Towards the global HydroSHEDS-X dataset: DEM pre-processing for the derivation of rivers and catchments
Bibliographic record
Abstract
The increased availability and accuracy of recent remote sensing data accelerates the development of data products for hydrological modelling. Most hydrological models rely on the accurate representation of the Earth’s terrestrial surface including all waterways from small mountain streams to great lowland rivers in order to compute discharge. In light of this, the creation of the HydroSHEDS-X database, which is currently developed in an international collaborative project between the German Aerospace Center (DLR), McGill University, Confluvio Consulting, and the World Wildlife Fund, represents a new source for global digital hydrographic information. HydroSHEDS-X is the second version of the well-established HydroSHEDS database, which is freely available at https://hydrosheds.org. While the first version was derived from the digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM), the foundation of HydroSHEDS-X are the elevation data of the TanDEM-X mission (TerraSAR-X add-on for Digital Elevation Measurement), which was created in partnership between the German Aerospace Center (DLR) and Airbus. HydroSHEDS-X benefits from the higher resolution of the underlying TanDEM-X DEM given its resolution of 0.4 arc-seconds worldwide including regions with latitudes higher than 60° North, which are not covered by the SRTM DEM. Details of this high-resolution DEM are preserved in the HydroSHEDS-X dataset by applying enhanced pre-processing techniques. This pre-processing of the elevation data comprises DEM infills for invalid and unreliable areas, an automatic coastline delineation with manual quality control, the generation of an open water mask, and the reduction that are caused by distortions of vegetation and settlements. The pre-processed DEM is further treated at a resolution of 3 arc-seconds to obtain a hydrologically conditioned DEM. Derived from this hydrologically conditioned version of the DEM, the HydroSHEDS-X core products comprise flow direction and flow accumulation maps as gridded datasets. The core products are complemented with secondary information on river networks, lake shorelines, catchment boundaries, and their hydro-environmental attributes in vector format. Finally, the database is completed with associated products. Available in standardized spatial units and at multiple scales starting from a resolution of 3 arc-seconds, HydroSHEDS-X is fully compatible with its original version and thus provides a consistent and easy-to-use database for hydrological applications from local to global scale. The main release of HydroSHEDS-X is scheduled for 2022 under a free license.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".