The impact of global reservoir expansion on the present-day climate  
Bibliographic record
Abstract
By now, humans have constructed more than 45 000 large reservoirs across the globe, increasing the global lake area with 8%. These reservoirs have large impacts on freshwater processes and resources by impounding continental runoff and altering river flows. So far, the impact of reservoirs on the climate remains largely unknown, as they are typically not represented in current Earth System Models (ESMs). This is remarkable, as two-way interactions between reservoirs and climate are likely to alter hydrological extremes and impact future water availability. Here we present the implementation of the role of reservoirs in the Community Terrestrial Systems Model (CTSM), a land surface model, by accounting for the increase in open water surfaces due to reservoir construction throughout the 20th century. To this end, we allow lake area to expand in the model, while ensuring that the surface energy and mass balances remain closed. We use reservoir and lake extent from the state-of-the-art Global Reservoir and Dams (GRanD) and HydroLAKES data sets. By conducting both land-only and coupled simulations with CTSM and the Community Earth System Model (CESM), we assess the added value of accounting for reservoir expansion in the land surface model performance and investigate their impacts on the mean climate and extremes. Globally, the effect of reservoirs on temperatures and the surface energy balance is small, but responses can be substantial locally, in particular for grid cells where reservoirs make up a large fraction. Our results show that reservoirs reduce temperature extremes and moderate the seasonal temperature cycle, by up to -1.5 K (for reservoirs covering > 15% of the grid cell). This study is an important step towards incorporating human water management in ESMs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".