Improving Streamflow Predictions over Indian Catchments using Long Short Term Memory Networks

Abstract

Streamflow modelling plays a critical role in water resource management activities. The “physically based" models require high computation resources and large amounts of input meteorological data which results in high operating costs and longer running times. On the other hand, with advancements in deep-learning techniques, data-driven models such as long short-term memory (LSTM) networks have been shown to successfully model non-linear rainfall-runoff relationships through historically observed data at a fraction of computation cost. Moreover, using physics-informed machine learning techniques, the physical consistency of data-driven models can be further improved. In this study, one such method is applied where we trained a physics-informed LSTM network model over 278 Indian catchments to simulate streamflow at a daily timestep using historically observed precipitation and streamflow data. The ancillary data included meteorological forcings, static catchment attributes, and Noah-MP simulated land surface states and fluxes such as soil moisture, latent heat, and total evapotranspiration. The LSTM model's performance was evaluated using error metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE) and its components, along with skill scores based on 2x2 contingency matrix for hydrological extremes. The trained LSTM model shows improved performance in simulating streamflow over the catchments compared to the physically based model. This will be the first study over India to generate reliable streamflow simulations using a hybrid state-of-the-art approach, which will be beneficial to policy makers for effective water resource management in India.