Flood Risk Vulnerability Detection based on the Developing Topographic Wetness Index Tool in Geographic Information System

Finding vulnerability to flooding locations is a crucial part of sensible urban development and effective natural disaster management. Globally, there has been a noticeable rise in the frequency of floods in recent years, which affects human habitation and several economic sectors. This calls for the employment of various prevention measures, wherein the assessment of vulnerability to flooding is crucial. The main objective of the present study is to introduce the best procedure for the identification of flood risk vulnerability detection using geographical information systems techniques and decision-making, based on a comparative evaluation of various scenarios. In this context, The current study will develop a Topographic Wetness Index (TWI) tool for the detection of these risks which can deal with the stream orders, calculate the length of the valley, and then show the outputs by thematic maps. The procedure with the developed adaptive tool has been applied to identify Flood Risk Vulnerability in Erbil city and some surrounding areas. The results of this paper indicated the existence of different levels of the TWI, which were classified into five classes. The procedure of this study has an advantage over other traditional methods since it takes into account mainly statistics data that are linked to the TWI which can be easily customized in detecting risk Vulnerability.


Introduction
Flood, more than any other hydrometeorological natural disaster on the planet, causes massive economic, social, and environmental losses and damages [1].Authorities and experts designate vulnerability flood risk zones to assess and communicate the degree of vulnerability to flooding for a specific location.These zones assist in informing emergency preparedness, land-use planning, and development decisions to reduce the potential harm caused by flooding events.The concept of vulnerability flood risk zones takes multiple factors into account to determine the level of risk a specific area faces when it comes to flooding [2], [3], [4], [5], and [6].There are some key components and considerations: 1.1 Geographical Location: An area's flood risk is greatly influenced by its geographic location, which includes its proximity to bodies of water like rivers, lakes, or coastlines.In general, areas nearer bodies of water are more vulnerable.1.2 Historical Flood Data: When determining the flood risk, historical flood data-such as records of previous flood events and their intensity is crucial.Locations with a history of frequent flooding are thought to be more susceptible.Flood risk experts and government agencies frequently classify areas into different flood risk zones based on these both factors and others; these zones are usually designated with labels such as "high risk," "moderate risk," and "low risk."When it comes to urban planning, disaster preparedness, and response operations, these designations aid decision-makers.A Geographic Information System (GIS) is a powerful tool for detecting vulnerability in flood risk zones [7], [8] and [9].It combines geographical data, such as maps, satellite imagery, and other spatial information, with analytical tools to assess and visualize the potential impact of flooding in different areas.

Study area, location topography, and geology
Erbil is located in the northeastern zone of Iraq and is determined between 44° and 45° E longitude and 36° and 37° N latitude (Fig. 1).Geologically, it is located in the foothill zone of the stable shelf tectonic unit of Iraq, which is a part of the low-folded zone.The area under investigation is surrounded by mountains, hills, and valleys.From the north and northeast, the area is bounded by the Sharabout and Kasnazan hills.Bestana Hills can be found in the southeast, while Awana Mountain can be found in the southwest.The area is bounded to the northwest by the Dameer Dagh hills and the Baranati Plateau.It is worth noting that the middle part of Arbil Plain is mostly covered by Quaternary sediments accumulated as a result of weathering and erosion [10], (Fig. 2A), and the elevation of study area ranging from (203) to (1076) meters (Fig. 2B).There are some bodies of water on either side of Erbil city [11].

TWI concept
The Topographic Wetness Index (TWI) is a terrain analysis technique used in hydrology and geomorphology to estimate the potential wetness or soil moisture content in a landscape based on its topography.It helps in identifying areas that are more likely to be wet or have a higher potential for water accumulation.The TWI is calculated using a combination of slope and flow accumulation, which are derived from a Digital Elevation Model (DEM) of the terrain.
The TWI is typically calculated using the following equation: TWI = ln(a / tan(beta)) [13] Where: TWI is the Topographic Wetness Index.ln is the natural logarithm.a is the specific contributing area (also known as the flow accumulation) for each grid cell.tan(beta) is the tangent of the slope angle for each grid cell.Let's break down the components of the equation: Specific Contributing Area (a): A specific contributing area represents the upslope contributing area to a specific point on the landscape.It is often computed as the accumulation of flow or the number of grid cells that drain into a specific cell in the DEM.It is a measure of how much water from the surrounding area is likely to flow into a given point.This value is calculated using flow routing algorithms, such as the D8 or D-infinity algorithms, which determine the flow direction of water across the terrain.

Slope (beta):
The slope angle (beta) for each grid cell is the angle of the terrain's steepness at that point.It is typically calculated using the slope formula, which is the elevation change (vertical distance) divided by the horizontal distance between grid cells: Where: z1 is the elevation of the cell of interest.z2 is the elevation of the neighboring cell to which water flows.d is the horizontal distance between the two cells.
The TWI values are calculated for each grid cell in the DEM, and they provide an index of relative wetness or moisture potential across the landscape.Higher TWI values indicate areas with a higher potential for water accumulation and wetness, while lower values indicate drier areas.
TWI is often used in hydrological and environmental modeling to identify areas prone to saturation, groundwater recharge, or potential wetland formation.It is a valuable tool for understanding and managing water resources and ecosystems in a given area.

