Uncertainty Modeling in The Assessment of Climate Change Impacts on Water Resources Management
Bibliographic record
Abstract
Climate change has significant impacts on water resource systems. The objective of this study is to assess climate change impacts on water resource management. The methodology includes (a) the assessment of uncertainty introduced by choice of precipitation downscaling methods; (b) uncertainty assessment and quantification of the impact of climate change on projected streamflow; and (c) uncertainty in and impact of climate change on the management of reservoirs used for hydropower production. The assessment is conducted for two future time periods (2036 to 2065 and 2066 to 2095). The study area, Campbell River basin, British Columbia, Canada, consists of three reservoirs (Strathcona, Ladore and John Hart). A new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics (e.g. mean, standard deviation). To account for different uncertainty sources, four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs), six downscaling models (DSMs), are considered, and the differences in projected variables of interest are analyzed. For streamflow generation a hydrologic model is used. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by GCMs or RCPs. It is also observed that the summer (June, July & August) and fall (September, October & December) flows into Strathcona dam (British Columbia) will decrease, while winter (December, January & February) flows will increase in both future time periods. In addition, the flow magnitude becomes more uncertain for higher return period flooding events in the Campbell River system under climate change than the low return period flooding events. To assess the climate change impacts on reservoir operation, in this study a system dynamics model is used for reservoir flow simulation. Results from system dynamics model show that as the inflow decreases in summer and fall, it also affects reservoir release and power production. It is projected that power production from downstream reservoirs (LDR & JHT) will decrease more drastically than the upstream reservoir (SCA) in both future time periods considered in this study.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".