Temporal compounding increases economic impacts of atmospheric rivers in California.

Temporally compounding atmospheric river (AR) events cause severe flooding and damage in California. However, the contribution of temporal compounding to AR-induced loss has yet to be systematically quantified. We show that the strongest ARs are more likely to be part of sequences, which are periods of elevated hydrologic hazard associated with temporally clustered ARs. Sequences increase the likelihood of flood-related impacts by 8.3% on AR days and 5.4% on non-AR days, and across two independent loss datasets, we find that ARs within sequences have over three times higher expected losses compared to ARs outside of sequences. Expected losses also increase when the preceding AR is higher intensity, when time since the preceding AR is shorter, and when an AR is the second or later event within a sequence. We conclude that temporal compounding is a critical source of information for predicting an AR's potential consequences.

A methodology to estimate postdisaster unmet housing needs using limited data: Application to the 2017 California wildfires.

In the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation without delays.