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Core Ideas Macropore flow may be enhanced in partly frozen soil. We developed a dual‐permeability modeling approach for frozen soils. Four test cases of increasing complexity were evaluated. Modeling results are in agreement with current process understanding. Measured data are lacking for a more quantitative evaluation of the model. Preferential flow may become significant in partially frozen soils because infiltration can occur through large, initially air‐filled pores surrounded by a soil matrix with limited infiltration capacity. The objectives of this study were to develop and evaluate a dual‐permeability approach for simulating water flow and heat transport in macroporous soils undergoing freezing and thawing. This was achieved by introducing physically based equations for soil freezing and thawing into the dual‐permeability model MACRO. Richards' equation and the heat flow equation were loosely coupled using the generalized Clapeyron equation for the soil micropore domain. Freezing and thawing of macropore water is governed by a first‐order equation for energy transfer between the micropore and macropore domains. We assumed that macropore water was unaffected by capillary forces, so that water in macropores freezes at 0°C. The performance of the model was evaluated for four test cases: (i) redistribution of water in the micropore domain during freezing, (ii) a comparison between the first‐order energy transfer approach and the heat conduction equation, (iii) infiltration and water flow in frozen soil with an initially air‐filled macropore domain, and (iv) thawing from the soil surface during constant‐rate rainfall. Results show that the model behaves in accordance with the current understanding of water flow and heat transport in frozen macroporous soil. To improve modeling of water and heat flow in frozen soils, attention should now be focused on providing experimental data suitable for evaluating models that account for macropore flow.

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta‐models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072–1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years as they offer a more transparent way to characterize risk and evaluate uncertainty than the traditional risk assessment paradigms. In this study, a novel probabilistic approach applying a BN for risk calculation was further developed and explored by linking the calculation a risk quotient to alternative future scenarios. This extended version of the BN model uses predictions from a process-based pesticide exposure model (World Integrated System for Pesticide Exposure -WISPE) in the exposure characterization and toxicity test data in the effect characterization. The probability distributions for exposure and effect are combined into a risk characterization (i.e. the probability distribution of a risk quotient), a common measure of the exceedance of an environmentally safe exposure threshold. The BN model was used to account for variabilities of the predicted pesticide exposure in agricultural streams, and inter-species variability in sensitivity to the pesticide among freshwater species. In Northern Europe, future climate scenarios typically predict increased temperature and precipitation, which can be expected to cause an increase in weed infestations, plant disease and insect pests. Such climate-related changes in pest pressure in turn can give rise to altered agricultural practices, such as increased pesticide application rates, as an adaptation to climate change. The WISPE model was used to link a set of scenarios consisting of two climate models, three pesticide application scenarios and three periods (year ranges), for a case study in South-East Norway. The model was set up for the case study by specifying environmental factors such as soil properties and field slope together with chemical properties of pesticides to predict the pesticide exposure in streams adjacent to the agricultural fields. The model was parameterized and evaluated for five selected pesticides: the three herbicides clopyralid, fluroxypyr-meptyl, and 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), and the two fungicides prothiocanzole and trifloxystrobin. This approach enabled the calculation and visualization of probability distribution of the risk quotients for the future time horizons 2050 and