Inland Flooding Damages in the United States: Historical Trends and Climate Change Implications

Thursday, 18 December 2014
Cameron W Wobus1, Richard Streeter1, Russell Jones1 and Jeremy Martinich2, (1)Stratus Consulting, Boulder, CO, United States, (2)U.S. EPA - Climate Change Division, Washington, DC, United States
Inland flooding causes billions of dollars of damage in the United States each year, and these amounts could increase with more extreme precipitation events. We use flood damage data from the National Climatic Data Center to examine the nature of historical flood damages in the continental United States. Using the location information for each flood event, we link each damaging flood to the nearest stream channel, and to the nearest meteorological station from NCDC. Using this combined dataset, we find that 1) flood events associated with first order streams (typically <300 km2) account for approximately 50% of inland flooding damages nationwide; 2) the number of damaging events in the historical record decreases as a power function of stream order; and 3) the distribution of monetary damages per event is similar across all stream orders. Using this information, we evaluate potential changes in monetary damages from flooding of small catchments under different climate change scenarios. We extract daily precipitation data from the historical record for each damaging event, and determine the approximate recurrence interval of the precipitation associated with each flood event. Then, using modeled daily precipitation projections from the IGSM-CAM model, we project regional changes in the magnitude and frequency of extreme events under two climate change mitigation scenarios compared to a reference scenario. For each scenario, we translate changes in flood frequency into flood damages using the observed historical distribution of damages. We then compile damage projections by region and by scenario to estimate the potential monetary value of inland flooding damages averted by climate change mitigation policies.