Assessing the Relative Contributions of Input, Structural, Parameter, and Output Uncertainties to Total Uncertainty in Hydrologic Modeling
Bibliographic record
Abstract
The simulation of physical environments by hydrologic models has become common as computational power has increased. It is well known that, to simulate the hydrology of a physical environment, simplifications of that environment are needed. The simplified versions of hydrologic processes generate uncertainty, in addition to ingesting uncertainty from input data. The uncertainty from one modeling step affects the next through propagation. Although computational power has increased through time, the computational demand for uncertainty analysis still remains a common limiting factor on the level of detail an uncertainty analysis can be conducted with. This thesis generates an estimate of total uncertainty propagated from input, structural, and parameter uncertainties for the Nelson River in the Lower Nelson River Basin near the outlet to Hudson Bay, as part of the BaySys project. Each source of uncertainty was relatively partitioned for determination of the most valuable source of uncertainty for consideration in an operational environment with a limited computational budget. The results of this thesis show the complex spatial and temporal variation present in gridded climate data. This thesis also presents an ensemble-based methodology to account for the input uncertainty associated with gridded climate data subject to propagation. The ensemble of input data was propagated through an ensemble of hydrologic models. Relative sensitivities of model parameters were shown to vary temporally and based on performance metrics, suggesting that aggregated performance metrics obscure information. Lastly, relative partitions of uncertainty were compared through cumulative distribution functions. Accounting for all sources of uncertainty appeared valuable towards improving streamflow predictability, however, structural uncertainty may be the most valuable in an operational environment with a limited computational budget followed by input, and parameter uncertainty.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".