An initial parameter estimation approach for urban catchment modelling. Issue 2 (7th February 2023)

• Record Type: Journal Article
• Authors: Gong, Siming; Ball, James E; Surawski, Nicholas
• Is Part Of: Urban water journal. Volume 20:Issue 2(2023)
• Page Start: 171
• Page End: 183
• Publication Date: 2023-02-07
• Subjects: Catchment modelling -- land use land cover (LULC) -- parameterisation -- Deep Learning; Municipal water supply -- Management -- Periodicals; Water-supply -- Planning -- Periodicals; 628.1
• DOI: 10.1080/1573062X.2022.2155851
• Languages: English
• ISSNs: 1573-062X

Abstract

ABSTRACT: Catchment modelling is an effective approach to simulating the rainfall-runoff process. Estimating parameters is challenging in urban catchments with heterogeneous land use land cover (LULC). This paper describes a novel and reproducible approach to initialise the parameters for modelling catchment hydrology. The Alexandra canal catchment, Australia, was selected as the study catchment. A pixel-based LULC map was generated from the catchment's orbital image using the Deep Learning (DL) techniques. Integrate the LULC map with subcatchment delineation and hydraulic layout, group LULC attributes to compute the area and imperviousness fraction at pixel scale. The distance from each pixel to the subcatchment outlet was vectorised to estimate flow length. The cumulative likelihood and Kolmogorov–Smirnov (KS) were adopted to describe the parameter distributions, evaluate the goodness-of-fit and LULC effect. Results reflect a limited effect of LULC on flow length, and the approach can initialise the parameters for conceptual catchment modelling systems.