Simulating pesticide leaching and runoff in rice paddies with the RICEWQ-VADOFT model

There is a current need to simulate leaching and runoff of pesticide from rice (Oryza sativa L.) paddies for assessing environmental impacts on a valuable agricultural system. The objective of this study was to develop a model for determining predicted environmental concentration (PEC) in soil, runoff, and ground water through the linkage of two models, rice water quality model (RICEWQ) and vadose zone transport model (VADOFT), to simulate pesticide fate and transport within a rice paddy and underlying soil profile. Model performance was evaluated with a field data set obtained from a 2-yr field experiment in 1997 and 1998 in northern Italy. The predictions of amount of pesticide running off from the paddy field and accumulating in the paddy sediment were in agreement with measured values. Leaching into the vadose zone accounted for approximately 19% of the applied dose, but only a small amount of chemical (<0.1%) was predicted to reach ground water at a 5-m depth due to sorption and transformation in the soil. The permeability of the soil and the water management practices in the paddy field were shown to have a strong influence on pesticide fate. These factors need to be well characterized in the field if model predictions are to be successful. The combined model developed in this work is an effective tool for exposure assessments for soil, surface water, and ground water, in the particular conditions of rice cultivation.     

TurfPQ, a pesticide runoff model for turf

Environmental assessments of golf courses and other turf systems must often rely on mathematical modeling. However, in the case of pesticide runoff, successful modeling applications are rare. Available models were developed for agricultural applications and have seen very limited testing for turf. TurfPQ is a pesticide runoff model developed exclusively for turf. The model is based on a curve number calculation for runoff volume and linear partitioning of pesticide into adsorbed and dissolved components during a precipitation or irrigation event. Calibration is optional, so the model can be applied, using default parameter values, to situations where runoff and chemical loss data are unavailable. TurfPQ was tested with default parameter values for 52 pesticide runoff events involving six pesticides measured in plot studies in four states. The model typically produced conservative overpredictions of pesticide runoff, particularly with strongly adsorbed pesticides. Mean predicted pesticide runoff was 2.9% [corrected] of application, compared with an observed mean of 2.1%. TurfPQ captured the dynamics of the pesticide runoff events well with R2 = 0.65 [corrected]. Sensitivity analyses indicated that prediction errors could be reduced by better estimates of adsorption parameters and runoff curve numbers. However, even with default parameters, TurfPQ predictions are at least as accurate as those produced by more complex models.     

Time Series Analysis of Nebraska Daily Rainfall Data to Simulate Atrazine Runoff1

Abstract: Measured atrazine concentrations in Nebraska surface water have been shown to exceed water‐quality standards, posing risks to humans and to the ecosystem. To assess this risk, atrazine runoff was simulated at the field‐scale in Nebraska based on the pesticide component of the AGNPS model. This project’s objective was to determine the frequency that the atrazine concentration at the field outlet exceeded three different atrazine water‐quality criteria. The simulation was conducted for different farm management practices, soil moisture conditions, and five Nebraska topographic regions. If the criteria were exceeded, a risk to the drinking water consumer or freshwater aquatic life was hypothesized to exist. Three pesticide fate and transport processes were simulated with the model. Degradation was simulated using first‐order kinetics. Adsorption/desorption was modeled assuming a linear soil‐water partitioning coefficient. Advection (runoff) was based primarily on the USDA‐NRCS curve number method. Daily rainfall from the National Weather Service was used to compute the soil moisture conditions for the 1985‐2000 growing seasons. After each runoff event, the pesticide runoff concentration was compared with each of the three atrazine water‐quality criteria. The results show that environmental receptors (i.e., freshwater aquatic species) are exposed to unacceptable atrazine runoff concentrations in 20‐50% of the runoff events.     

