Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

Remote Sensing Applications: Society and Environment 14 (2019) 60–74

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Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India

T

Sumit Das Department of Geography, Savitribai Phule Pune University, Pune 411007, India

ARTICLE INFO

ABSTRACT

Keywords: Flood susceptibility Flood hydrology Unit stream power AHP GIS Ulhas river, India

Flood is a common natural disaster that causes immense damage to the natural environment, construction and casualties every year around the world. The effectiveness of flood is a function of several criterions such flood power, magnitude, frequency, duration of the flow, changes of the planform and cross-section geometry in a river etc. Alternatively, the prevention of flood depends on assessment of various factors which are related to flood occurrence. In an international scale, it may not be possible to prevent flooding by some full scale assessment of environmental factors. However, deterrence of regional floods can easily be done through flood susceptibility mapping. The present work represents an innovative analysis of flood mapping through the analytical hierarchy process (AHP) and hydro-geomorphic response to the floods by implementing geospatial analysis and unit stream power modelling. The subject field is applied in Ulhas catchment, India. The hydrologic assessment of yearly peak discharge shows that the hydro-station located at Badlapur is capable to transport upto 0.5 m boulder during extreme flood events. However, there is no transformation of the cross-section geometry during 2005–2012 at Badlapur. The flood susceptibility map is constructed based on twelve influencing parameters, i.e. elevation, slope, distance from drainage network, geomorphology, drainage density, flow accumulation, rainfall, land-use, geology, stream power index, topographic wetness index and curvature of the topography. Established along the resultant flood susceptibility map, it is found that about 25% area of the Ulhas catchment is under very high flood susceptibility. The efficiency of the analytical hierarchy process is examined by employing area under the curve (AUC) method which shows considerable accuracy (84%). The present study bridges the gap between the hydro-geomorphic assessment of the flood and the geospatial approach towards flood susceptibility.

1. Introduction Among diverse categories of natural disasters such as landslides, tsunami, earthquake, volcanic eruption etc., flood is considered as the most common and disastrous phenomena that occur almost everywhere around the world (Doocy et al., 2013; Das, 2018a; Termeh et al., 2018). Even though, it is impossible to prevent catastrophic flood events; however, implementation of appropriate methods can provide the geomorphic effectiveness, the magnitude, frequency (Gupta, 1988; Wohl, 1992; Rajaguru et al., 1995; Gupta et al., 1999). Moreover, through these analyses the disaster management can become easier (Cloke and Pappenberger, 2009). The study of Kowalzig (2008) suggests that annually around 170 million people are affected by floods, worldwide. Hence, flood risk management needs to overcome geographic locations and socioeconomic limitations (Degiorgis et al., 2012; Kazakis et al., 2015). Present days, with regards to the climate change, the occurrence of

floods are highly dynamic and catastrophic (Burner et al., 2018). Variations in climatic circumstance strongly affect the runoff of a river, the rate of snow melting in glaciated region, evapotranspiration (Horton, 2005; Dobler et al., 2012; Fischer et al., 2015) and therefore the flood frequency and magnitudes (Kundzewicz, 2002; Pilling and Jones, 2002; Köplin et al., 2014). There is a strong requirement to have consistent flood power evaluations to make the flood management easier (Cameron et al., 2000). The methods involve the analyses of effectiveness of extreme floods are generally based on temporal variability of the hydrology (Šraj et al., 2016; Wilby et al., 2008). A majority of the researches evaluating the effectiveness of floods, flood frequency and magnitude generally emphasis on the peak discharge (Cameron, 2006; Madsen et al., 2014). The geomorphic effectiveness of an extreme flood, which is an event or a combination of several occurrences to affect the landscape is regulated by multiple factors such as flow competence, stream power, sequence of events, channel geometry etc. (Wolman and Miller, 1960; Wolman and Gerson, 1978; Nanson, 1986; Miller, 1990;

E-mail address: [email protected]. https://doi.org/10.1016/j.rsase.2019.02.006 Received 25 September 2018; Received in revised form 29 January 2019; Accepted 14 February 2019 Available online 14 February 2019 2352-9385/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Location map of the study area with the spatial distribution of historical floods. Notice the star symbol on the map which is indicating the location of hydrostation (Badlapur) considered in this study. The inset map is indicating the location of Ulhas basin with respect to the Indian peninsula.

Richards, 1999). Hence, the assessment of flood effectiveness varies depending on the controlling factors, one river to another, temporally (Magilligan et al., 1998). The prediction of flood susceptibility can decrease the flood associated fatalities and economic losses. Demarcation of flood sensitive areas is a strategic component in any flood mitigation strategy (Sarhadi et al., 2012; Chapi et al., 2017). In recent years, hazard mapping and flood susceptibility analyses through remote sensing and GIS tools are done by many researchers which provided considerable good accuracy (Pradhan et al., 2009; Bates, 2012; Rahmati et al., 2016a, 2016b; Wanders et al., 2014; Fekete, 2009; Nikoo et al., 2016). Instances of geospatial models in flood studies include analytical hierarchy process (Chen et al., 2011; Kazakis et al., 2015; Rahmati et al., 2016b; Das, 2018a), frequency ratio (Pradhan, 2010; Kornejady et al., 2014, 2015; Lee et al., 2012; Tehrany et al., 2015a), weights of evidence (Tehrany et al., 2014; Rahmati et al., 2016a), fuzzy logic (Pierdicca et al., 2010; Pulvirenti et al., 2011; Zou et al., 2013), artificial neural networks (Kia et al., 2012), support vector machines (Tehrany et al., 2015a, 2015b), adaptive neuro-fuzzy inference system (ANFIS) (Bui et al., 2018a, 2018b), biogeography based optimization and BAT algorithms (Ahmadlou et al., 2018), reduced error pruning trees (Khosravi et al., 2018), multivariate adaptive regression splines (Moghadam et al., 2018) are used. However, no conclusion can be made for the selection of the best model for flood susceptibility. In Ulhas catchment, the occurrence of flood is very frequent and

