of the crowdsourced observations and their additional value in different types of
catchments. In addition, the adopted simple hydrologic and flow propagation models
neglect some of the physical processes in complex floodplains (e.g. lamination/
reservoir effects). The internal states of the hydrologic model where crowdsourced
observations are supposed to be observed should be calibrated, since unbiased
models are necessary to optimise data assimilation frameworks [34]. Moreover,
real-life crowdsourced observations provided by citizens using static social and
dynamic social sensors have to be used to further validate the results obtained in
this research.
Overall, with this research we demonstrated that the choice of the proper mathematical model and updating technique to be used for flood forecasting may vary
according to the data availability, location of the sensors, type of forecast, etc.
Acknowledgements This research was funded in the framework of the European FP7 Project
WeSenseIt: Citizen Observatory of Water, grant agreement No. 308429.
References
1. Hinkel J, Lincke D, Vafeidis AT, Perrette M, Nicholls RJ, Tol RSJ, Marzeion B, Fettweis X,
Ionescu C, Levermann A (2014) Coastal flood damage and adaptation costs under 21st century
sea-level rise. Proc Natl Acad Sci 111(9):3292–3297. https://doi.org/10.1073/pnas.1222469111
2. Jongman B, Hochrainer-Stigler S, Feyen L, Aerts JCJH, Mechler R, Botzen WJW, Bouwer LM,
Pflug G, Rojas R, Ward PJ (2014) Increasing stress on disaster-risk finance due to large floods.
Nat Clim Chang 4(4):264–268. https://doi.org/10.1038/nclimate2124
3. McLaughlin D (2002) An integrated approach to hydrologic data assimilation: interpolation,
smoothing, and filtering. Adv Water Resour 25(8–12):1275–1286. https://doi.org/10.1016/
S0309-1708(02)00055-6
4. Solomatine DP, Wagener T (2011) Hydrological modelling. In: Wilderer P (ed) Treatise on
water science. Elsevier, Amsterdam, pp 435–457
5. Todini E, Alberoni P, Butts M, Collier C, Khatibi R, Samuels P, Weerts A (2005) ACTIF best
practice paper–understanding and reducing uncertainty in flood forecasting. In: Balabanis P,
Lumbroso D, Samuels P (eds) International conference on innovation, advances and implementation of flood forecasting technology, Troms, Norway
6. Pappenberger F, Matgen P, Beven KJ, Henry J-B, Pfister L, de Fraipont P (2006) Influence of
uncertain boundary conditions and model structure on flood inundation predictions. Adv Water
Resour 29(10):1430–1449. https://doi.org/10.1016/j.advwatres.2005.11.012
7. Koutsoyiannis D (2010) HESS opinions “a random walk on water”. Hydrol Earth Syst Sci 14
(3):585–601. https://doi.org/10.5194/hess-14-585-2010
8. Montanari A, Koutsoyiannis D (2012) A blueprint for process-based modeling of uncertain
hydrological systems. Water Resour Res 48(9):W09555. https://doi.org/10.1029/
2011WR011412
9. Alfonso L, Tefferi M (2015) Effects of uncertain control in transport of water in a river-wetland
system of the Low Magdalena River, Colombia. Transport of water versus transport over water.
Springer, Cham, pp 131–144
10. Domeneghetti A, Vorogushyn S, Castellarin A, Merz B, Brath A (2013) Probabilistic flood
hazard mapping: effects of uncertain boundary conditions. Hydrol Earth Syst Sci 17
(8):3127–3140. https://doi.org/10.5194/hess-17-3127-2013
Exploring Assimilation of Crowdsourcing Observations into Flood Models
229
catchments. In addition, the adopted simple hydrologic and flow propagation models
neglect some of the physical processes in complex floodplains (e.g. lamination/
reservoir effects). The internal states of the hydrologic model where crowdsourced
observations are supposed to be observed should be calibrated, since unbiased
models are necessary to optimise data assimilation frameworks [34]. Moreover,
real-life crowdsourced observations provided by citizens using static social and
dynamic social sensors have to be used to further validate the results obtained in
this research.
Overall, with this research we demonstrated that the choice of the proper mathematical model and updating technique to be used for flood forecasting may vary
according to the data availability, location of the sensors, type of forecast, etc.
Acknowledgements This research was funded in the framework of the European FP7 Project
WeSenseIt: Citizen Observatory of Water, grant agreement No. 308429.
References
1. Hinkel J, Lincke D, Vafeidis AT, Perrette M, Nicholls RJ, Tol RSJ, Marzeion B, Fettweis X,
Ionescu C, Levermann A (2014) Coastal flood damage and adaptation costs under 21st century
sea-level rise. Proc Natl Acad Sci 111(9):3292–3297. https://doi.org/10.1073/pnas.1222469111
2. Jongman B, Hochrainer-Stigler S, Feyen L, Aerts JCJH, Mechler R, Botzen WJW, Bouwer LM,
Pflug G, Rojas R, Ward PJ (2014) Increasing stress on disaster-risk finance due to large floods.
Nat Clim Chang 4(4):264–268. https://doi.org/10.1038/nclimate2124
3. McLaughlin D (2002) An integrated approach to hydrologic data assimilation: interpolation,
smoothing, and filtering. Adv Water Resour 25(8–12):1275–1286. https://doi.org/10.1016/
S0309-1708(02)00055-6
4. Solomatine DP, Wagener T (2011) Hydrological modelling. In: Wilderer P (ed) Treatise on
water science. Elsevier, Amsterdam, pp 435–457
5. Todini E, Alberoni P, Butts M, Collier C, Khatibi R, Samuels P, Weerts A (2005) ACTIF best
practice paper–understanding and reducing uncertainty in flood forecasting. In: Balabanis P,
Lumbroso D, Samuels P (eds) International conference on innovation, advances and implementation of flood forecasting technology, Troms, Norway
6. Pappenberger F, Matgen P, Beven KJ, Henry J-B, Pfister L, de Fraipont P (2006) Influence of
uncertain boundary conditions and model structure on flood inundation predictions. Adv Water
Resour 29(10):1430–1449. https://doi.org/10.1016/j.advwatres.2005.11.012
7. Koutsoyiannis D (2010) HESS opinions “a random walk on water”. Hydrol Earth Syst Sci 14
(3):585–601. https://doi.org/10.5194/hess-14-585-2010
8. Montanari A, Koutsoyiannis D (2012) A blueprint for process-based modeling of uncertain
hydrological systems. Water Resour Res 48(9):W09555. https://doi.org/10.1029/
2011WR011412
9. Alfonso L, Tefferi M (2015) Effects of uncertain control in transport of water in a river-wetland
system of the Low Magdalena River, Colombia. Transport of water versus transport over water.
Springer, Cham, pp 131–144
10. Domeneghetti A, Vorogushyn S, Castellarin A, Merz B, Brath A (2013) Probabilistic flood
hazard mapping: effects of uncertain boundary conditions. Hydrol Earth Syst Sci 17
(8):3127–3140. https://doi.org/10.5194/hess-17-3127-2013
Exploring Assimilation of Crowdsourcing Observations into Flood Models
229