Surrogate modeling for large-scale integrated hydrologic modeling: A case study in deep learning, model inversion, and hybrid methods across the Continental United States

Use of complex, high-resolution integrated hydrologic models offer the most comprehensive and detailed representations of groundwater, surface water, and land surface processes, but are challenging to use for forecasting tasks due to high computational costs and parameter uncertainty. On the flipside, machine learning approaches are highly accurate and can be computationally frugal for targeted tasks, but are difficult to audit and must be retrained to adapt to new tasks or domains.

In this work we present several case studies of using deep learning surrogate modeling approaches for integrated hydrologic modeling that alleviates many of the weaknesses of taking a purely physically based or purely data driven approach. We first show how deep learning surrogates can readily achieve orders of magnitude speedup over the original physically based models with high degree of accuracy, which allows for on demand forecasting. While this approach is great for generating forecasts from the original model configuration, it is still challenging to adapt to new scenarios such as use in parameter calibration or running long simulations such as climate change scenarios. We close the presentation by discussing recent work to address these challenges using model inversion techniques and by developing hybrid model emulation strategies.