quite destructive in nature. Almost every year, the cities become inundated which are closer to the main river, during monsoon. Therefore, it is essential to understand the flood dynamics of the study area and prediction of an accurate flood susceptibility map for prevention of future damage. During literature review, it is also found that the prediction of flood susceptibility map through geospatial techniques is common work in present days. However, for flood mitigation, the requirement of hydro-geomorphic understanding of previous floods is crucial, which is lack in such studies. Therefore, in this study, an attempt has been made to understand the hydro-geomorphic response of Ulhas river to the previous floods, occurred in the last 3 decades together with geospatial mapping of flood susceptibility zones in Ulhas catchment, India. Bringing the geomorphic perspective of the flood effectiveness and the geospatial mapping of potential flood regions into a single study, for better understanding and mitigation is the major innovation of this present study. 2. Study area The Ulhas river is approximately 160 km long and the catchment drains about 4700 km2 area in Western Maharashtra, India (Fig. 1). The dendritic river collects all of its surface water that enters in the catchment from heavy precipitation by monsoon. The annual rainfall in the study area is about 2500 mm. The physical barrier of Western Ghat intercepts the Arabian branch of south-west monsoon, which brings 61

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Meteorological Department (IMD, Pune); (iv) Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) of 90 m spatial resolution are obtained from CGIAR official website (http:// srtm.csi.cgiar.org/); (v) Landsat 8 imagery (Date: 16-may-2018) are collected from USGS official website (https://earthexplorer.usgs.gov/). These data are used to generate thematic layers in ArcGIS environment (Fig. 3). Later, all the thematic layers have been converted into 90 m spatial resolution. Finally, by conjunctively applying analytical hierarchy process (AHP) to the Ulhas catchment, involving 12 susceptibility criteria, the flood susceptibility map is produced. The procedure for the preparation of each layer and their relation to flood susceptibility is discussed below.

Fig. 2. Distribution of average monthly rainfall in the study area. The highlighted months are indicating monsoon season. About 95% rainfall occurs in June-Sept months.

3.1. Elevation

high volume of moisture to the study area. As a result, a very high amount of rainfall occurs (June-Sept) in the Ulhas catchment (Fig. 2). The Ulhas basin is located at the foot of Western Ghat, which is a part of Konkan Coastal Belt (KCB). The bedrock in this region consists of Deccan flood basalt of Cretaceous-Tertiary age (Courtillot et al., 1986; Duncan and Pyle, 1988; Pande et al., 1988; Widdowson and Cox, 1996). The stratigraphic record of the study area indicates that the flood basalt was erupted by Reunion hotspot during the northward movement of Indian subcontinent about 65 Ma before present (Subbarao and Hooper, 1989; Mitchell and Widdowson, 1991; Cox, 1983). The coastal plain of the Ulhas basin shows thick deposits of alluvium with a considerable matrix of coarse material (Das, 2017). Ulhas basin shows a large number of geologic lineaments which are mostly oriented in NNE-SSW direction (Das and Pardeshi, 2018a). Due to the spectacular prominent escarpment of Western Ghat, the study area shows several distinct geomorphic features in the Ulhas catchment. Many workers believe that the present location of Western Ghat is the result of parallel retreat and the retreat is not uniform along the 1600 km long ranges (Radhakrishna, 1993; Widdowson and Cox, 1996). However, Ulhas catchment is the zone of maximum retreat of the Western Ghat. Therefore, the source of the Ulhas river shows an extraordinary and conspicuous feature which is most prominent in this region is the beheaded valley (Kale and Subbarao, 2004; Kale, 2010). The development of the beheaded valley in this region is occurred by the rapid headword erosion of the Ulhas river and its tributaries and the retreat of escarpment which consumes a part of the Upland plateau (Kale and Subbarao, 2004). Another distinguished geomorphic feature in the study area is plateau outlier. Plateau outliers are the detached masses from the main plateau, which is separated from the main escarpment by deep and narrow valley (Kale, 2010). Such feature can be seen in the eastern section of Ulhas basin. Due to the semi-circular shape and dendritic drainage pattern, heavy rainfall for a long duration frequently creates a disastrous flood situation in the western part of the catchment where most of the main tributaries meet the main river. The major causes of such floods in Ulhas catchment are torrential rainfall and the land-use changes in recent years due to high population pressure of Mumbai metropolitan. Thane and Badlapur are the major towns in Ulhas basin which are affected by frequent floods, once in every two years. During the monsoon in 2018, the district disaster management department of Thane declared about 84 villages which are prone to flood (Hindustan times, 2018). The floods in Ulhas river lead to damages the infrastructure, collapse the rail-bridge, disruption of traffic, public services and fatalities (The Hindu, 2002).

