Statistical models are an essential tool to model, forecast and understand
the hydrological processes in watersheds. In particular, the modeling of time
lags associated with the time between rainfall occurrence and subsequent
changes in streamflow, is of high practical importance. Since water can take a
variety of flowpaths to generate streamflow, a series of distinct runoff pulses
from different flowpath may combine to create the observed streamflow time
series. Current state-of-the-art models are not able to sufficiently confront
the problem complexity with interpretable parametrization, which would allow
insights into the dynamics of the distinct flow paths for hydrological
inference. The proposed Gaussian Sliding Windows Regression Model targets this
problem by combining the concept of multiple windows sliding along the time
axis with multiple linear regression. The window kernels, which indicate the
weights applied to different time lags, are implemented via Gaussian-shaped
kernels. As a result, each window can represent one flowpath and, thus, offers
the potential for straightforward process inference. Experiments on simulated
and real-world scenarios underline that the proposed model achieves accurate
parameter estimates and competitive predictive performance, while fostering
explainable and interpretable hydrological modeling