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.
Research article
| 
14 Feb 2017
Research article |
| 14 Feb 2017
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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- How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? J. Valdes-Abellan et al. 10.2136/vzj2018.07.0142
- Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
- Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping A. Annis et al. 10.5194/hess-26-1019-2022
- Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring L. de Vos et al. 10.1029/2019GL083731
- 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
- Citizen science flow – an assessment of simple streamflow measurement methods J. Davids et al. 10.5194/hess-23-1045-2019
- 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
- Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. 10.1111/jfr3.12895
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- Refraction-based waterlogging depth measurement using solely traffic cameras for transparent flood monitoring J. Qin & P. Shen 10.1016/j.jhydrol.2025.132917
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- 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
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Latest update: 02 Apr 2025
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...
