Dynamic Feedback Coupling of Continuous Hydrologic and Socio-Economic Model Components of the Upper Thames River Basin
Notice bibliographique
Résumé
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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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».