Automatic Estimation of Parameter Transfer Functions for Distributed
Hydrological Models - Function Space Optimization Applied on the mHM
Model
Abstract
FSO is a symbolic regression method that allows for automatic estimation
of the structure and parameterization of transfer functions from
catchment data. The FSO method transforms the search for an optimal
transfer function into a continuous optimization problem using a text
generating neural network (variational autoencoder). mHM is a widely
applied distributed hydrological model, which uses transfer functions
for all its parameters. For this study, we estimate transfer functions
for the parameters saturated hydraulic conductivity and field capacity.
To avoid the influence of parameter equifinality, the remaining mHM
parameter values are optimized simultaneously. The study domain consists
of 229 basins, including 7 major basins for Training and 222 smaller
basins for validation, distributed across Germany. 5 years of data are
used for training und 35 years for validation. By validating the
estimated transfer functions in a set of validation basins in a
different time period, we can examine the FSO estimated transfer
functions influence on model performance, scalability and
transferability. We find that transfer functions estimated by FSO lead
to a robust performance when being applied in an ungauged setting. The
median KGE of the validation basins in the validation time period is
0.73, while the median KGE of the 7 training basins in training time is
0.8. These results look promising, especially since we are only using 5
years of training data, and show the general applicability of FSO for
distributed hydrological models.