A Flexible Multi-Scale Framework to Simulate Lakes and Reservoirs in Earth System Models
Bibliographic record
Abstract
Lakes and reservoirs are an important part of the terrestrial water cycle. However, relatively little attention has been given to lake and reservoir water balance modelling, their impacts, and interaction with complex terrestrial system processes. In this work, we present the implementation of lakes and reservoirs into mizuRoute, a vector-based routing model (termed mizuRoute-Lakes) that is agnostic to the choice of hydrologic or land model. In this work, we demonstrate capabilities of mizuRoute-Lake in modeling the water balance of lakes and reservoirs namely (1) data-driven lake/reservoir models; (2) multi-model lake models; and (3) abstraction from lakes, reservoirs, and river segments. Applications presented in this work are at global, regional, and local scales. The data-driven and parametric capabilities that are provided in mizuRoute enable incorporating past or future altimetry data (e.g. from the Surface Water and Ocean Topography, SWOT, mission for estimation of lakes and reservoirs storage) or information from water management model simulations regarding water demand and reservoir operation under climate change scenarios. We believe the capabilities presented in mizuRoute-Lake will enable the modellers to diagnose and compare water balance models in a more rigorous manner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".