Quasi-Online Groundwater Model Optimization Under Constraints of Geological Consistency Based on Iterative Importance Sampling
Maximilian Ramgraber , Matteo Camporese, Philippe Renard, Paolo Salandin & Mario Schirmer
| Résumé |
The increasing use of wireless sensor networks and remote sensing
permits real‐time access to environmental observations. Data
assimilation frameworks tap into such data streams to autonomously
update and gradually improve numerical models. In hydrogeology,
such methods are relevant in areas of long‐term interest in
water quality and quantity, for example, in drinking water
production. Unfortunately, accurate hydrogeological predictions
often demand a degree of geological realism, which is difficult to
reconcile with the operational limitations of many data
assimilation frameworks. Alluvial aquifers, for example, are
sometimes characterized by paleo‐channels of unknown extent
and properties, which may act as preferential flow paths. Gradually
optimizing such fields in real‐time or
quasi‐real‐time settings is a formidable task. Besides
subsurface properties, ill‐specified model forcings are a
further source of predictive bias, which an optimizer could learn
to compensate. In this study, we explore the use of a
quasi‐online optimizer based on the iterative batch
importance sampling framework for a groundwater model of a field
site near Valdobbiadene, Italy. This site is characterized by the
presence of paleo‐channels and heavily exploited for drinking
water production and irrigation. We use Markov chain Monte Carlo
steps to explore new parameterizations while maintaining
consistency between states and parameters as well as conformance to
a multipoint statistics training image. We also optimize a
preprocessor designed to compensate for potential bias in the model
forcing. We achieve promising and geologically consistent
quasi‐real‐time optimization, albeit at the loss of
parameter uncertainty. |
| Mots-clés |
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| Citation | Ramgraber , M., Camporese, M., Renard, P., Salandin, P., & Schirmer, M. (2020). Quasi-Online Groundwater Model Optimization Under Constraints of Geological Consistency Based on Iterative Importance Sampling. Water Resources Research, 56(6), 26777-26798. |
| Type | Article de périodique (Anglais) |
| Date de publication | 4-2020 |
| Nom du périodique | Water Resources Research |
| Volume | 56 |
| Numéro | 6 |
| Pages | 26777-26798 |
| URL | https://doi.org/10.1029/2019WR026777 |