An optimized indirect method to estimate groundwater table depth
anomalies over Europe based on Long Short-Term Memory networks
Abstract
Long Short-Term Memory (LSTM) networks are a deep learning technology to
exploit long-term dependencies in the input-output relationship, which
has been observed in the response of groundwater dynamics to atmospheric
and land surface processes. We introduced an indirect method based on
LSTM networks to estimate monthly water table depth anomalies (wtd_a)
across Europe from monthly precipitation anomalies (pr_a). The network
has further been optimized by including supplementary
hydrometeorological variables, which are routinely measured and
available at large scales. The data were obtained from daily integrated
hydraulic simulation results over Europe from 1996 to 2016, with a
spatial resolution of 0.11° (Furusho-Percot et al., 2019), and separated
into a training set, a validation set and a test set at individual
pixels. We compared test performances of the LSTM networks locally at
selected pixels in eight PRUDENCE regions with random combinations of
monthly pr_a, evapotranspiration anomaly, and soil moisture anomaly
(θ_a) as input variables. The optimal combination of input variables
was pr_a and θ_a, and the networks with this combination achieved
average test R^2 between 47.88% and 91.62% in areas with simulated
wtd ≤ 3 m. Moreover, we found that introducing θ_a improved the ability
of the trained networks to handle new data, indicating the substantial
contribution of θ_a to explain groundwater state variation. Therefore,
including information about θ_a is beneficial, for instance in the
estimation of groundwater drought, and the proposed optimized method may
be transferred to a real-time monitoring of groundwater drought at the
continental scale using remotely sensed soil moisture observations.
Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J.
and Kollet, S.: Pan-European groundwater to atmosphere terrestrial
systems climatology from a physically consistent simulation, Sci. data,
6(1), 320, doi:10.1038/s41597-019-0328-7, 2019.