A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides
Surface-water divides can be delineated by analyzing digital elevation models. They might, however, significantly differ from groundwater divides because the groundwater surface does not necessarily follow the surface topography. Thus, in order to delineate a groundwater divide, hydraulic-head measu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2020.554845/full |
| _version_ | 1818929189467717632 |
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| author | Jonas Allgeier Ana González-Nicolás Daniel Erdal Daniel Erdal Wolfgang Nowak Olaf A. Cirpka |
| author_facet | Jonas Allgeier Ana González-Nicolás Daniel Erdal Daniel Erdal Wolfgang Nowak Olaf A. Cirpka |
| author_sort | Jonas Allgeier |
| collection | DOAJ |
| description | Surface-water divides can be delineated by analyzing digital elevation models. They might, however, significantly differ from groundwater divides because the groundwater surface does not necessarily follow the surface topography. Thus, in order to delineate a groundwater divide, hydraulic-head measurements are needed. Because installing piezometers is cost- and labor-intensive, it is vital to optimize their placement. In this work, we introduce an optimal design analysis that can identify the best spatial configuration of piezometers. The method is based on formal minimization of the expected posterior uncertainty in localizing the groundwater divide. It is based on the preposterior data impact assessor, a Bayesian framework that uses a random sample of models (here: steady-state groundwater flow models) in a fully non-linear analysis. For each realization, we compute virtual hydraulic-head measurements at all potential well installation points and delineate the groundwater divide by particle tracking. Then, for each set of virtual measurements and their possible measurement values, we assess the uncertainty of the groundwater-divide location after Bayesian updating, and finally marginalize over all possible measurement values. We test the method mimicking an aquifer in South-West Germany. Previous works in this aquifer indicated a groundwater divide that substantially differs from the surface-water divide. Our analysis shows that the uncertainty in the localization of the groundwater divide can be reduced with each additional monitoring well. In our case study, the optimal configuration of three monitoring points involves the first well being close to the topographic surface water divide, the second one on the hillslope toward the valley, and the third one in between. |
| first_indexed | 2024-12-20T03:40:51Z |
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| id | doaj.art-b08ec1ac3c0c4deabec899dab0c79fec |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-20T03:40:51Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-b08ec1ac3c0c4deabec899dab0c79fec2022-12-21T19:54:45ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632020-11-01810.3389/feart.2020.554845554845A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater DividesJonas Allgeier0Ana González-Nicolás1Daniel Erdal2Daniel Erdal3Wolfgang Nowak4Olaf A. Cirpka5Center for Applied Geoscience, University of Tübingen, Tübingen, GermanyInstitute for Modelling Hydraulic and Environmental Systems (LS3/SimTech), University of Stuttgart, Stuttgart, GermanyCenter for Applied Geoscience, University of Tübingen, Tübingen, GermanyTyrns AB, Göteborg, SwedenInstitute for Modelling Hydraulic and Environmental Systems (LS3/SimTech), University of Stuttgart, Stuttgart, GermanyCenter for Applied Geoscience, University of Tübingen, Tübingen, GermanySurface-water divides can be delineated by analyzing digital elevation models. They might, however, significantly differ from groundwater divides because the groundwater surface does not necessarily follow the surface topography. Thus, in order to delineate a groundwater divide, hydraulic-head measurements are needed. Because installing piezometers is cost- and labor-intensive, it is vital to optimize their placement. In this work, we introduce an optimal design analysis that can identify the best spatial configuration of piezometers. The method is based on formal minimization of the expected posterior uncertainty in localizing the groundwater divide. It is based on the preposterior data impact assessor, a Bayesian framework that uses a random sample of models (here: steady-state groundwater flow models) in a fully non-linear analysis. For each realization, we compute virtual hydraulic-head measurements at all potential well installation points and delineate the groundwater divide by particle tracking. Then, for each set of virtual measurements and their possible measurement values, we assess the uncertainty of the groundwater-divide location after Bayesian updating, and finally marginalize over all possible measurement values. We test the method mimicking an aquifer in South-West Germany. Previous works in this aquifer indicated a groundwater divide that substantially differs from the surface-water divide. Our analysis shows that the uncertainty in the localization of the groundwater divide can be reduced with each additional monitoring well. In our case study, the optimal configuration of three monitoring points involves the first well being close to the topographic surface water divide, the second one on the hillslope toward the valley, and the third one in between.https://www.frontiersin.org/articles/10.3389/feart.2020.554845/fullgaussian process emulationpreposterior data impact assessorbayesian analysisuncertainty quantificationoptimal design of measurementsdelineation |
| spellingShingle | Jonas Allgeier Ana González-Nicolás Daniel Erdal Daniel Erdal Wolfgang Nowak Olaf A. Cirpka A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides Frontiers in Earth Science gaussian process emulation preposterior data impact assessor bayesian analysis uncertainty quantification optimal design of measurements delineation |
| title | A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides |
| title_full | A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides |
| title_fullStr | A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides |
| title_full_unstemmed | A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides |
| title_short | A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides |
| title_sort | stochastic framework to optimize monitoring strategies for delineating groundwater divides |
| topic | gaussian process emulation preposterior data impact assessor bayesian analysis uncertainty quantification optimal design of measurements delineation |
| url | https://www.frontiersin.org/articles/10.3389/feart.2020.554845/full |
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