3.2. Flood Conditioning factors
Flood conditioning factors are a location's numerous physical and environmental aspects determining its flooding vulnerability (Sanyal, Joy, and Xi Xi Lu. 2004). These elements are critical in predicting the likelihood and severity of flooding disasters (Tehrany, Mahyat et al. 2019). The selection of these factors depends upon the geography and type of flood susceptibility modelling, here 17 Flood conditioning factors are Elevation, Slope, Aspect, Curvature, Flow Accumulation, Distance from River, Drainage Density, Soil, Geology, Geomorphology, Curve Number, Land use Landcover, NDVI, Rainfall, Topographic Wetness Index (TWI), Stream Power Index(SPI), Sediment Transportation Index (STI) are taken according to literature review. Minimum, and Maximaum values of flood conditioning factors together with their sources are displayed in the Table 1.
Table -1
Factor
|
Range
|
Source
|
Elevation
|
Min -5
Max 1188
|
SRTM 1 arcsecond (30m) DEM
|
Slope
|
Min 0
Max 74.3293
|
Aspect
|
Min -1
Max 359.703
|
Curvature
|
Min -17.3365
Max 20.8249
|
Flow Accumulation
|
Min -1
Max 359.703
|
Distance From River
|
Min 1
Max 6
|
Open Street Map River network
|
Drainage Density
|
Min 0
Max 4.758
|
Geology
|
Min 1
Max 6
|
Bhukosh
|
Geomorphology
|
Min 1
Max 8
|
Soil Type
|
Min 1
Max 3
|
FAO soil map
|
Curve Number
|
Min 0
Max 91
|
GCN 250 new global gridded curve numbers for hydrologic modelling and design
|
LULC
|
Min 0
Max 4
|
Landsat 8 using GEE
|
NDVI
|
Min -0.065
Max 0.5308
|
Landsat 9 using ArcGIS
|
Rainfall
|
Min 2389.14
Max 3593.89
|
Chirp dataset using GEE
|
STI
|
Min 0
Max 729.086
|
SRTM 1 arcsecond(30m) DEM
|
TWI
|
Min 2.60408
Max 22.5374
|
SPI
|
Min -13.8155
Max 9.98717
|
3.2.1 Elevation: Elevation is vital in flood susceptibility mapping since it directly affects water flow during flooding. Lower-elevation areas are more vulnerable to flooding than higher-elevation areas (Vojtek, Matej, and Jana Vojteková. 2019).
3.2.2 Aspect: In the context of flood susceptibility, aspect refers to the direction that a slope faces. It is an essential topographic element that can alter the transport and storage of water during rainstorm events, influencing the likelihood of flooding in a specific area (Nsangou,Daouda, et al. 2022).
3.2.3 Slope: Slope refers to the steepness or gradient of the terrain. It is a significant topographic feature that affects water circulation during rainstorm occurrences as water flows from a higher to a lower slope (Khosravi, Khabat, et al. 2018).
3.2.4 Curvature: Curvature is defined as the rate of slope change or degree of bending of terrain. Curvature influences the direction of water flow across the landscape. Positive curvature (convex) areas tend to shed water away from their centers, whereas negative curvature (concave) areas absorb water (Al-Abadi, Alaa M. 2018).
3.2.5 Flow accumulation: Flow accumulation reflects the number of upstream cells pouring into each cell of the DEM; it aids in understanding the accumulation of water flow in various areas, suggesting potential flood-prone regions (Vojtek, Matej, and Jana Vojteková. 2019).
All the flood conditioning factors, Elevation, Slope, Aspect, Flow Accumulation, and curvature, were calculated using the SRTM 30 m DEM.
3.2.6 Drainage Density: Flooding is more frequent in areas near rivers or streams, especially following severe rains or when rivers breach their banks. Higher drainage density implies a greater density of stream channels, implying better drainage and lower flood danger (Choubin, Bahram, et al. 2019).
3.2.7 Distance to River: The distance from a river is a critical factor in flood susceptibility modelling, influencing the likelihood and severity of flooding in adjacent areas. (Choubin, Bahram, et al. 2019). The distance to the river was calculated using Euclidian distance from the river at intervals of 100, 200, 300, 400, 500, and > 500 m.
Drainage density and distance from the river were calculated from the OpenStreetMap (OSM) river map network.
