Bibliographic record
Abstract
“The problem of finding water for the needs of man is not new. What is new is the magnitude and extent of the acceleration of demand” (UNESCO 1974). Forty-five years later, this axiom is more relevant than ever. During my career that spans more than 35 years in five different countries, I have seen the science of hydrogeology developed from a purely physical science into a multidisciplinary science, a physical-chemical-social-political science. This has reshaped my approach to designing and carrying out hydrogeological studies. I will list some of the most important lessons I have learned over the years: Recognition of groundwater as an essential water resource for food security and maintaining ecosystems, assessing groundwater requires cooperation and buy-in at all levels, communication is essential outside the realm of scientific publications; simply presenting facts is not enough to inform the public's beliefs, or influence policy-makers, the need to be more persuasive and speak to our audience's values, and the need to keep up with new technologies and research strategies. Today, I see groundwater as a constantly evolving state of interconnected affaires … not always connected. The assessment of aquifers and the construction of conceptual models are made independently, without links to the social and political needs. Aquifer assessment, monitoring, and flow models are still not fully integrated in the management of groundwater resources. Most governments take decisions based on political duration periods, regardless of the groundwater time-scale effects (much slower). Large gaps on data, information and knowledge on aquifers and groundwater in many countries persist. Overexploitation of groundwater continues, showing that this is a common resource subject to the classic tragedy of the commons. International cooperation on transboundary aquifers is still very poor. Good governance of groundwater is still lacking. There is a huge gap in public and private institutions dedicated to groundwater, which do not allow a proper governance of this valuable resource. My outlook for the future, however, is an optimistic one. Groundwater will play a very important role in scenarios of changing climate. Groundwater will play an equally important role in poverty reduction within the scope of the United Nations Sustainable Development Goals (SDG6, 2016). While knowledge about groundwater remains deficient in many countries around the world, greater investments in innovative technologies will fill those gaps quickly. Groundwater awareness between society, scientists, and government are becoming more common. The increase in knowledge, communication, and transparency will motivate more concrete and efficient decision-making and management actions. The notion of aquifers (geological boundaries) is slowly disappearing, and nested (multiscale) groundwater-flow systems are becoming the units of study. Groundwater databases around the world will become interoperable and available online as open sources. In the social and legal areas, integrated surface water and groundwater models will merge with economic and social models creating unique water-management models. New technologies will facilitate much of this. Increase in the integrated water management practices with a set of remote-sensing images and databases constantly updated, recoverable in real time, and free of charge. New innovative methodologies for estimating river flow using radar (RADARSAT) images will develop, for example, Teledebit will be applied in the evaluation of rivers' flow in places not measured with hydrometric stations (Chokmani et al., 2016). These surface water flows could be used to couple groundwater-surface water models in real time. Synthetic Aperture Radar is used to track groundwater levels and make measurements of pumping-induced subsidence in real time. Use of GRACE-FO (gravity fields measured by follow-on GRACE mission) to calculate groundwater storage changes, recharge and discharge at the aquifer scale, combined with the use of quantitative indicators for groundwater management. In summary, we will need to identify the most critical groundwater-related technic-scientific, social and legal factors, as well as society and environmental issues, to quantify the amount of required groundwater that is available for all users in a sustainable way. Information acquired in real time with advanced technology will be the backbone that supports future water management scenarios.
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 | 0.000 | 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 | 0.000 |
| 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 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".