Accurately and Efficiently Predicting High-Resolution Inundation using a Hybrid Machine Learning and Physics-Based Approach

Predicting high-resolution inundation at large spatial and temporal scales is important in understanding future water availability and flood risk. Classically, hydrologic models have been used to model inundation, but the computational expense associated with applying hydrologic models at large scales has motivated the use of other methodologies, such as machine learning. We propose a hybrid physics-based and machine learning modeling approach to produce high-resolution inundation maps at a much lower computational cost than high-resolution physics-based modeling while still maintaining high accuracy. This methodology is then tested in a representative watershed in Colorado, USA. The proposed hybrid physics-based and machine learning modeling approach consists of a coarse spatial resolution hydrologic model and a random forest downscaling postprocessing step. First, a 1km resolution integrated surface-subsurface hydrologic model, ParFlow-CLM, is ran for the spatial and temporal domain of interest. Then, the resultant modeled inundation as well as additional climate and geographical parameters are fed into a random forest model which predicts inundation at a higher spatial resolution. We tested this methodology in a 1800km2 watershed in Colorado, USA during the 2019 water year to predict modeled inundation produced by a 100m resolution hydrologic model. In our test case, this hybrid methodology predicted whether each fine resolution cell is inundated at each hourly timestep correctly >97% of the time and maintained high accuracy in unseen timesteps as well as in unseen locations within the same region. We will also discuss next steps to predict real-world inundation by training the random forest model on 30m resolution satellite data. This study shows the potential for this methodology to be applied at the continental scale to predict high-resolution inundation accurately and efficiently.