Spatial modeling of soil heterogeneities and their impacts on runoff, sediment and total phosphorus loss
Bibliographic record
Abstract
Located in southern Quebec, at the northeastern extremity of Lake Champlain, the Missisquoi Bay is subject to eutrophication arising from excess nutrients, predominantly phosphorus (P), contributed by agricultural runoff. Land use patterns, agronomic practices, soil properties, and geomorphology have an impact on soil P. Studies have used hydrologic models [e.g., the Soil and Water Assessment Tool (SWAT)] to characterize P loadings from the region's agricultural watersheds. The lack of a proper understanding of the impact of spatial variability and heterogeneity of soil properties on the prediction of runoff, sediment and nutrient movement has proven a major challenge. In order to overcome this, field surveys, spatial variability characterization of P through geostatistics, heterogeneity quantification and hydrologic modeling using SWAT were undertaken. An extensive geostatistic study of soil properties was followed by the use of SWAT to predict runoff, sediment and total phosphorus (TP). Soil surveys carried out in the summers of 2011 and 2012 measured soils physical and chemical properties. Variogram analysis characterized the spatial variability of soil test phosphorus (STP). Ordinary kriging (OK) was used to estimate STP values at unsampled locations. Due to OK's smoothing effect, some high value areas were underestimated, while some low areas were overestimated. Compared to OK, sequential Gaussian simulation (SGS) helped characterize the uncertainty and provides better estimates at non-sampled locations. Areas above the STP threshold at which P has the potential to move to freshwater after precipitation events, combined with topographic factors, were identified. The uncertainty in variogram parameters (sill, nugget and range) was characterized using a Bayesian Hierarchical framework, aiding in understanding the complexity and heterogeneity in the STP dataset attributable to land use patterns. The Posterior mean and 95% credible confidence intervals of the variogram parameters and STP values were developed. An Independent Component Analysis (ICA) technique, which overcame the problem of matrix inversion in co-simulation, served in the decomposition of spatially-correlated geochemical variables. This implementation was tested on three correlated variables: magnesium, calcium and iron. The measured soil properties required by SWAT were regionalized and clustered using a Regionalization with Constrained Clustering and Partitioning (REDCAP) algorithm. Five maps were created based on 5, 10, 15, 20 and 24 part partitioning. Each of these maps had different measures of heterogeneity and each was used as inputs for five different configurations of SWAT. Mean monthly flow, sediment and total P load from April 2001 to December 2002 were used to assess model performance before and after calibration. Overall, there was no significant difference in runoff simulation between any of the five map configurations, which might be due to the impacts of the SCS-CN (soil conservation service-curve number) method in simulating runoff. In the study watershed, using a higher resolution (number of regions) of soil data did not improve predictions of monthly streamflow, sediment or TP.
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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".