Conferring to the expert’s opinion, elevation is the prime factor to control floods of an area (Pradhan, 2009; Botzen et al., 2012; Mojaddadi et al., 2017). Water continually flows from higher elevations to the lower and flat lowland areas may flood faster than the locations in a higher elevation (Fernandez and Lutz, 2010; Dahri and Habib, 2017). The elevation map is prepared based on SRTM DEM of 90 m spatial resolution and the classification is done based on natural breaking in ArcGIS (Fig. 4a). 3.2. Slope In hydrological assessment, slope of an area expresses a fundamental role to regulate the surface discharge. In such hydrological studies, it is a very important topographic factor (Pradhan, 2009; Tehrany et al., 2013; Mojaddadi et al., 2017; Das, 2018a; Das et al., 2018). A strong positive correlation can be found between the slope of an area and the surface flow velocity (Fernandez and Lutz, 2010; Das, 2018b). Additionally, gradient partially controls the infiltration process. The surface runoff increases significantly as the gradient increases; consequently, the infiltration decreases (Das and Pardeshi, 2018b). As a result of this, regions with a sudden decrease of slope, having higher probability of flood as a massive volume of water become stationary which causes a severe flood situation (Pradhan, 2009; Li et al., 2012). Slope is highly related to the flow regulation towards downstream, which can be perceived in stream power models (Baker et al., 2009). Çelik et al. (2012) stated that higher magnitude of the slope may accelerate precipitation related runoff. The slope map is created directly from the DEM in ArcGIS environment by using surface tools after sinking the data gaps of the elevation model (Fig. 4b). 3.3. Distance from the drainage network The expansion of a flood event depends on the distance of a region from the drainage network (Predick and Turner, 2007). Regions located near to the drainage network, generally suffer flooding higher than areas that are far away as the nearby locations are within flow path (Mahmoud and Gan, 2018). The distance from the river relating to the flood susceptibility can be subjective. Many researchers gave their expert opinion in their studies. Pradhan (2009) considered the regions within 90 m distance from the drainage network are more vulnerable to the flood. Samanta et al. (2016) considered that the regions located less than 100 m distance are highly flood prone whereas distance more than 2000 m have very low flood potential. Several studies indicated that the terrestrial water storage such as lakes, ponds, dams are also associated with flooding at a regional level (Antonelli et al., 2008; Reager et al., 2014). Based on this paradigm, regions within a distance of 0.5, 1, 1.5 and 2 km from the drainage network are classified as very high, high, moderate and low flood vulnerable, respectively (Fig. 4c).

3. Preparation of geospatial layers A large amount of multi-source geospatial data have been used in this study to prepare the flood susceptibility map: (i) geological quadrangle map published by geological survey of India (GSI); (ii) geomorphology map of India, published by survey of India (SOI); (iii) pixel based daily rainfall data from 1963 to 2013, acquired from India 62

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Fig. 3. The methodological steps adapted to perform in this study. Notice that the study is divided into two broad sections, unit stream-power modelling and geographic information system based flood susceptibility mapping.

generally leads to higher flood susceptibility (Lehner et al., 2006). The flow accumulation, by name it indicates the flow accumulation of a pixel from the surrounding pixels which indicates the zones of runoff (Mahmoud and Gan, 2018). In this paper, flow accumulation is generated from elevation model in ArcGIS, using flow accumulation command after calculation of flow direction (Fig. 4f).

3.4. Geomorphology The geomorphic arrangement of a region has significant importance towards flooding. Hence, geomorphology can be an important factor for flood susceptibility assessment. According to Slater et al. (2015), geomorphology is one the prime driver of flood hazards, probably more common, but less important compared to the hydrology. The regions located in low lying flood plain are more prone towards flooding compared to the structural hilly regions. Additionally, the coastal low lying flat regions are more vulnerable to the coastal floods as well. The geomorphology map is created based on the map published by Survey of India, in the ArcGIS environment (Fig. 4d).

3.7. Rainfall A large amount of previous literatures establish the relationship between the rainfall and the flood occurrence of an area (Goel et al., 2000; Zhang and Smith, 2003; Rozalis et al., 2010; Hong et al., 2018a, 2018b; Zhao et al., 2018). It cannot exactly be determined to what extent an increase of rainfall will cause a flood situation (Kay et al., 2006). Instead, it can be said that rainfall is the principle factor for the occurrence of flood in any environmental conditions (Segond et al., 2007). For flood potential mapping, rainfall was selected by one of the principle influencing component by numerous researchers, worldwide (Tehrany et al., 2013a; Tehrany et al., 2015a, 2015b; Tien Bui et al., 2016a, 2016b; Das, 2018a). Preparation of rainfall map in this study is based on pixel based daily rainfall data of 63 years (Fig. 5a).

3.5. Drainage density Higher probability of flooding is strongly associated with higher drainage density as it indicates greater surface runoff. The drainage density map of Ulhas catchment is computed from the drainage network map using line density command in ArcGIS (Fig. 4e). Kumar et al. (2007) indicated that higher surface runoff is generated in the regions having a higher drainage density compared to the areas with low drainage density. Thus, the expansion of flood risk may depend on the drainage density, which is a critical factor for runoff generation (Ogden et al., 2011; Mahmoud and Gan, 2018).

