Analysis of Precipitation Trends and Prediction in Selected Cities in the Southeast Louisiana
Bibliographic record
Abstract
The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".