Development of a fully integrated hydrological fate and transport model for plant protection products: incorporating groundwater, tile drainage, and runoff
Bibliographic record
Abstract
Introduction Plant protection products (PPPs) such as pesticides and herbicides are experiencing increased use worldwide. In the context of PPP authorization and registration, water exposure assessments (drinking water and aquatic exposure) use numerical modeling to simulate relevant hydrological processes and exposure pathways. A common practice for estimating PPP leaching to groundwater, PPP loading onto surface water via tile drainage, or PPP transport via runoff utilizes multiple one-dimensional models, each representing a separate exposure pathway. Separate analysis of individual exposure pathways can result in disparate assumptions being made that represent relative worst-case scenarios for each pathway, rather than an integrated reasonable worst-case scenario for all pathways. Methods The interplay between PPP degradation, leaching to groundwater, transport in tile drainage, and runoff is well-suited for simulation using an integrated surface–subsurface hydrologic and chemical fate and transport model. This study presents functionality added to HydroGeoSphere (HGS), a three-dimensional, fully integrated, surface–subsurface hydrologic model. HGS was verified against other recognized models: PRZM, HYDRUS, PEARL, PELMO, and MACRO. Added features include automatic irrigation, non-linear adsorption, temperature and soil water content-dependent degradation, and solute uptake by plant roots. Results and Discussion HGS results for leaching of PPP mass to groundwater showed the highest correlation, lowest error, and lowest bias relative to PEARL model results. Simulation of macropore flow to tile drains in HGS produced an intermittent tile drain flow in summer that resulted in generally lower peak effluent concentrations compared to the MACRO model. Simulation of runoff in HGS produced a higher total runoff compared to the PRZM model, attributed to lower evapotranspiration in HGS. Use of the integrated HGS model resulted in a greater agreement in water balance components relative to using multiple models to simulate individual hydrologic pathways.
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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 | 0.001 |
| 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".