3.2.8 Geology: Geology influences flood susceptibility through permeability, drainage, erosion, and sedimentation; these geological factors interact with climatic elements like rainfall and human factors like land use to determine an area's overall flood susceptibility (Kaya, C. M., & Derin, L. (2023). The study area has six geological classes, as shown in Fig. 3(a). Acid intrusive /Granite is majorly present in the eastern part and contains the Charnockite Gneissic complex in the western coastal part of Kottayam.
3.2.9 Soil: Soil characteristics influence water infiltration, runoff, and storage, playing a significant role in flood susceptibility modelling (Kaya, C. M., & Derin, L. 2023). Kottayam has three major soil classes: clay loam, sandy clay loam, and loam; a significant part of Kottayam has clay loam soil texture, which has moderate infiltration capacity.
3.2.9 Geomorphology: Geomorphology, the study of the physical features of the surface of the earth and their relation to its geological structures, plays a crucial role in flood susceptibility modelling (Mahmood, S., & Rahman, A. U. 2019). Kottayam has eight geomorphological classes, which give the overall idea of the surface feature. Pedi plain is the primary class present, and the geomorphological map also shows the idea about the active flood plain present in Kottayam. Geology, Soil, and Geomorphology were collected from their respective source, reclassified, and resampled to 30 m raster corresponding to the resolution of other flood conditioning factors.
3.2.10 Land Use Land Cover: LULC classification was done using Landsat-8 surface reflectance image (Level-2) using Random Forest algorithm on GEE with five classes: Water body, Built-up, Forest, Barren Land, and Vegetation with an overall accuracy above 90%.
3.2.11 The Normalized Difference Vegetation Index (NDVI): NDVI is an essential variable in flood susceptibility modelling because it gives important information on vegetation cover and health, which can influence flood dynamics (Saha, Asish, et al. 2021). NDVI was calculated using Landsat-9 image in ArcGIS.
NDVI is calculated using the following formula:
NDVI = (NIR - Red)(NIR + Red) (1)
In this formula, NIR represents the reflectance in the near-infrared wavelength range, and Red represents the reflectance in the red wavelength range.
3.2.12 Curve Number (CN): CN is a dimensionless number that represents the runoff potential of a particular area based on its land use, soil type, slope, and antecedent moisture condition before the flood event. The greater the CN value, the greater the likelihood that the land will generate more runoff during a rainy event, making it more susceptible to floods (Seleem Omar et al., 2022). CN calculation has been carried out in GEE at 250m spatial resolution for hydrological modelling (Jaafar, Hadi H, et al. 2019) and resampled at 30 meters afterward.
3.2.13 Rainfall: Rainfall is a critical variable in determining flood risk. The amount, intensity, and rainfall duration in each location all impact the risk and severity of floods. In this research, the CHIRPS (Climate Hazards Group Infrared Precipitation with Station data) rainfall dataset used in the GEE platform for years 2018, 2019, 2020, 2021, and 2022 taken to account for recent rainfall trends and an average of all years taken as representative rainfall amount (at 30m resolution) for flood susceptibility modelling. Rainfall for Kottayam varies from 2389mm to 3789mm.
3.2.14 The Stream Power Index (SPI): SPI is a hydrological indicator used to estimate river erosion and sediment transport potential in flood susceptibility evaluations. It quantifies the erosive strength of flowing water in a river or stream (Saravanan, Subbarayan, et al. 2023). SPI calculated as
SPI = As*tan β (2)
Where A represents the area of the basin, and β is the slope in degrees.
Sediment Transportation Index (STI): STI accounts for the effect of topography on erosion and qualitatively explains the process of erosion and deposition (Wang, Yi, et al. 2020). STI is calculated as
STI = As22.130.6 Sin β0.08961.3 (3)
Where As is basin area, β is slope in degree.
3.2.15 Topographic Wetness Index (TWI): TWI is a hydrological parameter. It represents the potential for water accumulation in a particular location based on its topographic characteristics (Das, Sumit. 2020). TWI is calculated as
TWI = lnαtanβ (4)
Where α represents the upslope contributing area (in square meters) per unit contour length. β represents the local slope or gradient of the terrain.
All these indices, STI, SPI, and TWI, were calculated in ArcGIS software using DEM as primary data for calculation.
All predictor variable values taken in this study have been shown in Fig. 2, Fig. 3, and Fig. 4, respectively.