CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China
Bibliographic record
Abstract
Abstract. Large-scale hydrologic modeling at the national scale is an increasingly important effort worldwide to tackle ecohydrologic issues induced by global water scarcity. In this study, a surface water–groundwater integrated hydrologic modeling platform was built using ParFlow, covering the entirety of continental China with a resolution of 30 arcsec. This model, CONCN 1.0, offers a full treatment of 3D variably saturated groundwater by solving Richards' equation, along with the shallow-water equation at the ground surface. The performance of CONCN 1.0 was rigorously evaluated using both global data products and observations. RSR values (the ratio of the root mean squared error to the standard deviation of observations) show satisfying performance with regard to streamflow, yet the streamflow is lower in the endorheic, Hai, and Liao rivers due to uncertainties in potential recharge. RSR values also indicate satisfying performance in terms of the water table depth of the CONCN model. This is an intermediate performance compared to two global groundwater models, highlighting the uncertainties that persist in current large-scale groundwater modeling. Our modeling work is also a comprehensive evaluation of the current workflow for continental-scale hydrologic modeling using ParFlow and could be a good starting point for modeling in other regions worldwide, even when using different modeling systems. More specifically, the vast arid and semi-arid regions in China with substantial sinks (i.e., the endpoints of endorheic rivers) and the large uncertainties in potential recharge pose challenges for the numerical solution and model performance, respectively. Incompatibilities between data and the model, such as the mismatch of spatial resolutions between models and products and the shorter, less frequent observation records, necessitate further refinement of the workflow to enable fast modeling. This work not only establishes the first integrated hydrologic modeling platform in China for efficient water resources management but will also benefit the improvement of next-generation models worldwide.
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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 | 0.001 | 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".