The Role of Sensitivity Analysis in Groundwater Risk Modeling for Pesticides

Fontaine, Don D.; Havens, Patrick L.; Blau, Gary; Tillotson, Patricia M.

doi:10.1017/s0890037x00036101

Cited by 38 publications

(39 citation statements)

References 5 publications

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“…It has an obvious effect on the relative ranking of ArcPRZM-3 predicted bentazon leaching in comparison with the detected bentazon concentration. The runoff curve number of antecedent moisture condition II for cropping was also found sensitive in other studies (e.g., [31,34,44]). …”

Section: Effect Of Runoff Curve Number Of Antecedent Moisture Conditisupporting

confidence: 55%

“…We found that the rank of well 7 along with other wells (10, 16, 28, and 32) changed from 9th to 3rd by increasing horizon compartment thickness. Similarly, the ranks were changed from (i) 3rd to 4th for wells (35,15,42), (ii) 4th to 6th for wells (25,21,22,44), (iii) 5th to 8th for well 45, (iv) 6th to 5th for well 19, and (v) 8th to 9th for well 35. The bentazon leaching rank for wells (26 and 20) did not change and remained same (i.e., 2nd) in both cases.…”

Section: Effect Of Horizon Compartment Thickness On Bentazon Simulationsmentioning

confidence: 99%

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Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Akbar

2013

CLEAN Soil Air Water

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The extensive use of pesticides for increasing the agricultural production is affecting the quality of groundwater. The objectives of this article are to (i) develop pesticide relative leaching ranks for well sites, (ii) develop maps for human health risks due to pesticide applications, and (iii) identify the most significant parameters in pesticide simulations for groundwater vulnerability assessment. The methods include (i) development of acifluorfen relative leaching ranks for 25 well sites using ArcPRZM-3, (ii) development of health risk maps using model simulated maximum dissolved bentazon concentrations on the basis of USA drinking water quality guidelines, (iii) sensitivity analysis for 14 ArcPRZM-3 input parameters using the Plackett-Burman method. ArcPRZM-3 is a user-friendly system for spatial modeling of pesticide leaching from surface to groundwater. Thirteen acifluorfen relative leaching potential ranks were developed in which the pesticide leaching decrease from 1 to 13. The model predicted ranks for well 34 and well 9 were 2nd and 3rd, respectively, and acifluorfen was detected in both wells during the physical monitoring. The percentages of high health risks in the agricultural areas were 48.38 and 72.72% for Randolph and Independence Counties, respectively. The most significant parameters were thickness of horizon compartment, runoff curve number of antecedent moisture condition II for cropping, soil bulk density, and total application of pesticide. The irrigation, soil permeability, and numerical dispersion could impact the pesticide leaching in soils toward groundwater. The ArcPRZM-3 system could be efficiently applied for spatial modeling and mapping of pesticide concentrations for groundwater vulnerability assessment on a large scale.

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Section: Effect Of Runoff Curve Number Of Antecedent Moisture Conditisupporting

confidence: 55%

Section: Effect Of Horizon Compartment Thickness On Bentazon Simulationsmentioning

confidence: 99%

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Akbar

2013

CLEAN Soil Air Water

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“…Other pesticide leaching models were also reported to be, among other parameters, sensitive to environmental fate parameters such as equilibrium soil-water partition (k d-PSL ), bulk density, and soil water content. 32,[59][60][61] e 1st-order dissolution rate constant SRRC decreased with time, as applied herbicide usually dissolves completely within 1 DAHA. 6) e sensitivity of the herbicide dissolution rate (Fig.…”

Section: Herbicide Concentrations In Paddy Watermentioning

confidence: 99%

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

et al. 2012

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is paper demonstrates the procedures for probabilistic assessment of a pesticide fate and transport model, PCPF-1, to elucidate the modeling uncertainty using the Monte Carlo technique. Sensitivity analyses are performed to investigate the in uence of herbicide characteristics and related soil properties on model outputs using four popular rice herbicides: mefenacet, pretilachlor, bensulfuron-methyl and imazosulfuron. Uncertainty quanti cation showed that the simulated concentrations in paddy water varied more than those of paddy soil. is tendency decreased as the simulation proceeded to a later period but remained important for herbicides having either high solubility or a high 1st-order dissolution rate. e sensitivity analysis indicated that PCPF-1 parameters requiring careful determination are primarily those involve with herbicide adsorption (the organic carbon content, the bulk density and the volumetric saturated water content), secondary parameters related with herbicide mass distribution between paddy water and soil (1st-order desorption and dissolution rates) and lastly, those involving herbicide degradations.

