Methodologies for analyzing intrinsic and required DEM accuracy for hydrological applications of flash floods
Bibliographic record
Abstract
For extreme events like flash floods, infiltration is considered to be negligible, and the morphology of the watershed is considered to be the most significant factor. Thus, digital elevation models (DEMs) are the most efficient data source to define the catchment surface. To determine if the DEM is adapted, it is essential to analyze both the intrinsic accuracy and the required accuracy. In this paper we propose two complementary methodologies to analyze and evaluate these two kinds of DEM accuracy that relate to the hydrological applications to flash floods. The first methodology relates to the intrinsic accuracy: a diagnostic method analyzes the accuracy and stability of the extracted hydrographic network at catchment scale. To obtain correct positioning of the channels, results show the strong influence of the topographic context and the need for associating extra data on rivers, especially in flat areas. The impact of accuracy is evaluated through a scheme based on DEM grid rotation. A step by step and iterative process gives a fuzzy evaluation of the hydrographic network. These techniques also highlight the strong scale dependence of the extracted network. The second methodology relates to the required accuracy. To analyze the sensitivity to the DEM accuracy, we propose an approach by comparing the results of hydraulic models obtained from a "real" description of the river topography and from this description distorted by a numerical noise characterizing the lack of accuracy of the DEM. The impact of this noise on the overflows on the major bed is analyzed. The results show that the accuracy required by the thematicians is often greater than that strictly required by modeling, which opens interesting prospects to reduce the phases of data acquisition.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".