Improving hydrogeological characterization using groundwater numerical models and multiple lines of evidence
Benavides Höglund, Nikolas LU
(2024)
In LUNDQUA THESIS
97.
- Abstract
- Groundwater is Earth’s largest liquid freshwater resource. It is a significant component of the hydrological cycle and a buffer that sustains
rivers and freshwater-dependent ecosystems during droughts. Approximately half of the world’s population depends on groundwater for
drinking water, food, and hygiene. It is used extensively in agricultural irrigation, food production and for industrial processes. However,
pollution and over-exploitation pose serious risks to its sustainability, representing a global problem manifested on a local scale. Therefore, the
responsible management of groundwater is critical to ensure its quality and availability for future generations.
Informed decision-making on groundwater... (More) - Groundwater is Earth’s largest liquid freshwater resource. It is a significant component of the hydrological cycle and a buffer that sustains
rivers and freshwater-dependent ecosystems during droughts. Approximately half of the world’s population depends on groundwater for
drinking water, food, and hygiene. It is used extensively in agricultural irrigation, food production and for industrial processes. However,
pollution and over-exploitation pose serious risks to its sustainability, representing a global problem manifested on a local scale. Therefore, the
responsible management of groundwater is critical to ensure its quality and availability for future generations.
Informed decision-making on groundwater management requires the underground, i.e. the material in which groundwater is stored and through
which it flows, to be characterized. This thesis focuses on how this characterization can be improved by using groundwater numerical models
as a framework for assimilating diverse types of data, including direct and indirect measurements of groundwater and underground properties,
as well as expert knowledge. The scope of this thesis is twofold. Firstly, it investigates the extent and manner in which groundwater numerical
models are currently applied within the industry to solve groundwater-related problems, as analyzed through the current state of the art in
decision-support modelling. For practical reasons, this investigation focuses on applications in Sweden, but highlights insights applicable in an
international context. Secondly, it explores methods for improving hydrogeological characterization through the assimilation of conventional
and unconventional data types, with a focus on contaminated sites. These data types are then evaluated in terms of their contribution towards
reducing the uncertainty of model predictions, providing insights on the value of information.
The findings highlights a significant gap between important academic advances in groundwater modelling and practical application within the
industry, tracing this discrepancy to a lack of inclusion of concepts such as data assimilation and uncertainty quantification in groundwater
education. Suggestions for improvement are presented, which include the formulation of flexible guideline recommendations for practitioners
and the inclusion of aforementioned concepts in groundwater education. Additional findings highlights the high value of unconvential data,
demonstrating that, depending on the model prediction, they can be as valuable as conventional measurements of hydraulic head or more.
This likely challenges the prevailing line of thinking within the industry, but also presents an opportunity for improved modelling workflows
among practitioners willing to embrace new concepts. This thesis presents tangible examples for how this can be achieved in order to improve
hydrogeological site characterization, demonstrated using transparent and reproducible model workflows of two contaminated sites in Sweden. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ff184277-fe61-4081-896a-8458a354f4aa
- author
- Benavides Höglund, Nikolas
LU
- supervisor
- opponent
-
- Professor Högh Jensen, Karsten, University of Copenhagen Department of Geosciences and Natural Resource Management
- organization
- publishing date
- 2024-08-15
- type
- Thesis
- publication status
- published
- subject
- keywords
- groundwater, groundwater modelling, contamination, parameter estimation, data assimilation, uncertainty analysis
- in
- LUNDQUA THESIS
- volume
- 97
- pages
- 137 pages
- publisher
- Lunds universitet, Media-Tryck
- defense location
- Pangea, Geocentrum II, 2nd floor, number 229.
- defense date
- 2024-09-06 09:15:00
- ISSN
- 0281-3033
- ISBN
- 978-91-87847-85-1
- 978-91-87847-84-4
- project
- Improving hydrogeological characterization using groundwater numerical models and multiple lines of evidence
- language
- English
- LU publication?
- yes
- id
- ff184277-fe61-4081-896a-8458a354f4aa
- date added to LUP
- 2024-08-07 12:09:46
- date last changed
- 2024-08-08 11:19:09
@phdthesis{ff184277-fe61-4081-896a-8458a354f4aa,
abstract = {{Groundwater is Earth’s largest liquid freshwater resource. It is a significant component of the hydrological cycle and a buffer that sustains<br/>rivers and freshwater-dependent ecosystems during droughts. Approximately half of the world’s population depends on groundwater for<br/>drinking water, food, and hygiene. It is used extensively in agricultural irrigation, food production and for industrial processes. However,<br/>pollution and over-exploitation pose serious risks to its sustainability, representing a global problem manifested on a local scale. Therefore, the<br/>responsible management of groundwater is critical to ensure its quality and availability for future generations.<br/><br/>Informed decision-making on groundwater management requires the underground, i.e. the material in which groundwater is stored and through<br/>which it flows, to be characterized. This thesis focuses on how this characterization can be improved by using groundwater numerical models<br/>as a framework for assimilating diverse types of data, including direct and indirect measurements of groundwater and underground properties,<br/>as well as expert knowledge. The scope of this thesis is twofold. Firstly, it investigates the extent and manner in which groundwater numerical<br/>models are currently applied within the industry to solve groundwater-related problems, as analyzed through the current state of the art in<br/>decision-support modelling. For practical reasons, this investigation focuses on applications in Sweden, but highlights insights applicable in an<br/>international context. Secondly, it explores methods for improving hydrogeological characterization through the assimilation of conventional<br/>and unconventional data types, with a focus on contaminated sites. These data types are then evaluated in terms of their contribution towards<br/>reducing the uncertainty of model predictions, providing insights on the value of information.<br/><br/>The findings highlights a significant gap between important academic advances in groundwater modelling and practical application within the<br/>industry, tracing this discrepancy to a lack of inclusion of concepts such as data assimilation and uncertainty quantification in groundwater<br/>education. Suggestions for improvement are presented, which include the formulation of flexible guideline recommendations for practitioners<br/>and the inclusion of aforementioned concepts in groundwater education. Additional findings highlights the high value of unconvential data,<br/>demonstrating that, depending on the model prediction, they can be as valuable as conventional measurements of hydraulic head or more.<br/>This likely challenges the prevailing line of thinking within the industry, but also presents an opportunity for improved modelling workflows<br/>among practitioners willing to embrace new concepts. This thesis presents tangible examples for how this can be achieved in order to improve<br/>hydrogeological site characterization, demonstrated using transparent and reproducible model workflows of two contaminated sites in Sweden.}},
author = {{Benavides Höglund, Nikolas}},
isbn = {{978-91-87847-85-1}},
issn = {{0281-3033}},
keywords = {{groundwater; groundwater modelling; contamination; parameter estimation; data assimilation; uncertainty analysis}},
language = {{eng}},
month = {{08}},
publisher = {{Lunds universitet, Media-Tryck}},
school = {{Lund University}},
series = {{LUNDQUA THESIS}},
title = {{Improving hydrogeological characterization using groundwater numerical models and multiple lines of evidence}},
url = {{https://lup.lub.lu.se/search/files/192638099/Nikolas_Benavides_H_glund_-_WEBB.pdf}},
volume = {{97}},
year = {{2024}},
}