Water, Governance and Sustainability: A Case Study of Water Allocation in Whiteman's Creek, Ontario
Bibliographic record
Abstract
This research focuses on the role of water governance in building resilience and fostering sustainability in socio-ecological systems (SES). Water governance refers to the structures, processes and actors – and the dynamic interactions among them – that facilitate and influence decisions affecting water resources and aquatic ecosystems in terms of their collective influence on sustainability of SES. As human water demands grow and the impacts of climate change set in, water governance regimes are increasingly challenged to provide sufficient water to support livelihood and economic activities while also protecting the life-supporting functions of freshwater ecosystems. The objective of this thesis was to understand and assess whether governance arrangements for water allocation in Ontario are effectively addressing this challenge. \n \nA broad literature review focused on three overlapping bodies of literature – (1) sustainability, resilience and systems thinking, (2) governance and planning, and (3) water policy and management. From this review, a conceptual framework was developed to guide understanding and assessing the effectiveness of water governance arrangements to enhance resilience and foster sustainability. The framework includes seven criteria: socio-ecological system integrity; equity; efficiency; transparency and accountability; participation and collaboration; precaution and adaptation; and, integration. \n \nA case study of water allocation was undertaken in Whiteman’s Creek watershed, a sub-watershed of the Grand River in southwestern Ontario, where water scarcity is a persistent concern and where conditions are anticipated to worsen under climate change, posing problems for both human livelihoods and the integrity of the creek ecosystem. Data for the case study were collected through content analysis of documents, records and websites and through semi-structured interviews with key informants. The conceptual framework was used to synthesize the data into a narrative from which recommendations for strengthening water governance were proposed. \n \nWater governance is increasingly taking on forms more distributed or polycentric in structure and more inclusive, collaborative and participatory than previous models built largely on top-down, centralized decision making. This shift is viewed by many as a critical element for building resilience and sustainability. While the governance regime for water allocation in Whiteman’s Creek reflects these general trends, the case study findings suggest that Ontario’s existing water governance system is not capable to deal effectively with more frequent and prolonged drought conditions anticipated in Whiteman’s Creek as the climate changes. \n \nIntroduction of decentralized governance arrangements over the past decade, primarily the Ontario Low Water Response (OLWR) plan, has enhanced capacity in Whiteman’s Creek to cope with recurring low water conditions. Yet when pressed with extreme drought conditions, as experienced during the period of field work for this thesis, the challenge of satisfying both instream water needs and withdrawal uses reveals weaknesses in the governance system, including unclear decision-making criteria (e.g., related to hydrological thresholds), uncertainty related to roles and responsibilities of various actors, and generally limited capacity for precaution and adaptation. \n \nRecommendations are proposed for improving water governance in Whiteman’s Creek, and in Ontario more broadly. Ecologically-based thresholds should be integrated into water management regimes to ensure sufficient water is secured to sustain aquatic ecosystem integrity and to provide clarity on limits to permitted allocation and OWLR thresholds. More broadly, a focus on building adaptive capacity and engaging in anticipatory planning will be central to building resilience and fostering sustainability in Whiteman’s Creek.
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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".