Nat Hazards DOI 10.1007/s11069-015-1605-1 ORIGINAL PAPER
Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya Gowhar Meraj • Shakil A. Romshoo • A. R. Yousuf • Sadaff Altaf Farrukh Altaf
•
Received: 5 April 2014 / Accepted: 1 January 2015 ! Springer Science+Business Media Dordrecht 2015
Abstract In Himalayan region, it is very important to generate detailed terrain information for identifying the causes of natural hazards such as debris flows, debris floods, and flash floods, so that appropriate corrective measures are initiated for reducing the risk of the people and property to these disasters. Basic watershed morphometrics coupled with the land-cover and slope information are useful for assessing the hazard vulnerability. The terrain characteristics govern the surface hydrology and have profound influence on the incidence and magnitude of natural hazards, particularly floods. The present work is a comparative study of two watersheds of Jhelum basin (upper Indus basin in Kashmir). In this research, we make an integrated use of the Linear Imaging Self-Scanner satellite data and Advanced Spaceborne Thermal Emission and Reflection Radiometer digital elevation model, supported with extensive field information, in a GIS environment for assessing the surface hydrological behavior of Lidder and Rembiara watersheds of the Jhelum basin. Knowledge-driven modelling approach has been used to evaluate the runoff potential of the watersheds to assess the flood vulnerability downstream. The results revealed that Lidder watershed exhibits lesser basin lag time compared to Rembiara watershed for a storm event. Further, due to higher population density in the Lidder downstream, this watershed is also socially more vulnerable to flooding than Rembiara. The methodology and results of this study shall help in formulating better flood mitigation strategies in this part of the Himalayan region, where the observation network of hydrometeorological and other land surface parameters is either missing or very scanty.
G. Meraj (&) ! S. A. Romshoo ! S. Altaf ! F. Altaf Department of Earth Sciences, University of Kashmir, Hazratbal, Srinagar 190006, Jammu and Kashmir, India e-mail:
[email protected] G. Meraj ! A. R. Yousuf Department of Environmental Science, University of Kashmir, Hazratbal, Srinagar 190006, Jammu and Kashmir, India A. R. Yousuf National Green Tribunal, Government of India, New Delhi, India
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Keywords Kashmir Himalaya ! Jhelum basin ! Hydrology ! Morphometry ! Runoff potential ! Flood vulnerability
1 Introduction Natural hazards in the form of floods and debris flows pose major threat to the people living in the high mountain regions (Watson and Haeberli 2004). During the last few decades, there has been an increase in the frequency of cataclysmic natural hazards particularly those that are triggered by water, such as floods in the mountainous regions of the globe (Sepu´lveda and Padilla 2008; Korup and Clague 2009). The main reasons for the observed increase in these hazards are attributed to increase in the global temperature due to the effects of climate change; population growth and inhabitation in the high slope zones; loss of wetlands, deforestation, and unrestrained land-use change (Berz et al. 2001; McBean 2004; IPCC 2007; Peduzzi et al. 2009; Meraj et al. 2012; Bhat et al. 2013, 2014; Romshoo and Rashid 2014). Himalayas are especially affected by floods, as they have the precipitous relief, witness heavy torrential rainstorms, frequent cloud bursts, and have a history of natural hazards amplified by melting of snow and glaciers (Ebi et al. 2007). Hence, floods are the major physical threats to the sustainable development in the Himalaya (Ives 2004). During 1954–1990, more than 2,700 billion rupees were spent on flood control measures in India, but in this period, the annual flood damage increased by nearly 40 times and the annual flood-affected areas increased by 1.5 times (Agarwal and Narain 1996). The valley of Kashmir is one of the most vulnerable flood hazard-prone Himalayan regions in India (Sen 2010; Meraj et al. 2013). Historically, Kashmir Himalayan region has witnessed heavy casualties and loss of property due to flooding (Singh and Kumar 2013). Due to the inadequacy of the data necessary for understanding the mountainous land surface and hydrological processes, the assessment of floods is often hindered in Himalaya (Mirza 2005). Moreover, the application of physically based models is limited due to the complex nature of the related events as well as the limitation of the observational data (Chaponnie`re et al. 2008; Romshoo et al. 2012). As an alternative, assessments are usually qualitative, based on the evaluation of the terrain characteristics, the surface hydrological characterization, and the experience gained through previous events (Costa 1988; Costa and Schuster 1988; Haeberli et al. 1989; Dutto and Mortara 1992; Clague and Evans 1994). Surface hydrological behavior is one of the main drivers behind floods (Barredo and Engelen 2010). It is rapid or delayed hydrological response, which often makes a watershed vulnerable to flooding and is a function of the watershed characteristics like geomorphology, topography, land use–land cover, geology, and soil (Romshoo et al. 2012). Morphometric parameters such as stream order, drainage density, stream frequency, and elongation ratio play a key role in governing the surface hydrology of the watershed (Chow 1964; Strahler 1964; Ward and Robinson 2000; Hudson and Colditz 2003). Further, during storm events, topography is presumed to be the primary factor controlling the hydrological response of a watershed (Brasington and Richards 1998) and is also major cause for triggering debris flows and landslides (Bates and De Roo 2000; Zaz and Romshoo 2012). Besides land cover, other terrain characteristics such as geology, soil, and geomorphology have a significant influence upon the surface hydrological behavior of a watershed, since it is largely related to its permeability and water-holding capacity (Barry and Chorley 1998;
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Ward and Robinson 2000; Rashid et al. 2011). Deforestation can amplify the vulnerability and magnitude of floods, landslides, and mass movements, particularly in the mountainous regions (Bosch and Hewlett 1982; Arnell 2002). Hence, it is very important to holistically assess the watershed characteristics in order to analyze the hydrological response of watersheds, to come up with better understanding of the processes involved in the occurrence of floods (Simonovic and Li 2003). In this research, we have evaluated watershed characteristics (drainage, land cover, and slope) that make the downstream areas of a watershed prone to flooding (Bhat and Romshoo 2009; Diakakis 2011; Meraj et al. 2013). The study has been carried out in the two watersheds of the Jhelum basin (upper Indus basin), i.e., Lidder and Rembiara. The former is a right-bank tributary watershed of the Jhelum basin in the greater Himalayan range and latter is the left-bank tributary in the Pir Panjal range. This study has used a comparative qualitative index (total runoff score) in the two watersheds to determine the extent and magnitude of the flood vulnerability downstream. It is very important to assess the relative flood vulnerability in the left- and right-bank tributary watersheds of the Jhelum basin, since both the sides have demonstrated different flood vulnerability due to the differential socio-geoenvironmental setting. These two watersheds are the representative watersheds among the 24 watersheds of the Jhelum basin. The study is based on the integrated use of satellite remote sensing, geographic information system (GIS), and detailed field observation techniques for better understanding of the influences of watershed characteristics on hydrological processes and flooding. The findings of this research shall be of tremendous practical use in planning flood hazard management and mitigation strategies in the Himalaya in general and Kashmir Himalaya in particular, as there is scarcity of ground-based observational data which often hampers the parameterization of the more complex physically based flood models (Van De Wiel et al. 2011; Romshoo et al. 2012; Altaf et al. 2014).
