Hybrid Neural Hydrology: Integrating Physical and Machine Learning Models for Enhanced Predictions in Ungauged Basins

Accurate prediction of streamflow in ungauged basins is a fundamental challenge in hydrology. The lack of hydrological observations and the inherent complexities in ungauged regions hinder accurate predictions, posing significant hurdles for water resource management and forecasting. Over time, efforts have been made to tackle this predicament, primarily utilizing physical hydrological models. However, these models need to be revised due to their reliance on site-specific data and their struggle to capture complex nonlinear relationships. Recent work by Kratzert et al. (2018) suggests that nonlinear regression models such as LSTM neural networks (Hochreiter Schmidhuber, 1997) may outperform traditional physically based models. The authors demonstrate the application of LSTM models to ungauged prediction problems, noting that information about physical processes might not have been fully utilized in the modeling setup. In response to these challenges, this research explores a novel approach by introducing a Hybrid Neural Hydrology (HNH) approach by fusing the strengths of physical hydrological models like Statkraft Hydrology Forecasting Toolbox (SHyFT), developed at Statkraft and the Distributed Regression Hydrological Model (DRM), developed by Matheussen at Energi with machine learning model, specifically Neural Hydrology, developed by F. Kratzert and team. By combining the information and structural insights of physically based models with the flexibility and adaptability of machine learning models, HNH seeks to leverage the complementary attributes of these methodologies. The combination is achieved by fusing the uncalibrated physical model with an LSTM based model. This hybridization seeks to enhance the model's adaptability and learning capabilities, leveraging available information from various sources to improve predictions in ungauged areas. Furthermore, this research investigates the impact of clustering catchments based on area to improve model performance. The data used in this research includes dynamic variables such as precipitation, air temperature, wind speed, relative humidity, and observed streamflow obtained from sources such as the internal database at Energi, The Norwegian Water Resources and Energy Directorate (NVE), The Norwegian Meteorological Institute (MET), ECMWF (ERA5) and static attributes such as catchment size, mean elevation, forest fraction, lake fraction and reservoir fraction obtained from CORINE Land Cover and Hydedata (www.hoydedata.no). This study presents HNH as a novel approach that synergistically integrates the structural insights of physical models with the adaptability of machine learning. Preliminary findings indicate promising outcomes from testing in 65 catchments in southern Norway. This suggests that information about physical processes and clustering catchments based on their similarities significantly improves the prediction quality in ungauged regions. This discovery underscores the potential of using hybrid models and clustering techniques to enhance the performance of predictive models in ungauged basins.