Flood modelling using synthesised citizen science urban streamflow observations

Abstract

The increase in floods and flash floods over the last decades has motivated researchers to develop improved methodologies for flood risk prevention and warning. Flood forecasting models available today have evolved technologically but are subject to limitations due to the lack of data and limited community participation. This paper presents the Hydrological Alert Model with Participatory Basis (HAMPB) model, an approach for integrating water level data reported by citizens, which has the advantage of being inexpensive and potentially highly available, with traditional data to improve flood forecasting. The model assimilates spatiotemporal water levels measured in the field when they are available through a real‐time estimator. We added random perturbations of up to |10| and |15| cm to those data using the Monte Carlo Method to mimic the uncertainty in citizen science data collection. Applying the HAMPB model for urban nested‐scale catchments (0.11 km2 ≤ Area ≤ 21.84 km2) in Brazil shows: (a) significant improvements in flood simulations when field data was assimilated even considering the volunteered data uncertainty; (b) capability to update simulations in more than one point in the semi‐distributed hydrological model by a regionalization method; and (c) flood hazard indexes and their uncertainties show better estimations using field data for updating.

Towards urban resilience through Sustainable Drainage Systems: A multi-objective optimisation problem

“…The Mineirinho catchment has a history of recurrent flooding, particularly over the past decade, leading to significant economic loss due to growing commercial establishments in the city (Fava et al, 2018;de Abreu et al, 2019). There have been attempts to manage this regular flooding by rectifying and/or channelizing the rivers and streams within the city leading to exacerbation of flooding in the city's lowlands.…”

How can flood modelling advance in the “big data” age?

“…The third data type I mentioned above is where I see the most potential to advance in the “big data” age. In my opinion, we now have a unique opportunity to exceptionally advance flood modelling capability and accuracy thanks to the advances in remote sensing‐based data products and technologies (Coops, Kearney, Bolton, & Radeloff, ; Shen, Anagnostou, Allen, Brakenridge, & Kettner, ), big data science (Jianga, Zheng, Babovic, Tian, & Han, ; Manfreda & Samela, ; Mobley, Sebastian, Highfield, & Brody, ; Pollard, Spencer, & Jude, ), and integration of citizen observations into modelling (Assumpção, Popescu, Jonoski, & Solomatine, ; Fava, Abe, Restrepo‐Estrada, Kimura, & Mendiondo, ; Rollason, Bracken, Hardy, & Large, ; Wang, Mao, Wang, Rae, & Shaw, ). In recent years, remote‐sensing techniques have shown encouraging results in delineating water surfaces and estimating water levels (Ciana, Marconcini, & Ceccato, ; Clement, Kilsby, & Moore, ; Huang et al, ; Pekel, Cottam, Gorelick, & Belward, ).…”

Socio-Hydrology Observatory for Water Security under climate change: novel global methods for local solutions

“…Although some authors assigned possible values of flood memory parameters adopted in Equation ( 3), future flood risk assessment works should consider alternatives for validate its parameters. Citizen science opportunities have demonstrated that observations, knowledge, and beliefs from locals can provide valuable measures in ungauged basins and outline possible changes in behaviour (Abreu, 2019;Fava et al, 2019;Mondino et al, 2020;Souza et al, submitted). Therefore, citizens' engagement is not only a data source, but it can be a step toward risk reduction and resilience increase as well (Mao et al, 2017).…”