Groundwater temperature variations in the Turin metropolitan area (Piedmont, NW Italy): what is the future?
Bibliographic record
Abstract
Monitoring the qualitative and quantitative state of groundwater is a fundamental tool for investigating and preventing the effects of climate change and anthropic activities on water resources. This study represents the first investigation into the dependency of shallow groundwater temperature (GWT) on climate variability in the Turin metropolitan area (Piedmont, NW Italy).First, a study of GWT and air temperature (AT) data on a regional scale in the time period 2010-2019 was carried out in order to understand the relationship between the two temperatures. It was possible to observe that GWT shows a general increase throughout the Piedmont Po plain, with a mean rise of 0.85 °C/10 years while AT has a mean increase of 1.69 °C/10 years.Given these results, a 3D groundwater flow and heat transport model for the Turin metropolitan area (approximately 130 km2) has been developed using the Smoker/Heatflow numerical code. For building the model 2 different measurement campaigns of GWT in the area has been carried out during 2022. The objective of the modelling was to better understand flow and heat transport dynamics in the shallow aquifer; moreover, a further aim was to analyse how climate change and the urban heat island of the city influences GWT, also from a forecasting perspective.Following calibration of the model with the available data, future predictions has been made using AT data from different IPCC scenarios for the city of Turin. It has been chosen to use RCP 4.5 and RCP 8.5. The results showed for the RCP 4.5 scenario, the maximum GWT reached is 17.9 °C with an average increase of 1°C from 2022 to 2099; for the RCP 8.5 scenario the maximum GWT reached is 19.2°C with an average increase of 1.5°C from 2022 to 2099.The development and application of this model has made possible to simulate variations in GWT on a local and city-scale in order to better understand how urban GWT will respond to the different climate scenarios in the perspective of better future management of the resource.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".