An early warning system (EWS), as defined by the United Nations, is a climate change mitigation tool that leverages information technology for risk monitoring, alert communication, practical action, and risk awareness [16]. Auckland has implemented monitoring and warning systems as part of its EWS, but these efforts have yet to prevent flood impacts entirely. Flood susceptibility maps enhance the EWS by enabling authorities to analyse vulnerable areas and respond swiftly to warnings. Flood mapping and sensitivity analysis are crucial components, identifying flood-prone areas based on spatial factors [17]. This approach supports experts and communities in proactive flood prevention.

GIS is a tool designed to store, manage, analyse, and visualise geographical data [18]. It enables the modelling and representation of spatial information, offering solutions to intricate planning and management challenges [19]. Each layer in a GIS represents data with specifics associated with specific locations and established relationships [20]. According to [21], GIS techniques are intended to offer an organised framework for effectively manipulating and analysing all information, which enables the timely identification of possible hazard zones. Recently, advancements in GIS and remote sensing have been incorporated into evaluating geoenvironmental disasters. This integration has significantly progressed flood susceptibility mapping, flood hazard assessment, and flood management strategies [22].

The authors of [23] define Multi-Criteria Decision Making (MCDM) as a method for tackling complex decision problems involving multiple criteria. The process of ranking alternatives in an MCDM model involves three key steps: identifying relevant criteria and alternatives, assigning weights to criteria, and applying numerical measures to evaluate how alternatives impact these criteria [24]. Subsequently, numerical values are processed to generate a ranking score for each alternative [25]. The adoption of MCDM tools in flood risk management, as noted by [26], can offer substantial benefits. AHP stands out as the most widely used MCDM technique in flood hazard mapping due to its user-friendliness and versatility [27]. This current study on Flood Susceptibility Mapping for Auckland adopted an AHP approach based on GIS to identify the flood risk zones. Evidence suggests that GIS and AHP Multi-Criteria Decision Analysis (MCDA) is the most suitable method, as determined through an analysis of comparable articles published in high-impact journals [28].

The Analytic Hierarchy Process (AHP) is a pairwise comparison method that uses multi-level hierarchies and priorities [29]. A key advantage of AHP is its ability to handle adaptive changes with minimal inconsistency using decidable data. Many researchers have applied AHP to develop weighted criteria for flood susceptibility mapping. For example, [30] used AHP to assign criteria weights, integrating them with GIS techniques like layer overlay and raster processing to produce a flood hazard map for the Philippines. Similarly, [31] applied AHP with nine factors and a weighted linear combination to analyse flood hazards and public preparedness in Abidjan, successfully identifying and mapping flood risk areas using GIS.

The susceptibility analysis is to identify key factors that ensure reliable and accurate results. Drawing on a comprehensive literature review, this study integrates widely recognised factors that influence flood susceptibility, including proximity to slope [32], aspect [33], elevation [34], and Topographic Wetness Index (TWI) [35] other critical variables include rainfall [36] drainage density [37], Normalised Difference Vegetation Index (NDVI) [38] and land use and land cover (LULC). Together, these factors form the basis for an effective and comprehensive susceptibility mapping framework.

Therefore, the narrowed-down problem statement revolves around the challenge of leveraging GIS technologies to develop and implement robust flood mitigation measures specifically for urban environments. Several studies have explored flood risk in New Zealand, but there is a notable gap regarding a comprehensive GIS-based approach specifically focused on Auckland. The Northland region has been the focus of flood risk studies, with particular emphasis on using community-based flood maps to explain flood hazards, as highlighted in such published studies as [39], [40]. Their study lacks attention to technological tools like GIS, particularly for urban areas such as Auckland. Therefore, this research will evaluate the suitability of GIS techniques in mapping urban flood susceptibility, identify and assess areas at risk of flooding, and suggest innovative flood mitigation measures enabled by GIS technology.

Figure 1 outlines the application of MCDA analysis for flood risk mapping. It begins by integrating various data sources, including the DEM, satellite images, rainfall data, and land use/land cover (LULC) information. Each input is processed to extract relevant factors like elevation, TWI, slope, drainage density, land use, rainfall, and NDVI. These factors undergo reclassification to standardise their values for analysis. Finally, a weighted overlay of the reclassified data generates a flood risk map, aiding in flood susceptibility assessment.

