Unraveling the Relationships between Trend of Dam Inflows, Hydrometeorological Variables, and Vegetation in Western and Southwestern United States
Bibliographic record
Abstract
Abstract This paper explored temporal changes in magnitude and seasonality of low, median, and high inflows of 51 dams across the western and southwestern United States over the 1993–2022 period. Changes in precipitation, air temperature (an indicator of snowpack and evaporation), soil moisture, and vegetation were also examined to identify potential reasons for the temporal trends in dam inflows. Using monotonic and nonmonotonic tests, we found a general downward trend in dam inflows, particularly across the Upper Colorado and California regions. More than 30% of the dams showed a downward trend in their annual median inflows, high inflows during spring, and median inflows during fall. The downward trend of dam inflows was associated with decreasing precipitation and soil moisture and increasing temperatures. While vegetation exhibited positive associations with inflows, it did not seem to be a primary factor for explaining the inflow trends. We also observed shifts in the seasonality of low and high inflows; there was an increase in the proportion of inflows occurring during summer and fall and a decrease in winter proportions for low inflows. Similarly, high inflows exhibited an increase in spring proportions and a decrease in fall proportions. Our changepoint analyses detected nonmonotonic trends between 2002 and 2012 in ∼13% of the dams; the majority were located in the Upper Colorado and California regions. More than half of these changepoints were in 2011, likely due to widespread droughts then. Our study has implications for reservoir managers to identify changes that dams experience over time and assist them in proposing actions that maintain the dams’ functionality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".