Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective

The assessment of the effectiveness of the unimodal and bimodal models to evaluate the water flow through nature-based solutions substrates

Nature-based solutions are popular techniques for managing stormwater. Most of them allow porous media as their main layer. The description of the Soil Water Retention Curve (SWRC) as the Unsaturated Hydraulic Conductivity Curve (UHCC) is often required to run the hydrological simulations with the physically based models. Using the unimodal and bimodal models to assess the SWRC and UHCC of soils is a widespread technique but their evaluation is often present in literature only in terms of curve fitting. Based on these assumptions, this work presents the performance assessment of the van Genuchten unimodal and bimodal models by functional evaluation of them based on the runoff from several substrates. Four substrates were investigated to define the structure, the SWRC, and the UHCC. Results showed that all substrates had a bimodal behaviour with lowest values of RMSE (RMSE_Θ = 0.0023 to 0.0037, RMSE_K = 0.0636 to 0.1284). Finally, a numerical simulation using the HYDRUS-1D model was performed for a three-month data set to check the effectiveness of the unimodal model instead of the bimodal one. The findings have shown that the unimodal model must be preferred instead of the bimodal because it has fewer parameters and assured low discrepancies in runoff volume (ε=0.00% to 6.25%).

A novel multiscale biophysical model to predict the fate of ionizable compounds in the soil-plant continuum

Soil pollution from emerging contaminants poses a significant threat to water resources management and food production. The development of numerical models to describe the reactive transport of chemicals in both soil and plant is of paramount importance to elaborate mitigation strategies. To this aim, in the present study, a multiscale biophysical model is developed to predict the fate of ionizable compound in the soil-plant continuum. The modeling framework connects a multi-organelles model to describe processes at the cell level with a semi-mechanistic soil-plant model, which includes the widely used Richards-based solver, HYDRUS. A Bayesian probabilistic framework is used to calibrate and assess the capability of the model in reproducing the observations from an experiment on the translocation of five pharmaceuticals in green pea plants. Results show satisfactory fitting performance and limited predictive uncertainty. The subsequent validation with the cell model indicates that the estimated soil-plant parameters preserve a physically realistic meaning, and their calibrated values are comparable with the existing literature values, thus confirming the overall reliability of the analysis. Model results further suggest that pH conditions in both soil and xylem play a crucial role in the uptake and translocation of ionizable compounds.

On the Use of Mechanistic Soil-Plant Uptake Models: A Comprehensive Experimental and Numerical Analysis on the Translocation of Carbamazepine in Green Pea Plants

Food contamination is a major worldwide risk for human health. Dynamic plant uptake of pollutants from contaminated environments is the preferred pathway into the human and animal food chain. Mechanistic models represent a fundamental tool for risk assessment and the development of mitigation strategies. However, difficulty in obtaining comprehensive observations in the soil-plant continuum hinders their calibration, undermining their generalizability and raising doubts about their widespread applicability. To address these issues, a Bayesian probabilistic framework is used, for the first time, to calibrate and assess the predictive uncertainty of a mechanistic soil-plant model against comprehensive observations from an experiment on the translocation of carbamazepine in green pea plants. Results demonstrate that the model can reproduce the dynamics of water flow and solute reactive transport in the soil-plant domain accurately and with limited uncertainty. The role of different physicochemical processes in bioaccumulation of carbamazepine in fruits is investigated through Global Sensitivity Analysis, which shows how soil hydraulic properties and soil solute sorption regulate transpiration streams and bioavailability of carbamazepine. Overall, the analysis demonstrates the usefulness of mechanistic models and proposes a comprehensive numerical framework for their assessment and use.

On the Use of a Real-Time Control Approach for Urban Stormwater Management

The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.