Impacts of Beetle Kill on Modeled Streamflow Response in the North Platte River Basin
Bibliographic record
Abstract
A beetle epidemic has been sweeping its way across the western United States and into portions of southern Canada that has caused millions of acres of forests to ultimately die. This beetle outbreak, that many have come to know simply as “beetle kill”, has caused many scientists to feel that such dramatic changes in land cover could potentially alter the hydrology throughout much of the West. One of the most important hydrological processes that beetle kill has the potential to impact is streamflow. This paper attempts to evaluate the hydrological impacts on streamflow from land cover change due to beetle kill in the North Platte River Basin (NPRB), by utilizing a hydrological model, Variable Infiltration Capacity (VIC). VIC is a land surface hydrological model that, for this analysis, has been calibrated and validated for the periods of 1950-1980 and 1981-2000, respectively, by using daily meteorological forcings and monthly streamflow data. In order to quantify the impacts on streamflow, land cover was changed by decreasing forest canopy coverage in order to mimic beetle kill for five different simulations, based on results obtained from basin level estimates of canopy loss, with error, using remote sensed data. Based on these five simulations, an increase of approximately 1% to 10% in decadal streamflow was observed for a decrease of 16% to 40% in forested land cover. Additionally, the average change in forest cover of 28% produced an increase in decadal streamflow of roughly 5%. However, based on model limitations and general assumptions, this estimate of increased streamflow was likely a high estimate. Given beetle kill did not fully manifest itself in the NPRB until roughly 2007/2008, modeling the proposed changes in land cover for the period 1950-
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".