Understanding Impacts of Groundwater Extraction on Flow Dynamics in Multi-aquifers in the Ho Chi Minh City Area, Vietnam
Bibliographic record
Abstract
Understanding the dynamic characteristics of the groundwater flow budget is crucial for effective water management. This study investigates the groundwater budget in the Ho Chi Minh City (HCMC) area by the application of groundwater flow modeling. A 3D model was built to simulate groundwater flow in aquifer systems in Ho Chi Minh City, using extensive hydrogeological data from 400 borehole logs, along with climate, hydrological, and groundwater extraction data. The model was calibrated in both steady-state and transient conditions, with monthly data from 1995–2007, and then validated using five years of monthly groundwater level monitoring data from 2008–2012. In general, the calibrated and validated models show a good match between calculated and observed groundwater levels, with R2 values > 0.8. The model results illustrate that recharge rates in HCMC vary according to local geological conditions and fluctuate seasonally due to changes in climate factors such as rainfall and evaporation. The annual recharge rate did not significantly change during 1995–2012. A groundwater depression cone was observed in the city center, with a maximum groundwater level approximately 50 meters below mean sea level (bmsl). Groundwater extraction increased sixfold from 1995–2012, which is the main cause of groundwater level decline. This study also found that the river system plays an important role in maintaining the groundwater balance in the area. As a result, increased groundwater exploitation has induced a fourfold rise in river leakage, while groundwater discharge to the river decreased by 35% during the same period. These changes potentially contribute to increased risks to groundwater quality, a reduction in base flow within the river system, and greater vulnerability of the natural ecosystem.
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 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".