3.8. Land-use The flood frequency of an area can strongly be influenced by the land-use pattern and its temporal evolution (Benito et al., 2010; GarcíaRuiz et al., 2008). According to García-Ruiz et al. (2008) the land use of an area has supreme prominence for hydrological responses at different time periods. Consequently, Beckers et al. (2013) demonstrated that the

3.6. Flow accumulation According to Kazakis et al. (2015) flow accumulation is one of the most essential parameters in flood mapping. High flow accumulation 63

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Fig. 4. Maps are representing flood controlling components. a. elevation; b. slope; c. distance from river; d. geomorphology; e. drainage density; f. flow accumulation.

changes in land use can accelerate the flood probability of an area. In this study, the land-use map is prepared using Landsat 8 data by processing in Erdas Imagine software. Supervised classification is done to prepare a total number of six land use classes: (i) waterbodies; (ii) agricultural land; (iii) natural vegetation; (iv) scrub forest; (v) barren land; and (vi) built up areas (Fig. 5b).

3.9. Geology The temporal flood of an area has strong ability to affect the stream profile due to the variation of geology (Reneau, 2000) and it can be considered as an important factor as it amplifies the degree of a flood event (Xu et al., 2001; Kazakis et al., 2015). Moreover, geology of an area can deliver substantial information regarding the occurrence of paleo-flood events (He et al., 2007). A strong correlation can be found 64

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Fig. 5. Maps are illustrating various flood controlling components. a. rainfall; b. land-use; c. geology; d. stream power index; e. topographic wetness index; f. topographic curvature.

between the permeability of a rock and the infiltration rate. Consequently, impermeable rocks favor the surface runoff, which can trigger floods. The geology map of Ulhas catchment is prepared based on Geological Quadrangle and the parent rock type data (NBSS-LUP), which shows four major types of geology: (i) coastal sediment; (ii) alluvium; (iii) massive basalt; and (iv) highly weathered basalt (Fig. 5c). A geological formation with higher permeability will lead to higher

infiltration process while impermeable layer will increase higher surface runoff. 3.10. Stream power index (SPI) According to Knighton (1999) stream power index is significantly important for many processes in the fluvial environment. Catastrophic 65

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transformation of the channel, because of the high stream power is found in the study of Fuller (2008). The stream power index can be described as a significant performance of stream channel erosion and sediment transport (Barker et al., 2009; Hong et al., 2018a). Calculation of stream power index is done based on the equation given by Moore et al. (1991):

SPI = As tan

(1)

where, As indicates the specific catchment area, and β represents the slope gradient. In the present study, calculation of SPI is directly done in SAGA GIS using a digital elevation model of the study area (Fig. 5d). 3.11. Topographic wetness index (TWI) The topographic wetness index is a physical representation of floodinundation areas, which is an important component of a river catchment (Soulsby et al., 2010; Hong et al., 2018a). TWI of a catchment indicates two types of measurements, are flat lands and hydrographic positions (Papaioannou et al., 2015). There is a strong relationship between the geomorphology and the TWI of an area. TWI values are generally higher in floodplain environments (Adam and David, 2011). TWI can be calculated using the following expression given by Moore et al. (1991):

TWI = ln

As tan

Fig. 6. Year wise variation of flood hydrology in study area. a. The graph is representing year wise peak discharge at Badlapur site. The red line indicates mean + 1 standard deviation of the peak discharge to discriminate the extreme flood years. b. The graph represents a variation of stream power during the peak discharge of each year. The different colors in the graph are indicating required stream power for transportation of different size sediment particles (Based on Williams, 1983).

(2)

here, As represents the cumulative upslope area draining a pour-point and tan β indicates the slope angle at the pour-point. In this study, TWI has been directly calculated in SAGA GIS using digital elevation models (Fig. 5e).

= QS / w the Eq. (3) can be re-written as:

= v

3.12. Curvature

(3) (4)

while

Topographic curvature has a crucial importance on runoff and the infiltration process of an area (Cao et al., 2016). A study done by Hudson and Kesel (2000) found that the curvature between 1.0 and 2.0 is having higher probability of flooding. For the accurate representation of flow velocity, it is beneficial to include curvature as it supports the projection of the water depth (Horritt, 2000). In the present study, curvature is calculated in the ArcGIS environment by using surface tools (Fig. 5f).

= RS

(5)

where, ω is unit stream power in Wm-2, γ is the specific weight of the clear water (9800 Nm-2), Q is discharge in m3 s-1, S is slope, w is width of the cross section in m, τ is boundary shear stress in Nm-2, v is flow velocity in m s-1, R is the hydraulic radius which is equivalent to the mean depth in m. The effectiveness of a flood can be understood by obtaining knowledge regarding the bed-load transport of each flood event. As stream power increases, larger sediment particles start moving towards downstream. The threshold of stream power required to transport different size of bed material can be determined by using an empirical power-law equation given by Williams (1983) (cited by Kale and Hire, 2004):

4. Methodology 4.1. Flood hydrology and the unit stream power model The geomorphic effectiveness of a flood is determined by the unit stream power and the degree of turbulence (Wohl, 1993; Baker and Kale, 1998; Kale and Hire, 2004). Hence, assessment of long-term hydrological data and hydraulic parameters can provide substantial information regarding the effectiveness of floods (Baker and Costa, 1987). Assessment of the flood hydrology is done in the Ulhas river by utilizing the daily discharge (Q) data for a period of 1981–2014for Badlapur hydro-station, acquired from the Water Resource Information System of India (India-WRIS) portal (http://www.india-wris.nrsc.gov.in/). To understand the flood hydrology, the peak discharge is plotted against years (Fig. 6a). Extreme flood years are computed by the summation of mean and one standard deviation (Mean + 1SD) of the peak discharge data. However, more advance knowledge of flood effectiveness can be computed by numerical stream power modelling (Scorpio et al., 2018). The unit stream power of the Badlapur station for peak discharge of each year is computed using the following formulae (Leopold et al., 1964; Baker and Costa, 1987; Kale and Hire, 2004):

