Abstract
AbstractIn this paper we seek to understand the nature of flood spatial dependence over the conterminous United States. We extend an existing conditional multivariate statistical model to enable its application to this large and heterogenous region and apply it to a 40‐year data set of ~2,400 U.S. Geological Survey gauge series records to simulate 1,000 years of U.S. flooding comprising more than 63,000 individual events with realistic spatial dependence. A continental‐scale hydrodynamic model at 30 m resolution is then used to calculate the economic loss arising from each of these events. From this we are able to compute the probability that different values of U.S. annual total economic loss due to flooding are exceeded (i.e., a loss‐exceedance curve). Comparing these data to an observed flood loss‐exceedance curve for the period 1988–2017 shows a reasonable match for annual losses with probability below 10% (e.g., >1 in 10‐year return period). This analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Analysis of the set of stochastic events and losses yields new insights into the nature of flooding and flood risk in the United States. In particular, we confirm the strong relationship between flood affected area and event peak magnitude, but show considerable variability in this relationship between adjacent U.S. regions. The analysis provides a significant advance over previous national flood risk analyses as it gives the full loss‐exceedance curve instead of simply the average annual loss.
Highlights
IntroductionWhile intense and isolated thunderstorms can cause extreme flows and flooding that affect only single river gauging sites (e.g., the Hepner storm of 1903 in Eastern Oregon and the Boscastle flood of 2004 in the United Kingdom), extreme rainfall more typically occurs as a result of larger‐scale and organized weather systems which can cause flooding in a number of different basins along the storm track
While intense and isolated thunderstorms can cause extreme flows and flooding that affect only single river gauging sites, extreme rainfall more typically occurs as a result of larger‐scale and organized weather systems which can cause flooding in a number of different basins along the storm track
The spatial pattern of maximum flood return periods caused by a single weather event at a set of gauge sites across a region is usually characterized by spatial dependence: If we observe an extreme flow at a particular site there is a positive probability that nearby sites will be experiencing high flows as a result of the same weather event
Summary
While intense and isolated thunderstorms can cause extreme flows and flooding that affect only single river gauging sites (e.g., the Hepner storm of 1903 in Eastern Oregon and the Boscastle flood of 2004 in the United Kingdom), extreme rainfall more typically occurs as a result of larger‐scale and organized weather systems which can cause flooding in a number of different basins along the storm track. “Nearby” in this case could either mean within the same river basin or in proximal basins, and in addition implies that there will be some distance decay in the spatial dependence between sites as the separation between them increases This dependence structure is asymptotic (Keef et al, 2012), as in the limit where the separation distance between sites approaches zero, the probability that two sites experience the same flood return period approaches 1. Spatial patterns of flooding are characterized by asymptotic independence, whereby for very distant sites there will be zero probability that the highest flows will be caused by the same storm (Keef et al, 2012) This is because there are physical limits to the maximum size of storm systems, this may be very large for major regional flooding events (e.g., Mississippi 1993, Central Europe 2003 and 2013, Pakistan 2010, Bangladesh 2011). The spatial pattern in flood return period for an event is commonly referred to as the event footprint (e.g., Rougier, 2013)
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