Figure 1.

Multi-criteria decision making for flood risk mapping

Auckland, which is the focus of this study, lies in the northern part of New Zealand's North Island, between latitudes 36°45'–37°10' south and longitudes 174°30'–175°10' east, covering approximately 1,086 km². Mount Eden, at 196 meters, is the highest point in central Auckland, as illustrated in Figure 2. The Tasman Sea borders Auckland to the west, the Hauraki Gulf and Pacific Ocean to the east, the Waikato region to the south, and the Northland region to the north. The Tamaki River, in the east, connects through tributaries near urban Auckland. Auckland's geography features two principal harbours: Waitemata Harbour to the north and Manukau Harbour to the south, forming a distinctive isthmus. The Waitemata Harbour drainage basin is vital for water management. Urban areas consist of flat to rolling terrain, while surrounding regions are rugged with volcanic cones and ranges [41]. Around 80% of the region is urbanised or semi-urbanised, shaped by its unique volcanic and coastal landscapes [42].

Figure 2.

The map of New Zealand showing the location of Auckland [43]

The rate of temperature increase has accelerated in recent decades, as shown in Figure 3. While annual temperature fluctuations are evident, the overall pattern suggests a long-term warming trend. The steeper slopes of the trend lines for the more recent periods further confirm that temperature rise has intensified over time. Such a rise in temperature can significantly impact flood risks. Warmer air holds more moisture, leading to heavier and more intense rainfall, which increases the likelihood of flooding.

Figure 3.

Average Mean Surface air temperature [°C] according to [44]

Rainfall data.

Figure 4 shows the annual precipitation of Auckland (1903–2023) graph, which indicates fluctuations in rainfall over the past century, with periods of both increasing and decreasing trends. While precipitation was highly variable in the early 20th century, the data suggest a gradual increase in recent decades, particularly after 2000. It could indicate a shift in rainfall patterns, which contributes to higher flood risks.

Figure 4.

Annual precipitation [mm] of Auckland (19032023) [45]

Table 1 shows the monthly precipitation data for 2019–2023 highlighting significant variations and extreme rainfall. The year 2023 experienced much higher rainfall, especially in January and February, compared to the previous years. These heavier rainfall amounts are essential for understanding recent floods and recognising climate trends that might affect future weather. By looking at these patterns, the study can identify times when heavy rainfall is more likely to cause flooding. The rainfall data presented in Table 1 were collected from a single station, ensuring consistency in measurement and allowing for a focused analysis of precipitation trends over the study period. The station is located within the analysed area and provides comprehensive monthly precipitation records.

Table 1.

Average rainfall data from a single station in the Auckland region [45]

	Rainfall [mm]
Year/Month	2019	2020	2021	2022	2023
January	26.5	10.9	74.06	27.39	317.57
February	33.71	10.6	87.46	148.75	381.03
March	67.17	32.63	151.67	109.12	49.7
April	73.51	42.3	113.62	116.14	115.55
May	34.9	133.21	52.43	115.77	275.4
June	57.37	125.15	134.7	134.03	191.55
July	110.31	94.34	109.84	281.31	159.9
August	117.67	149.89	139.26	171.34	82.35
September	126.63	47.4	166.3	175.29	154.45
October	76.9	48.83	116.1	135.71	102.35
November	44.82	142.32	79.23	207.26	58.21
December	64.13	27.23	77.83	129.72	75.22

Figure 5.

Method used to develop the flood susceptibility map for the Auckland region

The primary methodology of this study involves GIS-based multi-criteria decision analysis for flood susceptibility mapping. Creating a susceptibility map for the research area requires several multi-source geospatial datasets. Consequently, the influencing factors are collected from various sources, including digital elevation model (DEM), Landsat 8 imagery, soil type, and rainfall data, as shown in Figure 5.

The spatial database for flood influencing factors comprises rainfall, geology, land use, topographic wetness index (TWI), drainage density, slope, normalised difference vegetation index (NDVI), and digital elevation model (DEM). Five of these factors can be extracted from the DEM using the spatial analyst tool in the ArcGIS Pro software, as explained in the flowchart shown in Figure 6.

