Connectivity of post‐fire runoff and sediment from nested hillslopes and watersheds
Bibliographic record
Abstract
Abstract Wildfire increases the potential connectivity of runoff and sediment throughout watersheds due to greater bare soil, runoff and erosion as compared to pre‐fire conditions. This research examines the connectivity of post‐fire runoff and sediment from hillslopes ( < 1.5 ha; n = 31) and catchments ( < 1000 ha; n = 10) within two watersheds ( < 1500 ha) burned by the 2012 High Park Fire in northcentral Colorado, USA. Our objectives were to: (1) identify sources and quantify magnitudes of post‐fire runoff and erosion at nested hillslopes and watersheds for two rain storms with varied duration, intensity and antecedent precipitation; and (2) assess the factors affecting the magnitude and connectivity of runoff and sediment across spatial scales for these two rain storms. The two summer storms that are the focus of this research occurred during the third summer after burning. The first storm had low intensity rainfall over 11 hours (return interval <1–2 years), whereas the second event had high intensity rainfall over 1 hour (return interval <1–10 years). The lower intensity storm was preceded by high antecedent rainfall and led to low hillslope sediment yields and channel incision at most locations, whereas the high intensity storm led to infiltration‐excess overland flow, high sediment yields, in‐stream sediment deposition and channel substrate fining. For both storms, hillslope‐to‐stream sediment delivery ratios and area‐normalised cross‐sectional channel change increased with the percent of catchment that burned at high severity. For the high intensity storm, hillslope‐to‐stream sediment delivery ratios decreased with unconfined channel length (%). The findings quantify post‐fire connectivity and sediment delivery from hillslopes and streams, and highlight how different types of storms can cause varying magnitues and spatial patterns of sediment transport and deposition from hillslopes through stream channel networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".