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

Fate of pesticides in combined paddy rice-fish pond farming systems in northern Vietnam

During the last decades, high population growth and export-oriented economics in Vietnam have led to a tremendous intensification of rice production, which in turn has significantly increased the amount of pesticides applied in rice cropping systems. Since pesticides are toxic by design, there is a natural concern on the impact of their presence in the environment on human health and environmental quality. The present study was designed to examine the water regime and fate of pesticides (fenitrothion, dimethoate) during two consecutive rice crop seasons in combined paddy rice-fish pond farming systems in northern Vietnam. Major results revealed that 5 and 41% (dimethoate), and 1 and 17% (fenitrothion) of the applied mass of pesticides were lost from the paddy field to the adjacent fish pond during spring and summer crop seasons, respectively. The decrease of pesticide concentration in paddy surface water was very rapid with dissipation half-life values of 0.3 to 0.8 and 0.2 d for dimethoate and fenitrothion, respectively. Key factors controlling the transport of pesticides were water solubility and paddy water management parameters, such as hydraulic residence time and water holding period. Risk assessment indicates that the exposure to toxic levels of pesticides for aquaculture (, ) is significant, at least shortly after pesticide application.     

Numerical modeling of diazinon transport through inter-row vegetative filter strips

A numerical simulation model of pesticide runoff through vegetative filer strips (PRVFS) was developed as a tool for investigating the effects of pesticide transport mechanisms on VFS design in dormant-sprayed orchard. The PRVFS model was developed applying existing theories such as kinematic wave theory and mixing zone theory for pesticide transport in the bare soil area. For VFS area, the model performs flow routing by simple mass accounting in sequential segments and the pesticide mass balance by considering pesticide washoff and adsorption processes on the leaf, vegetative litter, root zone and soil. Model sensitivity analysis indicated that pesticide transfer from surface soil to overland flow and pesticide washoff from the VFS were important mechanisms affecting diazinon transport. The VFS cover ratio and rainfall intensity can be important design parameters for controlling diazinon runoff using inter-row VFS in orchard. The PRVFS model was validated using micro-ecosystem simulation of diazinon transport for 0, 50 and 100% VFS cover conditions. The PRVFS model is shown to be a beneficial tool for evaluating and analyzing possible best management practices for controlling offsite runoff of dormant-sprayed diazinon in orchards during the rainy season.     

MODEL PREDICTIONS OF WATERSHED HYDROLOGIC COMPONENTS: COMPARISON AND VERIFICATIION1

ABSTRACT: Model predictions of the relatively simple soil compartment model SESOIL are compared with those of the more data-intensive terrestrial ecosystem hydrology model AGTEHM. Comparisons were performed using data from a deciduous forest stand watershed, a grassland watershed, and two agricultural field plots. Good agreement was obtained between model predictions for annual values of infiltration, evapotranspiration, surface runoff, and groundwater runoff. SESOIL model predictions also compare well with empirical measurements at the forest stand and the grassland watersheds.     

Modeling macropore flow effects on pesticide leaching: inverse parameter estimation using microlysimeters

Macropore flow is a key factor determining pesticide fate, but models accounting for this process need parameters that cannot be easily measured. This study was conducted to investigate the use of inverse techniques to estimate parameters controlling macropore flow and pesticide fate in the dual-permeability model MACRO. Undisturbed columns were sampled at three landscape positions (hilltop, slope, hollow) with contrasting texture and organic carbon content. Transient leaching experiments were performed for an anionic tracer and the herbicide MCPA (4-chloro-2methylphenoxy acetic acid) during a 4-mo period, first under natural rainfall, and then under controlled irrigation in the laboratory. The tracer breakthrough for the liner-textured soil from the hilltop showed strong evidence of macropore flow, resulting in a rapid leaching of MCPA, while leaching was minimal from the organic-rich hollow soil, since macropore flow was weaker and adsorption stronger. The MACRO model was linked to the inverse modeling program SUFI (Sequential Uncertainty Fitting) to enable calibration of nine key model parameters. Based on calculated model efficiencies, MACRO-SUFI gave generally good predictions of water movement and tracer and pesticide transport, although some errors were attributed to difficulties in simulating the effects of soil moisture on degradation and the timing of water outflows. Even after calibration, significant uncertainties remained for some key parameters controlling macropore flow. Nevertheless, the parameter estimates were significantly different between landscape positions and could also be related to basic soil properties. The posterior uncertainty ranges could probably be reduced with a more exhaustive sampling of the parameter space and improved experimental designs.     

