NH-SWE: A new Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series (1950-2022)
Bibliographic record
Abstract
Ground-based observation datasets of Snow Water Equivalent (SWE) are scarce. In contrast, numerous long-term and good quality ground observations of snow depth are available. Furthermore, an increasing number of models can accurately convert snow depth to SWE. We present a novel dataset of SWE time series over the Northern Hemisphere based on in-situ observations of snow depth. We convert snow depth to SWE using the DeltaSNOW model and we present a method to generalise the conversion model for global use. We calibrate the model over a wide range of climates with the SNOTEL dataset and we regionalise the model parameters based on climate variables. We evaluate this approach on independent datasets such as the Canadian SWE dataset and other European SWE datasets. The key strengths of the modelling approach and the SWE dataset are the excellent performance of peak SWE and timing of snowmelt season onset. The final SWE dataset contains 11,003 stations with daily SWE and snow density time series distributed across the Northern Hemisphere, including mountain regions, at the point scale, and spanning the period 1950-2022. The dataset is available and free to access. It can be used for a variety of applications including validation of remote sensing of snow, hydrological modelling, water resources assessment and climate change impact analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".