Understanding socio-groundwater systems: framework, toolbox, and \nstakeholders’ efforts for analysis and monitoring groundwater resources
Notice bibliographique
Résumé
Groundwater, the predominant accessible reservoir of freshwater storage on Earth, plays an important role as a human-natural life sustaining resource. In recent decades there has been an increasing concern that human activities are placing too much pressure on the resource, affecting the health of the ecosystem. However, because groundwater it is out of sight, its monitoring on both global and local scales is challenging. In the field of groundwater monitoring, modelling tools have been developed for improving insights into groundwater characteristics, dynamics, and patterns in the hydrological cycle. Although models are accessible, the drawbacks are: they are not always locally affordable, data are not always reliable, and stakeholders are not included. Global assessments are important, but problems might be exacerbated if we do not examine groundwater from another perspective by considering that its related problems are largely local. An example of this is the groundwater system in Yucatan, Mexico, a place where groundwater is the only source of freshwater for the population and where inhabitants have to deal with pollution problems, salt intrusion, biodiversity loss and resource degradation. Consequently the water situation can quickly reach critical conditions and even small disturbances may have dramatic consequences. \n \nGoal \nThe goal of this research project is to “develop a research toolbox for understanding and developing a monitoring system for socio-groundwater systems. The toolbox includes a transdisciplinary methodology to develop a Socio-Groundwater model, to investigate factors that influence the way in which groundwater resources are managed, to support monitoring efforts and approaches to contribute to groundwater literacy”. The work described in this thesis deals with the design of the framework, characterization and modelling of the system by (i) developing a model by analyzing the relevant fluxes of the groundwater system, (ii) eliciting the stakeholders mental models, their values regarding groundwater use and management, and (iii) proposing a groundwater toolbox to integrate different methods with environmental activities for a better groundwater resource management. \n \nMethods \nThis thesis is part of a larger project organized in three phases: development of the framework, the toolbox and monitoring. Five different, interrelated, methodologies for system analysis have been developed and employed, including: 1 material flow analysis (to quantify groundwater flows associated with present-day economic sectors), 2 mental models (to analyze stakeholders’ risk perception regarding groundwater pollution, by eliciting mental models), 3 underwater exploration (to obtain insights about current status of local wells and sinkholes), 4 community-based conservation (to integrate local values, beliefs and perceptions into groundwater conservation), 5 environmental activism (to directly involve stakeholders in local well clean-ups, and community events). These methods were developed in a transdisciplinary process with stakeholders spanning sectors including: NGOs, local communities and policy makers. \n \nAnalysis and results \nWe analyzed, in a unique way, groundwater in Yucatan, Mexico, where no other sources of freshwater exist. Applying system analysis and bringing local and scientific knowledge, we adapted our framework and methods to understand sociogroundwater systems. Data was obtained from a range of different sources: literature, national and local statistics, stakeholder’s workshops, expert opinions, expert consultation, local interviews, and estimations. Investigation of flows by applying MFA helped us to develop the first Local Groundwater Balance Model. The results from this revealed: a) high wastewater emissions into the aquifer (ca. 6.4 hm3 year−1). Wastewater ranges from grey water to wastewater with high concentrations of organic matter (i.e., discharges from pig farms) and alkaline discharges (i.e., tortilla industry); b) all wastewater emissions are discharged directly into the aquifer without treatment; c) poor recycling practices (<1%, relative to the total water emissions). Mental models of local members and experts were elucidated and discrepancies were found regarding risk perceptions. The results revealed that experts’ conceptions were rather unclear and incomplete, based on characteristics of the system. They described fluxes erroneously, vaguely, and depicted unclear flows representing the system.Experts perceive a clear connection with pollution and related health risks. Locals described groundwater as clean drinking water, originating from rainfall, but stored in the ocean. They described fluxes erroneously and they were not aware of the concept of groundwater stored in a porous medium. The local population did not see any connection between pollution (i.e., use of pesticides) and health related risks. Overall, the mental models did not overlap since locals did not have a clear understanding of their influence on the system whereas experts did. Interviews revealed a profound sense of loss of local and traditional knowledge, a strong desire to learn about groundwater, to restore cultural practices and to revitalize local values. Contemporary governance status and regimes of the cenotes are mostly mixed or unclear. Community respondents did not seem to associate contamination of cenotes and the governance regime, and what that might imply for stewardship responsibilities. Interviewees did not understand that all cenotes are part of a single interconnected groundwater system, and cultural values did not seem to be considered. Speleological records obtained during underwater explorations evidenced current hotspots of pollution due to bad waste disposal local practices. Cenotes explored indicated bad waste disposal practices in open-illegal dumping. Through sinkhole clean-ups, locals were aware of groundwater sensitivity, hazardous materials associated with human activities (i.e., agriculture) and this provided insights into local pollution sources. We collected ca. 400 kg of plastics and solid waste from just one of the cenotes explored. Plastic bags, cups, cans and cigarette butts were amongst the top ten items collected. Direct involvement with policy makers, experts and locals was found to be key to guide and validate the project stages in an iterative process, and simultaneously narrowed the gap between science, policy-making and society towards groundwater sustainability. \n \nConclusions \nIn the study of the groundwater system in Yucatan, we found that technical solutions to groundwater problems are of importance; however, local stakeholder involvement is crucial. We agree with the relevance of models, but we offer a much more comprehensive view and approach to local groundwater problems since we involved stakeholders during the research process. This is a reliable and versatile methodology that meaningfully contributes to groundwater sustainability and literacy. It can be adapted to the specific social, economic, political, and environmental setting of different regions. A real transformation is required in how we value, manage and characterize groundwater systems since hydrological-only models and singlediscipline approaches seemed to have failed. The proposed toolbox, framework and approaches help to identify the patterns of changes in biosphere leading to changes in inequality, as a driver of resource use patterns. To implement SDG 6, our toolbox specifically supports the following Targets 6.3, 6.4, 6.5, and 6.6 (a & b) by: facilitating examination of hotspots of pollution, distribution of flows of hazardous substances, minimizing release of chemicals, ensuring sustainable withdrawals, revealing water extraction trends and sectors with major consumption, strengthening participation of local communities, and safeguarding the ecosystem by recognizing traditional ecological knowledge as an informal norm of monitoring. Our results support the current global monitoring framework of SDG 6, by acknowledging the importance of community participation and that groundwater literacy is essential to effectively address SDG 6 and its water related targets.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,007 |
| Communication savante | 0,006 | 0,013 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».