= 0.079 dg1.27

(6)

where dg is the intermediate diameter of the sediment grain in mm. Theoretical approximations designate that the maximum unit stream power necessary to move a pebble (64 mm), cobbles (256 mm), boulder (0.5 m) and boulder (1 m) is 16, 90, 212, and 510 Wm-2. 4.2. Analytical hierarchy process Saaty (1980) introduced the analytical hierarchy process (AHP) which is a semi-quantitative decision making approach using weights through pairwise comparison between different factors without inconsistencies. According to Saaty and Vargas (2001) the AHP method comprises several stages: (i) itemization of a decision making problem into component factors; (ii) preparation of the components in hierarchical order; (iii) depending on the relative importance of each factor, assignment of numerical values according to their relevance; (iv) 66

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Table 1 Comparison matrix and relative score of each parameter. Parameters

Elevation

Slope

Distance from river

Geomorphology

Drainage density

Flow Accumulation

Rainfall

Land-use

Geology

SPI

TWI

Curvature

Elevation Slope Distance from river Geomorphology Drainage density Flow accumulation Rainfall Landuse Geology SPI TWI Curvature

1 1/2 1/3 1/4 1/4 1/5 1/5 1/6 1/6 1/7 1/8 1/8

2 1 1/2 1.3 1/3 1/4 1/4 1/5 1/5 1/6 1/7 1/8

3 2 1 1/2 1/3 1/3 1/4 1/4 1/5 1/6 1/6 1/7

4 3 2 1 1/2 1/3 1/3 1/4 1/4 1/5 1/6 1/7

4 3 3 2 1 1/2 1/3 1/3 1/4 1/5 1/6 1/7

5 4 3 3 2 1 1/2 1/3 1/4 1/5 1/6 1/7

5 4 4 3 3 2 1 1/2 1/3 1/5 1/5 1/6

6 5 4 4 3 3 2 1 1/2 1/4 1/5 1/6

6 5 5 4 4 4 3 2 1 1/2 1/4 1/5

7 6 6 5 5 5 5 4 2 1 1/2 1/3

8 7 6 6 6 6 5 5 4 2 1 1/2

8 8 7 7 7 7 6 6 5 3 2 1

building a comparison matrix; and (v) computation of normalized eigenvector which determine the weights of each component. The comparison of different components is made by assigning scores based on their relative influences and relative importance (i.e. 1. Equally significant; 3. Moderately more significant; 5. strongly more significant; 7. Very strong more significant; 2, 4, and 6 are the transition scores). In the pairwise comparison matrix, the rows follow the inverse score of each component and its significance with other factor (Table 1). For instance, for flood susceptibility model, the elevation is having slight higher significance than slope; thus, the score of elevation is 1 and 2 for slope. Hence, in the next now slope is having a score of ½ and similar method is applied to all components. After that, standardized a pairwise comparison matrix is built where diagonal elements are equal to 1, using the following expression:

A = (aij )n* n =

a11 a12 … a1n 1 a21 a22 … a2n , aii = 1, aij = , aij aji an1 an2 … ann

0

where, CR is the consistency ratio, CI represents the consistency index, RI stands for the random index, λmax is the principle eigenvalue of matrix, and n is the number of total components in the matrix. Based on Saaty’s (1980) paper, the random index (RI) values are implemented to calculate the consistency ratio. The random index depends on the number of components in the comparison matrix. When the number of components is 12, the value of RI is 1.54 referencing to the Saaty’s (1980) paper. In this study, if the consistency ratio (CR) value is < 0.10, the computation is accepted and it indicates a reasonable accuracy for the pairwise comparison matrix. In case of the CR is > 0.10, the comparison matrix indicates inconsistency and the scores are readjusted according to Saaty (1977). In the later phase, all the components contemplated in this research are assessed separately and classified into sub-categorical orders. Table 3 indicates that the CR values for all components are lower than 0.10, which indicates the preferences made in this study are highly consistent. Finally, integration of all the components and their weights in flood susceptibility index (FSI) is done based on weighted linear sum in the ArcGIS environment using the following expression:

(7)

The scores of pairwise comparisons are normalized to compute the standard pairwise comparison matrix through the weighted arithmetic mean method (Eq. (7)) (Table 2). The determination of the weights for all components are determined by the mean row method in the standard pairwise comparison matrix. The maximum characteristic root can be expressed as:

n

FSI =

The consistency of the AHP method is done by the following expression (Saaty, 1980):

CR =

CI RI

(9)

while,

CI =

max n n 1

(11)

where, FSI represents flood susceptibility index, Wi is the weight value of each component, Ri is the weight value of sub-category of components, n is the number of factors. There are several advantages to implement AHP method for flood susceptibility analysis are (Long and De Smedt, 2012; Kayastha et al., 2013): (i) the decision rules are based on experience and the knowledge of experts; (ii) all the information are taken into account for structural judgement; (iii) The weights of each component are automatically calculated based to principle eigenvector, when consensus is reached and (iv) by using consistency index values, the inconsistency can easily

