Can the use of CCTV images improve urban flooding forecasts?

By Sanita Vetra-Carvalho

Urban flooding can result from intense rainfall, flash floods, coastal floods or river floods, the same as in rural areas. However, in cities, unlike in rural areas, there is very little open soil available for water storage and most floodwater needs to be to be transported to surface water or the sewage system. It is possible for rainwater entering the sewer system in one part of the city to exit through a manhole, flooding a different part of the city. Having early and accurate warning of potential flooding allows cities to prepare the drainage systems by ensuring there is an adequate water drainage capacity through sewage system and draining canals.

Due to the inherent complexity of cities characterized by a dense network of buildings with basements, roads, public transport, and a large number of people and businesses operating in close proximity, flooding in urban areas can be extremely costly and disruptive. The good news is that there are growing amounts of data available about our cities, such as CCTV images and citizen-sourced smartphone images, as well as scientific river gauge data and satellite images. Urban areas are rich with observation networks such as CCTV cameras looking at buildings, streets, parking lots, and rivers with images being available on a minute timescale. Many cities have a dense network of such cameras, and it is often open access, for example, London Traffic Cameras (JamCams) have around 800 CCTV traffic cameras distributed around London freely open to the community. Other organisations such as Highway Traffic Cameras have many cameras across England monitoring motorways and pass through/around cities. River cameras (e.g. Farson Digital Watercams) are another source of free open data (Figure 1).

Figure 1. Map of London Traffic Cameras, with a camera looking at the Thames

Currently this source of information is not used in producing flood forecasts, however, CCTV images have the potential to be very valuable in producing more accurate urban flood forecasts. The way to make most of such information is to use a technique called data assimilation (DA) which combines a model forecast with observations such as river levels and water extent in streets to produce a more accurate flood forecast. Using information from CCTV images and assimilating them directly into a flood forecasting model is one of the novel ideas behind the Data Assimilation for the REsilient City (DARE) project with an aim to assess the impact and benefit such novel observations can offer for urban area flood forecasting and improve the accuracy of urban flood forecasts.

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