to processed data but optical imagery tends to be affected by cloud contamination. Retrospective analysis of flood recurrence, in contrast, does not require immediacy in the data supply and can rely on temporal compositing methods to overcome cloud contamination.

The flood risk spatial datasets that can be obtained from this site contain estimated maximum flood inundation extent for South Asia. These products were produced using the original data from Moderate Resolution Imaging Spectroradiometer (MODIS), an optical sensor with a spatial resolution (pixel size) of 250 to 500 meters. The estimation algorithm is described in detail in Amarnath et al (2012) and was validated using ALOS AVINIR / LANDSAT (optical sensor) and ALOS PALSAR (microwave sensor) data. The 500 m grid flood risk products were further analyzed to show how changes in population, land use or shifting climatic patterns may affect future inundation patterns. The data hence also explain where people are vulnerable to floods, where crop losses may occur, for insurance purposes, and where authorities should prioritize investments. More flood products will be added to this site as the work progresses on methodology improvement and in other regions.

Publications

Amarnath, G.; Ameer, M.; Aggarwal, P.; Smakhtin, V. 2012. Detecting spatio-temporal changes in the extent of seasonal and annual flooding in South Asia using multi-resolution satellite data. In Civco, D. L.; Ehlers, M.; Habib, S.; Maltese, A.; Messinger, D.; Michel, U.; Nikolakopoulos, K. G.; Schulz, K. (Eds.). Earth resources and environmental remote sensing/GIS applications III: proceedings of the International Society for Optics and Photonics (SPIE), Vol.8538, Amsterdam, Netherland, 1-6 July 2012. Bellingham, WA, USA: International Society for Optics and Photonics (SPIE). 11p.

Amarnath, G. 2013. An algorithm for rapid flood inundation mapping from optical data using a reflectance differencing technique. Journal of Flood Risk Management, 12p. http://dx.doi.org/10.1111/jfr3.12045

Enquiries should be addressed to: Giriraj Amarnath