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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- 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
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53 citations as recorded by crossref.
- Characterizing Consecutive Flooding Events after the 2017 Mt. Salto Wildfires (Southern Italy): Hazard and Emergency Management Implications G. Esposito et al. 10.3390/w11122663
- 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
- Analysing the value of environmental citizen-generated data: Complementarity and cost per observation L. Alfonso et al. 10.1016/j.jenvman.2021.114157
- 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
- Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications P. Yang & T. Ng 10.1002/2017WR020682
- 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
- Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model M. Mazzoleni et al. 10.1029/2018WR023768
- 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
- Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers M. Milašinović et al. 10.2166/hydro.2021.078
- Improving Hydrological Models With the Assimilation of Crowdsourced Data P. Avellaneda et al. 10.1029/2019WR026325
- The Techno-politics of Crowdsourced Disaster Data in the Smart City E. Wolff & F. Muñoz 10.3389/frsc.2021.695329
- 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
- Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. 10.1029/2022WR034092
- Data Assimilation in Hydrologic Routing: Impact of Model Error and Sensor Placement on Flood Forecasting M. Mazzoleni et al. 10.1061/(ASCE)HE.1943-5584.0001656
- Citizen science supporting agricultural monitoring with hundreds of low-cost sensors in comparison to remote sensing data C. Corbari et al. 10.1080/22797254.2022.2084643
- Citizen observations contributing to flood modelling: opportunities and challenges T. Assumpção et al. 10.5194/hess-22-1473-2018
- Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring C. Restrepo-Estrada et al. 10.1016/j.cageo.2017.10.010
- Value of quality controlled citizen science data for rainfall-runoff characterization in a rapidly urbanizing catchment G. Kebede Mengistie et al. 10.1016/j.jhydrol.2024.130639
- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng 10.1029/2018WR023857
- Multipurpose Use of Artificial Channel Networks for Flood Risk Reduction: The Case of the Waterway Padova–Venice (Italy) R. Mel et al. 10.3390/w12061609
- The value of citizen science for flood risk reduction: cost–benefit analysis of a citizen observatory in the Brenta-Bacchiglione catchment M. Ferri et al. 10.5194/hess-24-5781-2020
- Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins M. Dal Molin et al. 10.1029/2022WR031929
- The promise of a “people-centred” approach to floods: Types of participation in the global literature of citizen science and community-based flood risk reduction in the context of the Sendai Framework E. Wolff 10.1016/j.pdisas.2021.100171
- Advances in the application of smartphones in hydrology L. Li et al. 10.1080/02508060.2023.2204636
- Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations A. Van Wesemael et al. 10.1016/j.advwatres.2019.05.025
- Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil F. Horita et al. 10.1016/j.ijdrr.2018.01.034
- Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017) D. Viero 10.5194/hess-22-171-2018
- Flood modelling using synthesised citizen science urban streamflow observations M. Fava et al. 10.1111/jfr3.12498
- Optimal floodgate operation for river flood management: The case study of Padova (Italy) R. Mel et al. 10.1016/j.ejrh.2020.100702
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- Citizen science pioneers in Kenya – A crowdsourced approach for hydrological monitoring B. Weeser et al. 10.1016/j.scitotenv.2018.03.130
- 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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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...
