Waldowski et al. (2025) Data Assimilation in Integrated Subsurface Flow Models—Making Optimal Use of Cross‐Compartmental Interactions

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2025
• Date: 2025-11-29
• Authors: Bastian Waldowski, Harrie‐Jan Hendricks Franssen, Insa Neuweiler
• DOI: 10.1029/2025wr041570

Research Groups

Not specified in the abstract.

Short Summary

This study investigates the risks and benefits of cross-compartmental data assimilation (DA) in integrated subsurface flow models, revealing that while single-compartment DA has trade-offs, multivariate assimilation of both soil moisture and groundwater tables yields the most robust predictions for root zone soil moisture.

Objective

• To study the potential risks and benefits of cross-compartmental data assimilation (DA) in integrated subsurface flow models (comprising the vadose zone and groundwater aquifers) using observations from multiple heterogeneous virtual realities.

Study Configuration

• Spatial Scale: Integrated subsurface flow systems (vadose zone and groundwater aquifers), with analysis of spatially averaged quantities at the land surface and within aquifers.
• Temporal Scale: Forecasting of states and fluxes; analysis of subsequent forecast improvements.

Methodology and Data

• Models used: Ensemble Kalman filter (for data assimilation), Integrated subsurface flow models.
• Data sources: Observations drawn from multiple heterogeneous virtual realities (synthetic data).

Main Results

• Updating soil moisture with point observations consistently improves spatially averaged soil moisture predictions at the land surface but frequently deteriorates estimates of the groundwater table height (worse for models with non-resolved layers).
• Bias correction and vertical localization can mitigate the deterioration of groundwater table estimates.
• Groundwater table assimilation limited to updating aquifer states can improve the estimation of spatially averaged groundwater tables (RMSEs on average reduced by an unspecified amount in test examples), but balancing fluxes introduced by rigorously taking out partly saturated parts impair these benefits.
• Additionally updating pressure heads in an area above the groundwater table can, on average, reduce RMSEs of groundwater table estimates by more than double (specific factor not provided).
• Multivariate assimilation of both soil moisture and groundwater tables leads to the best results for predicting root zone soil moisture.
• Groundwater recharge predictions could often be improved in a subsequent forecast without DA if groundwater tables had been updated before.

Contributions

• Systematic investigation of the potential risks and benefits of cross-compartmental data assimilation in integrated subsurface flow models.
• Identification of specific data assimilation strategies (e.g., bias correction, vertical localization, multivariate assimilation, updating pressure heads above the groundwater table) to improve forecasts across different compartments.
• Demonstration of the potential for improved subsequent groundwater recharge predictions through prior groundwater table updates.

Funding

Not specified in the abstract.

Citation

@article{Waldowski2025Data,
  author = {Waldowski, Bastian and Franssen, Harrie‐Jan Hendricks and Neuweiler, Insa},
  title = {Data Assimilation in Integrated Subsurface Flow Models—Making Optimal Use of Cross‐Compartmental Interactions},
  journal = {Water Resources Research},
  year = {2025},
  doi = {10.1029/2025wr041570},
  url = {https://doi.org/10.1029/2025wr041570}
}