Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis

Abstract

Abstract. Water ponding and pluvial flash flooding (PFF) on roadways can pose a significant risk to drivers. Furthermore, climate change, growing urbanization, increasing imperviousness, and aging stormwater infrastructure have increased the frequency of these events. Using physics-based models to predict pluvial flooding at the road segment scale requires notable terrain simplifications and detailed information that is often not available at fine scales (e.g., blockage of stormwater inlets). This brings uncertainty into the results, especially in highly urbanized areas where micro-topographic features typically govern the actual flow dynamics. This study evaluates the potential for flood observations collected from Waze – a community-based navigation app – to estimate the likelihood of PFF at the road segment scale. We investigated the correlation of the Waze flood reports with well-known flood observations and maps, including the National Flood Hazard Layer (NFHL), high watermarks, and low water crossings data inventories. In addition, highly localized surface depressions and their catchments are derived from a 1 m resolution bare-earth digital elevation model (BE-DEM) to investigate the spatial association of Waze flood reports. This analysis showed that the highest correlation of Waze flood reports exists with local surface depressions rather than river flooding, indicating that they are potentially useful indicators of PFF. Accordingly, two data-driven models, empirical Bayes (EB) and random forest (RF) regression, were developed to predict the frequency of flooding, a proxy for flood susceptibility, for three classes of historical storm events (light, moderate, and severe) in every road segment with surface depressions. Applying the models to Waze data from 150 storms in the city of Dallas showed that depression catchment drainage area and imperviousness are the most important predictive features. The EB model performed with reasonable precision in estimating the number of PFF events out of 92 light, 41 moderate, and 17 severe storms with 0.84, 0.85, and 1.09 mean absolute errors, respectively. This study shows that Waze data provide useful information for highly localized PFF prediction. The superior performance of EB compared to the RF model shows that the historical observations included in the EB approach are important for more accurate PFF prediction.

Validating the Quality of Volunteered Geographic Information (VGI) for Flood Modeling of Hurricane Harvey in Houston, Texas

“…Understandably, the availability and usability of such VGI projects in other parts of the world might depend on a list of factors, including, but not limited to, hazard impacts, physical and social vulnerability, disaster preparation, the digital divide, civil awareness, etc. Nevertheless, previous studies revealed some success in initiating and implementing VGI projects for flood management [4,6,[16][17][18]. Future work can examine the quality of other crowdsourced data for flood modeling and other applications [19].…”

Influence of Terrain Factors on Urban Pluvial Flooding Characteristics: A Case Study of a Small Watershed in Guangzhou, China

“…Hence, urban flood risk analysis should prioritize areas characterized by subsidence [81]. Effectively describing depressions helps to improve the accuracy and efficiency of flood simulations [12,82]. The use of spatial interpolation methods to compute the terrain curvature parameters in this study provides an efficient means of characterizing the depth of depressions.…”

“…Urban pluvial flooding is believed to be highly related to rainfall patterns, urban surface characteristics (including terrain and land feature variables), and drainage system performance [7][8][9][10]. However, the specific mechanisms of these factors remain highly uncertain [5,6,[11][12][13]. As a result, researchers have devoted considerable efforts to analyzing the triggering factors of urban pluvial flooding, to identify the mechanisms that cause this disaster and take targeted measures to reduce the risk of urban pluvial flooding [7,11,[14][15][16][17].…”

A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data

“…Public concerns regarding road flooding hazards have created pressure to develop fine-grained and accurate models for hydrological simulation. Hydrological modeling is based on a relatively well-established theory that can provide approximations of real-world hydrological systems and has been widely used in many road-related studies (Versini et al, 2010;Yin et al, 2016;Safaei-Moghadam et al, 2023). Because hydrological modeling is subject to uncertainty that arises from the oversimplified reflection of hydrological systems, initial and boundary conditions, and lack of true knowledge, parameters for hydrological models must be carefully calibrated prior to their application to practical problems, so that models can closely match the historical trends (Gupta et al, 1998).…”

“…Citizens can voluntarily or passively act as human sensors to generate georeferenced data to improve flood monitoring. Many studies have leveraged crowdsourced social media data (Brouwer et al, 2017;Sadler et al, 2018;Zahura et al, 2020), mobile phone data (Yabe et al, 2018;Balistrocchi et al, 2020), and taxi GPS data (She et al, 2019;Kong et al, 2022). However, most previous works have concentrated on using big data either for flood mapping or mining spatiotemporal patterns (Restrepo-Estrada et al, 2018), and parameter calibration for ungauged roads based on big data remains a problem.…”