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Data assimilation using sequential Monte Carlo methods in hydrological applications

Douglas Antonio Plaza Guingla (2013)
abstract
Hydrologic Data Assimilation concerns the application of state estimation methods to hydrologic models. The hydrologic models used in this dissertation correspond to a lumped conceptual model (rainfall-runoff model) and a distributed physically-based model (land surface-atmosphere transfer scheme). Hydrologic systems are highly nonlinear with complex dynamics. Therefore, nonlinear/non-Gaussian estimation techniques should be used in the inference of the states and/or parameters of the hydrologic models. In this sense, sequential Monte Carlo methods (a.k.a. particle filters) have captured the interest of the scientific community and nowadays are widely utilized in complex state estimation problems. This dissertation introduces two data assimilation methods which are based on Kalman and particle filtering theory: the ensemble Gaussian particle filter and the standard particle filter with parameter resampling. The first hydrologic variable to be assimilated is the discharge of water at the outlet of the Zwalm catchment which is located in East-Flanders. The second variable of interest is the volumetric soil moisture content in the study area located in the Grand Duchy of Luxembourg. These variables are assimilated separately in experiments with different setups and different study areas. Overall, the results indicate an improvement in the estimation of the model output flows when the proposed methods are applied to specific hydrologic data assimilation problems.
Please use this url to cite or link to this publication:
author
promoter
UGent
organization
alternative title
Data-assimilatie met behulp van sequentiele Monte Carlo-methoden in hydrologische toepassingen
year
type
dissertation
publication status
published
subject
keyword
Kalman filter, Rainfall-Runoff hydrologic models, Sequential data assimilation, physically-based hydrologic models, particle filter, non-linear/non-Gaussian state estimation
pages
154 pages
publisher
Ghent University, Department of Electrical energy, Systems and Automation
place of publication
Ghent, Belgium
defense location
Gent: Jozef-Plateauzaal (Jozef-Plateaustraat 22)
defense date
2013-10-03 16:00
ISBN
9789085786313
language
English
UGent publication?
yes
classification
D1
copyright statement
I have retained and own the full copyright for this publication
id
4142344
handle
http://hdl.handle.net/1854/LU-4142344
date created
2013-09-23 11:04:40
date last changed
2017-01-16 10:43:52
@phdthesis{4142344,
  abstract     = {Hydrologic Data Assimilation concerns the application of state estimation methods to hydrologic models. The hydrologic models used in this dissertation correspond to a lumped conceptual model (rainfall-runoff model) and a distributed physically-based model (land surface-atmosphere transfer scheme). Hydrologic systems are highly nonlinear with complex dynamics. Therefore, nonlinear/non-Gaussian estimation techniques should be used in the inference of the states and/or parameters of the hydrologic models. In this sense, sequential Monte Carlo methods (a.k.a. particle filters) have captured the interest of the scientific community and nowadays are widely utilized in complex state estimation problems. This dissertation introduces two data assimilation methods which are based on Kalman and particle filtering theory: the ensemble Gaussian particle filter and the standard particle filter with parameter resampling. The first hydrologic variable to be assimilated is the discharge of water at the outlet of the Zwalm catchment which is located in East-Flanders. The second variable of interest is the volumetric soil moisture content in the study area located in the Grand Duchy of Luxembourg. These variables are assimilated separately in experiments with different setups and different study areas. Overall, the results indicate an improvement in the estimation of the model output flows when the proposed methods are applied to specific hydrologic data assimilation problems.},
  author       = {Plaza Guingla, Douglas Antonio},
  isbn         = {9789085786313},
  keyword      = {Kalman filter,Rainfall-Runoff hydrologic models,Sequential data assimilation,physically-based hydrologic models,particle filter,non-linear/non-Gaussian state estimation},
  language     = {eng},
  pages        = {154},
  publisher    = {Ghent University, Department of Electrical energy, Systems and Automation},
  school       = {Ghent University},
  title        = {Data assimilation using sequential Monte Carlo methods in hydrological applications},
  year         = {2013},
}

Chicago
Plaza Guingla, Douglas Antonio. 2013. “Data Assimilation Using Sequential Monte Carlo Methods in Hydrological Applications”. Ghent, Belgium: Ghent University, Department of Electrical energy, Systems and Automation.
APA
Plaza Guingla, D. A. (2013). Data assimilation using sequential Monte Carlo methods in hydrological applications. Ghent University, Department of Electrical energy, Systems and Automation, Ghent, Belgium.
Vancouver
1.
Plaza Guingla DA. Data assimilation using sequential Monte Carlo methods in hydrological applications. [Ghent, Belgium]: Ghent University, Department of Electrical energy, Systems and Automation; 2013.
MLA
Plaza Guingla, Douglas Antonio. “Data Assimilation Using Sequential Monte Carlo Methods in Hydrological Applications.” 2013 : n. pag. Print.