Probabilistic assessment of herbicide runoff from Japanese rice paddies: The effects of local meteorological conditions and site-specific water management

Abstract

Uncertainty assessments of herbicide losses from rice paddies in Japan associated with local meteorological conditions and water management practices were performed using a pesticide fate and transport model, PCPF-1, under the Monte Carlo (MC) simulation scheme. First, MC simulations were conducted for ve di erent cities with a prescribed water management scenario and a 10-year meteorological dataset of each city. e e ectiveness of water management was observed regarding the reduction of pesticide runo . However, a greater potential of pesticide runo remained in Western Japan. Secondly, an extended analysis was attempted to evaluate the e ects of local water management and meteorological conditions between the Chikugo River basin and the Sakura River basin using uncertainty inputs processed from observed water management data. e results showed that because of more severe rainfall events, signi cant pesticide runo occurred in the Chikugo River basin even when appropriate irrigation practices were implemented.

Inverse analysis to estimate site‐specific parameters of a mathematical model for simulating pesticide dissipations in paddy test systems

“…Similarly, the effect of the parameter variations on the model cost was visualized by the ‘modCRL’ function and quantified by the standardized rank regression coefficient (SRRC), which is the robust sensitivity measure estimated from the rank‐transformed regression model. A detailed explanation of SRRC is available elsewhere . In this study, the SRRCs of the varying parameters were calculated using the ‘src’ function in the R package ‘sensitivity.’…”

PCPF‐M model for simulating the fate and transport of pesticides and their metabolites in rice paddy field

“…Uncertainty and sensitivity analyses of the PCPF‐1 and PCPF‐NB models using Monte Carlo (MC) techniques have been reported . The soil adsorption coefficient and the degradation rate constants of the pesticide were reported to be extremely sensitive parameters for forecasting the concentration of the parent pesticide in PW and PSL.…”

“…The MC simulations consisted of 250 iterations as this sample size provided stable and consistent results for the PCPF‐1 and PCPF‐NB models . The input parameters of the PCPF‐M model selected for the uncertainty and sensitivity analyses were attributed minimum and maximum values which were equal to −10% and +10%, respectively, of the deterministic values presented in Table …”

Development and validation of the SPEC model for simulating the fate and transport of pesticide applied to Japanese upland agricultural soil

“…32,33) The method relies on a stepwise regression analysis that computes standard rank regression coefficients (SRRCs) for the predictors (inputs) that have the most significant influence on the predictions (outputs). By ranking the input parameters by absolute values of SRRCs, the model's most sensitive parameters can be highlighted.…”