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Record W4319934689 · doi:10.13031/ja.15283

Calibration and Validation of RZWQM2-P Model to Simulate Phosphorus Loss in a Clay Loam Soil in Michigan

2023· article· en· W4319934689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the ASABE · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsLoamEnvironmental scienceHydrology (agriculture)PhosphorusDrainageWater qualitySoil waterSoil scienceGeologyEcologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Highlights RZWQM2-P was tested and validated for clay loam soil using daily discharge and load data. The model performed satisfactorily in predicting hydrology and TP load, but DRP prediction was unsatisfactory. Inability of the model to simulate P loss in subsurface drainage discharge after fertilization event was one of the reasons for the unsatisfactory model performance. Abstract. Phosphorus (P) loss and transport through subsurface drainage systems is a primary focus for addressing harmful algal blooms in freshwater systems. The recent development of the phosphorus (P) routine of the Root Zone Water Quality Model (RZWQM2-P) has the potential to enhance our understanding of the fate and transport of P from subsurface-drained fields to surface water. However, there is a need to test the model under different fertilization, soil, climate, and cropping conditions. The objective of this study was to test the model's performance with daily drainage discharge, dissolved reactive phosphorus (DRP), and total phosphorus (TP) load collected from a subsurface-drained field with clay loam soil. We calibrated RZWQM2-P using two years of measured data. Subsequently, we validated RZWQM2-P using a year and nine months of measured data. We used the Nash-Sutcliffe model efficiency (NSE) and percentage bias (PBIAS) statistics for the RZWQM2-P model evaluation. The results showed that the model performance was “good” (daily NSE = 0.66 and PBIAS = -7.16) in predicting hydrology for the calibration period. For the validation period, the hydrology prediction of the model was “very good” (daily NSE = 0.76), but it had a “satisfactory” underestimation bias (PBIAS = 23.57). The model’s performance was “unsatisfactory” in simulating DRP for both calibration (daily NSE = 0.31 and PBIAS = -61.50) and validation (daily NSE = 0.32 and PBIAS = 43.68) periods. The P model showed “satisfactory” performance in predicting TP load for both calibration (daily NSE = 0.46 and PBIAS = -32.41) and validation (daily NSE = 0.39 and PBIAS = 42.90) periods, although both periods showed “unsatisfactory” percent bias. The underperformance may have been due to the model’s inability to partition fertilizer P into different P pools under high water tables or ponding conditions when using daily data. In conclusion, the RZWQM2-P model performed well for drainage discharge with daily data, but further investigation is needed to improve the P component of the model. Keywords: Field-scale modeling, Nutrient load, Phosphorus modeling, Subsurface drainage, Tile drainage, Water Quality.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.235
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it