Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty

Abstract

Abstract. Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in southwestern Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM), greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set for an important agricultural production area in south-western Sweden based on monthly change factors for 2070-2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

“…Using multiple climate models with different greenhouse gas emission scenarios allows the integration of more variability (Fernández et al, 2017). The climate change component of the model could have been improved by using more recent and alternative climate models, as recommended by, for example, Steffens et al (2014) and Moe et al (2022). These authors mentioned that using an ensemble of various global and regional climate models and various greenhouse gas emission scenarios would potentially enable more robust predictions of environmental impacts such as chemical exposure in the future.…”

Using a Bayesian network model to predict effects of pesticides on aquatic community endpoints in a rice field – A southern European case study

“…The use of multiple climate models with different greenhouse gas emission scenarios allows integration of more variability (Fernández et al, 2017). The model could he been improvement by using more and different climate models as some papers by Steffens et al (2014) and Moe et al (2022) recommended. These mentioned that using an ensemble of various lobal and regional climate models together with various greenhouse gas emission scenarios would potentially enable more robust estimations of pesticide losses in future.…”

Reply to reviewer comment 1

“…Printer-friendly version Discussion paper for projecting some important hydrological variables. Another study also demonstrated that model parameters could be of major importance for projecting changes in waterquality (Steffens et al, 2014).…”