Investigation of the nonlinear hydrologic response to precipitation forcing in physically based land surface modeling
Bibliographic record
Abstract
This paper is concerned with the effect of precipitation forcing on land surface hydrological variables predicted by a physically based land surface scheme. The aspects considered are the differences in precipitation input across varying sensor measurements and temporal scales of aggregation. Precipitation accumulations at 1-, 2-, 3-, and 6-h time scales are derived on the basis of standard 5-min rain gauge rainfall measurements, hourly rain gauge calibrated WSR-88D radar rainfall estimates, and passive microwave calibrated half-hourly satellite infrared rain retrievals. The spatial resolution of the rainfall estimates is fixed to 1° grid boxes. The off-line community land model (CLM) is used to simulate land surface parameters on the basis of external meteorological forcing parameters. The study region and data consist of two vegetation-distinct (high and low vegetation cover) sites in Oklahoma. The data used include one warm season (May–August 2002) of in situ meteorological data from the Oklahoma Mesonet. The CLM is forced with the three different rainfall input datasets for varying temporal scales (1–6 h). Relative difference statistics in terms of rainfall and land surface parameters are presented between the two remote sensing rain retrievals and the gauge rainfall measurements used as reference. Results show that the hydrological response is nonlinear and strongly dependent on the error characteristics of the retrieval (e.g., more temporal correlated rainfall error results in higher error propagation in land surface parameters). We also investigate the temporal lag correlation of the error in rainfall with the error in the various land surface hydrological variables. Time resolution is shown to have an effect on the error statistics of the hydrologic variables. Coarse time resolutions are associated with errors of lower variance and higher correlation.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".