Modeling Temporal Accessibility of an Urban Road Network during an Extreme Pluvial Flood Event

Extreme flood events are expected to increase in wet regions as a result of climate change (Tabari 2020). Relative sea level rise, shifts in population patterns, and changes in land use create additional uncertainty in the management of future risks (Wing et al. 2018). These factors create serious challenges for flood risk managers, engineers, and environmental planners in their efforts to sustainably work with water while protecting public safety and infrastructure in flood-prone communities. Roadway networks are a critical aspect of urban infrastructure systems and express unique vulnerabilities to flood hazards (Abdulla et al. 2020). Spatial constraints on roadway coverage in a given region (e.g., property ownership, cost, and vertical design considerations) can lead to roads being built in flood-prone areas. Beyond the immediate hazard to motorists associated with inundation hotspots, the indirect impacts of a loss of accessibility to critical resources may affect entire communities (Miller 2019).

The direct impact of flood hazards on motorists, roads, and transportation disruption has been analyzed. The widespread availability of regional and continental flood hazard data sets (Wing et al. 2018) facilitates the identification of flood-prone roadway segments. These flooded segments may present a life-threatening situation for motorists. Ashley and Ashley (2008) report that vehicles were involved in 63% of the flood-related fatalities when the setting of occurrence or activity was known. Pavement lifespan and long-term maintenance costs are also impacted by road flooding. Lu et al. (2020) studied the effects of flood hazards on road pavement and showed that damage ratios increase as the number of flood cycles increase. Structural failure of drainage infrastructure (e.g., bridges and culverts) has also been linked to flood submergence. In a study in the Pacific Northwest, the USDA found that plugged culverts accounted for 28% of storm-induced road damage based on their analysis of 352 sites (Copstead and Johansen 1998). As suggested by Coles et al. (2017), there is a need to better understand the broader consequences of flooded roads beyond the direct impacts associated with flood hotspots. The proliferation of two-dimensional (2D) hydrodynamic modeling simulations coupled with recent advancements in remotely sensed digital topographic surface products make it possible to analyze the effects of flood hotspots as well as time-varying flood phenomena on local, regional, and continental scales. These methods can be coupled with high-resolution roadway network data to detect latent vulnerabilities related to street flooding. One such vulnerability is the inaccessibility of critical locations to motorists during an extreme flood event.

The accessibility of roadway networks during flood events is a well-studied topic. The problem of flood-induced disruptions on roadway networks can be initially conceptualized as a specific type of impairment in a broader context of transportation network robustness (Schneider et al. 2011). Albano et al. (2014) used a GIS-based approach to estimate the degree of accessibility to emergency response structures in support of strategic planning and emergency response efforts for the floodplain of the Bradano River located in Italy. Their study evaluated the impacts to ambulance and fire services during the peaks of various design storm flooding scenarios based on MIKE FLOOD simulations. Coles et al. (2017) also integrated hydrodynamic modeling to evaluate flood-induced vulnerabilities including impacts on emergency service areas and travel times during the flood peak. Gori et al. (2020) examined time-varying accessibility impacts in Houston, Texas, due to flooding from Hurricane Harvey. Their multidisciplinary study focused primarily on fluvial flooding sources using Hydrologic Engineering Center’s River Analysis System (HEC-RAS) models associated with the Brays and Greens Bayou subregions. Key differences were revealed in the time-varying accessibility impacts owing to social vulnerability factors (Gori et al. 2020). Abdulla et al. (2020) also selected a neighborhood in the City of Houston to analyze flood-induced road network vulnerabilities under different impairment scenarios. Their methodology included the usage of an SIS network diffusion model that used the giant connected component (GCC) framework to measure the flood impacts on the network. However, Loreti et al. (2022) studied the effects of local impacts on roadway networks during extreme floods and concluded that the GCC may not be as relevant as the accessibility of specific localities where critical services can be obtained. The previous studies suggest that the local accessibility to specific refuge points is a key aspect in terms of accurately evaluating the total impact of flood-induced disruptions on transportation networks. Such considerations should also factor into the development of equitable mitigation strategies that account for existing disparities in access to critical resources (Gori et al. 2020). The emphasis of the preceding studies has been mainly on impacts induced by fluvial (i.e., flooding from river/channel overflow) flood hazards.

