Dynamic Feedback Coupling of Continuous Hydrologic and Socio-Economic Model Components of the Upper Thames River Basin
Bibliographic record
Abstract
The main contribution of this work consists of formulating a novel simulation framework used in analysis of climate change impact assessment. The model developed consists of a continuous hydrologic component coupled (via feedback) to a socioeconomic component developed using system dynamics. The hydrologic component of the model responds to changes in socio-economic conditions (such as changing economic, demographic and land use patterns), while socio-economic conditions are continually influenced by hydrologic quantities (such as available ground water recharge, flow and precipitation). As the two components are connected via feedback, each dynamically influences, and is influenced by, the other thereby mimicking such interactions in the real world. The combined model represents a comprehensive integrated water resources management tool developed to test climatic impact and change of both hydrologic and socio-economic conditions in the area. The model is developed for the Upper Thames River basin, located in southwestern Ontario, Canada. The study area encompasses a number of growing urban and rural communities, with the largest community being the City of London. The entire basin is approximately 3,500 km2, with an approximate population of 420,000 (of which 350,000 live in the City of London). Agricultural land occupies approximately 80% of basin’s land area, while forest cover and urban land take up about 10% each. Hydrology of the basin is quantified with a continuous hydrologic model component, and incudes detailed modules describing snow accumulation and melt; losses; transformation of surface excess to river runoff; representation of baseflow; as well hydrologic river routing methods. The socio-economic characteristics are expressed with a component describing dynamics of urban and rural population; business and housing, as well as detailed land and water use patterns. The overall (or combined) model couples two components, and is thus capable of testing a wide range of socioeconomic policies and management strategies (like changes in demographics, housing, jobs, land and water use practices), as well as able to produce detailed hydrologic output (like frequency of floods/drought, timing and regularity of flows) typically used for impact analysis and/or engineering design. Simulation of the model is performed for three different climate scenarios (no change, increased precipitation, and increased temperature) obtained from an external weather generator (a tool used to simulate alternate regional climate characteristics based on historical information as well as latest knowledge of global climate models). The climate scenarios are coupled with a range of socio-economic scenarios where different management strategies are explored (such as proceeding with the belief that regional water resources are infinite, implementing a strict water conservation policy, a combination of water conservation with limiting land development, as well as implementing a switch from ground to surface water use basin wide). The main findings revealed by simulation of different scenarios include the following: (i) climate change has the potential to significantly alter flooding characteristics of the region by increasing risk levels (and its corresponding frequency of occurrence) of extreme conditions; (ii) frequency of extremes of drought conditions are likely to remain at their current levels and, (iii) the most significant regional socio-economic factor is availability of water, shown as a limiting agent to growth of population and regional economy. Recommendations are suggested to area’s water resources professionals that urge them to consider revising existing management guidelines in light of knowledge of altered hydrologic and socio-economic conditions in the basin as a result of climatic change.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".