The new HydroSHEDS v2.0 database derived from the TanDEM-X DEM
Bibliographic record
Abstract
The increased availability and accuracy of recent remote sensing data accelerates the development of high-quality data products for hydrological modelling. Accurate representation of the Earth's surface, including all water-related features, is crucial for simulating runoff and other hydrological processes. HydroSHEDS v2.0, the second and refined version of the well-established HydroSHEDS dataset, provides global seamless high-resolution hydrographic information. Developed through an international collaboration involving the German Aerospace Center (DLR), McGill University, Confluvio Consulting, and World Wildlife Fund, HydroSHEDS v2.0 builds on the TanDEM-X mission's digital elevation model (DEM) to offer enhanced accuracy and expanded geographic coverage compared to its predecessor. While the first HydroSHEDS version relied on the Shuttle Radar Topography Mission (SRTM) DEM, HydroSHEDS v2.0 benefits from the TanDEM-X DEM, which provides a higher resolution of 0.4 arc-seconds globally and includes regions beyond 60°N latitude, previously uncovered by SRTM. Advanced pre-processing techniques ensure that HydroSHEDS v2.0 preserves the high-resolution details of the TanDEM-X DEM. These techniques include the generation of a global inland water mask and its usage for filling invalid and unreliable DEM areas, delineating global coastlines with manual quality control, and reducing distortions caused by vegetation and urban areas. A sequence of automated hydrological conditioning steps further refines the DEM, incorporating void filling, outlier correction, and algorithms to optimize hydrological consistency. Finally, extensive manual corrections using various ancillary data sources improve river network delineation in areas where high uncertainties exist for DEM-derived products, such as areas with flat terrain or anthropogenically modified landscapes. The resulting hydrologically conditioned DEM has a resolution of 1 arc-seconds and ensures accurate derivation of hydrologic flow connections, forming the basis for core products such as flow direction and flow accumulation maps. In the final HydroSHEDS product, these gridded datasets are complemented by secondary vector-based information on river networks, nested catchment boundaries, and associated hydro-environmental attributes. Together, these products create a standardized, multi-scale database in the same structure and format as the original version and supports applications ranging from local to global scales. HydroSHEDS v2.0 offers a consistent and easy-to-use framework for hydrological and hydro-ecological research. The main release, scheduled to start in 2025 under a free license, will provide researchers and practitioners with a robust tool for diverse applications. A demonstration of the novel data products and the pre-processing workflow be presented for selected test sites.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".