An integrated modeling system to simulate the impact of land-use changes on hydrological processes in the Elbow River watershed in Southern Alberta
Bibliographic record
Abstract
The Elbow River watershed (ERW), in southern Alberta, Canada, covers an area of 1,238 km2 and is subjected to considerable pressure for land development due to the rapid population growth in the City of Calgary. In this study, a comprehensive modeling system was developed to investigate the impact of land-use change on hydrological processes considering the complex surface-groundwater interactions existing in the watershed. Specifically, a spatially explicit land-use change model was coupled with MIKE-SHE and MIKE-11, a distributed physical based catchment and channel flow model. The modeling system was designed such that it has the following unique features: simulate dominant land-use changes in a spatially distributed way using a spatially explicit land-use change model, integrate spatially distributed land-use based parameters through the coupling of the land-use change model and the hydrological model, use comprehensive mechanisms to simulate the surface water and groundwater processes and their interactions, and incorporate a flexible design so that new land-use change plans can be incorporated easily for scenario analysis. Following a rigorous sensitivity analysis along with the calibration and validation of the integrated models, four land-use change scenarios were simulated for the period 2016-2031: business as usual (BAU), new development concentrated within the Rocky View County (RV-LUC) and in the Hamlet of Bragg Creek (BC-LUC) respectively, and development based on projected population growth within the ERW (P-LUC). The simulation results reveal that the rapid urbanization and deforestation create an increase in overland flow, and a decrease in evapotranspiration, baseflow, and infiltration mainly in the east sub-catchment of the watershed. Furthermore, BC-LUC is the most preferable scenario, while the BAU scenario with the same amount of new built-up area is found less preferable in terms of the impact on overland flow and baseflow. The scenario P-LUC is found the least preferable out of all scenarios mainly due to the aggressive new development associated to the high population growth. The land-use/hydrological modeling system described in this study is resourceful and could be used to reduce the negative impact of land-use changes on the hydrological processes in the Elbow River watershed. This study is the first of this nature carried out in the Elbow River watershed, and is unique mainly due to its comprehensive framework that facilitates spatial explicit land-use change modeling, physical based and distributed hydrologic modeling, and the connection between these via distributed land-use based parameters. Furthermore, the design of this modeling system is flexible enough to extend this study to consider more aspects of the environment, e.g., incorporate climate change data to evaluate the impact of land-use and climate changes on hydrological processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".