A simple groundwater scheme in the TRIP river routing model: global off-line evaluation against GRACE terrestrial water storage estimates and observed river discharges
Bibliographic record
Abstract
Abstract. Groundwater is a non-negligible component of the global hydrological cycle, and its interaction with overlying unsaturated zones can influence water and energy fluxes between the land surface and the atmosphere. Despite its importance, groundwater is not yet represented in most climate models. In this paper, the simple groundwater scheme implemented in the Total Runoff Integrating Pathways (TRIP) river routing model is applied in off-line mode at global scale using a 0.5° model resolution. The simulated river discharges are evaluated against a large dataset of about 3500 gauging stations compiled from the Global Data Runoff Center (GRDC) and other sources, while the terrestrial water storage (TWS) variations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission help to evaluate the simulated TWS. The forcing fields (surface runoff and deep drainage) come from an independent simulation of the Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model covering the period from 1950 to 2008. Results show that groundwater improves the efficiency scores for about 70% of the gauging stations and deteriorates them for 15%. The simulated TWS are also in better agreement with the GRACE estimates. These results are mainly explained by the lag introduced by the low-frequency variations of groundwater, which tend to shift and smooth the simulated river discharges and TWS. A sensitivity study on the global precipitation forcing used in ISBA to produce the forcing fields is also proposed. It shows that the groundwater scheme is not influenced by the uncertainties in precipitation data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".