Landscape indicators of groundwater nitrate concentrations: an approach for trans‐border aquifer monitoring
Bibliographic record
Abstract
Abstract Groundwater is the primary source of nearly half the freshwater used in drinking and cooking worldwide. Excess nitrate contamination of groundwater is a growing health concern, particularly in regions of intensive agriculture. Monitoring the world's nearly 500 transboundary aquifer bodies is complicated by the complexities associated with multi‐jurisdictional governance, disparities in data collection, and inconsistencies in geospatial data among countries. In a region where elevated groundwater nitrate concentrations are linked to overlying land use practices and land cover, we develop landscape indicators characterizing likely sources and examine their correspondence with groundwater nitrate concentrations. We evaluate an aquifer spanning the United States and Canada (the Abbotsford–Sumas aquifer) and ask two primary questions: Are nitrate concentrations in the aquifer changing over time? How well do landscape indicators help explain patterns of groundwater nitrate concentrations? To answer our first question, a time series (2005–2013) of groundwater nitrate concentrations was examined for 15 shallow wells using Mann–Kendall trend analysis tests. Nitrate concentrations in nine of the fifteen monitoring wells decreased while two increased over time. To answer our second question, a seamless harmonized cross‐border land cover mosaic was created using available U.S. and Canadian land use and land cover data. Landscape indicators such as crop type (proportion of raspberries, forage, and pasture, etc.) were measured in terrestrial zones of influence (of varying sized radii which incorporated groundwater flow direction) surrounding each well. Backward stepwise regression was used to identify parsimonious models of landscape indicators which explain nitrate concentrations in the United States and Canada. The proportions of different berry types (e.g., blueberries and raspberries, and mixed berries), forage/pasture, and area of renovations explained 21–72% of the variance in groundwater nitrate concentrations, depending on zonal scale, direction, and/or jurisdiction. As very few studies have quantitatively linked groundwater nitrate concentrations to land use, land cover, or land use practices, our work provides an important transportable approach that is highly relevant to other regions facing similar management challenges.
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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) | 0.002 | 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".