SWMHMS - SMALL WATERSHED MONTHLY HYDROLOGIC MODELING SYSTEM1

ABSTRACT: SWMHMS is a conceptual computer modeling program developed to simulate monthly runoff from a small nonurban watershed. The input needed to run model simulations include daily precipitation, monthly data for evapotranspiration determination (average temperature, crop consumptive coefficients, and percent daylight hours), and six watershed parameter values. Evapotranspiration was calculated with the Blaney-Criddle equation while surface runoff was determined using the Soil Conservation Service curve number procedure. For watershed parameter evaluation, SWMHMS provides options for both optimization and sensitivity analysis. Observed runoff data are required along with the model input previously mentioned in order to conduct parameter optimization. SWMEIMS was tested with data from six watersheds located in different regions of the United States. Model accuracy was generally found to be very good except on watersheds having substantial snowfall accumulation. In having only six watershed parameters, SWMHMS is less complex to use than many other computer programs that calculate monthly runoff. Consequently, SWMHMS may find its greatest application as an educational tool for students learning principles of hydrologic modeling, such as parameter evaluation procedures and the impacts of input data uncertainty on model results.     

Modeling the Fate and Transport of Plunging Inflows to Onondaga Lake

The transport and fate of two plunging tributaries, Onondaga and Ninemile Creeks, in Onondaga Lake, New York, are quantified based on application of hydrodynamic/transport models. Short‐term transport is simulated with a three‐dimensional Estuary Lake and Coastal Ocean Model (ELCOM), while the longer term fate is represented by a previously validated one‐dimensional model (UFILS4). The validation of ELCOM for the vertical distribution of tributary inflow into the lake's water column is demonstrated for four dye tracer experiments. The models are applied for three years to represent the dynamics of transport and fate for the two tributaries, with ELCOM predictions serving as input for UFILS4. The models together quantify the distribution of these inflows between the upper mixed layer (UML) and stratified depths, and the subsequent transport from stratified depths to the UML by vertical mixing. Substantial short‐term variations are predicted for both tributaries in response to variability in hydrology and weather. Increased inflow to the UML is predicted for high runoff periods. The fraction of Ninemile Creek's inflow directly entering the UML is predicted to be 50% greater than for Onondaga Creek due to Ninemile's lower negative buoyancy. The plunging phenomenon has important water quality implications, by reducing the effective loading to the UML, particularly for constituents with large rates of loss/transformation relative to the rate of vertical transport from stratified depths.     

STREAM DISCHARGE PREDICTION VIA A GRID BASED SOIL WATER ROUTING WITH PADDY FIELDS1

ABSTRACT: A grid based daily hydrologic model for a watershed with paddy fields was developed to predict the stream discharge. ASCII formatted elevation, soil, and land use data supported by the GRASS Geographic Information System are used to generate distributed results such as surface runoff and subsurface flow, soil water content, and evapotranspiration. The model uses a single flow path algorithm and simulates a water balance at each grid element. A linear reservoir assumption was used to predict subsurface runoff components. The model was applied to a 75.6 km² watershed located in the middle of South Korea, and observed stream flow hydrographs from 1995 and 1996 were compared to model predictions. The stream flow predictions of 1995 and 1996 generally agreed with the observed flow, resulting in a Nash‐Sutcliffe efficiency R² of 0.60 and 0.62, respectively. The hydraulic conductivity for percolating water through the saturated layer affected baseflow generation. The levee height of the paddy influenced the time and magnitude of the surface runoff, depending on irrigation management. The model will be used for making low flow management decisions by evaluating the role of each land use to stream flow, especially in case of paddy decrease by gradual urbanization of a watershed.     