Figure 6.

Flowchart for the data collection methodology using ArcGIS Pro

The slope map is generated directly from the DEM raster. To calculate TWI, flow direction and flow accumulation must be extracted and processed using the raster calculator in the spatial analyst tool. Using the same flow accumulation data, the spatial analyst tool also produces drainage density and distance from drainage.

Elevation significantly influences flood occurrence, as water flows rapidly from higher to lower elevations, making low-lying areas more prone to flash flooding. The elevation map of Auckland, presented in Figure 7, was generated using a Digital Elevation Model (DEM) sourced from the United States Geological Survey (USGS) and processed in ArcGIS. Elevation in Auckland ranges from −34 to 700 meters, with higher elevations in inland hilly areas and lower elevations near coastlines and flatlands. These variations are critical for assessing flood risks, as low-lying areas are particularly vulnerable.

Figure 7.

Elevation map of Auckland

Flood risk increases in areas with lower slope values, as flatter terrain allows runoff to move quickly, heightening flood susceptibility. Conversely, steeper terrain slows runoff, reducing flood risk. The slope map of Auckland shown in Figure 8. was created using a DEM and ArcGIS's spatial analyst tool. Slope values, measured in degrees, range from 0.001–4.418 (low) to 23.564–75.105 (high). Central and northern Auckland, with lower slope values highlighted in green, are more prone to flooding due to rapid runoff, while the western areas, with higher slopes, exhibit reduced flood risks.

Figure 8.

Slope map of Auckland

The Topographic Wetness Index (TWI) is a vital metric for identifying areas prone to wetland formation and high overland water flow potential. It helps assess flood susceptibility by highlighting zones likely to accumulate water. The TWI map is derived from slope and flow accumulation data and analysed using a Digital Elevation Model (DEM). Calculated with ArcGIS Pro's raster calculator, TWI values reflect topographical characteristics. In Auckland, as depicted in Figure 9, TWI values range from −9.4 to 12.2. Lower values, shown in red, indicate steeper slopes and smaller drainage areas, suggesting reduced runoff accumulation. Higher values, found in flatter regions or areas with larger drainage zones, signify greater water accumulation and flood risk.

Where As denotes the total area of upslope drainage, tanβ – local slope gradient, and C = 0.00.

Figure 9.

Topographic Wetness Index (TWI) map

The drainage density map is calculated from flow accumulation, which can be derived from DEM data using the “line density” tool within the spatial analyst tools of ArcGIS Pro software. Figure 10 presents the drainage density distribution for Auckland, with values ranging from the lowest (0.1−31.9) to the highest (127.7−159.6). The central and some southeastern parts of the region, where red and orange colours are prominent, are the areas most at risk. These areas have the highest drainage density values (127.7−159.6), indicating that they are more prone to the risks associated with high surface water runoff.

Figure 10.

Drainage density map

Rainfall is a primary driver of floods, as heavy rains can overwhelm river systems and lead to excess surface runoff when infiltration capacity is exceeded. Rainfall data for this study were obtained from the Climatic Research Unit (CRU). Figure 11 shows the annual rainfall distribution across Auckland, with the northern areas receiving the least rainfall (34.92–150.18 mm). Moving south, rainfall increases, with the central region experiencing moderate levels (150.19–338.89 mm, shown in yellow and orange). The southern and southeastern areas receive the highest rainfall (415.8–503.79 mm), making them more prone to flooding due to higher water availability. This rainfall gradient is critical for assessing flood risks and managing water resources.

Figure 11.

Rainfall map (20192023)

Land use and land cover are critical in influencing soil stability and water infiltration, making them significant factors in flood susceptibility mapping. Areas with dense vegetation reduce surface runoff by slowing water movement from precipitation to the ground, promoting infiltration. Conversely, impervious surfaces such as concrete and asphalt limit water absorption, leading to an increase in surface runoff and elevating the risk of flooding. The LULC map shown in Figure 12 was generated using 2023 Landsat 8 imagery acquired from the United States Geological Survey (USGS) website. From the LULC map obtained, 29% of Auckland consists of fallow land, 26% of forest, 22% of agricultural activities or cultivated, 13% of settlements and 1 % of water.

