Estimating stream solute loads from fixed frequency sampling regimes: the importance of considering multiple solutes and seasonal fluxes in the design of long‐term stream monitoring networks
Bibliographic record
Abstract
Abstract Reduced sampling frequency is known to increase the error associated with estimates of stream solute load. However, the extent to which the magnitude of error differs among commonly measured solutes and across seasons is unclear. In this study, a high sampling frequency data set from two forested streams (one upland‐draining and one wetland‐draining stream) in south‐central Ontario was systematically sub‐sampled to simulate weekly, fortnightly and monthly fixed frequency sampling regimes for 12 stream solutes. We found that solutes which had a higher degree of temporal variation in concentration (i.e. higher %RSD) had poorer precision ( C v ) in estimates of annual load relative to solutes with a lower %RSD. In addition, the magnitude and direction of bias varied considerably among solutes and were related to differences in spring concentration‐discharge relationships ( m [spring Q vs C] ) among the 12 solutes. Solutes which decreased in concentration with increases in spring flow (i.e. m [spring Q vs C] <0) exhibited positive bias in annual load while solutes which increased in concentration with increases in spring flow (i.e. m [spring Q vs C] >0) were negatively biased. In terms of differences between seasonal and annual load errors, precision was generally lower for estimates of seasonal load relative to annual load while bias varied in both magnitude and direction among seasons. When the root mean square error (RMSE) of load estimates was compared to a threshold of acceptable error (<15%), the proportion of solutes attaining acceptable levels of uncertainty ranged from 11/12 for annual load estimates at a weekly sampling frequency to only 4/12 at a monthly frequency when both annual and seasonal loads were considered. Our results demonstrate that commonly measured solutes do not behave uniformly in response to changes in sampling frequency and that estimates of seasonal loads are often less accurate than estimates of annual load. These findings provide important insights into the design of stream monitoring programs and the evaluation of existing long‐term data sets. Copyright © 2015 John Wiley & Sons, Ltd.
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 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 | 0.001 |
| 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".