Simulating Pan-Arctic Runoff With a Macro-Scale Terrestrial Water Balance Model
Bibliographic record
Abstract
A terrestrial hydrological model, developed to simulate the high-latitude water cycle, is described along with comparisons to observed data across the pan-Arctic drainage basin for the period 1980--2001. Gridded fields of plant rooting depth, soil characteristics (texture, organic content), vegetation, and daily time series of precipitation and air temperature provide the primary inputs used to derive simulated runoff at a grid resolution of 25 km across the pan-Arctic. The Pan-Arctic Water Balance Model (P/WBM) includes a simple scheme for simulating daily changes in soil frozen and liquid water amounts, with the thaw/freeze model (TFM) driven by air temperature, modeled soil moisture content, and physiographic data. P/WBM-generated maximum summer active-layer thickness estimates differ from a set of observed data by an average of 12\,cm at 27 sites in Alaska, with many of the differences within the variability (1 $\sigma$) seen in field samples. Simulated long-term annual runoffs are in the range 100 to 400\,mm year$^{-1}$, with highest runoffs found across northeastern Canada, southern Alaska, and Norway. Lower simulated runoff is noted along the highest latitudes of the terrestrial Arctic in North America and Asia. Good agreement exists between simulated and observed long-term seasonal (winter, spring, summer/fall) runoff to the 10 Arctic sea basins ($r$ = 0.84). Model water budgets are most sensitive to changes in precipitation and air temperature, while less affect is noted when other model parameters are altered. Increasing daily precipitation by 25\,% amplifies annual runoff by 50 to 80\,% for the largest Arctic drainage basins. Ignoring soil ice by eliminating the TFM sub-model results in runoffs which are 7 to 27\,% lower than the control run. The spatial and temporal variability of freshwater export along continental margins is also explored. This flux represents a merging of simulated discharge and observed data. The results of model sensitivity experiments, along with other uncertainties in both observed validation data and model inputs, emphasize the need to develop improved spatial data sets of key geophysical quantities---particularly climate time series---to better estimate terrestrial Arctic hyrological budgets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".