Compound vs. single droughts: different impacts on river water quality revealed by multi-decadal observations
Bibliographic record
Abstract
• Proposed a framework for revealing how water quality responds to various types of droughts. • Identified positive response relationship of TC, TN, and TP to drought duration and severity. • Random forest can be used to simulate TC, TN, and TP based on drought characteristics. • Compound droughts have more complex impacts on water quality than single drought. Droughts impact both water quantity and quality. Research is needed on how water quality (e.g., total carbon, TC; total nitrogen, TN; and total phosphorus, TP) responds to different droughts (single or compound) and their characteristics (i.e., duration and severity). It also needs to be addressed whether these water quality indicators during different droughts can be simulated based on drought characteristics. We used 1979–2018 hydro-meteorological and water quality data from Harp Lake in south-central Ontario, Canada. First, we analyzed trends and annual distribution of relevant factors. Then, we identified meteorological, hydrological, and compound droughts using the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI), by applying run theory to extract the drought characteristics. Lastly, we established a response relationship of TC, TN, and TP to different drought characteristics, and used random forest method to construct simulation models for TC, TN, and TP during droughts. The simulation models were evaluated using correlation coefficient (CC) and Nash-Sutcliffe Efficiency (NSE). The results showed that: ( i ) In the HP4 watershed, TC showed a significant upward trend ( p < 0.01) over 40 years of long-term data, while TN and TP showed a significant downward trend ( p < 0.01). ( ii ) The concentration of TC did not show significant changes in drought, while TN concentration increases, and TP concentration decreases. ( iii ) The random forest model can effectively simulate the cumulative concentrations of TC, TN, and TP during drought periods (CC > 0.90, NSE > 0.60). However, the simulation results for compound droughts are poorer than for other drought types, highlighting the greater complexity of compound drought impact on river water quality relative to single droughts. These findings may aid in water quality management during droughts and may have broader applications in environmental monitoring and water resource management.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".