  The Sihl river, located near the city of Zurich in Switzerland, is under
continuous and tight surveillance as it flows directly under the city's main
railway station. To issue early warnings and conduct accurate risk
quantification, a dense network of monitoring stations is necessary inside the
river basin. However, as of 2021 only three automatic stations are operated in
this region, naturally raising the question: how to extend this network for
optimal monitoring of extreme rainfall events?
  So far, existing methodologies for station network design have mostly focused
on maximizing interpolation accuracy or minimizing the uncertainty of some
model's parameters estimates. In this work, we propose new principles inspired
from extreme value theory for optimal monitoring of extreme events. For
stationary processes, we study the theoretical properties of the induced
sampling design that yields non-trivial point patterns resulting from a
compromise between a boundary effect and the maximization of inter-location
distances. For general applications, we propose a theoretically justified
functional peak-over-threshold model and provide an algorithm for sequential
station selection. We then issue recommendations for possible extensions of the
Sihl river monitoring network, by efficiently leveraging both station and radar
measurements available in this region.
