1.3 January 2015 Malawi Floods from a Remote Sensing Perspective

Tuesday, 12 January 2016: 9:15 AM
Room 338/339 ( New Orleans Ernest N. Morial Convention Center)
Andrew Kruczkiewicz, IRI/Red Cross Red Crescent Climate Centre, Palisades, NY; and H. Cen, B. Moneymaker, J. Lessel, R. Kufandiko, J. Pagona, E. Coughlan de Perez, A. Sweeney, K. Ndungu, and P. Ceccato

In January 2015, extended periods of extreme rainfall caused a series of flood events throughout southern Africa. With little or no warning, the floods took communities by surprise, resulting in the displacement of over 230,000 residents and causing 276 fatalities (Malawi Government, 2015). The meteorological situation was complex, with both riverine floods and flash floods occurring in various parts of the region within a narrow temporal frame. Parts of Malawi and Mozambique were amongst the hardest hit regions, with internally displaced people still residing in tented shelter camps as of August 2015.

Disaster management groups and humanitarian organizations responded using information available to them in the days after the flood, in the form of flood maps and anecdotal evidence. These organizations relied on remotely-sensed satellite data to evaluate initial disaster impact and design response programs.

In partnership with the Malawi Red Cross National Society, we compared ground-truth data (locations of shelter site of internally displaced people (IDPs) and origins of IDPs) with the spatial distribution of remotely sensed and model driven flood products derived from NASA sensors, MODIS, SSM-I and AMSU-A and Landsat data from USGS. Future, to explore the potential predictive capabilities of geophysical variables, we evaluated the relationship between soil moisture (data from the advanced scatterometer (ASCAT) provided by the European Space Agency and rainfall (data from the Tropical Rainfall Measuring Mission (TRMM) by provided by NASA).

Outputs suggest that flood signal variations across flood map can be great, creating a situation of potential uncertainty when rapid response actions need to be taken by humanitarian and government organizations. Furthermore, relative to ground truth data, certain maps able to detect certain types of floods at a higher skill than others. Also, the relationship between soil moisture and floods vary based on flood type, with antecedent soil moisture conditions potentially acting as a primary driver in the prediction of heightened risk of flash floods, while appearing to be of little importance in the prediction of river floods. Lastly, 6-day rainfall forecasts were analyzed for various spatial and temporal scales over regions impacted by both floods to analyze signal skill relative to flood type.

In the same way that the geophysical characteristics of flood vary spatially and temporally by flood type, so too does the linked humanitarian action. The results of this study will increase the ability to forecast and monitor flood events in Africa, benefiting organizations involved with disaster preparedness and relief efforts, with a specific emphasis on providing analysis for the development of forecast based financing mechanisms to release funding before a disaster based on pre determined triggers and actions (Coughlan de Perez et al. 2015). Project outputs suggest further exploration of the relationship of coupled variables, such as forecasted rainfall and antecedent soil moisture status, could be useful in developing thresholds to trigger humanitarian action in Mozambique.

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