Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter. Issue 11 (23rd November 2018) - Tang, Qi; Schilling, Oliver S.; Kurtz, Wolfgang; Brunner, Philip; Vereecken, Harry; Hendricks Franssen, Harrie‐Jan
Abstract: Flood events can change the riverbed topography as well as the riverbed texture and structure, which in turn can influence the riverbed hydraulic conductivity ( K rb ) and river‐aquifer exchange fluxes. A major flood event occurred in the Emme River in Switzerland in 2014, with major implications for the riverbed structure. The event was simulated with the fully integrated hydrological model HydroGeoSphere. The aim was to investigate the effect of the spatial and temporal variability of riverbed topography and K rb on predictions of hydraulic states and fluxes and to test whether data assimilation (DA) based on the ensemble Kalman filter (EnKF) can better reproduce flood‐induced changes to hydraulic states and parameters with the help of riverbed topography changes recorded with an unmanned aerial vehicle (UAV) and through‐water photogrammetry. The performance of DA was assessed by evaluating the reproduction of the hydraulic states for the year 2015. While the prediction of surface water discharge was not affected much by the changes in riverbed topography and in K rb, using the UAV‐derived postflood instead of the preflood riverbed topography reduced the root‐mean‐square error of predicted heads (RMSE [ h ]) by 24%. If, in addition to using the postflood riverbed topography, also K rb and aquifer hydraulic conductivity ( K aq ) were updated through DA after the flood, the RMSE ( h ) was reduced by 55%. We demonstrate how updating of K rb and K aq based on EnKF and UAV‐based observations of riverbed topography transience after a major flood event strongly improve predictions of postflood hydraulic states. Key Points: Drone information on riverbed topography and hydraulic head data provide useful information for the simulation of river‐aquifer systems Parameter estimation via data assimilation allows better estimation of hydraulic heads and slightly better estimation of river discharge