Assessing resilience of a dual drainage urban system to redevelopment and climate change

Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM

“…In spite of this sensitivity, the validity of SWMM calibration is almost never assessed in common applications of SWMM that predict urban runoff production across changing climates (e.g., Bai et al., 2018; Panos et al., 2021; Waters et al., 2003), changing land cover characteristics (e.g., Jang et al., 2007; Panos et al., 2018), or combinations of the two (Hung et al., 2020; but see Johannessen et al., 2019; Li et al., 2012; Mittman et al., 2012). Lack of assessment of transferability of effective parameters means modelers lack of guidance about how to apply calibrated models to make predictions under future environmental conditions.…”

“…Many different initial‐to‐future transitions are possible in theory, yet SWMM is often used to predict changes to infiltration and runoff resulting from future development (increased imperviousness) and/or climate change (increased rainfall intensity; e.g., Hung et al., 2020; Kim et al., 2017; Panos et al., 2018, 2021). To explore the implications of non‐transferability for these cases, we plotted the distributions of errors that arise when calibrated parameter sets from initial conditions $${{\phi }_{imp}}^{i}$$ = 0.1 and $${p/{k}_{s}}^{i}$$ = 0.25 were subsequently used to make predictions under three different future conditions: (i) land cover change, $${{\phi }_{imp}}^{f}$$ = 0.9 (Δ ϕ imp = 0.8); (ii) climate change, that is, increased rainfall intensity, $${p/{k}_{s}}^{f}$$ = 4 (Δ p / k s = 3.25); and (ii) both land cover + climate change, $${{\phi }_{imp}}^{f}$$ = 0.9 (Δ ϕ imp = 0.8) and $${p/{k}_{s}}^{f}$$ = 4 (Δ p / k s = 3.25).…”

“…Using the EPA SWMM, we quantify the errors in runoff predictions associated with calibration transfer uncertainty arising from changes to climate and land cover. SWMM provides a suitable case study model, as it is one of the most widely used urban stormwater models (Niazi et al., 2017), and is frequently employed to predict the response of urban hydrology to changes in climate (Panos et al., 2021), land cover (Panos et al., 2018; Waters et al., 2003), and management (e.g., low impact development, LID; Bai et al., 2018; Hekl & Dymond, 2016). LID is often designed (and modeled) to receive overland flow from a contributing subcatchment; sizing and effectiveness of LID controls for subcatchment scale mitigation are therefore largely independent of downstream hydraulics.…”

“…Thus, we would expect the distributions of AE RD to roughly mirror those of AE IF and PE IF . We plotted the resulting errors (PE IF , AE IF , and AE RD ) against the transitions, that is, Δp/k s and Δϕ imp .Many different initial-to-future transitions are possible in theory, yet SWMM is often used to predict changes to infiltration and runoff resulting from future development (increased imperviousness) and/or climate change (increased rainfall intensity; e.g.,Hung et al, 2020;Kim et al, 2017;Panos et al, 2018Panos et al, , 2021. To explore the implications of non-transferability for these cases, we plotted the distributions of errors that arise when calibrated parameter sets from initial conditions 𝐴𝐴 𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖 = 0.1 and 𝐴𝐴 𝐴𝐴∕𝑘𝑘𝑠𝑠 𝑖𝑖 = 0.25 were subsequently used to make predictions under three different future conditions: (i) land cover change, 𝐴𝐴 𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖 𝑓𝑓 = 0.9 (Δϕ imp = 0.8); (ii) climate change, that is, increased rainfall intensity, 𝐴𝐴 𝐴𝐴∕𝑘𝑘𝑠𝑠 𝑓𝑓 = 4 (Δp/k s = 3.25); and (ii) both land cover + climate change, 𝐴𝐴 𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖 𝑓𝑓 = 0.9 (Δϕ imp = 0.8) and 𝐴𝐴 𝐴𝐴∕𝑘𝑘𝑠𝑠 𝑓𝑓 = 4 (Δp/k s = 3.25).…”

Application of PCSWMM for the 1-D and 1-D–2-D Modeling of Urban Flooding in Damansara Catchment, Malaysia

“…Hence, a commercial version of SWMM known as Personal Computer Storm Water Management Model (PCSWMM) is used, incorporating a stand-alone Geographic Information System (GIS) for integrated 1-D-2-D and quasi-2-D modeling. The reliability and efficiency of PCSWMM have garnered popularity in many countries, such as Korea [25], Cambodia [26,27], Norway [28], China [29], Thailand [30], the United States [31], Australia [32], the Philippines [33], and India [34]. In Malaysia, SWMM modeling with PCSWMM is less commonly done, though Hasan et al [35] and Sidek et al [36] used a different version of the stormwater modelling tool, XPSWMM, developed by XP Solutions to investigate the hydrological and hydraulic characteristics of the Aur River and Jeluh River catchments, respectively.…”