Deep learning for hydrological modelling: from benchmarking to concept formation
Keywords: environmental modelling
deep learning
hydrology
hydrological modelling

Hydrological modelling seeks to address the question: what happens to water once it falls on the land surface? Water can flow into river systems, it can pass through soils into the subsurface, it can be absorbed by the biosphere, or it can be released back into the atmosphere as evaporation. The ultimate purpose of hydrological modelling is twofold, to improve our predictions about the system of interest, and to understand how the system works. In recent decades, advances in science and technology have been made by using techniques from the field of Deep Learning, whereby flexible models are calibrated on large datasets to deduce relationships and make predictions. These techniques have begun to be applied across the environmental sciences.
In this thesis I will explore a particular model architecture for deriving relationships between inputs and outputs from data, to provide accurate simulations of hydrological systems as well as to improve our understanding of the hydrological processes themselves. The Long Short-Term Memory (LSTM) is a neural network architecture from the field of Deep Learning which has shown promise for time-series modelling. This model architecture was chosen for its correspondence with our perceptual model of hydrology, whereby we consider the hydrological system to be characterised by a description of its state, and processes that govern the transfer of energy and materials from that state. This input-state-output architecture is similar in many ways to traditional process-based and conceptual models. However, unlike these models the LSTM is capable of searching a much wider range of possible functions that map inputs to outputs, capable of learning any process that can be deduced from the data, as opposed to being limited by the encoding in the traditional models.
The chapters that make up this thesis first demonstrate that the LSTM is an appropriate architecture for rainfall-runoff modelling on the island of Great Britain. I trained the model using meteorological and catchment averaged attributes as input, and river discharge as outputs over a large sample of catchments. In comparison with often used conceptual model architectures, I show that the LSTM demonstrates state-of-the-art performance and justify further interrogation of what the model has learned. In a follow up study, I explore what the LSTM has learned about the hydrological system by taking the trained model weights and interpreting them with reference to intermediate stores of water that relate the meteorological inputs to the outputs of discharge. Despite the complexity of translating rainfall to discharge, hydrology is not limited to rainfall-runoff modelling. The final chapter in this thesis turns to the problem of forecasting a satellite-derived vegetation health metric which is used operationally as a proxy for drought conditions. Like rainfall-runoff modelling, the system being simulated is driven by the complex interaction of meteorological and land surface attributes, however, the target variable is now a store of water (vegetation) rather than a flux (discharge).
This dissertation provides the hydrological community with three important outcomes. Firstly, the LSTM model results are provided as a benchmark for future work looking to develop a national rainfall runoff model for Great Britain. Secondly, this dissertation demonstrates a method used elsewhere in machine learning research that allows a scientist to diagnose what the LSTM has learned about the hydrological system. Finally, this dissertation demonstrates the utility of the LSTM in a drought monitoring context, forecasting a satellite derived vegetation health metric with the potential to improve the ability of national agencies to respond to drought events.
Ultimately, this dissertation offers a demonstration of the power of Deep Learning models in hydrology, and calls on the community to interrogate these tools further to not only advance our predictive goals, but also our scientific ones.