A Bayesian Decision Model for Optimum Investment and Design of Low-Impact Development in Urban Stormwater Infrastructure and Management

Abstract

Uncertainties concerning low-impact development (LID) practices over its service life are challenges in the adoption of LID. One strategy to deal with uncertainty is to provide an adaptive framework which could be used to support decision-makers in the latter decision on investments and designs dynamically. The authors propose a Bayesian-based decision-making framework and procedure for investing in LID practices as part of an urban stormwater management strategy. In this framework, the investment could be made at various stages of the service life of the LID, and performed with deliberate decision to invest more or suspend the investment, pending the needs and observed performance, resources available, anticipated climate changes, technological advancement, and users’ needs and expectations. Variance learning (VL) and mean-variance learning (MVL) models were included in this decision tool to support handling of uncertainty and adjusting investment plans to maximize the returns while minimizing the undesirable outcomes. The authors found that a risk-neutral investor tends to harbor greater expectations while bearing a higher level of risks than risk-averse investor in the VL model. Constructed wetlands which have a higher prior mean performance are more favorable during the initial stage of LID practices. Risk-averse decision-makers, however, could choose porous pavement with stable performance in the VL model and leverage on potential technological advancement in the MVL model.

Knowledge Atlas on the Relationship between Water Management and Constructed Wetlands—A Bibliometric Analysis Based on CiteSpace

“…Miklas Scholz started The United States is followed by China in second place in terms of the number of articles published (counts = 220). There are macroscopic studies on the CW dispersal in China [30][31][32], as well as research into the efficiency of the integrated management of CW media, plant management, etc. [33][34][35].…”

Opportunities for Leveraging Existing Hydrologic and Hydraulic Models Developed for Water Quantity Management to Mitigate Flooding Due to Extreme Precipitation

“…Climate change will make compliance with local and national clean water regulations (Pasquier et al 2019) even more challenging, especially for coastal, low-income, communities that are generally more susceptible to flood damages, displacement (Lee and Jung 2014) and other socioeconomic challenges (Bick et al 2021). Stormwater management strategies designed to historical climate conditions (De Sousa et al 2012), may not perform well under future conditions (Wang et al 2021). To avoid stranding these valuable assets, decision makers must factor climate change into infrastructure planning and design (Martinich and Crimmins 2019).…”