Articles | Volume 22, issue 1
https://doi.org/10.5194/hess-22-391-2018
© Author(s) 2018. 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-22-391-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Jan 2018
Research article |
| 17 Jan 2018
Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)
Maurizio Mazzoleni
CORRESPONDING AUTHOR
Integrated Water Systems and Governance Department, IHE Delft
Institute for Water Education, Delft, 2611AX, the Netherlands
Vivian Juliette Cortes Arevalo
Water Engineering and Management, University of Twente, Enschede, 7522
NB, the Netherlands
Integrated Water Systems and Governance Department, IHE Delft
Institute for Water Education, Delft, 2611AX, the Netherlands
Leonardo Alfonso
Integrated Water Systems and Governance Department, IHE Delft
Institute for Water Education, Delft, 2611AX, the Netherlands
Daniele Norbiato
Alto Adriatico Water Authority, Venice, Italy
Martina Monego
Alto Adriatico Water Authority, Venice, Italy
Michele Ferri
Alto Adriatico Water Authority, Venice, Italy
Dimitri P. Solomatine
Integrated Water Systems and Governance Department, IHE Delft
Institute for Water Education, Delft, 2611AX, the Netherlands
Water Resources Management department, Water Problems Institute,
Russian Academy of Sciences, Moscow, Russia
Water Resources Section, Delft University of Technology, Delft, 2628
CD, the Netherlands
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- Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping A. Annis & F. Nardi 10.1080/10095020.2019.1626135
- Rainfall‐Runoff Modeling Using Crowdsourced Water Level Data B. Weeser et al. 10.1029/2019WR025248
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- Water Level Measurements from Drones: A Pilot Case Study at a Dam Site E. Ridolfi & P. Manciola 10.3390/w10030297
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- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng 10.1029/2018WR023857
- A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment Y. Tao et al. 10.3390/s24217090
- Dynamic maps of human exposure to floods based on mobile phone data M. Balistrocchi et al. 10.5194/nhess-20-3485-2020
- Improving Hydrological Models With the Assimilation of Crowdsourced Data P. Avellaneda et al. 10.1029/2019WR026325
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- 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
- Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation M. Re et al. 10.1080/02626667.2022.2075266
- 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
- The Role of Traditional Knowledge in Coastal Adaptation Priorities: The Pamunkey Indian Reservation N. Hutton & T. Allen 10.3390/w12123548
- Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges F. Nardi et al. 10.1080/02626667.2020.1849707
- 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
- A coupled high-resolution hydrodynamic and cellular automata-based evacuation route planning model for pedestrians in flooding scenarios B. Li et al. 10.1007/s11069-021-04960-x
23 citations as recorded by crossref.
- High‐resolution urban flood model for risk mitigation validated with records collected by the affected community M. Re et al. 10.1111/jfr3.12524
- Supporting participative pre‐flood risk reduction in a UNESCO biosphere M. O'Grady et al. 10.1111/jfr3.12520
- Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping A. Annis & F. Nardi 10.1080/10095020.2019.1626135
- Rainfall‐Runoff Modeling Using Crowdsourced Water Level Data B. Weeser et al. 10.1029/2019WR025248
- Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data S. Dong et al. 10.1177/23998083211069140
- 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
- Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study F. Granata et al. 10.1016/j.jhydrol.2022.128431
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- Water Level Measurements from Drones: A Pilot Case Study at a Dam Site E. Ridolfi & P. Manciola 10.3390/w10030297
- 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
- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng 10.1029/2018WR023857
- A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment Y. Tao et al. 10.3390/s24217090
- Dynamic maps of human exposure to floods based on mobile phone data M. Balistrocchi et al. 10.5194/nhess-20-3485-2020
- Improving Hydrological Models With the Assimilation of Crowdsourced Data P. Avellaneda et al. 10.1029/2019WR026325
- Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model M. Mazzoleni et al. 10.1029/2018WR023768
- Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations X. Tian et al. 10.1016/j.scitotenv.2019.06.355
- 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
- A paradigm of extreme rainfall pluvial floods in complex urban areas: the flood event of 15 July 2020 in Palermo (Italy) A. Francipane et al. 10.5194/nhess-21-2563-2021
- Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends A. Karagiannopoulou et al. 10.3390/rs14051263
- 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
- Assessment of crowdsourced social media data and numerical modelling as complementary tools for urban flood mitigation M. Re et al. 10.1080/02626667.2022.2075266
- 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
- The Role of Traditional Knowledge in Coastal Adaptation Priorities: The Pamunkey Indian Reservation N. Hutton & T. Allen 10.3390/w12123548
3 citations as recorded by crossref.
- Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges F. Nardi et al. 10.1080/02626667.2020.1849707
- 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
- A coupled high-resolution hydrodynamic and cellular automata-based evacuation route planning model for pedestrians in flooding scenarios B. Li et al. 10.1007/s11069-021-04960-x
Latest update: 21 Nov 2024
Short summary
We investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of sensors for different scenarios of citizen involvement levels during the flood event occurred in the Bacchiglione catchment in May 2013. We achieve high model performance by integrating crowdsourced data, in particular from citizens motivated by their feeling of belonging to a community. Satisfactory model performance can still be obtained even for decreasing citizen involvement over time.
We investigate the usefulness of assimilating crowdsourced observations from a heterogeneous...
