HGS RESEARCH HIGHLIGHT – HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Tang, Q., Delottier, H., Kurtz, W., Nerger, L., Schilling, O. S., & Brunner, P. (2024). HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model. In Geoscientific Model Development (Vol. 17, Issue 8, pp. 3559–3578). Copernicus GmbH. https://doi.org/10.5194/gmd-17-3559-2024
“With the aim to provide a DA framework for operational real-time simulations of water quality and quantity in complex systems for which ISSHMs are typically the ideal decision-based modelling tools, we have developed a highly modular DA framework for ISSHMs based on PDAF and HGS.”
Fig. 2. Flowchart of the overall HGS-PDAF workflow.
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In their recent publication, Qi Tang and her team present an advancement in hydrological modelling: HGS-PDAF (version 1.0). This modular data assimilation framework is tailored specifically for integrated surface and subsurface hydrological modelling, offering a powerful tool for understanding and managing water resources in a changing environment.
A key aspect highlighted in the study is the versatility offered by combining an integrated surface and subsurface model, such as HGS with a flexible data assimilation framework like PDAF. This so-called HGS-PDAF framework, in contrast to traditional HGS simulations, extends the capabilities of the model by assimilating observational data and updating model states and parameters to adequately quantify model prediction uncertainty. This modular approach ensures that the model is flexible and adjustable to a dynamic system, ultimately enhancing the accuracy and reliability of model predictions. In particular, real-time data streams can be integrated into the modelling process, making HGS-PDAF particularly useful for operational modelling.
Furthermore, the scalability of HGS-PDAF demonstrates its potential for use in large-scale operational environments. This design ensures that it can leverage high performance computing resources, making it suitable for both research and practical applications in water resource management.
This research represents a significant advancement in hydrological modelling, providing a robust framework for understanding and managing water resources in the face of a dynamic environment. Through the innovative integration of HGS and data assimilation techniques, HGS-PDAF equips researchers with a powerful tool for addressing complex water resources challenges.
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Fig. 7. A synthetic experimental setup as an example case to illustrate the performance of HGS-PDAF. Three-dimensional view of the model domain and model boundary conditions. Contours represent the groundwater table depth below the surface. Locations of eight virtual observation wells are marked as stars. The location of the highly conductive paleochannel is indicated by a 500m/d iso-surface for hydraulic conductivity.
In this study, Qi Tang and her team introduce HGS-PDAF (version 1.0), a new tool for coupling the PDAF data assimilation framework with HGS. HGS-PDAF allows for the assimilation of a variety of observational data, including, hydraulic head, soil moisture, and solute concentration. They then demonstrated the usability and modularity of HGS-PDAF with a synthetic river-aquifer model using the standard Kalman Filter method.
Abstract:
This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.