Simulating solute transport in a structured field soil: uncertainty in parameter identification and predictions

Dual-permeability models have been developed to account for the significant effects of macropore flow on contaminant transport, but their use is hampered by difficulties in estimating the additional parameters required. Therefore, our objective was to evaluate data requirements for parameter identification for predictive modeling with the dual-permeability model MACRO. Two different approaches were compared: sequential uncertainty fitting (SUFI) and generalized likelihood uncertainty estimation (GLUE). We investigated six parameters controlling macropore flow and pesticide sorption and degradation, applying MACRO to a comprehensive field data set of bromide andbentazone [3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2dioxide] transport in a structured soil. The GLUE analyses of parameter conditioning for different combinations of observations showed that both resident and flux concentrations were needed to obtain highly conditioned and unbiased parameters and that observations of tracer transport generally improved the conditioning of macropore flow parameters. The GLUE "behavioral" parameter sets covered wider parameter ranges than the SUFI posterior uncertainty domains. Nevertheless, estimation uncertainty ranges defined by the 5th and 95th percentiles were similar and many simulations randomly sampled from the SUFI posterior uncertainty domains had negative model efficiencies (minimum of -3.2). This is because parameter correlations are neglected in SUFI and the posterior uncertainty domains were not always determined correctly. For the same reasons, uncertainty ranges for predictions of bentazone losses through drainflow for good agricultural practice in southern Sweden were 27% larger for SUFI compared with GLUE. Although SUFI proved to be an efficient parameter estimation tool, GLUE seems better suited as a method of uncertainty estimation for predictions.     

Predicting dissolved phosphorus in runoff from manured field plots

Dissolved inorganic P transport in runoff from agricultural soils is an environmental concern. Models are used to predict P transport but rarely simulate P in runoff from surface-applied manures. Using field-plot data, we tested a previously proposed model to predict manure P in runoff. We updated the model to include more data relating water to manure ratio to manure P released during water extractions. We verified that this update can predict P release from manure to rain using published data. We tested the updated model using field-plot and soil-box data from three manure runoff studies. The model accurately predicted runoff P for boxes, but underpredicted runoff P for plots. Underpredictions were caused by runoff to rain ratios used to distribute P into runoff or infiltration. We developed P distribution fractions from manure water extraction data to replace runoff to rain ratios. Calculating P distribution fractions requires knowing rainfall rate and times that runoff begins and rain stops. Using P distribution fractions gave accurate predictions of runoff P for soil boxes and field plots. We observed relationships between measured runoff to rain ratios and both P distribution fractions and a degree of error in original predictions, calculated as (measured runoff P/predicted runoff P). Using independent field-plot data, we verified that original underpredictions of manure runoff P can be improved by calculating P distribution fractions from measured runoff to rain ratios or adjusting runoff to rain ratios based on their degree of error. Future work should test the model at field or watershed scales and at longer time scales.     

