| Popis: |
Groundwater is essential for drinking water and economic development, yet its availability and quality are threatened by climate change, pollution, and rising demand. Effective groundwater management relies on accurate numerical models for flow and contaminant transport. Traditional calibration techniques often struggle with the uncertainty and spatial variability inherent in hydrogeological data. Although geostatistical simulations can represent this variability, their computational complexity limits their use in large-scale models. To overcome these challenges, ensemble methods like the Ensemble Kalman Filter (EnKF) and Ensemble Smoother (ES) have been introduced for model updates using spatiotemporal data. However, they face limitations in high-dimensional systems with sparse observational data, common in hydrogeology. This paper introduces an innovative data assimilation method combining Well-by-Well (WbW) and observation Type-by-observation Type (TbT) techniques. This approach utilizes local analysis to effectively calibrate large, complex groundwater models with limited observations, resulting in a more stable and accurate calibration process. The method is tested on a synthetic 3D model and a real regional groundwater flow model, showing significant improvements in calibration and predictions. A 3D synthetic model of a coastal aquifer with saltwater intrusion was developed to evaluate the WbW & TbT updates within the Ensemble Smoother with Multiple Data Assimilation (ES-MDA 4x) method. The results indicate improved calibration and reduced errors in hydraulic head and salt concentration predictions. This study demonstrates the robustness of the WbW & TbT method in calibrating the Ville Mercier regional hydrogeological model, showcasing its potential for complex hydrogeological settings. By updating parameters locally around each observation well, the WbW & TbT method addresses high-dimensional challenges while preserving data amplitude and managing the complexity of regional hydrogeological systems. Results confirm that this method enhances the accuracy and reliability of groundwater flow models, making it a vital tool for resource management amid environmental challenges. |
