Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".