Figure 12.

Land Use and Land Cover (LULC) Map

The Normalised Difference Vegetation Index (NDVI) is an essential tool for evaluating vegetation cover, which is crucial in reducing surface runoff and serving as a natural barrier against flooding. It is determined using the red (R) and near-infrared (NIR) bands from satellite data, such as Landsat 8, with values ranging between −1 and +1. High NDVI values suggest lush, healthy vegetation, while low values point to sparse or minimal vegetation. The NDVI map of Auckland, shown in Figure 13 and derived from Landsat 8 imagery, displays NDVI values ranging from −0.32 to +1. The formula for this index is:

Regions with lower NDVI values, shown in yellow and light orange, likely represent urbanised or developed areas with limited vegetation. Conversely, areas with higher NDVI values, illustrated in green and dark green, denote regions with thick vegetation, such as forests or parks, which play a key role in natural flood protection. This distribution highlights the varying capacity of different areas within Auckland to withstand and mitigate the effects of flooding based on their vegetation cover.

Figure 13.

Normalised Difference Vegetation Index (NDVI) map

The weighted linear combination (WLC) method assesses criterion relevance and assigns appropriate weights [48]. The Analytical Hierarchy Process (AHP) approach for WLC involves five key steps: (a) listing the unit factors; (b) organising these factors into a hierarchical structure; (c) assigning numerical values to the factors based on their importance; (d) generating a comparison matrix; and (e) calculating the normalised eigenvector to determine the weights of each factor. A combination of expert judgment and a literature review was used to assign numerical values, with the factors scored on a nine-point continuous scale [49] according to their relative importance, as shown in Table 2. The study employs the consistency ratio (CR) to evaluate the reliability of the pairwise comparison matrix. The procedure is initiated by calculating the Consistency Index (CI), using the following formula:

Where λmax is the principal eigenvalue of the matrix, and n is the number of criteria.

Next, the consistency ratio (CR) is calculated to determine if the pairwise comparison matrix is consistent, using:

In this formula, RI represents a random index value that depends on the size of the matrix [50]. A CR value of 0.10 or less is considered acceptable. However, a CR exceeding 0.10 indicates inconsistency in the assessments and implies the need for revisiting subjective judgments.

After assigning relative weights on a nine-point continuous scale following the method developed by [49], a comparison matrix was created where the diagonal elements are equal to 1. For example, the element corresponding to rainfall is of equal importance to itself, which means the diagonal element in the first row (for rainfall) is 1. Similarly, when elevation is compared with itself, the corresponding diagonal element will also be 1, and this pattern continues for all criteria. Next, the sum of the values in each column of the comparison matrix is calculated, as shown in Table 3. Following this, a normalised pairwise matrix is derived by dividing each element of the column by the sum of that column. The criteria weights are then determined by calculating the average of the elements in each row, i.e., by dividing the sum of the row elements by the number of criteria. The criterion with the highest weight is considered the most significant in the overall calculation. From the criteria weights obtained, the three most influential factors in this analysis are TWI, Rainfall, and Elevation, collectively accounting for over 50% of the total weight [52], [53].

Additionally, the consistency ratio (CR) was calculated to assess the consistency of the generated ratings. Using equations (3) and (4), the CR was found to be 0.05, which confirms the reliability of the assigned ratings. This value is deemed acceptable since it is below the threshold of 0.1. If the CR exceeds 0.1, it suggests that the evaluations may be too inconsistent to be considered dependable.

After determining the criteria weights, the flood susceptibility map was created using the weighted overlay tool in ArcGIS Pro. To use this tool effectively, each raster parameter was first reclassified according to its susceptibility class ratings. Then, the cell values were multiplied by their corresponding percentage influence, and the resulting rasters were combined. This process led to the generation of the final flood susceptibility map, which was categorised into five risk levels: very high risk, high risk, moderate risk, low risk, and very low risk, as illustrated in Figure 14. Table 3 presents the areas of different flood susceptibility zones along with the percentage of each risk level.

The map from Figure 14 highlights that certain northern and northeastern parts of the region, along with some offshore islands, have higher flood risk, whereas central and southern areas of Auckland generally exhibit lower flood susceptibility. The south and central parts of Auckland show a predominance of low to very low flood susceptibility, marked in green on the map. These areas are likely covered with more vegetation and forested land, which helps absorb rainfall and reduces the risk of flooding.

