Stochastic simulation of rainfall in the semi‐arid Limpopo basin, Botswana
Bibliographic record
Abstract
Abstract The application of spatial‐temporal stochastic rainfall models to semi‐arid or arid areas is expected to be particularly challenging because of the high variability of rainfall, sparse rain gauge networks with significant periods of missing rainfall and potential data quality issues. In this article, a generalized linear model (GLM) has been fitted to daily rainfall data from the period 1975–1999 for 13 gauges in a 7660 km 2 sub‐basin of the Limpopo basin in Botswana, with the objective of exploring applicability of the GLM for infilling historic records and for climate change analysis. Several relevant statistics of rainfall space‐time variability were used to analyse model performance, including use of an independent validation period and sites that were not used in the fitting. The GLM was considered to simulate rainfall adequately for the purpose of sub‐basin‐scale water resource studies, although the model uncertainty is high. The main factors affecting rainfall space‐time variability were found to be seasonality, autocorrelation of daily rainfall, altitude, latitude and longitude. Addition of large‐scale drivers of rainfall (pressure, humidity and temperature) further improved representation of inter‐annual variability, and this link to large‐scale climate potentially facilitates downscaling of global climate model outputs. Although the model was locally sensitive to data quality issues, there was no evidence that these issues affected sub‐basin scale analysis. Copyright © 2011 Royal Meteorological Society
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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 | 0.001 | 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 | 0.001 | 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 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".