(8)

Aw = maxw

(wi *ri) i=1

(10)

Table 2 Normalized and the weight values in the standardized pair-wise comparison matrix. Parameters

El

Sl

Dr

Geom

Dd

Fa

Rn

Lu

Geol

SPI

TWI

Curv

Weight (Wi)

El Sl Dr Geom Dd Fa Rn Lu Geol SPI TWI Curv

0.2891 0.1445 0.0964 0.0723 0.0723 0.0578 0.0578 0.0482 0.0482 0.0413 0.0361 0.0361

0.3636 0.1818 0.0909 0.0606 0.0606 0.0455 0.0455 0.0364 0.0364 0.0303 0.0259 0.0227

0.3596 0.2397 0.1199 0.0599 0.0340 0.0340 0.0300 0.0300 0.0240 0.0199 0.0199 0.0171

0.0329 0.2464 0.1643 0.0821 0.0411 0.0274 0.0274 0.0205 0.0205 0.0164 0.0137 0.0117

0.2680 0.2010 0.2010 0.1340 0.0670 0.0335 0.0223 0.0223 0.0167 0.0134 0.0112 0.0096

0.2552 0.2042 0.1531 0.1531 0.1021 0.0510 0.0255 0.0170 0.0128 0.0102 0.0085 0.0073

0.2137 0.1709 0.1709 0.1282 0.1282 0.0855 0.0427 0.0214 0.0142 0.0085 0.0085 0.0071

0.2061 0.1717 0.1374 0.1374 0.1030 0.1030 0.0687 0.0343 0.0172 0.0086 0.0069 0.0057

0.1718 0.1431 0.1431 0.1144 0.1144 0.1144 0.0858 0.0572 0.0286 0.0143 0.0072 0.0057

0.1494 0.1281 0.1281 0.1068 0.1068 0.1068 0.1068 0.0854 0.0427 0.0214 0.0107 0.0071

0.1416 0.1239 0.1062 0.1062 0.1062 0.1062 0.0885 0.0885 0.0708 0.0354 0.0177 0.0088

0.1194 0.1194 0.1045 0.1045 0.1045 0.1045 0.0896 0.0896 0.0746 0.0448 0.0299 0.0149

0.239 0.173 0.135 0.105 0.087 0.073 0.058 0.043 0.034 0.022 0.016 0.013

67

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Table 3 Sub-criteria of each parameter and the pairwise comparison matrix and their weights. Sr. No.

Factors

1

Elevation

2

3

4

5

6

7

8

9

10

11

12

Slope

Distance from river

Geomorphology

Drainage density

Flow accumulation

Rainfall

Landuse

Geology

SPI

TWI

Curvature

Classes

1

2

0–88 88–209 209–419 419–724 724–1394

1 1/2 1/4 1/7 1/8

1 1/2 1/5 1/7

0–4.08 4.08–10.61 10.61–19.86 19.86–33.74 33.74–69.38

1 1/2 1/3 1/4 1/6

1 1/2 1/3 1/5

< 0.5 0.5–1 1–1.5 1.5–2 >2

1 1/2 1/4 1/6 1/8

1 1/3 1/5 1/7

Coastal Plain Rocky benches Ridge/hills Slope facets Lava plateau

1 1/2 1/4 1/6 1/9

1 1/2 1/4 1/6

1.16–1.49 1.02–1.16 0.83–1.02 0.58–0.83 0.01–0.58

1 1/2 1/4 1/6 1/8

1 1/2 1/4 1/6

255,678–547,882 176,181–255,678 90,239–176,181 30,079–90,239 0–30,079

1 1/2 1/4 1/5 1/6

1 1/2 1/3 1/5

3472–3867 3114–3472 2805–3114 2483–2805 2045–2483

1 1/2 1/4 1/5 1/7

1 1/2 1/3 1/5

Waterbodies Built-up Agricultural land Scrub forest Natural vegetation Rocky outcrop

1 1/2 1/3 1/4 1/5 1/6

1 1/2 1/3 1/4 1/5

Alluvium Coastal sediment Basalt Weathered basalt

1 1/3 1/5 1/6

1 1/3 1/5

0–76 76–281 281–615 615–1192 1192–3256

1 1/2 1/3 1/4 1/5

1 1/2 1/3 1/5

16.20–25.42 12.31–16.20 9.51–12.31 7.25–9.51 2.46–7.25

1 1/2 1/4 1/6 1/7

1 1/3 1/5 1/7

− 10.08 to − 0.71 − 0.71 to − 0.18 − 0.18–0.11 0.11–0.79 0.79–9.18

1 1/2 1/4 1/5 1/6

1 1/2 1/3 1/4

3

4

5

6

CR 0.0479

68

1 1/3 1/5

1 1/2 1/4

1 1/3 1/5

1 1/2 1/4

1 1/2 1/4

1 1/3 1/5

1 1/2 1/4

1 1/2 1/3 1/5

1 1/3

1 1/2 1/3

1 1/3 1/5

1 1/2 1/3

1 1/3

1 1/2

1 1/3

1 1/2

1 1/2

1 1/3

1 1/2

1 1/2 1/4

1

1

1 1/3

1 1/2

0.0609

1

0.0114

1

0.0156

1

0.0642

1

0.0207

1

1 1/2

1

1 1/2

0.0196

0.0357

1

0.0826

0.0204

1

1

1

0.0695

0.0202

Weight (Ri) 0.462 0.274 0.156 0.071 0.038 0.420 0.266 0.167 0.095 0.053 0.445 0.297 0.147 0.073 0.037 0.475 0.266 0.142 0.075 0.042 0.468 0.268 0.144 0.076 0.044 0.441 0.249 0.174 0.090 0.046 0.459 0.254 0.151 0.087 0.049 0.376 0.246 0.165 0.108 0.064 0.040 0.546 0.268 0.125 0.061 0.413 0.272 0.159 0.097 0.059 0.439 0.299 0.148 0.074 0.039 0.457 0.249 0.146 0.090 0.058

