Toward sustainable stormwater management: Understanding public appreciation and recognition of urban Low Impact Development (LID) in the San Francisco Bay Area

Probabilistic assessment of failure of infiltration structures under model and parametric uncertainty

We focus on the quantification of the probability of failure (PF) of an infiltration structure, of the kind that is typically employed for the implementation of low impact development strategies in urban settings. Our approach embeds various sources of uncertainty. These include (a) the mathematical models rendering key hydrological traits of the system and the ensuing model parametrization as well as (b) design variables related to the drainage structure. As such, we leverage on a rigorous multi-model Global Sensitivity Analysis framework. We consider a collection of commonly used alternative models to represent our knowledge about the conceptualization of the system functioning. Each model is characterized by a set of uncertain parameters. As an original aspect, the sensitivity metrics we consider are related to a single- and a multi-model context. The former provides information about the relative importance that model parameters conditional to the choice of a given model can have on PF. The latter yields the importance that the selection of a given model has on PF and enables one to consider at the same time all of the alternative models analyzed. We demonstrate our approach through an exemplary application focused on the preliminary design phase of infiltration structures serving a region in the northern part of Italy. Results stemming from a multi-model context suggest that the contribution arising from the adoption of a given model is key to the quantification of the degree of importance associated with each uncertain parameter.

Multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development

Cost-effective runoff control scheme drafting involves localization, multi-sector coordination, and configuration of multifunctional infrastructures. Numerous independent variables, parameters, weights, and objectives make runoff control optimization quantitatively arduous. This study innovatively proposed a multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development. The quantitative methods of multi-objective evaluation, hydrological feature partition, and pressure-adapted multi-objective weight assignment were proposed. Remote sensing inversion of water quality, hydrological model simulation (using SWAT and SWMM software), landscape pattern index calculation, life cycle cost (LCC), life cycle assessment (LCA) on ecological impact, and NSGA-II optimization algorithm were applied. Wuhan, the most water-sensitive city in China, was studied as a case. Runoff control function (RCF), capital investment (CI), and ecological return on investment (EROI) served as optimized objectives. High, medium, and low built-up regions in Wuhan urban development planning district were extracted by topographic factors and landscape patterns, which comprised 28, 34, and 38% of the case area, respectively. Three corresponding hydrological models were then built to illustrate distinct runoff control cost-efficiency in each region. Pressure distributions on runoff control, economic constraints, and ecological resource scarcity were quantitatively evaluated. And four pressure zones were clustered, which occupied 36, 29, 16, and 19% of the case area, respectively. Then the zonal weighted optimization decision-making matrix (with 3 hydrological models and 5 wt) was established by overlaying the pressure zone and built-up zone. In high, medium, and low built-up regions, optimized solutions reduced annual runoff volume by 86, 82%, and 77%The average runoff investments per square meter of impervious underlying surface in high, medium, and low built-up regions were 34.2, 18.7, and 7.9 RMB yuan, respectively. Medium and low built-up regions may only need 55 and 23% of the high built-up region for the unitary impervious underlying surface to balance runoff control and ecological benefits. Runoff control and financial utilization efficiency enhance with hydrological differentiation zones. Thus, the optimization solutions are zonal adaptive, refined, comparable, replicable, and implementable.

Comprehensive analysis of waterlogging control and carbon emission reduction for optimal LID layout: a case study in campus

Nowadays, sponge city reconstruction has become the focus of research because of the increasingly serious urban waterlogging. Carbon emission reduction, waterlogging area reduction, cost, and other indicators were considered to explore the optimal sponge allocation scheme in the study area. The two-dimensional coupled model MIKE FLOOD was established to analyze the causes of waterlogging through numerical simulation. Low-impact development (LID) combination scenarios were set to analyze the control effects of waterlogging water and total runoff. The carbon emission reduction capacity and economic benefit of each scenario were calculated and evaluated. The analytic hierarchy process (AHP) was used to comprehensively evaluate the LID combination scenario and explore the optimal cost-benefit LID configuration scheme. The results show that the campus rainwater pipe network is under overload operation, and the number of overflow nodes accounts for up to 58.1% under the 3a rainfall return periods. After setting up LID measures, the runoff control rate can be increased by 26.15-42.84%, and the waterlogging area where the depth exceeds 15 cm can be reduced by 72.87-100%. If the energy conservation and emission reduction benefits and costs are considered at the same time, the layout scenario of 9% bioretention facility + 3% green roof + 3% permeable pavement can achieve the best benefits. The research can provide a reference for planning and reconstruction of sponge campus and residential areas.

Stormwater retention performance of green roofs with various configurations in different climatic zones

Green roof stormwater retention performance is fundamentally related to design configurations and climates. Efficient tools for assessing stormwater retention performance of green roofs with various configurations in different climates are highly desirable for practical applications. In this study, a hydrological model which can be used to simulate dynamic changes in moisture content and evapotranspiration of green roofs is developed and tested (with average Nash-Sutcliffe Efficiency of 0.8197 for calibration and 0.8252 for verification) using monitoring data (2018-2019) of four green roofs with various configurations. The model is applied to simulate long-term (1970-2018) moisture content, actual evapotranspiration, and retention performance of green roofs in eight cities across different climates of China. Green roofs built with engineered soil and Portulaca grandiflora show the largest evapotranspiration and thus provide the largest stormwater retention rates (R_r), while green roofs with light growing medium and Sedum lineare show the lowest evapotranspiration and R_r. R_r of green roofs increases as climate changes from humid to arid. Green roofs at Guangzhou (humid climate) provide the lowest R_r (28% ± 3%) caused by plenty of rainfall (1827 mm), while green roofs at Urumqi (desert climate) show the lowest mean annual actual evapotranspiration (167-269 mm) but provide the largest R_r (84% ± 5%) as a result of the lowest annual rainfall (282 mm). The results highlight that stormwater retention performance of green roofs could be enhanced through configuration optimization. However, a limiting factor in improving green roofs water retention rates may be the peculiarity of local climatic conditions.

A Water Resilience Evaluation Model for Urban Cities

Cities around the world are having to become more resilient to the effects of climate change and ongoing development pressures and consequently are seeking to improve urban water resource management. In order to analyze the key factors affecting a city’s water resilience, this research establishes an urban water resilience evaluation model. The DEMATEL method is used to calculate the intensity of the interaction among the water resilience indicators and to establish their causal relationships. Then, an ANP structured diagram is established and the weighting of the water resilience evaluation index for the city of Wuhan is obtained using the Super Decisions software. The key water resilience factors for Wuhan are shown to have significant mutual influences, with the main factors found to be the utilization of rainwater resources, the utilization of reclaimed water, and the total annual runoff control of the city. This method provides a sound basis for the evaluation of urban water resilience with scope to extend this to other cities. The results provide useful guidance for policy makers and other key stakeholders in supporting more sustainable urban development.