Improving flood prediction assimilating uncertain crowdsourced data into hydrologic and hydraulic models : Dissertation

275 Pages·2016·47.705 MB·English

by  Mazzoleni, Maurizio

IMPROVING FLOOD PREDICTION
ASSIMILATING UNCERTAIN
CROWDSOURCED DATA INTO
HYDROLOGIC AND HYDRAULIC MODELS

IMPROVING FLOOD PREDICTION ASSIMILATING
UNCERTAIN CROWDSOURCED DATA INTO
HYDROLOGICAL AND HYDRAULIC MODELS

DISSERTATION

Submitted in fulfilment of the requirements of
the Board for Doctorates of Delft University of Technology
and of
the Academic Board of the UNESCO-IHE Institute for Water Education
for the Degree of DOCTOR
to be defended in public on
Monday, 28 November 2016, at 10:00 hours
In Delft, the Netherlands

by

Maurizio MAZZOLENI
Master of Science in Environmental Engineering
University of Brescia, Brescia

born in Brescia, Italy
This dissertation has been approved by the
promotor: Prof. dr. D.P. Solomatine and
copromotor: Dr. L. Alfonso

Composition of the doctoral committee:
Chairman Rector Magnificus TU Delft
Vice-Chairman Rector UNESCO-IHE
Prof.dr. D.P. Solomatine, UNESCO-IHE/TU Delft, promotor
Dr. L. Alfonso, UNESCO-IHE, copromotor

Independent members:
Prof. dr. ir. A.W. Heemink, TU Delft
Prof. dr. ir. A. Weerts, Wageningen University
Dr. ir. H. Madsen, University of Copenhagen, Denmark
Prof. dr. ir J.A. Roelvink, UNESCO-IHE/TU Delft
Prof. dr. N.C. van de Giesen, TU Delft, reserve

This research was conducted under the auspices of the Graduate School for Socio-
Economic and Natural Sciences of the Environment (SENSE)
CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informal business

© 2016, Maurizio Mazzoleni

Although all care is taken to ensure the integrity and quality of this publication and
information herein, no responsibility is assumed by the publishers, the author nor
UNESCO-IHE for any damage to the property or persons as a result of the operation or
use of this publication and/or the information contained herein.

A pdf version of this work will be made available as Open Access via
http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons
Attribution-Non Commercial 4.0 International License,
http://creativecommons.org/licenses/by-nc/4.0/

Published by:
CRC Press/Balkema
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To my parents, Mirella and Luciano
To Jessica

