Sources of Surface Water in Space and Time: Identification of Delivery Processes and Geographical Sources With Hydraulic Mixing‐Cell Modeling
Bibliographic record
Abstract
Abstract Knowledge of the sources of surface water in riparian zones and floodplains is critical to understanding its role in runoff generation and impact on biogeochemical and ecological processes. In this study, we demonstrate the potential of integrated surface‐subsurface hydrologic modeling (HydroGeoSphere) in combination with a hydraulic mixing‐cell approach to decipher different sources of surface water and their mixing in space and time. We present a novel approach to processing the model data that allowed us to compare which mechanisms ultimately transferred water to the surface (delivery processes) and from where the surface water originated (geographical sources) for varying wetness states and phases of wetting or drying across 36 test locations within the riparian‐stream continuum of an intensively‐studied, humid‐temperate, forested headwater catchment (45 ha). Consistent with current process understanding for the study site, water exfiltrating from the subsurface was simulated as the dominant source for riparian surface water and intermittent streamflow. The model further helped to specify the relevance of different subsurface stores, revealing a wetness‐dependent activation of upslope source areas. Contributions of riparian overland flow and precipitation were minor during all investigated phases of wetting and drying. Moreover, the spatial variability of surface water sources proved to be smaller than expected for the heterogeneous patterns and frequencies of the surface saturation observed and simulated. Based on these findings, we discuss the value of hydraulic mixing‐cell modeling to complement the planning and interpretation of field investigations and to enhance process understanding regarding the spatio‐temporal sources of surface water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".