Physics Guided Machine Learning Methods for Hydrology

TL;DR

This paper introduces a physics-guided machine learning framework for hydrology that models intermediate processes explicitly, improving streamflow prediction accuracy while only requiring weather data during testing.

Contribution

It presents a novel multi-task learning approach that incorporates hydrological process understanding into machine learning models for streamflow prediction.

Findings

Improved streamflow prediction accuracy on a SWAT-simulated dataset.

Explicit modeling of intermediate hydrological processes enhances model performance.

Method is applicable across multiple catchments with data availability constraints.

Abstract

Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich understanding of the physical processes, a significant performance gap still remains which can be potentially addressed by leveraging the recent advances in machine learning. The goal of this work is to incorporate our understanding of hydrological processes and constraints into machine learning algorithms to improve the predictive performance. Traditional ML models for this problem predict streamflow using weather drivers as input. However there are multiple intermediate processes that interact to generate streamflow from weather drivers. The key idea of the approach is to explicitly model these intermediate processes that connect weather drivers to streamflow using a multi-task learning framework. While our proposed approach requires data about intermediate processes during training, only weather drivers will be needed to predict the streamflow during testing phase. We assess the efficacy of the approach on a simulation dataset generated by the SWAT model for a catchment located in the South Branch of the Root River Watershed in southeast Minnesota. While the focus of this paper is on improving the performance given data from a single catchment, methodology presented here is applicable to ML-based approaches that use data from multiple catchments to improve performance of each individual catchment.