Monitoring and modeling of runoff generating areas in a small agricultural watershed
Bibliographic record
Abstract
It is presently well-known that more than 50% of total water quality impairment originates from non-point sources (NPS) of pollution. As an important NPS pollutant, runoff from agricultural lands contributes to water quality problems more than other non-point sources. Therefore, the identification and quantification of runoff generation areas is crucial for source water protection and nutrient management. Runoff generation is a complicated landscape process, affected by various factors in different seasons of the year. In this research, monitoring and modeling were selected as two important approaches to identify the mechanisms of runoff generation, runoff generating areas (RGAs) and its variability in time and space in a small agricultural watershed in southern Ontario. A wireless sensor network (WSN) was designed to monitor runoff generating areas in the study watershed with the ability to measure the depth of surface runoff and soil moisture over ten minutes time intervals. Eight pressure and soil moisture sensors were installed at the outlet of eight fields in the watershed. Data from eighteen natural rainfall events for the period from July 2008 to April 2009 were analyzed to study the spatial and temporal variability of runoff generation areas in the study watershed. The results showed that runoff generating areas in the watershed are highly dynamic in summer, fall and spring with differences in 100%-contribution-status persistency. The results also indicated that 15% of the watershed generates 75% of surface runoff during summer, 100% in fall and 45% during spring. In spite of the dynamic nature of RGAs, the sensitivity of different fields in the watershed in response to rainfall events remained constant, such that some specific fields responded first in all three seasons. This finding led to the introduction of a Slope/Area index for the identification of sensitive fields in the study watershed. Statistical analyses of the factors affecting RGAs indicated that the factors affecting the spatial and temporal variability of RGAs in three seasons vary; however, the soil moisture and rainfall intensity played important roles in the runoff generation mechanism and variability of contributing areas in all seasons. Based on monitored results and field observations, a hydrological model was developed to simulate runoff generating area and to classify the sensitivity of the fields to runoff generation on the basis of the modified Soil Conservation Service Curve Number approach. The model was able to identify the fields that generate runoff and classify the sensitivity of the fields in the watershed. The developed model could simulate RGAs for the summer season with higher degree of accuracy than fall. The developed model needs further improvements for simulation of runoff generating area in the spring season.
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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) | 0.000 | 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".