How Spatial Resolutions Impact the Large‐Scale River Hydrodynamic Model Simulations: Analysis Focuses on Model Physics
Bibliographic record
Abstract
Abstract Large‐scale hydrodynamic models are vital for flood risk assessment and understanding the global water cycle; however, their results can include uncertainties related to spatial resolution. Few studies have evaluated hydrodynamic models across a range of spatial resolutions, with most focusing on a few variables (e.g., discharge) and often neglecting performance at ungauged sites or the role of parameter optimization. We addressed these limitations by comparing Catchment‐based Macro‐scale Floodplain (CaMa‐Flood) model simulations in the Amazon River basin at different spatial resolutions, using the higher resolution as a benchmark in each comparison. We found good inter‐resolution performance in simulating discharge and water depth, with coefficients of determination exceeding 0.88 in >80% of locations. The normalized Nash–Sutcliffe efficiencies for discharge and water depth were greater than 0.83 and 0.68, respectively, in more than 75% of locations, suggesting that most locations had consistent hydrodynamics. We detected large discrepancies in discharge between simulations at ∼2.5% of locations due to limited representation of bifurcation flow, floodplain conveyance, and backwater at river confluences in the model. Water depth also differed significantly at ∼3% of locations, mainly at headwaters, due to width bottleneck sections. Flood extent patterns differed minimally between simulations around the main stream and large sub‐streams, whereas improvements in the downscaling method are required for small sub‐streams. Our results demonstrate the need to improve the representation of bifurcation channels and floodplain parameterization for specific locations, although the general river hydrodynamics patterns were well‐captured by computationally efficient moderate‐resolution (i.e., 6 arcmin) CaMa‐Flood simulations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".