Exploring Assimilation of Crowdsourcing
Observations into Flood Models
M. Mazzoleni, Leonardo Alfonso, and D. P. Solomatine
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
2 Crowdsourced Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
3 Case Studies and Water-Related Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
3.1 Brue Catchment (UK) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
3.2 Bacchiglione Catchment (Italy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
4 Model Updating Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
4.1 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
4.2 Ensemble Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
4.3 Synthetic Flow Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
4.4 Estimation of the Observational Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
5 Assimilation of Flow Observations from Static Heterogeneous Sensors . . . . . . . . . . . . . . . . . . 220
5.1 Assimilation of Synchronous Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
5.2 Assimilation of Asynchronous Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Abstract This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological
and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan
and space-time coverage as well as their spatial distribution. The main part of the
chapter is dedicated to the optimal assimilation of heterogeneous intermittent data
within hydrological and hydraulic models. These approaches are designed to
M. Mazzoleni (*)
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden
e-mail: maurizio.mazzoleni@geo.uu.se
L. Alfonso
IHE Delft Institute for Water Education, Delft, The Netherlands
D. P. Solomatine
IHE Delft Institute for Water Education, Delft, The Netherlands
Delft University of Technology, Delft, The Netherlands
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 209–234, https://doi.org/10.1007/698_2019_403,
© Springer Nature Switzerland AG 2019, Published online: 13 September 2019
209
Observations into Flood Models
M. Mazzoleni, Leonardo Alfonso, and D. P. Solomatine
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
2 Crowdsourced Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
3 Case Studies and Water-Related Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
3.1 Brue Catchment (UK) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
3.2 Bacchiglione Catchment (Italy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
4 Model Updating Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
4.1 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
4.2 Ensemble Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
4.3 Synthetic Flow Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
4.4 Estimation of the Observational Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
5 Assimilation of Flow Observations from Static Heterogeneous Sensors . . . . . . . . . . . . . . . . . . 220
5.1 Assimilation of Synchronous Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
5.2 Assimilation of Asynchronous Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Abstract This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological
and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan
and space-time coverage as well as their spatial distribution. The main part of the
chapter is dedicated to the optimal assimilation of heterogeneous intermittent data
within hydrological and hydraulic models. These approaches are designed to
M. Mazzoleni (*)
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden
e-mail: maurizio.mazzoleni@geo.uu.se
L. Alfonso
IHE Delft Institute for Water Education, Delft, The Netherlands
D. P. Solomatine
IHE Delft Institute for Water Education, Delft, The Netherlands
Delft University of Technology, Delft, The Netherlands
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 209–234, https://doi.org/10.1007/698_2019_403,
© Springer Nature Switzerland AG 2019, Published online: 13 September 2019
209