An Evaluation Of The Impact Of Land Cover Change On Seasonal Evapotranspiration Estimates In The Upper Gundar River Basin, Tamilnadu, India
Bibliographic record
Abstract
The water cycle significantly impacts the Earth's atmospheric temperature and overall energy balance. In a warming climate, changes in the hydrologic cycle, both spatially (planetary, continental, and regional) and temporally (daily and yearly), are anticipated. Factors driving this change at a watershed scale include increased agricultural intensity, evolving land use patterns, and the development of industrial and urban areas. Studies also suggest an expected decline in overall runoff due to changing precipitation patterns and, notably, evapotranspiration (ET), encompassing plant transpiration and land evaporation. This outlines the importance of studying ET dynamics at a watershed scale. This research focuses on the Upper Gundar River Basin, part of the Gundar basin in Tamil Nadu, India. To estimate basin-scale ET, the widely used Surface Energy Balance Algorithm for Land (SEBAL) is employed. SEBAL utilizes satellite imagery, digital elevation models, and weather data during the time and date of satellite overpass to estimate actual evapotranspiration in the resolution consistent with the imagery. The initial phase involves land cover classification and change detection between summer and monsoon seasons annually for the years 2006, 2014, and 2021 using Landsat data with minimal cloud cover. The land cover classes that are identified are - water, built-up land, exposed soil, barren land, agricultural land, and the invasive species Prosopis Juliflora, which is widely prevalent in the region. A random forest approach is used due to its capability to handle complex datasets in heterogeneous landscapes. The subsequent phase involves validating the gridded Global Land Data Assimilation System (GLDAS) data by comparing it with in-situ data obtained from five stations in close proximity to the region of interest. The in-situ data and the GLDAS data are utilized to meet the specific requirements for the day and time of the acquisition date, respectively. The variables under comparison are average temperature, relative humidity, solar radiation, and reference evapotranspiration. This comparative analysis employs correlation coefficients and considers the monthly time scale corresponding to Landsat data acquisition. Identification of stations demonstrating the most agreement is conducted for each season. The final phase involves utilizing in-situ daily data and instantaneous GLDAS data during satellite overpass, alongside ASTER digital elevation model, for SEBAL computations on the same date-season combinations mentioned earlier. The in-situ data necessary for SEBAL is obtained by interpolating the data from five nearby weather stations to match GLDAS resolution. Comparisons between SEBAL-derived actual evapotranspiration estimates for different land cover classes and those from EEFlux, which operates on the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) algorithm, involve visual analysis via box plots and quantification using Root Mean Square Error (RMSE) and correlation coefficients. As the end goal, agricultural water requirements for cropped regions are calculated for each day by multiplying the actual evapotranspiration estimates with the area of the land cover class. Strategies to meet the water demand are discussed and outlined.
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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 | 0.001 |
| 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) | 0.000 | 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 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".