High computational cost is often the most limiting factor when running high-resolution hydrodynamic models to simulate spatial-temporal flood inundation behaviour. To address this issue, a recent study introduced the hybrid Low-fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behaviour of flood inundation extent by upskilling simulations from a low-resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process (Sparse GP) learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high-resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high-resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent-based LSG model rather than deriving it from water depth predictions.