Impact of fate properties, groundwater fluctuations and the presence of worm burrows on pesticide leaching assessments through golf areas

Karan, Sachin; Badawi, Nora; Jensen, Anne; Rosenbom, Annette E.

doi:10.1016/j.envpol.2021.117822

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The boundary condition, bgrad, being among the parameters with the largest contribution to the prediction uncertainty, controls the percolation out of the model domain (Equation 7). Obviously, the boundary conditions can affect the simulation results and as shown by, for example, Karan et al (2021) the choice in lower boundary condition may affect simulated concentrations, especially when estimating concentrations at or near the boundary. It was evident that the information content in the available observation data was sufficient to constrain bgrad as seen from the identifiability that was dominated by low singular value indices (Figure S2A in Supporting Information S1).…”

Section: Parameter Contribution To Prediction Uncertaintymentioning

confidence: 99%

“…Models are needed to quantify these fluxes, however, the inherent complexity of subsurface processes presents substantial challenges for numerical modeling and associated predictions (e.g., Beven, 2018;Gassmann, 2021), particularly in clay till settings characterized by preferential flows (Badawi et al, 2023;Villholth & Jensen, 1998). The presence of preferential flow pathways, such as macropores and root channels, creates a heterogeneous and non-uniform water content, leading to faster solute transport with less likelihood for degradation and thus potential for contamination of groundwater resources (Badawi et al, 2023;Brown et al, 2000;Karan et al, 2021;Nimmo, 2012;Rosenbom et al, 2014). A major challenge in numerical modeling of the variably saturated zone in settings with preferential flow paths is a lack of data to constrain the models adequately (N. Jarvis et al, 2007;Šimůnek et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Numerical Prediction Uncertainty and Data Worth Analysis of Solute Transport in an Agricultural Clay Till Setting With Preferential Flow

Karan,

Jensen,

Sonnenborg

2024

Water Resources Research

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With modern agricultural practices, it is essential to quantify flow and solute transport fluxes by numerical models and associated predictions. A major challenge in modeling preferential flow settings is the ability to constrain the often numerous parameters needed to physically represent these systems. Following this, there is a lack of understanding of what parameters and observations carry the most worth for a model to reduce its prediction uncertainty. Here, first‐order second moment (FOSM) analyses were used for a heavily monitored clay till field with preferential flow to investigate which parameters and observation types contribute the most to reducing the uncertainty of bromide predictions at various depths. Using a multi‐objective regularized optimization approach, a 1‐D preferential flow model was calibrated and subjected to FOSM analyses. Key parameters contributing to the prediction uncertainty of bromide concentrations in 0.25–3 m were limited to the lower boundary condition, the mass transfer coefficient, the hydraulic conductivity of the micro‐ and macropore, the macropore porosity, and the water content at wilting point. The data with the largest worth and ability to reduce the pre‐calibration prediction uncertainty were concentration observations closest to the sought prediction depth, drain concentrations, and averaged water table measurements from the entire field. The post‐calibration prediction uncertainty was increased primarily by removing concentration observations closest to the prediction depths. While this study provided new insights into parameter importance and data worth further research is required to understand if these findings apply broadly to clay till settings (or any soil setting) with preferential flow.

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Section: Parameter Contribution To Prediction Uncertaintymentioning

confidence: 99%