Uncalibrated modelling of conservative tracer and pesticide leaching to groundwater: comparison of potential Tier II exposure assessment models

Fox, Garey A.; Sabbagh, George J.; Chen, Wenlin; Russell, Mark H.

doi:10.1002/ps.1211

Cited by 23 publications

(18 citation statements)

References 27 publications

(56 reference statements)

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“…The flow rates and sediment concentration data from each experiment was modeled by fitting k d and τ c based on minimizing the sum of squared errors between observed and predicted flow rates during the experimental period. The quality of the model fit was assessed based on the root mean square error (RMSE) and a normalized objective function (NOF) (Fox et al ., ):

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {((), X_{i} - Y_{i})}^{2}}{n}}

NOF = \frac{italicRMSE}{X_{a}}

where X i and Y i are the observed and predicted values, respectively; X a is the mean of observed values; and n is the number of observations. In general, 1, 10, and 50% deviations from the observed values result in NOF values of 0.01, 0.10, and 0.50, respectively.…”

Section: Methodsmentioning

confidence: 94%

Section: Pipeflow Modelingmentioning

confidence: 99%

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Laboratory soil piping and internal erosion experiments: evaluation of a soil piping model for low‐compacted soils

Fox

Felice

Midgley³

et al. 2013

Earth Surf Processes Landf

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Mechanistic models have been proposed for soil piping and internal erosion on well‐compacted levees and dams, but limited research has evaluated these models in less compacted (more erodible) soils typical of hillslopes and streambanks. This study utilized a soil box (50 cm long, 50 cm wide and 20 cm tall) to conduct constant‐head, soil pipe and internal erosion experiments for two soils (clay loam from Dry Creek and sandy loam from Cow Creek streambanks) packed at uniform bulk densities. Initial gravimetric moisture contents prior to packing were 10, 12 and 14% for Dry Creek soil and 8, 12, and 14% for Cow Creek soil. A 1‐cm diameter rod was placed horizontally along the length of the soil bed during packing and carefully removed after packing to create a continuous soil pipe. A constant head was maintained at the inflow end. Flow rates and sediment concentrations were measured from the pipe outlet. Replicate submerged jet erosion tests (JETs) were conducted to derive erodibility parameters for repacked samples at the same moisture contents. Flow rates from the box experiments were used to calibrate the mechanistic model. The influence of the initial moisture content was apparent, with some pipes (8% moisture content) expanding so fast that limited data was collected. The mechanistic model was able to estimate equivalent flow rates to those observed in the experiments, but had difficulty matching observed sediment concentrations when the pipes rapidly expanded. The JETs predicted similar erodibility coefficients compared to the mechanistic model for the more erodible cases but not for the less erodible cases (14% moisture content). Improved models are needed that better define the changing soil pipe cross‐section during supply‐ and transport‐limited internal erosion, especially for piping through lower compacted (more erodible) soils as opposed to more well‐compacted soils resulting from constructing levees and dams. Copyright © 2013 John Wiley & Sons, Ltd.

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RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {((), X_{i} - Y_{i})}^{2}}{n}}

NOF = \frac{italicRMSE}{X_{a}}

Section: Methodsmentioning

confidence: 94%

Section: Pipeflow Modelingmentioning

confidence: 99%

Laboratory soil piping and internal erosion experiments: evaluation of a soil piping model for low‐compacted soils

Fox

Felice

Midgley³

et al. 2013

Earth Surf Processes Landf

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“… * Indicates aerobic soil metabolism half‐life that differs from values used in the EPA evaluation study …”

Section: Methodsmentioning

confidence: 99%

“…As PRZM‐GW is a relatively new model (the most recent release in 2014), evaluations of its performance, outside the EPA study, are limited and unavailable in the open literature. However, its base model, PRZM3.12.2, has previously been investigated for use as an EPA regulatory groundwater model …”

Section: Introductionmentioning

confidence: 99%

“…However, its base model, PRZM3.12.2, has previously been investigated for use as an EPA regulatory groundwater model. 4 The objectives of this paper were: (a) to evaluate PRZM-GW1.07 prediction performance (simulated with the 2014 set of EPA-developed standard environmental scenarios), and (b) to identify environmental fate input parameter ranges that are generally associated with patterns of under-or overprediction of observed groundwater concentrations by SCI-GROW2.3 and PRZM-GW1.07.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of predicted pesticide concentrations in groundwater from SCI‐GROW and PRZM‐GW models with historical monitoring data

Estes¹,

Pai²,

Winchell³

2015

Pest Management Science

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Numerical simulation of agricultural sediment and pesticide runoff: RZWQM and PRZM comparison

Chen

Zhang

DeMars

et al. 2017

Hydrological Processes

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Agricultural sediment and pesticide runoff is a widespread ecological and human health concern.Numerical simulation models, such as Root Zone Water Quality Model (RZWQM) and Pesticide Root Zone Model (PRZM), have been increasingly used to quantify off-site agricultural pollutant movement. However, RZWQM has been criticized for its inability to simulate sedimentation processes. The recent incorporation of the sedimentation module of Groundwater Loading Effects of Agricultural Management Systems has enabled RZWQM to simulate sediment and sedimentassociated pesticides. This study compares the sediment and pesticide transport simulation performance of the newly released RZWQM and PRZM using runoff data from 2 alfalfa fields in Davis, California. A composite metric (based on coefficient of determination, Nash-Sutcliffe efficiency, index of agreement, and percent bias) was developed and employed to ensure robust, comprehensive assessment of model performance. Results showed that surface water runoff was predicted reasonably well (absolute percent bias <31%) by RZWQM and PRZM after adjusting important hydrologic parameters. Even after calibration, underestimation bias (−89% ≤ PBIAS ≤ −36%) for sediment yield was observed in both models. This might be attributed to PRZM's incorrect distribution of input water and uncertainty in RZWQM's runoff erosivity coefficient. Moreover, the underestimation of sediment might be less if the origin of measured sediment was considered. Chlorpyrifos losses were simulated with reasonable accuracy especially for Field A (absolute PBIAS ≤ 22%), whereas diuron losses were underestimated to a great extent (−98% ≤ PBIAS ≤ −65%) in both models. This could be attributed to the underprediction of herbicide concentration in the top soil due to the limitations of the instantaneous equilibrium sorption model as well as the high runoff potential of herbicide formulated as water-dispersible granules.RZWQM and PRZM partitioned pesticides into the water and sediment phases similarly. According to model predictions, the majority of pesticide loads were carried via the water phase. On the basis of this study, both RZWQM and PRZM performed well in predicting runoff that carried highly adsorptive pesticides on an event basis, although the more physically based RZWQM is recommended when field-measured soil hydraulic properties are available.

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Uncalibrated modelling of conservative tracer and pesticide leaching to groundwater: comparison of potential Tier II exposure assessment models

Cited by 23 publications

References 27 publications

Laboratory soil piping and internal erosion experiments: evaluation of a soil piping model for low‐compacted soils

Laboratory soil piping and internal erosion experiments: evaluation of a soil piping model for low‐compacted soils

Comparison of predicted pesticide concentrations in groundwater from SCI‐GROW and PRZM‐GW models with historical monitoring data

Numerical simulation of agricultural sediment and pesticide runoff: RZWQM and PRZM comparison

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