Evaluation of climate change impact on plants and hydrology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change (CC) is the menace of the hour impacting every facet of human existence. Regional CC and its impact studies are crucial in that they contribute to global change. The current study aims to investigate the prevalence of CC in Charsadda, Pakistan and its impact on vegetation and hydrology of the region to understand microclimate variability contribution to global CC. Utilizing local climate data for 20 years (2001–2020), Modified Mann-Kendall and Sen’s Slope statistics were employed to determine monthly and seasonal trends in climate variables. Significant changing climate variables were regressed on Moderate resolution Imaging Spectroradiometer (MODIS) satellite dataset viz. normalized difference vegetation index (NDVI). Due to the prominent climate factor impacting vegetation, NDVI was further correlated to MODIS land surface temperature (LST). Floods being the conspicuous climate calamity were mapped for 2005 and 2010 using satellites Landsat 5 and 7 dataset viz. normalized difference water index (NDWI) with flood risk assessment by watershed delineation. The findings revealed significant ( p < 0.05) variability in climate variables (average monthly and summer maximum temperature, and average monthly and summer precipitation) that are driving CC and impacting vegetation and hydrology in the region. Temperature and solar radiation affect NDVI adversely while precipitation and relative humidity has positive impact on vegetation. NDVI varied greatly spatiotemporally, often increasing but worsening in some areas (Shabqadar, Abazai, Palai and Charsadda city with NDVI = 0.1–0.3) of the study region as a result of extreme weather events. Temporally, NDVI improved with an overall positive trend with a stage (2007–2016) of noticeable zigzag fluctuation. Spatial grids with higher LST (>40°C) were either devoid of or with sparse NDVI (<0.3) presenting global warming as peril to vegetation. NDWI maps (2005, 2010 floods) indicate that after floods wreaked havoc on the region altering the vegetation pattern revealing heavy irregular precipitation as the next to temperature in jeopardizing vegetation of the region. Lower elevation regions along the Swat and Kabul Rivers with a greater risk of flooding were identified by watershed delineation. The study suggests that local governments and stakeholders implement CC mitigation strategies and plans for vegetation restoration, flood alerts with post-flood management for regional sustainable development.
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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 | 0.002 | 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 | 0.001 |
| 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 it