Using public cloud computing infrastructure for rapid simulations of large-scale global reservoirs
Bibliographic record
Abstract
Water reservoirs play an important role in relation to water security, flood risk, agriculture production, hydropower, hydropower potential, and environmental flows. However, long-term daily information on reservoir volume, inflow and outflow dynamics are not publicly available. To enable deriving long-term reservoir dynamics for many reservoirs across the globe using a distributed hydrological model, large amounts of computer power are needed. Therefore, these types of simulations are generally performed on super computers. Nowadays, public cloud computing infrastructure offers interesting alternative and allows one to quickly access hundreds to thousands of computer nodes. The current work presents an example of making use of the public cloud offers by simulating the dynamics of 3236 headwater reservoirs on a Kubernetes Cluster on Microsoft Azure. Within the cloud, distributed model forcing and hydrological parameters at a 1-km grid resolution were derived using HydroMT, which subsequently were used by wflow_sbm to perform long-term hydrological simulation over the period 1970-2020. To enable operation in the cloud, usage is made of the Argo workflow engine, that is effective able to schedule the sequential execution of the HydroMT and wflow_sbm containers. Using this setup, all model simulation results were obtained in less than a week. We will present the executed modeling setup within the public cloud as well as present some of the results derived in this manner by comparing observations with in situ and satellite observations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".