Methodology
The methods used for calculating TWI are based on the DEM -analysis which determines the efficient procedure for identifying flood risk vulnerability.To achieve that, a tool was developed in Python based on the modular builder in ArcMap.The main Methods of this study consist of steps as illustrated in (Fig. 3).Identifying and delineating the drainage network based on the DEM based on the following procedure.DEMs may contain depressions or sinks that can impact drainage analysis.Use a "Fill" tool to remove these sinks and ensure a continuous flow of water.This is crucial for accurate drainage extraction.Flow direction calculate the flow direction from the filled DEM.The flow direction algorithm assigns a flow direction (usually in degrees) to each cell based on the steepest downhill slope.Calculate of flow accumulation to determine the number of cells that contribute flow to each cell.Higher flow accumulation values indicate areas with more accumulated flow and are typically associated with larger streams.Stream Network Delineation which is Define a threshold for flow accumulation to extract the main stream network.This threshold depends on the characteristics of the area and the desired level of detail.Higher thresholds will result in larger rivers.Finally, the stream order is Assign stream orders to the extracted stream network.Stream order indicates the hierarchical position of a stream segment within the drainage network.The Strahler stream order is commonly used.In this context, extracting basins from a Digital Elevation Model (DEM) involves identifying and delineating the areas that drain into specific pour points (or outlets) in the terrain.This process is commonly known as watershed or basin delineation.After perform these procedures, the flood risk vulnerability can be mapped based on the calculating TWI index

Conceptual model
Geographic information systems (GIS) use a conceptual model that is typically expressed diagrammatically to explain the executive processes' quantitative and qualitative data-related procedures.One of the most widely used conceptual models in geographic information systems (GIS) is the system diagram.It describes the key elements and connections of the model using both words and symbols [14].This model outlines the processes that occur during the input, analysis, and output stages as well as how data is arranged for usage by (GIS) [15].The variables found in the suggested knowledge tabulation of the diagram structure are formalized in this context by the conceptual model [16].The most significant benefit of conceptual modeling for scientific application design is that it lets users convey their application-specific knowledge without requiring complex function use processes or mathematical expressions [17].Some conceptual models have been interacting with GIS applications lately.The most significant of these models are produced by the model builder, a sub-program of the ArcGIS desktop software that is used to develop, modify, and maintain conceptual models.With the use of a model builder, one may create and automate one-step geoprocessing activities as well as visualize the workflow through graphic flowcharts.The conceptual model builder of the TWI index consisted of two parts.The first part deals with the slope, mod slope, and Tan slope.The second part of the conceptual builder model contains a set of tools that deal with the tools related to hydrology to extract the drainage stream networks (Fig. 4).

Results and Discussion
The performance of the various wetness indicators was assessed in this study based on how well they could forecast the spatial distributions of wetlands.Recognizing that spatially uniform parameterizations were applied is crucial.As a result, the expected distribution of wetlands is solely influenced by topography and is not the result of spatially variable parameters.It is a developing feature of the model simulation.The topographic wetness index (TWI) is calculated using an input fill sink and a flow accumulation DEM.The TWI analysis follows the stream networks analysis, which arranges a network of streams that exist in a specific region using flow accumulation information.The value of flow accumulation seen as a river is used to explain flow accumulation.The branches of the entire hydrographic network were digitized according to [18] the present study focused on the first, second, and third orders.Their pattern is dendritic to parallel.The main catchments are illustrated in (Fig. 1).It is observed that the average stream length increases towards a higher order of streams for all order catchments.Related to the TWI index analysis, The dark brown color in (Fig. 6) has an Index value greater than (15) TWI index.This location has a sloping slope, which increases the possibility of large water puddles.On the other hand, The yellow color refers to a low TWI index value.

Conclusions
The analysis of DEM data was found useful in the flood vulnerability analysis of the Erbil city.
The flowchart of this study is an efficient way of extracting and analyzing the TWI index.Integration of the TWI index with the zonal statistical analysis can identify the risk zone of flooding.It is concluded that this data will be very useful not only in assessing flood damage but also in disaster preparedness, which is one of the most important aspects of disaster management.

1 . 3 2 1. 5
Topography and Elevation: The land's topography and elevation are significant factors that affect vulnerability.Due to their increased susceptibility to flooding, low-lying areas, floodplains, and areas below sea level are deemed vulnerable.1.4 Infrastructure and Building Standards: An area's vulnerability may be impacted by the caliber and robustness of its buildings and infrastructure.1300 (2024) 012012 IOP Publishing doi:10.1088/1755-1315/1300/1/012012Population Density: Because more people and property are at risk during a flood event, areas with high population densities are frequently thought to be more vulnerable.1.6 Land Use and Zoning Regulations: Vulnerability may be impacted by local land use and zoning laws.Constructing in regions susceptible to flooding or marshes can heighten the danger to inhabitants and buildings alike.1.7 Flood Control Measures: Levees, dams, and drainage systems are examples of flood control measures.1.8 Weather patterns and climate change.

Figure 2 .
Figure 2. A. Geology of the Study area [12], B. Topography of the area.

Figure 3 .
Figure 3.The flowchart of the suggested method of the present study

Figure 4 .
Figure 4. Conceptual model builder of the TWI index

Figure 5 .
Figure 5.The interface of the developing tool.