Contribution of historical precipitation change to US flood damages

Abstract

Precipitation extremes have increased across many regions of the United States, with further increases anticipated in response to additional global warming. Quantifying the impact of these precipitation changes on flood damages is necessary to estimate the costs of climate change. However, there is little empirical evidence linking changes in precipitation to the historically observed increase in flood losses. We use >6,600 reports of state-level flood damage to quantify the historical relationship between precipitation and flood damages in the United States. Our results show a significant, positive effect of both monthly and 5-d state-level precipitation on state-level flood damages. In addition, we find that historical precipitation changes have contributed approximately one-third of cumulative flood damages over 1988 to 2017 (primary estimate 36%; 95% CI 20 to 46%), with the cumulative impact of precipitation change totaling $73 billion (95% CI 39 to $91 billion). Further, climate models show that anthropogenic climate forcing has increased the probability of exceeding precipitation thresholds at the extremely wet quantiles that are responsible for most flood damages. Climate models project continued intensification of wet conditions over the next three decades, although a trajectory consistent with UN Paris Agreement goals significantly curbs that intensification. Taken together, our results quantify the contribution of precipitation trends to recent increases in flood damages, advance estimates of the costs associated with historical greenhouse gas emissions, and provide further evidence that lower levels of future warming are very likely to reduce financial losses relative to the current global warming trajectory.

Keywords: climate change; flooding; precipitation.

Fig. 1.

Effect of state-level precipitation on flood damages. (A) Historical state-level trends in monthly flood damages. The nine National Centers for Environmental Information (NCEI) climate regions are outlined in dark gray: Northwest (NW), West (W), Southwest (SW), Northern Rockies and Plains (NR), South (S), Upper Midwest (UM), Central (C), Northeast (NE), and Southeast (SE). (B) Relationship between normalized flood damages and monthly precipitation at the state level using linear (blue line), quadratic (gray line), and binned (red line) models. Shading indicates the 95% CI estimated by bootstrapping states. Response functions are centered at mean monthly precipitation (0.04 SD) and mean log-normalized damage (1.8). Histograms show the distribution of monthly precipitation anomalies across all state-months (blue), the distribution of monthly precipitation anomalies during months with flood damage (light gray), and the distribution of total damages (in 2017 dollars) across monthly precipitation anomalies (dark gray). (C) Effect of precipitation on flood damages within each NCEI climate region (shown in A), for two precipitation variables: total monthly precipitation (black) and monthly maximum 5-d precipitation (gray). Effects are measured as the change in ln(normalized damages) per SD change in precipitation. Points show median coefficient estimates and vertical lines show the 95% CI around each point estimate. Filled circles indicate statistically significant (P < 0.05) differences between the regional coefficients and a pooled model (shown as a black dashed line for total monthly precipitation, same as the blue line in B). (D) Seasonal variations in the effect of monthly precipitation on flood damages for each region. Points show the median coefficient estimates for each season and region, and vertical lines show the 95% CI around each point estimate. Seasons are defined as December–January–February (DJF), March–April–May (MAM), June–July–August (JJA), and September–October–November (SON). Black dotted lines show the median coefficient estimate for each region (the same as black points in C), and gray shading shows the 95% CI (black lines in C).

Fig. 2.

Observed trends in monthly total and maximum 5-d precipitation. (A) Observed 1928-to-2017 trends in the 50th, 75th, and 95th percentiles of monthly precipitation, measured in SDs per decade. Trends are calculated on a 2.5 × 2.5° grid using quantile regression and the PRISM monthly precipitation product. (B) Same as A, but for monthly maximum 5-d precipitation. Trends are calculated on a 2.5 × 2.5° grid using quantile regression and the Climdex HadEX3 monthly Rx5day product.

Fig. 3.

Cumulative damages due to historical precipitation change. (A) Cumulative observed flood damages (gray) and estimated portion due to historical precipitation change (green) from 1988 to 2017. Error bars show the 95% CI for cumulative damages in 2017 (based on precipitation trends from 1928 to 2017). (B) Impact of historical precipitation change on cumulative flood damages in 2017 using various regression model specifications. From left to right, the models are the regional model (same as A), the regional-seasonal model (Fig. 1D), a regional model with lagged precipitation (SI Appendix, Fig. S2A), a linear model (Fig. 1B), and a quadratic model (Fig. 1B). (C) Sensitivity of cumulative damages from precipitation change to starting year of precipitation trend calculation. All estimates use the same regional regression model used in A.

Fig. 4.

Change in probability of exceeding early industrial baseline precipitation thresholds during the recent historical period, simulated by the CMIP5 global climate model ensemble. (A) Probability of exceeding the early industrial baseline (1860 to 1920) 50th, 75th, 95th, and 99th percentile monthly precipitation thresholds during the recent historical period (1988 to 2017). Probabilities are shown as a ratio relative to the probability during the baseline period, and are based on a 24-model ensemble (SI Appendix). Solid colors indicate strong model agreement (following the IPCC AR5 definition, when ≥66% of models agree with the direction of change shown on the map). Black stippling indicates <66% of models agree with the direction of change shown. (B) Same as A but for monthly maximum 5-d precipitation.

Fig. 5.

Projected changes in monthly total and maximum 5-d precipitation. (A) Projected change in the 50th, 95th, and 99th percentiles of monthly precipitation by 2081 to 2100 for RCP2.6. Changes are relative to the recent historical (1988 to 2017) period. Maps show the mean change across a 17-model ensemble (Methods). Solid colors indicate strong model agreement (following the IPCC AR5 definition, when ≥66% of models agree with the direction of change shown on the map). Black stippling indicates <66% of models agree with the direction of change shown. (B) Same as A but for RCP8.5. (C) Same as A but for monthly maximum 5-d precipitation. (D) Same as B but for monthly maximum 5-d precipitation.