Regional assessment of herbicide sorption and degradation in two sampling years

Sorption and degradation of the herbicide 2,4-D [2,4-dichlorophenoxyacetic acid] were determined for 123 surface soils (0 to 15 cm) collected in 2002 and in 2004 between 49 degrees to 60 degrees north longitude and 110 degrees to 120 degrees west latitude in Alberta, Canada. The soils were characterized by soil organic carbon content (SOC), pH, electrical conductivity, soil texture, cation exchange capacity, carbonate content, and total soil microbial activity. The 2,4-D sorption coefficients, Kd and Koc, were highly variable with coefficients of variation of 89 and 59%, respectively, at the provincial scale. Both Kd and Koc were well described by regression models with SOC and soil pH as variables, regardless of scale. Surprisingly, variations in 2,4-D mineralization were much smaller than variations in sorption. Variability in total 2,4-D mineralization was particularly low, with a coefficient of variation of only 7% at the provincial scale. Average 2,4-D half-lives in ecoregions ranged from 1.7 to 3.5 d, much lower than the field dissipation half-life of 10 d reported for 2,4-D in general pesticide property databases. Regression models describing degradation parameters were generally poor or not significant because 2,4-D mineralization was only weakly associated with measured 2,4-D sorption parameters and soil properties. As such, regional variations in herbicide sorption coefficients should be measured or calculated based on soil properties, to assign distinct pesticide fate model input parameters when estimating 2,4-D off-site transport at the provincial scale. Spatial variations in herbicide degradation appear less important for Alberta as 2,4-D half-lives were similar in soils across the province. The rapid mineralization of 2,4-D is noteworthy because 2,4-D is widely used in Alberta and perhaps adaptation of soil microbial communities allowed for accelerated degradation regardless of soil properties or the extent of 2,4-D sorption by soil.     

APPLICATION OF DIFFUSIVE TANK MODEL IN DRAINAGE ANALYSIS OF PADDY FIELDS1

ABSTRACT: A diffusive tank model has been successfully applied to the simulation of runoff from paddy fields in Japan because it can well describe the features of local water flows. The main goal of the study is to evaluate the performance of the diffusive tank model with the calibrated parameters obtained in Jyau‐Shi to simulate discharge from paddy fields in two experimental catchments located in the areas of Shing‐Ying and Ta‐Liao, Southwestern Taiwan. The simulations were verified by comparing the model results with observed runoff data from the two experimental catchments. The model predicted the discharge from the paddy fields well. This indicates that the model with the calibrated parameters may be used in other paddy fields in Taiwan.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

Predicting soil fumigant air concentrations under regional and diverse agronomic conditions

SOFEA (SOil Fumigant Exposure Assessment system; Dow AgroSciences, Indianapolis, IN) is a new stochastic numerical modeling tool for evaluating and managing human inhalation exposure potential associated with the use of soil fumigants. SOFEA calculates fumigant concentrations in air arising from volatility losses from treated fields for large agricultural regions using multiple transient source terms (treated fields), geographical information systems (GIS) information, agronomic specific variables, user-specified buffer zones, and field reentry intervals. A modified version of the USEPA Industrial Source Complex Short Term model (ISCST3) is used for air dispersion calculations. Weather information, field size, application date, application rate, application type, soil incorporation depth, pesticide degradation rates in air, tarp presence, field retreatment, and other sensitive parameters are varied stochastically using Monte Carlo techniques to mimic region and crop specific agronomic practices. Regional land cover, elevation, and population information can be used to refine source placement (treated fields), dispersion calculations, and risk assessments. This paper describes the technical algorithms of SOFEA and offers comparisons of simulation predictions for the soil fumigant 1,3-dichloropropene (1,3-D) to actual regional air monitoring measurements from Kern, California. Comparison of simulation results to daily air monitoring observations is remarkable over the entire concentration distribution (average percent deviation of 44% and model efficiency of 0.98), especially considering numerous inputs such as meteorological conditions for SOFEA were unavailable and approximated by neighboring regions. Both current and anticipated and/or forecasted fumigant scenarios can be simulated using SOFEA to provide risk managers and product stewards the necessary information to make sound regulatory decisions regarding the use of soil fumigants in agriculture.     

A COMPARISON OF DELTA CHANGE AND DOWNSCALED GCM SCENARIOS FOR THREE MOUNTAINOUS BASINS IN THE UNITED STATES1

ABSTRACT: Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical downscaling. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical downscaling (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEPINCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation‐runoff model. Results from this study show that, in the basins tested, a precipitation‐runoff model can simulate realistic runoff series for current conditions using statistically down‐scaled NCEP output. But, use of downscaled HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or downscaling approaches, future climate assessments based on either of these approaches must be treated with caution.