Morphometric properties and hydrological responses of sewer networks: cases studies from France.
Bibliographic record
Abstract
Catchment morphology and river network structure greatly condition hydrological response to flooding. While scaling laws have been established for natural catchments and Optimal Channel Networks (OCNs) (Rinaldo and Rodriguez-Iturbe, 1997; Moussa, 2003 & 2009), fewer works have looked into artificial urban water networks, namely stormwater and sewer networks. Optimal Channel Networks (OCNs) are defined based on a generative geomorphological mechanism minimizing the total energy dissipation. However, man-made networks are conceived based on engineering efficiency taking governed by local optimizations, both in time and space, for minimal costs. Hence questions arise regarding the applicability of OCN scaling laws to sewer networks and their potential impact on the shape of the Geomorphological Instantaneous Unit Hydrograph (GIUH).This work addresses these issues through a case study on twelve nested subcatchments of the Greater Paris combined sewer system (France). A two-step methodology is used. First, the morphometric properties are analysed using the reference Horton-Strahler, Rodríguez-Iturbe and Moussa-Bocquillon scaling laws. They are used in the second step to calculate four GIUHs: the reference Width Function (GWF), the Nash unit hydrograph (GN) using Horton-Strahler ratios, the Nash Unit Hydrograph equivalent (GNe) using Moussa- Bocquillon descriptors, and the Hayami function (GH) solution of the diffusive wave equation (Achour et al., 2023).In an effort to generalize the methodology to smaller catchments and Separate Sewer System (SSS), a case study is presented on a sub-network of the city of Montpellier (Southern France). The preliminary results show the need to adapt catchment delimitation methods. Indeed, while hillslopes are the main contributing areas to water flow in natural rivers, the flow in sewer networks is generated by individual production units such as residential, industrial and commercial units. Hence the methods traditionally used to automatically extract hydrographic networks from digital terrain models (DTMs) and delimit catchment boundaries lead to an overestimation of contributing areas. Thus, the geomorphological properties of Moussa and the power law of Rodriguez-Iturbe were not verified.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".