Using web-based observations to identify thresholds of a person's stability in a flow

Abstract

Flood risk assessment and mitigation are important tasks that should take advantage of rational vulnerability models to increase their effectiveness. These models are usually identified through a relevant set of laboratory experiments. However, there is growing evidence that these tests are not fully representative of the variety of conditions that characterize real flood hazard situations. This paper suggests a citizen science‐based and innovative approach to obtain information from web resources for the calibration of people's vulnerability models. A comprehensive study employing commonly used web engines allowed the collection of a wide set of documents showing real risk situations for people impacted by floods, classified according to the stability of the involved subjects. A procedure to extrapolate the flow depth and velocity from the video frames is developed and its reliability is verified by comparing the results with observation. The procedure is based on the statistical distribution of the population height employing a direct uncertainty propagation method. The results complement the experimental literature data and conceptual models. The growing availability of online information will progressively increase the sample size on which the procedure is based and will eventually lead to the identification of a probability surface describing the transition between stability and instability conditions of individuals in a flow.

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

“…Social media data can also be used to obtain information about the hazard itself . Examples of this include the determination of water levels (Aulov et al, ; de Albuquerque et al, ; Eilander et al, ; Jongman et al, ; Kongthon et al, ; Li et al, ; Smith et al, ; Vieweg et al, ) and water velocities (Le Boursicaud et al, ), including using such data to evaluate the stability of a person immersed in a flood (Milanesi et al, ). The analysis of Twitter data has also been able to provide information on a range of other information relevant to natural hazard management , including information on traffic and road conditions during floods (de Albuquerque et al, ; Kongthon et al, ; Vieweg et al, ) and typhoons (Declan, ), as well as information on damaged and intact buildings and the locations of key infrastructure, such as hospitals, during Typhoon Hayan in the Philippines (Declan, ).…”

Impact of the Rainfall Duration and Temporal Rainfall Distribution Defined Using the Huff Curves on the Hydraulic Flood Modelling Results

“…Four times higher maximum water velocities were obtained for the scenario with a constant rainfall intensity of 6 h than for the scenario with a short rainfall duration of 2 h and the 50% Huff curve for cross-section 1 on the right bank of the flooded area. The water velocity has an important impact both on the stability of human body, and vehicles in the floodwater [27][28][29][30]. Similarly, also, floodplain volumes for different scenarios differ for an order of magnitude (more than 10 times) (Table 3), which indicates that the design rainfall definition has a significant impact on the simulated floodwater dynamics.…”

“…However, the relationship between the design hyetograph selection and hydraulic modelling results remains unclear. Examples of modelling results include the flood extent or flow velocities over floodplains, which can have a significant impact on the stability of a human body or a vehicle in floodwaters [27][28][29][30]. Even though some researchers doubt the usefulness of the flood water flow velocities as the appropriate parameter to model flood damages [31], the implementation of the 2007 European Union (EU) Flood Directive governs the determination and zonation of hazards areas using a combination of flood water depths and flow velocities.…”

Flood Risk Zoning by Using 2D Hydrodynamic Modeling: A Case Study in Jinan City

“…Other physical factors such as water temperature, land surface type, and objects that may cause slippage or fall (e.g., "blown" manhole), are likely to have an impact on the risk for people or objects in the flood. Xia et al [18,19] and Milanesi et al [20,21] developed risk calculation methods …”