River restoration projects have been launched over the last two decades
to improve the ecological status and water quality of regulated rivers.
As most restored rivers are not monitored at all, it is difficult to
predict consequences of restoration projects or analyze why restorations
fail or are successful. It is thus necessary to implement efficient
field assessment strategies, for example by employing sensor networks
that continuously measure physical parameters at high spatial and
temporal resolution. This paper focuses on the design and implementation
of an instrumentation strategy for monitoring changes in bank
filtration, hydrological connectivity, groundwater travel time and
quality due to river restoration. We specifically designed and
instrumented a network of monitoring wells at the Thur River (NE
Switzerland), which is partly restored and has been mainly channelized
for more than 100 years. Our results show that bank filtration -
especially in a restored section with alternating riverbed morphology -
is variable in time and space. Consequently, our monitoring network has
been adapted in response to that variability. Although not available at
our test site, we consider long-term measurements - ideally initiated
before and continued after restoration - as a fundamental step towards
predicting consequences of river restoration for groundwater quality. As
a result, process-based models could be adapted and evaluated using
these types of high-resolution data sets.