Developing a global database of contemporary sediment yield observations
Bibliographic record
Abstract
For many decades, sediment yield (SY) observations have been collected around the world to analyze, monitor, and better understand the state and dynamics of various Earth system processes. These records are highly relevant for a wide variety of research applications, yet they remain poorly accessible, especially for large-scale studies. A main reason for this is the fact that many of these measurements are collected on an isolated basis, leading to inconsistencies across data sets. SY observations also suffer from large uncertainties in data quality: key factors such as location accuracy, sampling method and frequency, measuring period, and others vary greatly but are not systematically reported. To address these shortcomings and provide a standardized global reference for SY data, we are developing an extensive, coherent and georeferenced global database of contemporary SY observations. Through an extensive review of (grey) literature and contacts with numerous research groups, we already compiled SY observations for >8,000 catchments worldwide (comprising a total of >80,000 catchment years of observations). These observations are either derived from gauging station measurements or reservoir sedimentation rates. We assess the reliability of SY records and provide data quality indices based on available information such as measuring location, reported catchment area, sampling method and frequency, and measuring period. We further link the SY observations to the HydroSHEDS global river network, making them readily accessible and consistent with a wide array of hydro-environmental catchment variables also connected to the HydroSHEDS network. This new global SY database creates untouched opportunities for large-scale model development and statistical analyses of sediment-related factors and processes, such as soil erosion, sediment budgets, land cover and land use change impacts, or hydrological and sediment connectivity. Here we present a first overview of the data collected so far, its spatial patterns and its research potential.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".