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Profiling Causal Mechanisms of Evolving Flood Risk

The challenges that hydroclimate extremes pose to sustainable development around the world are both severe and growing. A documented increase in flooding across parts of the United States and globally coupled with new development in flood-prone areas has led to an alarming rise in humanitarian crises and societal costs, underscoring the need to better understand the nonstationary characteristics of these phenomena. There is a growing voice in the hydrologic community arguing that nonstationary statistical techniques used to describe changing flood risk over time need to be complemented by an understanding of the casual mechanisms and dominant climatic processes that modulate this risk.

Tropical moisture export driving the 2008 Iowa flood.

Our research seeks to establish a direct link between an emerging process-based knowledge of organized, large-scale climate dynamics linked to widespread flooding and extreme event frequency modeling often used for risk estimation. Our present focus is in the midlatitudes, where organized and persistent moisture transport originating in the tropics has been identified as one climate mechanism that can significantly influence the generation of hydroclimatic extremes. These tropical moisture exports (TMEs), also known as atmospheric rivers (ARs) when appearing as planetary-scale filaments of atmospheric moisture in satellite imagery, are often associated with some of the most extreme flood events in the instrumental record and produce the greatest socioeconomic damage.

Provided by NOAA/ESRL Physical Sciences Division

We are developing modeling frameworks that can support both seasonal predictions and long-term projections of future extreme event risk based on developing knowledge of these mid-latitude causal mechanisms. Such an understanding is indeed necessary to develop a consistent analytical framework to explain and predict oscillations and trends in extremes using both historic observations and long-term climate projections from global climate models, and may be particularly relevant when trying to estimate future extreme event risk at ungaged sites. Our goal is to promote the cost-effective design of reliable engineering solutions and financial mechanisms (e.g., insurance products) over annual to decadal planning horizons based on this scientific understanding.

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