Assessment and Projection of Water Budget over Western Canada using Convection Permitting WRF Simulations
Bibliographic record
Abstract
Abstract. Water resources in cold regions in western Canada face severe risks posed by anthropogenic global warming as evapotranspiration increases and precipitation regimes shift. Although understanding the water cycle is key in addressing climate change issues, it is difficult to obtain high spatial and temporal resolution observations of hydroclimatic processes, especially in remote regions. Climate models are useful tools for dissecting and diagnosing these processes, especially, convection-permitting (CP) high-resolution regional climate simulation provides advantages over lower-resolution models by explicitly representing convection. In addition to better representing convective systems, higher spatial resolution also better represents topography and mountain meteorology, and highly heterogeneous geophysical features. However, there is little work with convection-permitting regional climate models conducted over western Canada. Focusing on the Mackenzie and Saskatchewan river basins, this study investigated the surface water budget and atmospheric moisture balance in historical and RCP8.5 projections using 4-km CP Weather Research and Forecast (WRF). We compared the high-resolution 4-km CP WRF and three common reanalysis datasets: NARR, JRA-55, and ERA-Interim. High-resolution WRF out-performs the reanalyses in balancing the surface water budget in both river basins with much lower residual terms. For the pseudo-global warming scenario at the end of the 21st century with RCP8.5 radiative forcing, both the Mackenzie and Saskatchewan river basins show increases in the amplitude for precipitation and evapotranspiration and a decrease in runoff. The Saskatchewan river basin shows a moderate increase of precipitation in the west and a small decrease in the east. Combined with a significant increase of evapotranspiration in a warmer climate, the Saskatchewan river basin would have a larger deficit of water resources than in the current climate based on the PGW simulation. The high-resolution simulation also shows the difference of atmospheric water vapour balance in the two river basins is due to flow orientation and topography differences at the western boundaries of the two basins. The sensitivity of water vapour balance to fine-scale topography and atmospheric processes shown in this study demonstrates that high-resolution dynamical downscaling is important for large-scale water balance and hydrological cycles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".