A Framework for Sustainable Urban Drainage System Performance Modeling

Thesis Abstract

Urban areas increasingly confront hydrological extremes, where conventional drainage designs often fail to sustain performance under changing rainfall regimes, rapid urbanization, and climate variability. This study addresses the gap between traditional drainage design approaches and the need for adaptive, performance-driven frameworks that integrate sustainability criteria with urban flood resilience. The aim is to develop a framework for Sustainable Urban Drainage System (SUDS) performance modeling that can predict system reliability, water quality outcomes, and lifecycle environmental impacts under diverse rainfall scenarios and land-use configurations. Specific objectives are to (i) synthesize theories of resilience and multi-criteria decision analysis to formulate a conceptual model linking SUDS components to hydrological and water quality performance, (ii) calibrate and validate the framework using empirical data from 15 pilot catchments across three metropolitan regions, (iii) quantify trade-offs between runoff attenuation, pollutant removal efficiency, and life-cycle emissions using integrated modeling, (iv) examine the sensitivity of SUDS performance to climate variability, urban form, and maintenance regimes, and (v) develop a decision-support tool for planners and engineers to optimize SUDS design and operation under uncertainty. The methodology adopts a mixed-methods, theory-informed design. The research follows a pragmatist paradigm, combining quantitative hydrological simulation with qualitative stakeholder insights to ensure practical relevance. The population comprises urban catchments with implemented or proposed SUDS in five metropolitan areas. A stratified random sample selects 15 catchments representing varying imperviousness, drainage configurations, and governance contexts. Data collection instruments include (i) hydrodynamic and water quality sensor datasets (daily runoff volumes, pollutant concentrations, and antecedent moisture conditions) spanning at least five years, (ii) design documentation and maintenance logs for SUDS installations, and (iii) semi-structured interviews with municipal engineers and facility operators. Analytical techniques feature (a) Bayesian networks and multivariate regression to model hydrological performance and pollutant fate, (b) Monte Carlo simulations to propagate parameter and climate uncertainties, (c) structural equation modeling to test the proposed theoretical framework linking resilience constructs to measurable outcomes, (d) life-cycle assessment (LCA) to quantify environmental burdens and benefits, and (e) scenario analysis to explore policy-relevant futures. Model specification integrates a modular SUDS performance framework with dynamic links between rainfall inputs, surface runoff, infiltration, evapotranspiration, and pollutant removal pathways, calibrated against observed data. Validity and reliability are ensured through cross-validation, k-fold partitioning, and expert triangulation of model parameters. Ethical considerations include informed consent for interviews, secure data handling, and adherence to local governance regulations. Anticipated findings indicate that the integrated SUDS performance framework can robustly predict runoff attenuation, peak discharge reduction, and pollutant removal efficiencies across rainfall regimes, with uncertainties quantified by credible intervals. The study expects to identify threshold conditions where maintenance frequency or retrofitting becomes essential to sustain performance, and to reveal meaningful trade-offs between maximized water quality benefits and energy or material life-cycle costs. The framework is also expected to demonstrate that resilience-oriented design choices—such as variable storage capacity, adaptive outlet controls, and integrated green-blue-blue-green corridors—enhance system reliability under climate variability while delivering co-benefits for biodiversity and urban heat mitigation. The contribution to knowledge lies in a rigorously tested, transdisciplinary SUDS performance model that unites hydrology, environmental engineering, and sustainability assessment within a single decision-support framework, enabling evidence-based optimization under uncertainty. The study concludes that a unified performance-modeling framework enhances decision-making for sustainable urban drainage by enabling explicit evaluation of hydrological performance, water quality outcomes, and environmental footprints in an uncertainty-aware context. Recommendations include adopting the framework for municipal planning cycles, prioritizing data-enabled maintenance regimes, integrating the model into regional climate adaptation plans, and extending the approach to include social equity considerations in access to flood-resilient infrastructure. Further research is suggested to refine parameter estimation for rare rainfall events, incorporate remote sensing-derived inputs for broader applicability, and explore real-time model updating using IoT sensor networks.