WATER QUALITY MODELING BY MONTE CARLO SIMULATION<sup>1</sup>

Brutsaert, Willem F.

doi:10.1111/j.1752-1688.1975.tb00675.x

Cited by 11 publications

(4 citation statements)

References 1 publication

(1 reference statement)

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“…One option for dealing with these uncertainties is to take a probabilistic approach. Monte Carlo simulation is a common approach to generating probabilistic model inputs and simulation results where input distributions are randomly sampled a sufficient number of times to capture the variability of the inputs and model responses (Brutsaert 1975;Bobba et al 1996). The SWAT Application Scenario Generator was used to generate scenarios that sampled a wide range of possible application dates for each atrazine-treated HRU in a watershed.…”

Section: Adjustment Of Model Default Valuesmentioning

confidence: 99%

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Winchell¹,

Peranginangin

Srinivasan

et al. 2018

Integr Envir Assess & Manag

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Recent national regulatory assessments of potential pesticide exposure of threatened and endangered species in aquatic habitats have led to increased need for watershed-scale predictions of pesticide concentrations in flowing water bodies. This study was conducted to assess the ability of the uncalibrated Soil and Water Assessment Tool (SWAT) to predict annual maximum pesticide concentrations in the flowing water bodies of highly vulnerable small-to medium-sized watersheds. The SWAT was applied to 27 watersheds, largely within the midwest corn belt of the United States, ranging from 20 to 386 km 2 , and evaluated using consistent input data sets and an uncalibrated parameterization approach. The watersheds were selected from the Atrazine Ecological Exposure Monitoring Program and the Heidelberg Tributary Loading Program, both of which contain high temporal resolution atrazine sampling data from watersheds with exceptionally high vulnerability to atrazine exposure. The model performance was assessed based upon predictions of annual maximum atrazine concentrations in 1-d and 60-d durations, predictions critical in pesticide-threatened and endangered species risk assessments when evaluating potential acute and chronic exposure to aquatic organisms. The simulation results showed that for nearly half of the watersheds simulated, the uncalibrated SWAT model was able to predict annual maximum pesticide concentrations within a narrow range of uncertainty resulting from atrazine application timing patterns. An uncalibrated model's predictive performance is essential for the assessment of pesticide exposure in flowing water bodies, the majority of which have insufficient monitoring data for direct calibration, even in data-rich countries. In situations in which SWAT over-or underpredicted the annual maximum concentrations, the magnitude of the over-or underprediction was commonly less than a factor of 2, indicating that the model and uncalibrated parameterization approach provide a capable method for predicting the aquatic exposure required to support pesticide regulatory decision making.

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Section: Adjustment Of Model Default Valuesmentioning

confidence: 99%

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Winchell¹,

Peranginangin

Srinivasan

et al. 2018

Integr Envir Assess & Manag

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“…Konzelmann and others (1994) relate clear-sky emissivity to measured vapour pressure, e, and T at Swiss Camp, through a modified version of Brutsaert's (1975) equation applied to their data, yielding…”

Section: Longwave Radiationmentioning

confidence: 99%

Calibration and evaluation of a high-resolution surface mass-balance model for Paakitsoq, West Greenland

et al. 2012

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ABSTRACT. Modelling the hydrology of the Greenland ice sheet, including the filling and drainage of supraglacial lakes, requires melt inputs generated at high spatial and temporal resolution. Here we apply a high spatial (100 m) and temporal ( respectively. Model performance is evaluated, first, by comparing modelled snow and ice distribution with that derived from Landsat-7 ETM+ satellite imagery using normalized-difference snow index classification and supervised image thresholding; and second, by comparing modelled albedo with that retrieved from the MODIS sensor MOD10A1 product. Calculation of mass-balance components indicates that 6% of surface meltwater and rainwater refreezes in the snowpack and does not become runoff, such that refreezing accounts for 31% of the net accumulation.

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“…Monte Carlo simulation (MCS). has been described by Brutsaert (1975) as an artificial sampling method. Random observations are drawn from a specified parameter distribution space.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…1988). Often, the only way to make parameters more meaningful is by attaching a probability distribution to them (Brutsaert. 1975).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal modeling in an agricultural watershed

Hameed

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WATER QUALITY MODELING BY MONTE CARLO SIMULATION¹

Cited by 11 publications

References 1 publication

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Calibration and evaluation of a high-resolution surface mass-balance model for Paakitsoq, West Greenland

Spatio-temporal modeling in an agricultural watershed

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WATER QUALITY MODELING BY MONTE CARLO SIMULATION1

Cited by 11 publications

References 1 publication

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds

Calibration and evaluation of a high-resolution surface mass-balance model for Paakitsoq, West Greenland

Spatio-temporal modeling in an agricultural watershed

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Product

Resources

About

WATER QUALITY MODELING BY MONTE CARLO SIMULATION¹