Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models  book cover
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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models





ISBN 9781138035904
Published January 15, 2017 by CRC Press
240 Pages

 
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Book Description

In recent years, the continued technological advances have led to the spread of low-cost sensors and devices supporting crowdsourcing as a way to obtain observations of hydrological variables in a more distributed way than the classic static physical sensors. The main advantage of using these type of sensors is that they can be used not only by technicians but also by regular citizens. However, due to their relatively low reliability and varying accuracy in time and space, crowdsourced observations have not been widely integrated in hydrological and/or hydraulic models for flood forecasting applications. Instead, they have generally been used to validate model results against observations, in post-event analyses.

This research aims to investigate the benefits of assimilating the crowdsourced observations, coming from a distributed network of heterogeneous physical and social (static and dynamic) sensors, within hydrological and hydraulic models, in order to improve flood forecasting. The results of this study demonstrate that crowdsourced observations can significantly improve flood prediction if properly integrated in hydrological and hydraulic models. This study provides technological support to citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, leading to improved model forecasts and better flood management.

Table of Contents

1 Introduction
1.1 Background 
1.2 Motivation
1.3 Terminology
1.4 Research objectives
1.5 Outline of the thesis

2 Case studies and models
2.1 Introduction
2.2 Case 1 - Brue Catchment (UK) 
2.3 Case 2 - Bacchiglione Catchment (Italy) 
2.4 Case 3 - Trinity and Sabine Rivers (USA)
2.5 Case 4 - Synthetic river reach

3 Data assimilation methods
3.1 Introduction
3.2 Direct insertion
3.3 Nudging scheme
3.4 Kalman Filter
3.5 Ensemble Kalman Filter
3.6 Asynchronous Ensemble Kalman Filter

4 Assimilation of synchronous data in hydrological models
4.1 Introduction
4.2 Methodology
4.3 Experimental setup 
4.4 Results and discussion
4.5 Conclusions

5 Assimilation of asynchronous data in hydrological models
5.1 Introduction
5.2 Methodology
5.3 Experimental setup
5.4 Results and discussion
5.5 Conclusions

6 Assimilation of synchronous data in hydraulic models
6.1 Introduction
6.2 Methodology
6.3 Experimental setup
6.4 Results and discussions
6.5 Conclusions

7 Assimilation of synchronous data in a cascade of models
7.1 Introduction
7.2 Methodology
7.3 Experimental setup
7.4 Results and discussion
7.5 Conclusions

8 Conclusions and recommendations
8.1 Overview
8.2 Research outcomes
8.3 Limitations and recommendations

References

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Author(s)

Biography

Maurizio Mazzoleni was born in Brescia in November 1986. Mr. Mazzoleni graduated from University of Brescia, in Brescia, Italy, in May 2011. During his university studies he continued to pursue his interest in the flood protection by moving to UNESCO-IHE with the support of a scholarship awarded by University of Brescia to carry out his Master Thesis. Afterwards, he cooperate for 1 year within the KULTURisk Project as research fellow of the University of Brescia. Currently, Mr. Mazzoleni is a PhD candidate at UNESCO-IHE Institute for Water Education under the Department of Integrated Water Systems and Governance, Delft, The Netherlands. His research interest include hydrologic and hydrodynamic modelling, in particular he dealt with issue related to flood forecasting, data assimilation, flood inundation mapping, flood risk and uncertainty analysis, flood defence systems design and reliability analysis, statistical hydrology.