The spatial-temporal variations of rainfall-streamflow linkage across North America: A functional data analysis approach
Bibliographic record
Abstract
Achieving improved predictions in ungauged-basins or inferring the effects of climate and land-use changes on streamflow requires hydrologists to first learn the underlying mechanisms behind streamflow generation in gauged-basins. One way to characterize streamflow generation is by quantifying how catchments filter rainfall into streamflow. A simple and popular technique that displays the rainfall-streamflow linkage is the unit hydrograph. Though one could characterize and classify catchments based on their unit hydrographs, this approach implicitly implies that the function and response-time that link rainfall to streamflow are time-invariant. The celerity, and the function that links rainfall to streamflow in a given catchment, could vary from catchment to catchment as well as from season to season. This is primarily due to variations in antecedent wetness, temperature, vegetation transpiration and the ways climatic factors interact with biophysical factors, over time and over space. In this study, we utilize sparse historical functional linear models to quantify the time-variant rainfall-streamflow response function, across hundreds of catchments in North America. The function reflects the temporally varying relationship between rainfall and streamflow and can be used to infer temporally varying response times. We then attempt to relate catchment characteristics such geology, climate, and topography to the characteristics of rainfall-streamflow response function and response time, spatially and temporally. We argue that our study extracts generalizable and robust process understanding in a novel data-driven manner.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".