In comparison with fluvial flooding, pluvial floods (i.e., localized surface/flash-type flooding) are generally more challenging to predict (Houston et al. 2011). The time-varying impacts of pluvial flooding on road network accessibility have been relatively understudied in comparison with the more well-defined riverine and coastal flood hazards. Also, fluvial-centric studies tend to be limited to specific subwatersheds. This may lead to an underestimation of the system-wide flood-induced accessibility impairment on municipal jurisdictional scales (e.g., city and county boundaries) that constrain planners and managers but do not align with hydrologic boundaries.

The main objective in this paper is to develop a raster-based methodology capturing the time-varying accessibility of urban roadway networks on a countywide scale during an extreme flood scenario. The flood scenario considered here takes into account pluvial flooding on a jurisdictional scale (i.e., county/parish-wide basis), focusing on the accessibility of typical motorists to emergency centers and key hurricane evacuation points. The method is demonstrated in Lafayette Parish, Louisiana—one of the fastest growing municipalities in the state, which also experienced catastrophic damage during the historic flood event of August 2016. The time-varying accessibility of the entire network is quantified, including smaller neighborhood streets that are often neglected in large-scale studies. The impact of strategic flood mitigation of critical roadway corridors frequently impaired by pluvial flooding, including potential adverse effects, is also analyzed. The paper is organized in the following manner. Section “Methods” describes the study area, hydrodynamic modeling effort, and raster-based search procedure used to quantify regional accessibility. Section “Results” focuses on the main results of the work, and section “Discussion” discusses the key findings situated in the broader context of related studies and more general settings. Section “Conclusions” summarizes the main takeaways of the study and its limitations, as well as potential opportunities for future research.

By incorporating pluvial flooding, this study complements the study by Gori et al. (2020) that focused exclusively on fluvial flooding from Hurricane Harvey in 2017. The results show that fluvial models (e.g., FEMA Flood Insurance Studies) may estimate a similar percentage of peak flooded roads but may not accurately represent the spatial distribution in comparison with models that account for pluvial flooding. This study also demonstrates a specific example of ancillary human costs associated with pluvial flood hazards, which are expected to increase with climate change (Houston et al. 2011).

The results show that the 500-year 24-h duration event applied uniformly over Lafayette Parish approximated the August 2016 flood event in terms of the available HWM data ($\mathrm{NSE}=0.96$). This result corroborates the regional frequency classification arrived at by Brown et al. (2020). The 500-year RMSE of 0.37 m obtained in comparison with the HWM data from the 2016 event is consistent with the 0.28–0.32-m performance range presented by Neal et al. (2009) for whole city urban-flood simulations. Although the analysis accounts for depth-dependent roughness, the base $n$ resistance factor (0.067) falls well within the minimum RMSE contours as shown in Neal et al. (2009) for the Carlisle, UK, region. Spatial areal reduction factors (ARFs) were not applied, however the work by Kao et al. (2020) suggests that an ARF in the vicinity of 90% would be reasonable for this region given the size of the watershed. The usage of a design storm–based approach facilitates representative comparisons with other more frequent precipitation scenarios (e.g., 10- and 25-year). Actual storm events can also be easily incorporated using spatially heterogenous radar-based precipitation products and coupling with national hydrologic forecasting models as demonstrated in a recent study in this region by Saad and Habib (2021).

The results showed that the main evacuation routes exhibit nearly identical accessibility profiles despite their relatively large physical separation. The model showed that the evacuation points could be equally inaccessible to 80% of the network during a critical 6-h period near the flood peak. A comparison of the effects of storm intensity showed that both the 500- and 100-year design storm conditions led to very similar consequences from a network accessibility standpoint in this study region. The evacuation target exhibited a larger response to overall storm intensity compared to the emergency facilities. However, the results showed that the degree of inaccessibility to an evacuation target exceeded 75% during both the 500- and 100-year peak conditions. This means that at least 75% of the roadway network would not be able to safely navigate to this evacuation target during the peak of such extreme rain events. Anarde et al. (2018) noted that inaccessibility due to flooded bridges on evacuation routes can also jeopardize disaster response activities in hurricane regions. The combination of these factors underscores the latent hazards associated with intense rain events occurring during hurricane evacuation operations.

