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

Abstract

Predictions of urban runoff are heavily reliant on semi‐distributed models, which simulate runoff at subcatchment scales. These models often use “effective” model parameters that average across the small‐scale heterogeneity. Here we quantify the error in model prediction that arises when the optimal calibrated value of effective parameters changes with model forcing. The uncertainty this produces, which we refer to as “calibration parameter transfer uncertainty,” can undermine the usefulness of important applications of urban hydrologic models, for example, to predict the hydrologic response to novel climate or development scenarios. Using the urban hydrologic model SWMM (“Stormwater Management Model”) as a case study, we quantify the transferability of two calibrated effective parameters: subcatchment “width” and “connected impervious area.” Through numerical experiments, we simulate overland flow across a highly simplified synthetic urban landscape subject to a range of scenarios (combinations of storm events, soil types, and impervious areas). For each scenario, we calibrate SWMM “width” and “connected impervious area” parameters to the outcomes of a distributed model. We find that the calibrated values of these parameters vary with soil, storm, and land cover forcing. This variation across forcing parameters can result in prediction errors up to a magnitude of 60% when a calibrated SWMM is used to predict runoff following changes in climate and land cover. Such calibration transfer uncertainty is largely unaccounted for in urban hydrologic modeling. These results point to a need for additional research to determine how to use urban hydrologic models to make robust predictions across future conditions.

Development of a Multiobjective Automatic Parameter-Calibration Framework for Urban Drainage Systems

“…It has two characteristics: (1) hydrological runoff and (2) hydraulic runoff. The differences in runoff characteristics are driven by climate, land cover, impervious and pervious configurations, pervious area soil types, network topology, and pipe characteristics [28]. Hydrological runoff is a computed path that connects subcatchments between the runoff outlets (e.g., nodes).…”

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

“…With the acceleration of social progress and urbanisation, the gradual expansion of urban areas has made urban flood safety issues more important [1]. Flood safety issues, including efficient and stable urban inundation warning systems and the utilisation of stormwater technologies for urban water resource management, rely heavily on stable, accurate, and rapid model simulation techniques [2]. Numerical models of urban hydrodynamic processes are crucial non-engineering measures for flood prevention.…”

Parameter Sensitivity Analysis of SWMM model in an Airport Airfield Area

“…Various innovative sensitivity analysis methods have been proposed, including the logistic regression method, global sensitivity analysis based on the storm water management model, and local sensitivity analysis (Chen et al, 2017;Fatone et al, 2021;Szelag et al, 2022;Zakizadeh et al, 2022). Sytsma A quantified the transferability of two calibrated effective parameters, and a novel ensemble framework was proposed to achieve robust model generalization (Sytsma et al, 2022). Two global sensitivity analysis methods (i.e., VARS and Sobol) were illustrated and compared for the identification of influential parameters and their variations in hydrological dynamics (Hashemi and Mahjouri., 2022).…”