Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
<p>It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/4213/2023/gmd-16-4213-2023.pdf |
| _version_ | 1797772105097412608 |
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| author | H. Delottier J. Doherty P. Brunner |
| author_facet | H. Delottier J. Doherty P. Brunner |
| author_sort | H. Delottier |
| collection | DOAJ |
| description | <p>It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In
implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming
tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space
inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances
between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter
probability distribution. By directly focusing on the relationship between model outputs under historical conditions and predictions of system
behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using integrated surface
and sub-surface hydrologic models (ISSHMs) because these models are associated with long run times, numerical instability and ideally complex
parameterization schemes that are designed to respect geological realism. This paper demonstrates that DSI provides a robust and efficient means of
quantifying the uncertainties of complex model predictions. At the same time, DSI provides a basis for complementary linear analysis that allows the worth of available observations to be
explored, as well as of observations which are yet to be acquired. This allows for the design of highly
efficient, future data acquisition campaigns. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic river–aquifer
system. Predictions of interest are fast travel times and surface water infiltration. Linear and non-linear estimates of predictive uncertainty based
on DSI are validated against a more traditional uncertainty quantification which requires the adjustment of a large number of parameters. A
DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. The
example demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify predictive uncertainty and support data
worth analysis in complex hydrogeological environments.</p> |
| first_indexed | 2024-03-12T21:47:16Z |
| format | Article |
| id | doaj.art-6b251818a3674a20a58442b9cf9af789 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-03-12T21:47:16Z |
| publishDate | 2023-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-6b251818a3674a20a58442b9cf9af7892023-07-26T09:04:07ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-07-01164213423110.5194/gmd-16-4213-2023Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic modelH. Delottier0J. Doherty1P. Brunner2Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, SwitzerlandWatermark Numerical Computing, Corinda, QLD, AustraliaCentre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland<p>It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By directly focusing on the relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using integrated surface and sub-surface hydrologic models (ISSHMs) because these models are associated with long run times, numerical instability and ideally complex parameterization schemes that are designed to respect geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions. At the same time, DSI provides a basis for complementary linear analysis that allows the worth of available observations to be explored, as well as of observations which are yet to be acquired. This allows for the design of highly efficient, future data acquisition campaigns. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic river–aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and non-linear estimates of predictive uncertainty based on DSI are validated against a more traditional uncertainty quantification which requires the adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. The example demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify predictive uncertainty and support data worth analysis in complex hydrogeological environments.</p>https://gmd.copernicus.org/articles/16/4213/2023/gmd-16-4213-2023.pdf |
| spellingShingle | H. Delottier J. Doherty P. Brunner Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model Geoscientific Model Development |
| title | Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model |
| title_full | Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model |
| title_fullStr | Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model |
| title_full_unstemmed | Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model |
| title_short | Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model |
| title_sort | data space inversion for efficient uncertainty quantification using an integrated surface and sub surface hydrologic model |
| url | https://gmd.copernicus.org/articles/16/4213/2023/gmd-16-4213-2023.pdf |
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