Water Budget Analysis in Arid Regions, Application to the United Arab Emirates
Bibliographic record
Abstract
Population growth and economic development have impacted the capacity of water resources to meet demands in a number of arid countries. This study focuses on the United Arab Emirates (UAE) where low rainfall, high rate of growth and agricultural development are resulting in a dramatic depletion of groundwater resources and an increased dependence on desalination. A water budget for the region was developed. It represents the variations in groundwater storage as a balance of total precipitation, desalinated water and evapotranspiration. The components of the water budget are obtained from ground observations, documented information, models and remote sensing data, using Gravity Recovery and Climate Experiment (GRACE) satellites to estimate changes in groundwater storage and Tropical Rainfall Measuring Mission (TRMM) satellites and Global Land Data Assimilation System (GLDAS) data to obtain precipitation and soil moisture respectively. Results show a negative trend of 0.5 cm/year in groundwater levels corresponding to an average decrease of 0.86 km3/year during the study period (2003 to 2012). This negative trend indicates that the aquifers are not being recharged fast enough to compensate for human withdrawals. Most of the precipitation was found to be lost through evapotranspiration. A discussion of the current water budget components is presented and propositions are made for a sustainable use of water resources in the UAE, including a more efficient use of recycled water. This analysis is applicable to other Gulf countries and it can help to determine the optimal allocation of water resources to optimize agricultural productivity.
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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".