Articles | Volume 21, issue 2
https://doi.org/10.5194/hess-21-839-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-839-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?
Maurizio Mazzoleni
CORRESPONDING AUTHOR
UNESCO-IHE Institute for Water Education, Hydroinformatics Chair Group, Delft, the Netherlands
Martin Verlaan
Deltares, Delft, the Netherlands
Leonardo Alfonso
UNESCO-IHE Institute for Water Education, Hydroinformatics Chair Group, Delft, the Netherlands
Martina Monego
Alto Adriatico Water Authority, Venice, Italy
Daniele Norbiato
Alto Adriatico Water Authority, Venice, Italy
Miche Ferri
Alto Adriatico Water Authority, Venice, Italy
Dimitri P. Solomatine
UNESCO-IHE Institute for Water Education, Hydroinformatics Chair Group, Delft, the Netherlands
Delft University of Technology, Water Resources Section, Delft, the Netherlands
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- Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges F. Nardi et al. 10.1080/02626667.2020.1849707
- Opportunities for crowdsourcing in urban flood monitoring A. Helmrich et al. 10.1016/j.envsoft.2021.105124
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- Integrated community-based approaches to urban pluvial flooding research, trends and future directions: A review K. Azizi et al. 10.1016/j.uclim.2022.101237
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- Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment R. kumari et al. 10.1016/j.seta.2023.103398
- Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach G. Niu et al. 10.1029/2020WR029121
- Value of uncertain streamflow observations for hydrological modelling S. Etter et al. 10.5194/hess-22-5243-2018
- Collaborative spatial information as an alternative data source for hydrodynamic model calibration: a Pernambuco State case study, Brazil G. de Oliveira et al. 10.1007/s11069-023-06073-z
- Advances in Urban Stormwater Management in Japan: A Review Y. Shibuo & H. Furumai 10.20965/jdr.2021.p0310
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- Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects N. Njue et al. 10.1016/j.scitotenv.2019.07.337
- The potential of proxy water level measurements for calibrating urban pluvial flood models M. Moy de Vitry & J. Leitão 10.1016/j.watres.2020.115669
- State of continental discharge estimation and modelling: Challenges and opportunities for Africa K. Akpoti et al. 10.1080/02626667.2024.2402938
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- Towards improved knowledge about water-related extremes based on news media information captured using artificial intelligence J. Pita Costa et al. 10.1016/j.ijdrr.2023.104172
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- Decision support system for flood risk reduction policies: The case of a flood protection measure in the area of Vicenza G. Pantalona et al. 10.1017/dap.2021.23
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- Flood modelling using synthesised citizen science urban streamflow observations M. Fava et al. 10.1111/jfr3.12498
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- GFPLAIN and Multi-Source Data Assimilation Modeling: Conceptualization of a Flood Forecasting Framework Supported by Hydrogeomorphic Floodplain Rapid Mapping A. Annis & F. Nardi 10.3390/hydrology8040143
- Modelling support to citizen observatories for strategic Danube Delta planning: Sontea-Fortuna case study A. Venturini et al. 10.1080/09640568.2018.1523787
- Automatic water-level class estimation from repeated crowd-based photos of streams Z. Wang et al. 10.1080/02626667.2023.2240312
- Participatory early warning and monitoring systems: A Nordic framework for web-based flood risk management H. Henriksen et al. 10.1016/j.ijdrr.2018.01.038
- Rainfall‐Runoff Modeling Using Crowdsourced Water Level Data B. Weeser et al. 10.1029/2019WR025248
- The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
- Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras S. Jiang et al. 10.1029/2018WR024480
- High‐resolution urban flood model for risk mitigation validated with records collected by the affected community M. Re et al. 10.1111/jfr3.12524
3 citations as recorded by crossref.
- Using crowdsourced web content for informing water systems operations in snow-dominated catchments M. Giuliani et al. 10.5194/hess-20-5049-2016
- The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam L. de Vos et al. 10.5194/hess-21-765-2017
- Continuity vs. the Crowd—Tradeoffs Between Continuous and Intermittent Citizen Hydrology Streamflow Observations J. Davids et al. 10.1007/s00267-017-0872-x
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Latest update: 11 Oct 2024
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.
This study assesses the potential use of crowdsourced data in hydrological modeling, which are...