The emergency facilities showed greater variance in the percent of accessibility despite their relatively close proximity compared to the evacuation points. In general, the evacuation routes were more robust to accessibility impairments compared to the emergency facilities. This may reflect the impact of disparate roadway design standards (e.g., federal/state highways versus local road designs) on the urban-flood accessibility profiles of communities.

The preceding results provide quantifiable consequences that can be factored into resiliency planning and design efforts. Albano et al. (2014) provide a model of such urban flood–induced accessibility consequence analyses for the Puglia region of Italy. The model in the current study shows that mitigating the adverse consequences of street flooding on accessibility to emergency facilities can be achieved by targeting specific vulnerable transportation corridors. However, the analysis also demonstrates that such countermeasures have the potential to increase flood risks on adjacent communities in extremely flat topographic environments as a result of long and narrow floodplain obstructions. Such obstructions traditionally represent elevated roadways, but it is worthwhile to note that the more recent development and deployment of temporary hydraulic flood barriers can produce similar dam-like effects. More research is needed to evaluate the indirect hydraulic impact of these and other types of flood-fight devices, e.g., Huong et al. (2002), used in emergency flood scenarios.

This study presents a raster-based method to analyze the time-varying effects of flooded roads on system-wide network accessibility to discrete target points. Taking into account pluvial flooding, the method is demonstrated for the roadway network of Lafayette, Louisiana, during the 500-year design flood scenario. The results demonstrate that the peak percentage of inaccessibility of the network could be three to four times larger than the percent of impassibility when considering a safe driving depth for a typical motorist. The model is also used to identify and evaluate strategic mitigation alternatives while factoring in the hydraulic impact on surrounding areas. Hence, the presented framework yields insights that can inform the development of more flood-resilient transportation networks in the future. However, several improvements can be made. First, the raster-based accessibility calculation did not discriminate between paths, treating all potential pathways to a target resource as being equally viable routes. In reality, driver fatigue, traffic congestion, and other factors (e.g., one-way streets, medical facility preference, familiarity with potential route) may constrain the potential viable options for motorists. Flood impacts on driver speed and perceived path optimality were also not analyzed, but the present framework can be extended to incorporate such considerations. Smaller yet more frequent flood scenarios may also be beneficial to community planning efforts, and the design storm framework presented herein can be scaled to these higher storm frequencies straightforwardly. However, for these types of scenarios, the subsurface drainage system (not analyzed here given the size of the extreme event) will become more significant. The strictly 2D hydrodynamic model framework can be easily extended to a hybrid 1D/2D approach incorporating such local effects. The flood-fill search algorithm was selected for convenience, but other network search methods including diffusion models could have also been used (Abdulla et al. 2020).

The incorporation of continuous gauge measurements for stage and flow would also help provide the foundation for a more rigorous calibration of the hydrodynamic model. Such calibration is especially important for accurate representation of smaller, more frequent flood events. Also not analyzed was the deluge of crowd-sourced information (e.g., photo streams from pedestrians, drivers, and residents; commentary) on social media platforms that accompanies significant flood events. When coupled with video streams from traffic cameras and surveillance footage, these diverse data streams may reveal new insights about human/flood interactions that cannot be gleaned from gauge measurements. Methods aimed at improving the capture and synthesis of these time-limited data sets, while facilitating rapid access across a diverse spectrum of regional stakeholders, would help address this gap and enhance the articulation of flood consequences in the future.

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Examples of such data may include flood photos, high-water-mark data, or time-varying accessibility rasters for the storm scenarios considered herein.

The author would like to thank two anonymous referees for their helpful suggestions that led to an improved manuscript. This work was sponsored in part by the Louisiana DOT and Development Louisiana Transportation Research Center Transportation Innovation for Research Exploration (LTRC TIRE) program under Grant No. DOT-LT-1000269, and by an award from the National Academies of Sciences Gulf Research Fellowship Program.