EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimating high resolution exposure at landscape-level – on the development of a 3‑dimensional Gaussian puff droplet drift model 

Mike Fuchs1,2, Sebastian Gebler1, and Andreas Lorke2
Mike Fuchs et al.
  • 1BASF SE, Global Exposure Modelling Team, Limburgerhof, Germany
  • 2Institute for Environmental Sciences, Rhineland-Palatinate Technical University Kaiserslautern-Landau (RPTU), Landau in der Pfalz, Germany

Modelling environmental concentrations of pesticides at landscape-level is of growing interest for pesticide registration and product stewardship, including higher-tier studies in risk assessment, mitigation measures, monitoring support and decision making. Typically, processes such as runoff, drainage, and leaching are well represented in existing modelling concepts at point and landscape scale. However, the modelling of off-target spray drift is often neglected or simplified at the landscape-level scale due to its high computational costs. Attempts at implementing spray drift into landscape-level modelling often rely on an external calculation of drift curves with pesticide masses added directly to the channel network. Although this approach enables the estimation of drift entries based on the proximity of source areas to water bodies, it may be insufficient in representing the spatial distribution of spray drift depositions in the landscape.

Our modelling approach aims to enable computationally efficient landscape-level spray drift predictions, which account for short term and local weather conditions. Therefore, a spray drift model for ground application was developed, by combining a mechanistic droplet model with a 3D Gaussian puff model. The mechanistic droplet model predicts the trajectory and mass balance of individual representative droplets, based on environmental conditions and application operations. This trajectory is then combined with a 3D Gaussian puff model to predict pesticide concentrations in the landscape, which are used to predict pesticide deposition rates. The model considers important spray drift predictors such as weather conditions, drop size distribution, physio‑chemical properties of the active ingredient, and operational conditions. The model showed realistic and expected behavior for variations in important input parameters (e.g., different nozzle types, wind speed). Furthermore, validation against two spray drift field studies showed good agreement between simulated and observed values.

To increase the understanding of pesticide transport pathways at the landscape-level, it is planned to combine the spray drift model in a modular fashion with a high-resolution SWAT+ (Soil and Water Assessment Tool) model of an agriculturally dominated catchment in Germany. Moreover, the spray drift model is expected to be a useful tool in the elucidation of monitoring data and the assessment of ecotoxicological risks for non-target organisms.

How to cite: Fuchs, M., Gebler, S., and Lorke, A.: Estimating high resolution exposure at landscape-level – on the development of a 3‑dimensional Gaussian puff droplet drift model , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5361,, 2023.

Supplementary materials

Supplementary material file