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

Abstract

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.

Improving soil water storage with no‐till cover cropping in the Mississippi River Alluvial Basin

“…Soil hydraulic parameters were calibrated to optimize the simulation of soil water content within a 1.2‐m profile. Field capacity and K s were the sensitive parameters that affected runoff and soil profile water storage (Chen et al., 2017). We observed that soil water balance was highly sensitive to the K s value of the top 5‐cm horizon.…”

“…The NNSE was calculated by normalizing Nash–Sutcliffe efficiency (NSE) within a 0–1 range [NNSE = 1/(2‐NSE)]. Generally, R 2 ≥ 0.5, NNSE > 0.5, d ≥ 0.5, and |PBIAS| ≤ 50% were considered acceptable (Bennett et al., 2013; Chen et al., 2017). For performance evaluation, whole seasonal soil water data including 60 days of calibration were used in the 2019–2020 and 2020–2021 simulation periods.…”

“…Process-based models such as the root zone water quality model (RZWQM2) could be useful for accurately estimating runoff and deep percolation to compute crop water use or evapotranspiration (ET). The RZWQM2 can simulate the one-dimensional movement of water and partition rainfall into infiltration and runoff for a particular event and period (Chen et al, 2017;Q. L. Ma et al, 1998).…”

“…Process‐based models such as the root zone water quality model (RZWQM2) could be useful for accurately estimating runoff and deep percolation to compute crop water use or evapotranspiration (ET). The RZWQM2 can simulate the one‐dimensional movement of water and partition rainfall into infiltration and runoff for a particular event and period (Chen et al., 2017; Q. L. Ma et al., 1998). The model utilizes breakpoint rainfall data to generate time‐based infiltration and runoff predictions and calculates the amount of water required to satisfy macropore flow to generate rainfall excess in an infiltration timestep using Poiseuille's law (Q. L. Ma et al., 1998).…”

Numerical Modeling of Groundwater Pollution by Chlorpyrifos, Bromacil and Terbuthylazine. Application to the Buñol-Cheste Aquifer (Spain)

“…In many practical situations where not many data are available, using a simple model with fewer parameters is recommended [21]. Numerical modeling to simulating pesticide behavior is an attractive way to perform environmental assessment of agricultural situations [22][23][24][25]. As the first step of the assessment process, it is necessary to identify which pesticide should be used for a specific climate-crop-soil combination, as well as its application rates, so that both crops and the environment are protected.…”