Multi-decadal Floodplain Classification and Trend Analysis in the Upper Columbia River Valley, British Columbia
Bibliographic record
Abstract
Abstract. Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in landcover and hydro-climatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A time series landcover classification from the Landsat image archive was generated using a Random Forest algorithm from 1984 to 2022. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed landcover trends. The land cover classifier used to segment changes in wetland area and open water performed well (Kappa = 0.82). Over the last four decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and flow duration has been reduced. The frequency of both high discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snow melt from April to mid-May and are associated with seasonal changes in the vegetative communities over the 39-year period. The area of woody shrub landcover has increased in the spring (April to mid-May), peak flow period (late-May to July) and early fall (August to mid-September) by +6 % to +12 % since 1984. In the spring and early fall, the area of open water has decreased –3 % to –6 % since 1984, while it has increased 3 % during the peak flow period. The area of marsh land cover (mostly bulrush and cattails) has declined in every season by –29 % in spring, –19 % in the peak flow period and –5 % in early fall. These findings suggest that increasing temperatures have already impacted regional hydrology, wetland hydroperiod and floodplain landcover in the Upper Columbia Valley in Canada. Overall, there is substantial variation in seasonal and annual land cover reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing the areas of woody/shrubby habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open water habitats are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".