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A
CKNOLEDGMENTS
Once Nelson Mandela said “It always seems impossible until it's done”. Indeed,
during my PhD research I had this feeling many times, but it is because of the help
of my supervisors, colleagues, family and friends that I have finally made it.
First of all, I would like to express my sincere gratitude to my promotor Prof.
Dimitri Solomatine and co-promotor Leonardo Alfonso. Dear Dimitri, in these four
years you provided me with creative and innovative ideas, critical thought and
advice that inspired me during my PhD research. Thanks for always believing in
me, and involving me in numerous research activities and projects. Leo, thank you
for always being available for a nice word or suggestion, for sharing your ideas and
for the insightful discussions on innovative research topics.
This PhD research project was part of the WeSenseIt Project which was funded by
the Seventh Framework Programme of the European Union. I really enjoyed being
part of such an important project and I would like to thank all the partner for their
support. In particular, I appreciate the dedication of Martina, Daniele and Michele
of the Alto Adriatico Water Authority for sharing flood data together with their
hydrological and hydraulic models with us.
I am grateful to Jolanda, Anique and Jos for all the administrative support, Paula
Derkse for the suggestion for printing this thesis, Benno for helping me with the
samenvatting and Joanne for the proofread of the thesis. Thanks to all the
Hydroinformatics staff members, Ioana, Andreja, Schalk Jan, Biswa, and Gerald
for involving me in the teaching activities of our group.
I am very thankful to Prof. Dong-Jun Seo for the opportunity to spend four months
as a visiting scholar at the University of Texas at Arlington where I also met Dr.
Seong-Jin Noh. Dear D.J. and Seong-Jin, thank you very much for your hospitality
and your precious scientific advice on data assimilation, I'm sure in the future there
will be opportunities to strengthen the collaboration created between UTA and
UNESCO-IHE. Special thanks go to Behzad Z., Hamideh R., Hamideh H., Ray and
Bahzad N. for making me feel at home in Arlington.
Luigia, thank you very much for always being there when I needed it, for your
sincere comments on the readability of our papers and for the funny moments
during the coffee breaks. Thank you Giuliano for being not only a supervisor but
also a friend. It is also because of you if I am now here. I wish to you, Luigia and
Tommaso all the best.
i
There are no words to convey my feelings to all my friends in Delft. Thanks Yared
for the 3 years sharing the workspace, for the Saturday football in TU, for all the
fun during conferences and many other moments. Juan, project buddy, thanks for
all the discussions about data assimilation, for the support during the PhD and for
helping to finish all that vodka in Krakow. Kun, sometimes it was more difficult to
explain my jokes to you than data assimilation to my mother, thanks for all those
nice memories. Thanks Angi, my friiiend, for all the fonny moments and your
patience with me in the gym. Thanks Maribel, I could not ask for a better roommate,
even if we had some bread-related issues. Thank you to all of you guys, Benno,
Elena, Paolo, Jojo, Juliette, Aki, Pato, Miguel, Alex, Alessio, Alessandro, Neiler,
Vero, Mark, Fer, Peter, Alida, Pablo, Claudia, Sara, Arlex, ShanShan, Erika,
Andres, Juan Pablo, Paola, Natalia, Micah, Mario, Quan, Mohaned, Stefan,
Quintilia, Dibesh, Joanne, Imra, Hadi, Feroz, Marianne, Laura, Victor, and many
more for being here all this time.
I would like to express my immense gratitude to Vale and Matteo for their
continuous support, Ale and Livio for unforgettable moments in the mountains and
inspiring conversations and Davide and Michela for their Sunday pizza dinners and
wedding talks. I would like to thank also Cristian, Manuela, Ippolita, Gianni, Marco,
Ciro, Manuel and Laura for always finding time for me when I was back in Italy
even if very busy.
Last but not least, I would like to give a special thanks to someone who has been
constantly with me for these last four years, in the rainy and sunny days, in the
sad and happy moments and, many times, overnight. We had difficult moments
but at the end, somehow, it always “ran” smoothly. Thank you my dear Laptop.
Un ringraziamento speciale va ai miei genitori, Mirella e Luciano. Grazie per la
vostra fiducia, paziena e per avermi sempre sostenuto incondizionatamente in questi
ultimi quattro anni (e non solo) anche quando le mie scelte mi hanno portato
lontano da casa per molto tempo. Un pezzo di questo dottorato é anche vostro.
Grazie di cuore ai miei zii Rossella, Battista e Gianni per essere sempre stati li
quando c’era bisogno e per aver aiutato la mamma negli ultimi due anni. Grazie
Daniela per aver supportato ma soprattutto sopportato quel vecchio brontolone in
questi ultimi anni.
I do not think I would ever get to finish my PhD without you, my wife, my lover,
my best friend, Jessica. In these last four years you saw the best and worst parts
of me and you were always there to cheer me up. My journey would not have been
possible without your continuous and unlimited support and help. Thanks for your
love, encouraging me in all of my pursuits and inspiring me to follow my dreams.
Te amo
ii
S
UMMARY
Monitoring stations have been used for decades to measure hydrological variables,
and mathematical water models used to predict floods can be enhanced by the
incorporation of these observations, i.e. by data assimilation. The assimilation of
remotely sensed water level observations in hydrological and hydraulic modelling
has become more attractive due to their availability and spatially distributed nature.
In recent years, continued technological advances have stimulated a spread of low-
cost sensors that has triggered crowdsourcing as a way to obtain observations of
hydrological variables in a more distributed way than the classic static physical
sensor networks. The main advantage of using this type of sensors is that they can
be used not only by technicians, as with observations from traditional physical
sensors, but also by regular citizens. However, there are also drawbacks of using
these observations, e.g. their relatively limited reliability, varying accuracy in time
and space, and their irregular and non a-priori defined availability. For this reason,
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.
Model updating is a strategy that aims at improving models using observations. A
particular case of model updating is data assimilation, which often uses measured
data such as streamflow, soil moisture, etc. coming from static physical stations.
However, only a few studies have considered the integration of crowdsourced
observations into water-related models.
The main objective of this research is to investigate the benefits of assimilating
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. Standard data assimilation
approaches, such as Kalman filtering, ensemble Kalman filtering, nudging, etc. are
applied to the three different case studies to assimilate crowdsourced observations
of variable accuracy and random life-span. The results of this study demonstrate
that crowdsourced observations can significantly improve flood prediction if they
are properly integrated in hydrological and hydraulic models.
In particular, this research proved that assimilation of streamflow observations from
static physical sensors provides improvements in model performance, the magnitude
of which depends on the observation locations and model structure. In case of the
Brue catchment, the best model improvement is achieved by assimilating
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