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be detected and the entire computation can be revised. The only disadvantage of AHP method is that the preference of ranking of factors can differ from one researcher to another according to their existing knowledge, expertise and experience. 4.3. Computation of area under the curve (AUC) There are many methods developed to validate the geospatial models by different researchers, worldwide. The most accurate and convenient way to validate such model is field work which is a difficult job sometimes, however. Flood susceptibility maps can easily be verified using the historical flood inventory data. For this study, the historical flood locations are collected from the older unpublished reports, newspapers and physical field visit where personal communication with local people is done. There are several mathematical and statistical methods which are very common for validate similar studies such as success rate curve, area under the curve, chi-square test, calculation of flood density etc. (Kayastha et al., 2013). In this study, for the purpose of validation, area under the curve (AUC) has been followed, by which the efficiency and performance of the AHP model is assessed. AUC is calculated based on the cumulative area under different susceptibility of flood in one hand and the cumulative number of flood events in the different susceptible areas on the other hand. More specifically, the resultant flood susceptibility map has been categorized into twenty classes and the percentage of area in each class is computed. The percentage of historical floods in each susceptibility category also calculated. Later, the AUC curve has been plotted based on the computation.

Fig. 7. The channel cross section of Badlapur station. The graph is showing the measured cross sections during 2005 and 2012 (Source: WRIS). There is almost no variation in cross section geometry during this time period.

O’Connor, 1995). Another attempt is made to check if there is any change in cross section geometry, although this section is bed rock dominated. Cross sectional data of 2005 and 2012 shows that there is no significant change in the shape of cross section geometry (Fig. 7). Although, the hydro-station located at Badlapur doesn’t provide substantial information regarding the geomorphic effectiveness of the entire basin, it can clearly be understood easily from the unit stream power values that the downstream region can have stream power higher than several magnitudes than the Badlapur as many northern tributaries are joining the main channel of Ulhas just below the Badlapur. This could be a possible reason for Thane to be a flood sensitive region where flood is much destructive.

5. Results and discussion The results of the present study can be described in two major sections, where, the first section defines the geomorphic effectiveness of the peak discharge of the last three decades and the second section reveals the flood susceptibility of the Ulhas catchment and the degree of efficiency of the AHP model.

5.2. Flood susceptibility map The resulting flood susceptibility map generated by implementing AHP technique by integration of 12 related components to the flood is shown in Figs. 4 and 5. It is observed that most of the researchers develop the class boundaries based on their own expert opinion and there is no particular rule for classification, automatically (Ayalew et al., 2004; Kayastha et al., 2013). In the present study, the resultant flood susceptibility map has been classified into five categories based on the natural braking method in the ArcGIS environment. The observation shows that according to the model, about 25% of the Ulhas catchment is having a very high probability of flooding, while high, moderate, low and very low vulnerable regions cover 28%, 21%, 16% and 10% areas, respectively (Fig. 8). The regions which are highly vulnerable to the flood, generally show a combine characteristics of very low elevation, lower degree of slope and high proximity to the drainage network. Another notable subject is that the flood sensitive zone (FSZ) indicated in the flood susceptibility map where the highest number of historical floods are recorded, is the zone of confluence of all the tributaries of the Ulhas river. Additionally, the catchment area shows very circular shape which indicates higher probability of flood where tributaries join the trunk channel (Das and Pardeshi, 2018c). To understand the influence of each conditional factors to the flood susceptibility in Ulhas catchment, relationships between the flood components (grid based) and the flood susceptibility index (FSI) values are randomly plotted (Fig. 9a-h). Factors which are vector based or consisting defined features (i.e. distance from river, geology, geomorphology, land-use) are plotted as bar graphs and the mean FSI values are computed for each category (Fig. 9i-l). Higher explained variance are found in case of elevation, slope and drainage density, indicated by “R2” in the graph plots. Therefore, it is a clear symptomatic that these factors play a crucial role during a flood period in the study area. By contrast, curvature, rainfall and flow accumulation show lowest explained variance which clearly indicates the factors do not play any important role in the flood potential in the study area.

