Join Us for Future Floods: An Exploration of a Cross-Disciplinary Approach to Flood Risk Forecasting

Download Future Floods Program (As of 25 Feb 2015)

A workshop co-sponsored by the FASS Environment Research Cluster, the Institute of Water Policy (Lee Kuan Yew School of Public Policy) and AON Benfield Pte. Ltd.

When? 26-27 February 2015 (Thursday-Friday), Registration at 9AM
Where? Research Division Seminar Room (AS7 06-42)

ALL ARE WELCOME

Future Floods Poster FINAL

ABSTRACT

As flood damage increases in Asia and elsewhere, as a result of increased flood frequency, flood magnitude, and human vulnerability, there is a growing need for better forecasting of floods. The challenges are however enormous, with short and spatially sparse gauged records or no records, increasing evidence of statistical non-stationarity of precipitation and floods, climate change that may take the world into a domain not experienced during the industrial age, and an increasing demand for flood insurance in countries where assets and wealth are rapidly accumulating and are exposed to floods. This Workshop will bring together experts in both the ‘traditional’ hydrologic analysis of floods with those who use non-traditional sources of information about floods. Expertise will come from risk analysis, hydrology, mathematics/statistics, geomorphology/geology, geochronology, climatology, history and archives.

The Agenda begins by showcasing by the insurance industry’s three approaches to flood forecasting: Fully Probabilistic Models; Historical Scenarios; and Realistic Disaster Scenarios. This is followed by inputs from sources of knowledge that are not traditionally used by mainstream flood forecasters. A key challenge for risk modeling is to address Epistemic Uncertainty; that is, uncertainty about a phenomenon that we could in principle know about but do not in practice, and so is amenable to improvement by collecting more information. Here is where non-traditional sources of knowledge may assist those involved in this challenging field.

 

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