Figure 15 presents a pie chart illustrating the spatial distribution of flood risk levels in Auckland, derived from the corresponding flood risk map. The results reveal that a substantial proportion of the area (63%) falls within the moderate risk category. Additionally, 21% of the area is classified as low risk, while 16% is designated as high risk. Notably, the analysis indicates that no regions fall under the very high or very low risk classifications.

These findings suggest that the majority of Auckland is currently exposed to a moderate level of flood risk. However, the potential intensification of urban development and deforestation activities poses a significant threat to this balance. Without the implementation of robust flood management and land-use planning strategies, there is a heightened likelihood that areas presently considered moderate or low risk could transition into higher risk categories over time. Therefore, proactive measures are critical to sustaining current risk levels and enhancing the city's resilience to future flood events.

Figure 14.

Flood susceptibility map

Figure 15.

Percentages of flood susceptibility

Geographic Information Systems (GIS) is a powerful tool for capturing, analysing, and visualising spatial data, enabling efficient decision making in various fields, including environmental management and urban planning [54], [55]. GIS has been extensively used in the Philippines, one of the most disaster-prone countries, to assess and mitigate flood risks [15]. Due to frequent typhoons and heavy rainfall, government agencies have integrated GIS technology with topographical and meteorological data to develop flood-risk maps [14]. Studies in Davao Oriental have demonstrated the effectiveness of GIS-based flood risk assessment by incorporating multiple variables such as slope, elevation, drainage density, soil type, and population density [17]. Methods like the Analytic Hierarchy Process (AHP), Weights by Rank (WR), and Ratio Weighting (RW) have been used to classify flood-prone areas [17]. The results indicated that 95.99% of Davao Oriental falls within low to moderate flood risk zones, while coastal regions face higher susceptibility, necessitating urgent mitigation strategies [16]. Similarly, this study employs GIS-based spatial analysis to assess flood susceptibility in Auckland. The results highlight that northern and northeastern parts of the region, along with offshore islands, are at a higher risk of flooding, whereas central and southern Auckland exhibit lower susceptibility due to vegetation cover. These findings align with previous research emphasising the role of GIS in identifying flood-prone zones and supporting flood resilience planning [12], [56], [57].

The validation process assesses the accuracy of a flood susceptibility map by comparing its classified susceptibility zones with actual floodplain locations [58]. This ensures that the spatial prediction of flood-prone areas aligns with historical flood records, improving the reliability of flood risk assessment. The flood susceptibility map (raster) is classified into five susceptibility levels: Very Low (1), Low (2), Moderate (3), High (4), and Very High (5). The floodplain shapefile (vector) represents historically flooded areas based on Auckland Council data. Both datasets are loaded into ArcGIS Pro to ensure proper alignment and coordinate system consistency. The “Extract Values to Points” tool is used to sample flood susceptibility values at validation points. Each point is assigned a susceptibility class (15), allowing direct comparison with floodplain locations. A Spatial Join is performed to check whether high-susceptibility zones correspond to actual floodplain locations. The analysis examines how many of these points fall within the floodplain, indicating model accuracy.

The model's accuracy is determined using the formula:

Where Correctly Predicted Floodplain points = 24,932 and Total Floodplain points = 30,047.

The model achieved an accuracy of 82.98%, indicating strong agreement between risk zones and actual flood-prone areas.

This subsection outlines the recommended actions to enhance urban flood resilience in Auckland.

While GIS technology provides capabilities for flood mitigation, this study acknowledges several limitations. The limitation of this study is that it used only open-source spatial datasets with different resolutions and scales. The elevation of the building is also not considered in this study. Additional building height information can provide detailed and accurate information about the risks of buildings. Building height can be obtained by fusing stereo images of high resolution, i.e., 5 cm, which can be generated through aerial photographs or from resolution satellite sensors; this high-resolution DEM can give more accurate results. In addition, this study does not provide a detailed analysis of the precipitation patterns, which have not been thoroughly examined. This limitation may affect the understanding of their specific impact on urban flooding dynamics.