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Record W7053304610

Water-Energy-Food Nexus Assessment Framework for Integrated Resource Management

2022· dissertation· en· W7053304610 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)HydropowerResource (disambiguation)Food securityAgricultureSustainabilityUnintended consequencesResource management (computing)Production (economics)Climate change
DOInot available

Abstract

fetched live from OpenAlex

Water, energy, and food (WEF) security are fundamental to human welfare and sustainable development, and WEF sectors are inevitably interconnected. Conventional sectoral policy- and decision-making in ‘silos’ may lead to unintended consequences to other sectors beyond objectives and scales or cause maladaptation. The uncertainty in climatic and socio-economic changes increases the level of complexity and challenges in WEF resources management. Addressing these issues calls for a “nexus” approach that aims to untangle interactions among sectors and identify opportunities to reduce trade-offs while building synergies to promote overall resource use efficiency, resource security, and policy coherence. This thesis aims to advance the understanding of integrated resource management, in particular, water, energy, and food, under historical conditions and future changes by proposing a comprehensive water-energy-food nexus assessment framework (WEFNAF). Three steps have been designed to achieve this goal. First, a WEF nexus model using the system dynamics approach has been developed. This model is applied to the Province of Saskatchewan, Canada, and so is named WEF-Sask. WEF-Sask captures the essential linkages and feedback loops among the water, energy, and food sectors, including water use for energy and agricultural (rainfed and irrigated) production, energy demand for agricultural activities and water supplies, and bio-crops (wheat and canola) for bioenergy production. The hydropower-irrigation trade-offs and synergetic benefits from the expanded use of other renewable energy sources are highlighted. Second, WEF-Sask is coupled with a climate downscaling tool and hydrological models to investigate the WEF nexus behavior under ensembles of hydroclimatic conditions and policy options. Based on preset agricultural and hydropower production targets or thresholds, favorable and unfavorable scenarios are identified and suggestions to improve the nexus performance are provided. Third, sector-specific adaptation strategies in the agriculture sector are proposed in response to potential hydroclimatic changes in the future, including agronomic measures and genetic improvements in crop cultivars. These strategies are evaluated from a WEF nexus perspective, including crop yield, water use efficiency (WUE), green and blue water use, associated energy demand for irrigation water supply and application, and impacts on hydropower production under uncertain hydroclimatic conditions. This thesis provides a set of insightful findings and suggestions for WEF nexus management. The WEF-Sask model shows overall good performance and has the potential to help investigate trade-offs and synergies as well as test response options or evaluate mitigation and adaptation strategies in response to changing hydroclimatic or socioeconomic conditions. Sensitivity analysis shows that crop production is highly sensitive to climate change, while socioeconomic factors (e.g., population, GDP, crude oil reserve/price, natural gas reserve/price) significantly affect the energy and water sectors as well as greenhouse gas emissions. In short, total water demand is most sensitive to population, air temperature, and precipitation. Future hydroclimatic changes cause uncertainty in the WEF nexus, and water deficit resulting from possible decreasing transboundary flows and local dry weather in Saskatchewan significantly threaten the WEF nexus performance. Moreover, irrigation expansion intensifies hydropower-irrigation trade-offs in dry conditions. Renewable energy expansion (wind power expansion and expanded use of bioenergy), the most effective climate change mitigation option in Saskatchewan, brings synergetic benefits by saving water from thermal power cooling and reducing greenhouse gas emissions. However, the expanded use of bioenergy (ethanol and biodiesel) in transportation is likely to significantly reduce wheat and canola surplus (export potential), resulting in trade-offs between sustainable energy and food. Climate characterized by a large increase in temperature with less rainfall will likely reduce agricultural production, to which it will be difficult to adapt; however, irrigation expansion can be employed to more easily adapt to climates characterized by a moderate temperature increase with slightly less rainfall or higher temperature increase with slightly higher rainfall. Additionally, the benefits of irrigation expansion in Saskatchewan for total food and feed production are likely to be fully offset by climate change. Therefore, it is of great importance to adopt mitigation strategies that slow down the global warming rate to make adaptations easier. Largely or fully offsetting agricultural production losses from climate change is unlikely to be achieved through individual adaptation strategies in the agriculture sector. Instead, combining individual strategies of earlier planting date, cultivars with a larger growing degree days requirement, and lower soil water evaporation can significantly compensate for the agricultural production losses from climate change and increase crop water use efficiency while mitigating the environmental burden (e.g., blue water use, energy consumption for irrigation) and hydropower-irrigation trade-offs. This result indicates that strategies involving effective water use, such as reducing soil water evaporation (alone or combined with other individual adaptation strategies), can benefit the overall food, water, and energy sectors. This strategy seems appropriate for water-scarce regions where large irrigation expansion is infeasible. Moreover, if irrigation expansion is also included, the agricultural production losses from climate change can be almost fully offset; however, this strategy requires considerable extra water and energy use for irrigation and a reduction in hydropower production.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.006
GPT teacher head0.201
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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