1Eawag – Swiss Federal Institute of Aquatic Science and Technology, Überlandstr. 133, 8600 Dübendorf, Switzerland
2Institute of Geophysics, ETH Zürich, Sonneggstr. 5, 8092 Zürich, Switzerland
3Institute of Geophysics, University of Lausanne, Amphipole Unil Sorge, 1015 Lausanne, Switzerland
4Institute for Environmental Engineering, ETH Zürich, Wolfgang-Pauli-Str. 15, 8093 Zürich, Switzerland
5Center for Applied Geoscience, University of Tübingen, Sigwartstr. 10, 72076 Tübingen, Germany
*now at: Department of Geography, University of Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland
**now at: AHEAD, IIE, EPFL-ENAC, Station 2, 1015 Lausanne, Switzerland
Abstract. 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.