Using machine learning to identify hydrologic signatures with an
encoder-decoder framework
Abstract
Hydrologic signatures are quantitative metrics that describe a
streamflow time series. Examples include annual maximum flow, baseflow
index and recession shape descriptors. In this paper, we use machine
learning (ML) to learn an optimal equivalent of hydrologic signatures,
and use the learnt signatures to build rainfall-runoff models in
otherwise ungauged watersheds. Our model has an encoder-decoder
structure. The encoder is a convolutional neural net mapping historical
flow and climate data to a low-dimensional vector encoding describing
watershed function. The encodings are analogous to hydrological
signatures. The decoder uses a process-informed network structure to
predict streamflow based on current climate data, stored watershed
state, static watershed attributes and the encoding. The decoder
structure includes stores and fluxes similar to a classical hydrologic
model. For each timestep, the decoder predicts coefficients that
distribute precipitation between stores and store outflow coefficients.
The model is trained end-to-end on the U.S. CAMELS watershed dataset to
minimize streamflow error . Using learnt signatures as input to the
process-informed model improves prediction accuracy over benchmark
configurations that use classical signatures or no signatures. Median
NSE performance on 100 watersheds excluded from the training set was
0.69. The process-informed model structure simulates hydrologic dynamics
such as snow accumulation and melt, quickflow and baseflow. We interpret
learnt signatures by correlation with classical signatures, and by using
sensitivity analysis to assess their impact on modeled store dynamics.
We conclude that process-informed ML models and other applications using
hydrologic signatures may benefit from replacing expert-selected
signatures with learnt signatures.