Evaluating the performance of Long Short-Term Memory (LSTM) Networks for rainfall–runoff modelling in large catchments

The problem of hydrologic modelling in large catchments has been addressed by conceptual physical-based models. Deep machine learning techniques such as Long Short-Term Memory (LSTM) networks have also proven to be effective for rainfall-runoff modelling. This is a promising approach to include a diversity of inputs to capture complex processes without handling the entire complexity, cost and difficulty that represent to include in conventional models. However, LSTM has not been extensively tested on large catchments and for seasonal forecasts in countries such as Germany. When a large catchment needs to be modelled, several questions arise, such as whether the performance of LSTM is suitable for rainfall-runoff without human intervention, to what extent we can rely on these results and whether the involved physical processes are appropriately represented also at higher spatial resolutions? The proposed study addresses these questions by developing a framework that employs daily meteorological observations and ancillary geographical information. First, by training a single LSTM to generate surface runoff in the study area. Secondly, to assess model performance, LISFLOOD is used as a benchmark spatially distributed semi-physical rainfall-runoff model. Finally, the performance of the model is evaluated against the Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) efficiency criteria. The framework is illustrated using the Weser River Basin in Germany with a spatial resolution of 1 km and a daily time step. The proposed framework is expected to outperform calibrated physical-based models, to be suitable for seasonal forecasting, and to provide information for understanding the capabilities and limitations of the physics-based models and how the machine learning techniques take into account or are enforced to follow the physical laws.