the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Feb 2026
Confidence-Aware Framework for Mapping Satellite-Derived River Reaches to Gridded Routing Networks
Abstract. The Surface Water and Ocean Topography (SWOT) mission delivers reach-scale observations of river water surface elevation, contextualized by the vector-based SWORD database. Assimilating these observations into gridded routing models such as CTRIP is hindered by structural mismatches between object-based river geometries and pixel-based flow networks. We present a global, confidence-aware pipeline that assigns SWORD reaches to CTRIP pixels by combining geometric and hydrological criteria such as intersection, proximity, upstream-area consistency, reach length, and flow-direction alignment into a composite score. Each assignment receives a confidence tier (Tier 1: single; Tier 2: scored; Tier 3: fallback; Tier 4: unassigned), and Tier-2 cases are further refined by a confidence score (high/medium/low). Applied globally at 1/12°, the framework assigns >99 % of CTRIP pixels; the vast majority are resolved either unambiguously (Tier 1) or as high-quality scored matches (Tier 2–High), with no fallback assignments and <0.5 % unassigned. Independent diagnostics based on basin-hash continuity confirm hydrological integrity. Code and outputs (CSV, NetCDF, shapefiles) are openly available and directly usable for assimilation into CTRIP or can be applied to any other gridded river network, providing a reproducible foundation for bridging SWOT observations with global river routing models.
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- CEC1: 'Comment on egusphere-2026-509', Astrid Kerkweg, 13 Mar 2026 reply
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RC1: 'Comment on egusphere-2026-509', Anonymous Referee #1, 18 Apr 2026
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Review of the paper entitled : “Confidence-Aware Framework for Mapping Satellite-Derived River Reaches to Gridded Routing Networks”, by Kaushlendra Verma and Simon Munier
Dear authors,
Please find below my review and suggestions of minor to moderate revisions regarding this interesting technical note.
Best regards,
General
This technical note presents a pipeline whose objective is “to identify the single most hydrologically consistent SWORD reach for each valid CTRIP pixel.” The topic is relevant and timely, addressing an important challenge in linking vector-based river network datasets with gridded hydrological models.
Overall, the manuscript is well developed, and both the methodology and figures are interesting. The comments below mainly aim at improving clarity and reinforcing key aspects.
Some clarifications would be beneficial, particularly regarding topological configurations, sensitivity to weighting, the use of zoomed examples, and the justification of how hydrological connectivity and drainage consistency are preserved. In addition to drainage/mass considerations, a brief discussion of hydrodynamic implications, especially in the context of data assimilation, would further strengthen the manuscript.
In Section 3, the methodology is sound but could be made easier to follow with minor improvements in notation and a clearer description of the weighting optimization and selection process. Including a few zoomed examples of typical reach configurations within pixels would also help. It would be useful to clarify whether the algorithm ensures consistent basin drainage without duplication, which is important for mass conservation.
Providing the spatial resolution of the CTRIP grid and discussing sensitivity to resolution would help better position the approach. From a hydrodynamic perspective, a more detailed discussion of whether the reach attribution preserves network structure and supports consistent flow propagation would be valuable.
Figures are generally interesting but pixelated, and some global maps are difficult to interpret in detail; localized zooms could improve readability. In Figure 2, a brief comment on sensitivity to the weighting scheme would also be helpful.
Minor
- “Observations of water surface elevation (WSE), facilitating the estimation of river discharge (e.g., Biancamaria et al., 2016; Durand et al., 2016)”. Water surface slope is also a key hydraulic observable for discharge estimation, together with dry channel geometry. Discharge estimation from water surface geometry alone is an ill-posed problem which can be better constrained by hydrological closure (https://doi.org/10.1029/2024WR038455 and refs therein), hydrology (MGB, could be cited also as large scale vectorial H&H model?) here connected to vectorial river network)
- Recent raster–vector frameworks (https://doi.org/10.5194/egusphere-2026-1557, https://doi.org/https://doi.org/10.1029/2024WR038183)
- Area and connectivity conservative are important in hydrology-hydrodynamic consistency, for gradient back propagation in emerging differentiable hydrological–hydraulic modeling approaches for basin scale inference (https://doi.org/10.5194/gmd-15-6085-2022) and double H&H regionalisation (DOI: 10.22541/au.176901862.25424328/v1)
- Another recent ref that could be relevant regarding river network capture in large scale gridded models: https://doi.org/https://doi.org/10.1029/2024WR038183
- “extant solutions”
- Fig1 grey quarter circles on borders?
- L103, clarify “per Pfafstetter hydrological zone basis”
- L106, “in a hydrologically coherent”, clarify vs connectivity and mass/area conservation
- L113, clarify “ghost or unresolved reaches”
- L144 “cantered on”
- L146, “numerical instability” of what? You mean high values of A ? also improve readability of this inline equation and variable names in paragraph
- L155 and after, homogenize notations, bold in table 1. Clarify index i. ‘The candidate with the lowest score is selected” : clarify among list of reaches within a pixel? Is it the confidence score?
- L159, wheight adjustment by pfaster zone : geomorphological regularity or else assumed?
- L198, “The confidence levels assigned”, define it clearly in method section, also what is “reach-pixel mapping” vs index i in eq 1?
- “the mapping preserves upstream–downstream structure at pixel scale”, I have difficulties to understand/see that, could be clarified in methodo and results.
- Clarify “Looking ahead, the framework is readily extensible: adaptive weighting schemes, integration with alternative routing models, and incorporation of additional hydrological metrics could further refine assignment confidence”. Which other metric could help in this (from sword or else), why not done here. Clarify routing model you mean topology, complexity?
Citation: https://doi.org/10.5194/egusphere-2026-509-RC1
Model code and software
Confidence-Aware Framework for Mapping Satellite-Derived River Reaches to Gridded Routing Networks Kaushlendra Verma, and Simon Munier https://doi.org/10.5281/zenodo.18402332
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Dear authors,
in my role as Executive editor of GMD, I would like to bring to your attention our Editorial version 1.2: https://www.geosci-model-dev.net/12/2215/2019/
This highlights some requirements of papers published in GMD, which is also available on the GMD website in the ‘Manuscript Types’ section: http://www.geoscientific-model-development.net/submission/manuscript_types.html
In particular, please note that for your paper, the following requirement has not been met in the Discussions paper:
In order to simplify reference to your developments, please add the acronym/name of the mapping pipeline and a version number to the title of your article in your revised submission to GMD.
Yours, Astrid Kerkweg