An Evaluation of the Impact of Seasonal Land Cover Change on Evapotranspiration Estimates at the Catchment Scale in the Upper Gundar River Basin, Tamil Nadu, India
Bibliographic record
Abstract
Changes in the water cycle influence the energy balance of the Earth. The water cycle is represented using the water balance equation, in which Evapotranspiration (ET) is a vital parameter. One of the main drivers of the change in ET within a specific area is the change in land cover. This study focuses on estimating ET across the Upper Gundar River Basin located in the state of Tamil Nadu, India. Notable features of this landscape include agriculture throughout the year supported using an extensive network of tanks and borewells, and the presence of Prosopis juliflora, a widely prevalent invasive species known to consume groundwater and moisture. Due to the lack of spatial variability in point ET measurements, ET models using remote sensing imagery as the main forcing data have been widely used to assess the spatial variability and temporal variability based on the principle of surface energy balance. These models are collectively referred to as Surface Energy Balance (SEB) models. The model used in our study is the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET for two periods of the year, indicating mid-summer and the end of the northeast monsoon for the years 2006, 2014 and 2021. Since land cover changes drive ET, land cover classification and seasonal change detection are also performed for the same time periods. Imagery from Landsat satellites is used, and one image is chosen to represent the specific season. The major land cover classes chosen in our study are water, pre-growth, agriculture, Prosopis juliflora (prosopis), barren land, and exposed soil. Along with the Landsat imagery, to run SEBAL, Aster DEM is used along with in-situ weather data and GLDAS data. \nOver 90% levels of overall accuracy were achieved for all year-season combinations for the land cover classification. Using SEBAL, Actual Evapotranspiration (ETa) for all the classes is calculated except the water classes. Due to the lack of in-situ measurements, an intermodal comparison was performed with the EEFlux product available at the same resolution derived using the METRIC algorithm using land cover classes as units of comparison. The comparisons are carried out using correlation coefficient (r), root mean squared error (RMSE), and mean values. Highest mean values were observed for either the agriculture or prosopis class, and the lowest mean value was exhibited by the exposed soil class on all occasions. Within all summers, considering all the years, the average correlation coefficient and RMSE were 0.8, 1.2 mm/day, and for monsoon, the averages were 0.5 and 0.85 mm/day, indicating increased proximity during the monsoon season between SEBAL and EEFlux. Similarly, the range of mean values between classes in summer is 2.12 mm/day, 1.36 mm/day in the monsoon. In terms of the energy fluxes used to determine ETa, a decrease in monsoon is observed for soil heat flux (G), instantaneous net radiant energy (Rn_inst), and net radiation in a day (Rn_24). For sensible heat flux (H), classes with vegetation tend to have lower values in comparison to the classes without vegetation. Finally, average water outflux is calculated encompassing all classes by multiplying the area of a class with mean ETa, and the values observed in summer and monsoon alternatively for the years 2006, 2014, and 2021 in m3/day are 5,142,212, 3,534,906, 2,954,897, 4,046,322, 5,369,191, 4,512,596.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".