Assessing Flood-Prone Areas Using Stream Order Analysis: A Case Study of Auckland, New Zealand

Abstract

Purpose This study aims to assess flood-prone areas in Auckland, New Zealand, using stream order analysis as a geospatial tool to delineate flood risk zones. The research addresses the growing need for efficient flood risk mapping in urban settings by providing a simplified, yet effective, method for identifying high-risk zones. The central argument is that stream order classification offers critical insights into flood susceptibility, especially in rapidly urbanising regions.

Design/methodology/approach The methodology integrates a high-resolution digital elevation model (DEM) to derive flow direction and flow accumulation grids, simulating the natural movement of surface water. Using the Strahler stream order method, the study classifies water channels based on flow convergence patterns to pinpoint areas with greater flood potential. Geographic information system-based spatial analysis tools were used, and Landsat 8 imagery provided the environmental context.

Findings The analysis reveals that areas with higher stream orders, particularly within urbanised zones, are significantly more prone to flooding due to increased water accumulation. The stream order approach effectively identifies flood-prone areas without complex hydraulic modelling, making it a practical tool for preliminary flood risk assessments.

Originality/value This research presents an innovative application of stream order analysis as a macro-scale terrain screening tool for flood susceptibility mapping, offering a blend of simplicity and scalability suited to early-stage planning support and data-scarce contexts. Rather than replacing detailed hydraulic or site-specific assessments, this method complements existing high-resolution flood models by providing a low-data, catchment-scale first-pass indicator of flood-prone zones. While stream order analysis has been used in previous flood studies, this study’s unique contribution is its application to Auckland’s hydrological network, achieved by integrating a DEM and land-use/land-cover (LULC) data. The distinctive value of this study lies in its tailored application to Auckland, effectively linking stream order analysis with urban growth dynamics and recent significant flood events. Future studies could incorporate higher-resolution datasets and real-time hydrological inputs to refine predictions and enhance decision-support systems.