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Abstract Friction is one of the cruxes of hydrodynamic modeling; flood conditions are highly sensitive to the Friction Factors (FFs) used to calculate momentum losses. However, empirical FFs are challenging to derive, causing flood models to rely on surrogate observations (such as land cover) and introducing uncertainty. This research presents a laboratory‐trained Deep Neural Network (DNN), developed using flume experiments, to estimateManning's nbased on Point Cloud (PC) data. The DNN was deployed on real‐world lidar PCs to directly estimateManning's nunder regulatory and extreme storm events, showing improved modeling capabilities in both 1D and 2D hydrodynamic models. For 1D models, the lidar estimates decreased differences with values assigned by experts through engineering judgment. For 1D/2D coupled models, the lidar values produced better agreement with flood extents obtained from airborne imagery, while better matching flood insurance claim data for Hurricane Harvey. In both 1D and 1D/2D coupled models, lidar resulted in better agreement with validation gauges. For these reasons, the lidar values ofManning's nwere found to improve both regulatory models and forecasts for extreme storm events, while simultaneously providing a pathway to standardize the estimation of FFs. Changing from land cover to lidar estimates significantly affected fluvial and pluvial flood models, while surge flooding was generally unaffected. Downstream flow conditions were found to change the impacts of FFs to fluvial models. This manuscript introduces a reliable, repeatable, and readily accessible avenue for high‐resolution friction estimation based on 3D PCs, improving flood prediction, and removing uncertainty from hydrodynamic modeling. 

Research has demonstrated that natural disasters, like flooding that are increasing with climate change, can have profound mental health effects. Moreover, these outcomes are not experienced evenly across the population with disadvantaged populations like racial/ethnic minorities and lower socio-economic status individuals being more likely to report psychological diagnoses and symptoms related to floods. However, the mechanisms that could account for the link between social vulnerability and worry about the threat of flooding remain poorly understood. In this analysis, we use a 2022 survey of Houston-area residents to examine how perceived flood risk and subjective flood preparedness relate to racial/ethnic differences in worry about the threat of flooding. We find that both individual-level and area-level race/ethnicity are significantly related to greater worry about the threat of flooding. Further, this is partially mediated by perceived flood risk, but not subjective flood preparedness. This suggests that policies and infrastructure priorities that reduce risk rather than prepare households for flooding would accomplish more in closing the gap in social disparities in mental health outcomes from flooding. 

In this study, a novel framework was developed to provide a holistic damage assessment caused by severe hydrologic events whether individually or as a compound event. The novel framework uses a developed hurricane-specific water quality model, Environmental Fluid Dynamic Code-Storm Surge model (EFDC-SS) and an ArcGIS-based framework, the Facility Economic Damage and Environmental Release Planning (FEDERAP) to assess damages to the built and natural environment. The developed framework could be used to compare different hurricanes and storms with a focus on land inundation, spill destination in both land and water and their associated risks, as well as economic loss including both physical and secondary losses. The results showed different spreading mechanisms during surge and rainfall-based hurricanes. While storm surge pushed contaminants (from spills) upstream, the rainfall-based hurricane caused a larger footprint of contamination on land. Though different in spreading patterns, spills during both hurricane types can widely spread miles away from the release location in a very short period of time. The FEDERAP economic loss model showed that facility area, average land elevation, the number of storage tanks and process units at the facility, and daily production are key drivers in the calculated total losses for a given hydrologic event. 

Compound flooding is a physical phenomenon that has become more destructive in recent years. Moreover, compound flooding is a broad term that envelops many different physical processes that can range from preconditioned, to multivariate, to temporally compounding, or spatially compounding. This research aims to analyze a specific case of compound flooding related to tropical cyclones where the compounding effect is on coastal flooding due to a combination of storm surge and river discharge. In recent years, such compound flood events have increased in frequency and magnitude, due to a number of factors such as sea-level rise from warming oceans. Therefore, the ability to model such events is of increasing urgency. At present, there is no holistic, integrated modeling system capable of simulating or forecasting compound flooding on a large regional or global scale, leading to the need to couple various existing models. More specifically, two more challenges in such a modeling effort are determining the primary model and accounting for the effect of adjacent watersheds that discharge to the same receiving water body in amplifying the impact of compound flooding from riverine discharge with storm surge when the scale of the model includes an entire coastal line. In this study, we investigated the possibility of using the Advanced Circulation (ADCIRC) model as the primary model to simulate the compounding effects of fluvial flooding and storm surge via loose one-way coupling with gage data through internal time-dependent flux boundary conditions. The performance of the ADCIRC model was compared with the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model both at the watershed and global scales. Furthermore, the importance of including riverine discharges and the interactions among adjacent watersheds were quantified. Results showed that the ADCIRC model could reliably be used to model compound flooding on both a watershed scale and a regional scale. Moreover, accounting for the interaction of river discharge from multiple watersheds is critical in accurately predicting flood patterns when high amounts of riverine flow occur in conjunction with storm surge. Particularly, with storms such as Hurricane Harvey (2017), where river flows were near record levels, inundation patterns and water surface elevations were highly dependent on the incorporation of the discharge input from multiple watersheds. Such an effect caused extra and longer inundations in some areas during Hurricane Harvey. Comparisons with real gauge data show that adding internal flow boundary conditions into ADCIRC to account for river discharge from multiple watersheds significantly improves accuracy in predictions of water surface elevations during coastal flooding events.