5.1. Flood hydraulics in Ulhas catchment Before discussing the significant results, it is noteworthy to mention that in this study it is assumed that occurrence of the peak discharge for every year are bank-full for easier calculation. However, in reality the situation can differ from this theoretical modelling. The unit stream power model of Badlapur station is carried out based on the cross sectional data, acquired from WRIS. Table 4 shows all the geomorphic parameters and their values for this river cross section at Badlapur. Results show that the unit stream power ranges between 83 and 395 W m-2 (Fig. 6b). The stream power model indicates that in this region, the appraised values are not very high. However, during extreme floods the river can easily carry cobbles, pebbles as suspended load whereas boulders size up to 0.5 m can become bedload (Costa and Table 4 Cross-sectional parameters and their corresponding values. Parameters

Value

Width (m) Average depth (m) Area (m2) Width-depth ratio Slope gradient (s) Boundary shear stress (τ) (Nm−2)

240 6.05 1484 39.67 0.0022 130.64

The formula to calculate cross sectional area is width×depth. However, the cross-sectional area in this study is calculated based on the original cross profile data (segment wise width and depth) given in WRIS data archive. Slope gradient is calculated from digital elevation model. 69

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Fig. 8. Flood susceptibility map of the study area based on analytical hierarchy process (AHP). The box inside of map is indicating Thane region, which is highly flood sensitive zone (FSZ). The inset pie diagram is illustrating the area distribution under different susceptibility.

5.3. Validation of the flood susceptibility map

which is located in the middle reach of the Ulhas catchment. Hence, it is not possible to understand the geomorphic effectiveness of floods in the lower reach. (ii) To calculate unit stream power, an assumption has been made that all the peak discharges are bank-full. In reality, the wetted perimeter (the wetted cross-sectional area) varies with discharge. Therefore, the unit stream power estimated in this study is not always true except the discharge is bank-full (The estimated values are lower than the real hydraulic unit stream power in the channel as the cross-sectional area and the discharge velocity are having an opposite relationship). (iii) It is not possible to prepare flood inventory using satellite images as the location is situated in the escarpment of the Western Ghats. During the monsoon season, this area shows thick cloud cover due to the obstruction of high mountains. Preparation of a detailed flood inventory using old newspaper/unpublished reports and by field work (rugged terrain, forested area) is quite challenging for a large area.

The AUC value (0.84 or 84%) shows considerable accuracy and efficiency of the predicted flood map through AHP technique (Fig. 10). In recent times, several methods are developed to map flood potentiality of a region which shows very good accuracy. Tehrany et al. (2013) carried out bivariate and multivariate statistical models, support vector machine and frequency ratio with several other studies which show high efficiency of flood model (Tehrany et al., 2014, 2015a, 2015b). Machine learning methods are complex, but those methods are having ability to deliver the prediction report in a higher level of accuracy (Chen et al., 2017a, 2017b). However, the accuracy found in this study is higher enough to compare with such machine learning approaches. 5.4. Limitations and future works Despite of the systematic analysis of the hydro-geomorphic effectiveness and mapping of the flood potential regions, it was very challenging to perform this study due to several limitations. Therefore, a significant future works may deliver more comprehensive understanding of the floods which will help the policymakers for easier flood management in the study area. Major limitations are:

A further assessment of flood potentiality by employing a higher resolution data and more comprehensive methods such as modern machine learning or statistical techniques may provide a more accurate flood map in the Ulhas basin.

(i) Hydrological data is available only for one station (Badlapur) 70

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Fig. 9. (a-h). Relationship between the flood conditioning factors (grid based) and the flood susceptibility index (FSI). The graphs are plotted based on random points generated on the study area map, for which values of the conditioning factor and the FSI are plotted. (i-l). Bar graphs represent the mean FSI values for each defined (vector based) factors and each sub-category (for Fig. 9-l: Wa- waterbodies, Nv- natural vegetation, Bl- barren land, Al- agricultural land, Sf- scrub forest, Bu- built up areas).

6. Conclusion The frequency of high magnitude floods is generally very low but have significant consequences on the socio-economic activities of an area. To recognize the geomorphic effectiveness of a flood occurrence, long term hydrological data is prerequisite to assess. For the purpose of management and planning, flood susceptibility mapping is a convenient practice in regions that are prone to very frequent flooding. In this paper, an assessment of long term hydrological data has been assessed to understand the geomorphic effectiveness of the floods in the last three decades along with a geospatial assessment of flood susceptibility in Ulhas catchment. Stream power modelling is a prevailing method to understand the effectiveness of the flow in a river channel. The flood susceptibility map in this study is produced based on analytical hierarchy process which is a heuristic expert-decision making approach with the capability to check the consistency of the model. This method is considerably accurate and appropriate for flood susceptibility mapping due to the reasonable comparison of diverse influencing factors deprived of any inconsistency in the decision process. A total 12 causative components are considered for mapping the flood vulnerable regions, i.e. elevation, slope, distance from river, geomorphology, drainage density, flow accumulation, rainfall, land-use, geology, stream

Fig. 10. Area Under the Curve (AUC) related to the validation of the flood susceptibility model. The AUC value is 0.84 or 84%, which is considerably good accuracy for geospatial mapping of flood susceptibility.

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power index, topographic wetness index and curvature of the topography. The present study concludes that (i) the flood intensity in Ulhas river from a hydro-geomorphic viewpoint is quite high as medium-large boulders become bed load during high flow regime; (ii) Among the twelve flood conditioning factors, only three factors such as elevation, slope and drainage density are having major impact on the flood potential in Ulhas catchment; (iii) The accuracy of the AHP model for flood susceptibility has shown a considerably good accuracy. The flood susceptibility map presented in this paper can be a good source for engineers, policy makers, planners and administrative bodies for the prevention of floods in Ulhas catchment. Moreover, the methods used in this study, bridge the gap of understanding extreme floods and their effectiveness, which is absent in any other geospatial assessment of flood susceptibility. Hence, it is recommended to apply this approach for advance understanding of the magnitude and frequency of flood with the probable flood exposure in different regions around the world.

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