WISE: A spatially explicit carbon cycling model at the watershed scale
Bibliographic record
Abstract
Watershed-scale carbon cycling models characterize both vertical and lateral carbon fluxes and enable a comprehensive assessment of carbon budgets. However, existing models have limitations in representing hydrological flow pathways, which limits the accuracy of simulation results. In this study, we developed a spatially explicit watershed-scale carbon cycling model, named WISE (Watershed-based Integrated Simulator for the Environment), which incorporated detailed representation of hydrological connectivity across landscapes to examine the effects of hydrological flow pathways on watershed carbon cycling. First, a spatially explicit framework was designed to ensure that simulation units have clearly defined spatial locations and explicit flow relationships among them, including the interception of water and carbon by impoundments. Subsequently, four types of processes—hydrology, soil erosion, terrestrial carbon cycling, and aquatic carbon cycling—were represented and integrated based on this framework. Two representative case studies in an upstream watershed of the Ammersee lake in southern Germany and a well-measured catchment in northern Sweden were conducted to demonstrate the capacity of the developed model. Sensitivity analysis revealed that considering wetland interception improved KGE and NSE values by 0.28 and 0.54, respectively. By accurately simulating the processes of carbon transport and transformation in soil and inland waters, the WISE model provides a valuable tool for simulating and understanding watershed carbon cycling processes under global climate change scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".