At an estimated cost of 85–125 billion USD (https://coast.noaa.gov/states/fast-facts/hurricane-costs.html), Hurricane Harvey is the 2nd most expensive US tropical cyclone after adjustment for inflation. Damages were principally due to freshwater inland flooding resulting from the copious amounts of precipitation endured during the storm’s extended stall over the greater Houston, Texas region from 26 to 31 August 2017. According to a report by the Harris County Flood Control District, over 154,000 structures and 600,000 cars were flooded with 37,000 people relocated to shelters (Abbot et al. 2018). Furthermore, although well forecasted, most of the 70+ direct deaths were due to drowning in deep floodwaters (Halverson 2018; Jonkman et al. 2018). Soon after the event, a number of rapid attribution studies were published finding that the effects of anthropogenic global warming on the storm’s precipitation totals were significant (Emanuel 2017; Risser and Wehner 2017; van Oldenborgh et al. 2017; Wang et al. 2018). The latter three of these studies concluded that estimated lower bound on the anthropogenic increase in precipitation over the entire area was at least in accordance with Clausius-Clapeyron (C-C)-scaled increases (6–7%/C) of saturation specific humidity from the ~1C of attributable warming in the Gulf of Mexico (Stone et al. 2019). However, best estimates of the effect of this warming on Harvey’s precipitation in these studies were considerably larger (up to 24%). Trenberth et al. (2018) also concluded that record high ocean heat content, partly attributable to human consumption of fossil fuels, led to increased evaporation and hence precipitation. Kossin (2018) recently found that the translational speeds of North Pacific and North Atlantic tropical cyclones have significantly slowed since 1949. However, whether this slowdown is attributable to global warming is presently unknown, and Harvey’s stall was indeed a rare and very different phenomena. Finally, Patricola and Wehner (2018) recently found that while the expected anthropogenically induced increases in tropical cyclone maximum winds speeds may not have yet emerged, a human influence on precipitation has likely emerged in the most intense tropical cyclones. They also found locally super Clausius-Clapeyron scaling in the heaviest precipitation regions of some storms due to storm structural changes. This provides a plausible physical explanation for the magnitude of these best estimate Harvey attribution statements as the greater Houston area had the misfortune of being in that part of the storm.
However, changes in flood properties may not be linearly proportional to changes in extreme precipitation due to the complexities of the local hydrological properties. This study uses the previously published estimates of Harvey’s attributable precipitation increase due to global warming as a forcing factor to a series of hydraulic model simulations to more directly investigate the anthropogenic influence on the Harvey flood. The hydraulic simulations in this study are undertaken using the Fathom-US large-scale hydraulic modeling framework. The 30-m resolution US variant of the hydraulic modeling framework employed here is described by Wing et al. (2017), with the US variant itself being a development of the original global hydraulic model framework of Sampson et al. (2015). The model is able to represent both fluvial (riverine), pluvial (rainfall), and surge (coastal) flood hazards by generating (or being provided with) appropriate boundary conditions before solving simplified forms of the Saint-Venant shallow water equations over a regular 2D grid to simulate the flow of water across the land surface (Bates et al. 2010). The model explicitly represents both floodplain and in-channel flows using a subgrid method based on the principles outlined by Neal et al. (2012)).
The US-variant of the model has been extensively validated against the entire FEMA flood hazard catalog for the conterminous USA, demonstrating its ability to approach the accuracy of traditional local scale flood models (Wing et al. 2017). The specifics of the model setup deployed here for the Hurricane Harvey scenario simulations, and a detailed assessment of model performance relative to observations, are fully presented in Wing et al. (2019)). In summary, Wing et al. (2019) show the baseline simulation to describe the observed flooding of the greater Houston area well, with 78% of the observed wet pixels correctly identified as flooded, and only 17% of observed dry pixels are incorrectly identified as flooded. Overall, the ratio of type I to type II errors is 1.42, indicating that the hydraulic model tends to slight overprediction of flooding. Furthermore, the simulated high-water marks are on average about 1 m lower than observed (itself with an estimated confidence interval of ±0.5 m). For context, these errors compare favorably to the NOAA NWM-HAND model (Arctur 2018), which correctly identified only 46% of wet pixels while identifying 16% of observed dry pixels as wet to yield a large negative error bias of 0.31 under the same conditions (Wing et al. 2019). The extensively validated nature of the underlying model physics, coupled with the previously published validation of this particular model configuration against observations in the aftermath of Hurricane Harvey, demonstrates this model to be an appropriate tool with which to explore the sensitivity of the Greater Houston area flooding to perturbations in precipitation.
The design of our numerical experiments is straightforward. As a baseline flood, the hydraulic model is forced by observed 5-day accumulated precipitation estimates (26–30 August inclusive) obtained from the NOAA National Weather Service Advanced Hydrologic Prediction Service (https://water.weather.gov/precip/about.php). The effect of climate change on the Harvey flood is then explored by uniformly reducing the observed precipitation by a factor inversely proportional to the published attribution statements on precipitation magnitude. For instance, Risser and Wehner (2017) wrote that their best estimate was that climate change increased the total accumulated precipitation across the region by 24%. The simulated non-anthropogenic flood associated with this attribution statement is obtained by reducing the observed precipitation used to drive the hydraulic model by a factor of 1/1.24 = 0.81. We explore the sensitivity of Harvey flood statistics to a range of precipitation totals spanning 2/3 to twice the observed amount. A set of attribution statements about the simulated Harvey flood properties on 31 August 2017 can be then be made from those simulations corresponding to the published Harvey precipitation attribution statements.
Baseline river flows were set to 50% of bankfull discharge, and initial soil conditions were dry at the start of each simulation. Given the overwhelmingly extreme nature of Hurricane Harvey precipitation, model sensitivity to antecedent conditions such as soil saturation is minimal as soil infiltration capacity is almost immediately exceeded due to the very high rainfall intensities and total rainfall volumes. The antecedent conditions are kept the same for every simulated scenario. Of course, under climate change, it may be the case that the probability of wetter or drier antecedent conditions will change; however, there are so many degrees of freedom to antecedent conditions that they are currently beyond the scope of experiment within this study.
This type of extreme event attribution study is often termed a “storyline” approach (Shepherd 2016) as only a portion of the relevant factors is investigated. In addition to precipitation increases from climate change, other anthropogenic influences such as urbanization (Zhang et al. 2018; Sebastian et al. 2019) and land subsidence (Miller and Shirzaei 2019) are important factors in assessing overall flood risk. As a result, the attribution statements in this study are high conditional on only the climate change aspects of the total change in flood risk (Wehner et al. 2019). There are a few other studies completing the chain of attribution from extreme precipitation to flooding to financial losses. Kay et al. (2018) examined the change in flood risk in Great Britain during the winter of 2013/2014 by driving a nationwide hydrological model by very large ensembles of a regional climate model under realistic conditions and a variety of counterfactual conditions with the effects of anthropogenic climate change removed. Earlier, Schaller et al. (2016) used the same set of regional model simulations to drive a hydrological model of the Thames river catchment rather than the entirety of Great Britain. These analyses, as is the Frame et al. (2020) analysis of Hurricane Harvey attributable economic costs, used the change in probability and the associated fractional attributable risk of the flood properties of these events to estimate the damages resulting from the human interference in the climate system. The present study differs in that it uses a hydraulic model and perturbations to observed precipitation to estimate the detailed geographical changes in flood area and associated economic costs. All of these studies find that the attributable changes in flood damages depend greatly on the assumptions made about how much anthropogenic climate change has affected the precipitation responsible for the flood.