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“…The range of the perturbation may be a specific percentage (Barnes and Young, 1994;Ferreira et al, 1995) or determined from experimental measurements (Fontaine et al, 1992;Gwo et al, 1996). The most common form of sensitivity analysis is independent parameter perturbation (IPP), in which parameters are varied individually by a fixed percentage around a base value (Ferreira et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…The output responses of a model to parameter perturbation may be quantified by percentage change of selected output variables (Barnes and Young, 1994;Ferreira et al, 1995), relative change of output versus input (Nearing et al, 1990;Larocque and Banton, 1994), sensitivity coefficients from linear regression analysis (Fontaine et al, 1992;Tiscareno-Lopez et al, 1993;Gwo et al, 1996), and graphic response curves or probability distributions (Haan and Zhang, 1996;Ellerbroek et al, 1998). However, most sensitivity analyses were not designed to study variability in experimental measurements on model outputs, and could not be used to answer the question of what quantity of the experimental errors in input parameters was transferred to simulation output errors.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of tile drainage flow and crop yield on measured and calibrated soil hydraulic properties

Ma¹,

Malone²,

Heilman

et al. 2007

Geoderma

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Process-based agricultural system models require detailed description of soil hydraulic properties that are usually not available. The objectives of this study were to evaluate the sensitivity of model simulation results to variability in measured soil hydraulic properties and to compare simulation results using measured and default soil parameters. To do so, we measured soil water retention curves and saturated soil hydraulic conductivity (K sat ) from intact soil cores taken from a long-term experimental field near Nashua, Iowa for the Kenyon-Clyde-Floyd-Readlyn soil association. The soil water retention curves could be well described using the pore size distribution index (λ). Measured λ values from undisturbed soil cores ranged from 0.04 to 0.12 and the measured K sat values ranged from 1.8 to 14.5 cm/h. These hydraulic properties were then used to calibrate the Root Zone Water Quality Model (RZWQM) for simulating soil water content, water table, tile drain flow, and crop yield (corn and soybean) by optimizing the lateral K sat (LK sat ) and hydraulic gradient (HG) for subsurface lateral flow. The measured soil parameters provided better simulations of soil water storage, water table, and N loss in tile flow than using the default soil parameters based on soil texture classes in RZWQM. Sensitivity analyses were conducted for λ, K sat , saturated soil water content (θ s ) or drainable porosity, LK sat , and HG using the Latin Hypercubic Sampling (LHS) and for LK sat and HG also using a single variable analysis. Results of sensitivity analyses showed that RZWQM-simulated yield and biomass were not sensitive to soil hydraulic properties. Simulated tile flow and N losses in tile flow were not sensitive to λ and K sat either, but they were sensitive to LK sat and HG. Further sensitivity analyses using a single variable showed that LK sat in the tile layer was a more sensitive parameter compared to LK sat in other soil layers, and HG was the most sensitive parameter for tile flow under the experimental soil and weather conditions. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractProcess-based agricultural system models require detailed description of soil hydraulic properties that are usually not available. The objectives of this study were to evaluate the sensitivity of model simulation results to variability in measured soil hydraulic properties and to compare simulation results using measured and default soil parameters. To do so, we measured soil water retention curves and saturated soil hydraulic conductivity (K sat ) from intact soil cores taken from a long-term experimental field near Nashua, Iowa for the Kenyon-Clyde-Floyd-Readlyn soil association. The soil water retention curves could be well described using the pore size distribution index (λ). Measured λ values from undisturbed soil cores ranged from 0.04 to 0.12 and the measured K sat values ranged from 1.8 to ...

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The Role of Sensitivity Analysis in Groundwater Risk Modeling for Pesticides

Cited by 38 publications

References 5 publications

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Analysis of parameter uncertainty and sensitivity in PCPF-1 modeling for predicting concentrations of rice herbicides

Sensitivity of tile drainage flow and crop yield on measured and calibrated soil hydraulic properties

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