Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

Abstract

Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

“…This conclusion is in line with previous works that investigated decision-making and human factors in different control rooms [9,29]. For overcoming this challenge, common people can provide reliable and accurate volunteered geographic information from vulnerable areas [17], which thus supplements traditional data collection tools and enhances decision-making in control rooms [20,1,34,19]. Findings of this work however show that as any other information, this should reflect the decision-makers' requirements, otherwise it may be useless or even misused.…”

Value of uncertain streamflow observations for hydrological modelling

“…Recent studies successfully used daily stream level data (Seibert and Vis, 2016) and stream level class data (van Meerveld et al 2017) to calibrate hydrological models, and other studies demonstrated the potential value of crowdsourced stream level data for providing information on baseflow (Lowry and Fienen, 2013) to improve flood forecasts (Mazzoleni et al, 2017). However, further research is needed to determine if real crowdsourced stream level (class-) data is informative for the calibration of 10 hydrological models.…”

High‐resolution urban flood model for risk mitigation validated with records collected by the affected community

“…Literature is scarce about citizens' involvement in the generation of tools to calibrate and/or validate urban flood models. During the last years, new works have been presented where citizens' data begins to be important to improve urban flood models: Ciervo, Papa, Medina, and Bateman () used videos, interviews, and field measures for estimate flow and soil depositions during flash floods; Kutija et al () used water levels from citizens' pictures to validate a 2D hydrodynamic model; Le Coz et al () presented the use of large‐scale particle image velocimetry (LSPIV) analysis of citizens' videos to calibrate a 1D fluvial model; Yu, Yin, and Liu () collected flooded data through a website and used it to calibrate and validate a hydrological model; Mazzoleni et al (, ) investigated the integration of synthetic citizen data into hydrological models to improve the accuracy of real‐time flood forecasts; Smith, Liang, James, and Lin () propose a real‐time modelling framework to identify areas likely to be flooded using data obtained from Twitter; Wang et al () is developing a framework for using information collected from social media for setting parameters of a 2D flood model. In all cases the collected variable is the water level, in some of them the flow velocity is inferred (Le Coz et al, ; Smith et al, ), but in none of them the flood duration is reported.…”