Hydraulic and sediment transport metrics of river systems from inverse modelling of seismic ground motion data

Constraining bedload flux in rivers is a challenging objective, especially when the data need to be continuous, beyond-point estimates. Seismometers are potentially valuable alternatives to in‐stream devices, which involve extensive measurement infrastructure or labour‐intensive manual sampling that can be potentially dangerous. We present a Monte Carlo-based inverse approach to deducing hydraulic and bedload transport dynamics continuously, with high temporal resolution, from seismic data, that averages the system’s behaviour over tens of metres. Water depths and bedload fluxes can be reproduced with average deviations of 0.10 m and 0.02 kg/sm, respectively. The method is validated against synthetic data sets and independently measured metrics from several challenging streams: we show applications of the technique from a flash flood-dominated catchment in Israel (Nahal Eshtemoa), from an ice-covered subarctic river (Sävarån, Sweden), and from a typhoon-driven major mountain river in Taiwan (Liwu River). The presented approach is a generic method implemented in the R package ‘eseis’ that can be used with off-the-shelf seismic equipment, installed at safe distances from potentially hostile conditions with minimum site disturbance.