2 Geologic setting of the study area The unique geomorphologic setup of the Jhelum basin with heterogeneous lithology and varied hydrological conditions renders the basin all the more vulnerable to natural hazards particularly flooding. Keeping in view the above facts, two representative watersheds of the Jhelum basin—Lidder and Rembiara, on either banks of the axial river Jhelum—were chosen for the detailed studies involving morphometry, land cover, and slope analysis in order to understand their influences on the surface hydrological behavior. Figure 1 shows the location of the two representative watersheds chosen for this study. The Rembiara watershed falls in Pir Panjal range, and predominantly, two geologic formations, viz. Panjal trap and Karewa group of formations, occur in this watershed. Panjal trap lies on the top of the agglomerate slates and almost forms the central axis of the Pir Panjal range, where they attain a maximum thickness of about 100 m. Panjal traps consist mainly of basic rocks and a few intermediate and acidic rocks. Basic types are mainly basalt and andesitic basalt, while as acidic and intermediate rocks are represented by augite–andesite, trachyte, keraphyre, rhyolite, and acidic tuffs. The other dominant formation, i.e., Karewa group of formations, covers extensive areas in the watershed. The lower part of Karewa group is known as Hirpur formation. The lithological constituents of the group are clay, sandy clay, sand, conglomerate, and lignite. The Lidder watershed falls in the Greater Himalayan range and is the first major rightbank tributary of the Jhelum River. It comprises of various geologic formations. Among
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Fig. 1 Location of the Lidder and Rembiara watersheds. The upper left inset shows a map of the Jhelum basin. Its location with respect to the Indian Territory is shown as a green star on the Political map of India at the upper right inset. Locations of some of the important landmarks are shown as black stars. The map coordinates are in the UTM 43 (North) World Geodetic System (WGS-1984) reference system
all the watersheds of Jhelum basin, Lidder is mostly bedrock river. Shale slate greywacke is the oldest formation of the region and occupies considerable area of the watershed. The shale–greywacke group is overlain by the thick pile of quartzites and sandstone of various types intercalated with limestone beds (Raza et al. 1978; Wadia 1979; Husain 1998).
3 Materials and methods For accomplishing the research objectives outlined for this research, a number of approaches were employed that included the use of satellite remote sensing data, digital elevation data, and detailed field observations integrated in a knowledge-driven analytical framework using geospatial tools. 3.1 Data sets Survey of India (SOI) topographic maps of 1967 (1:50,000 scales), Indian Remote Sensing (IRS) P6 Linear Imaging Self-Scanning (LISS III) data with 23.5-m spatial resolution of October 21, 2008, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30-m resolution digital elevation model (DEM) were used in this study.
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Geometric correction of the IRS LISS III satellite data was done using SOI topographic map of 1967 at 1:50,000 scale as the reference map. More than 75 ground control points (GCPs) on the satellite image as well as on the topographic maps were used to derive a first (affine)-order polynomial transformation. Root-mean-square error (RMSE) of 0.45 was achieved. A nearest-neighbor interpolation method was used to resample the image into a Universal Transverse Mercator (UTM) projection, zone 43 North (Deepika et al. 2014). 3.2 Morphometry Morphometric parameters were derived using drainage generated from ASTER 30 m GDEM (Engelhardt et al. 2012; Altaf et al. 2013). Arc Hydro algorithm has been used for drainage generation and is logical, efficient, and consistent compared to the manual approach of morphometry evaluation from natural drainage. A detailed procedure for extracting drainage using Arc Hydro has been discussed by Youssef and Pradhan (2011). It incorporates the existing streams and lakes into the DEM for drainage analysis through a process termed as DEM manipulation. For this purpose, natural drainage present on the SOI topographic map was digitized and used to manipulate the DEM. For the proper determination of the flow direction and flow accumulation, DEM sinks were also identified and filled. Based on the cumulative number of the upstream cells draining into each cell, stream networks in both the watersheds were defined (O’Callaghan and Mark 1984). We used a critical threshold of 0.06, which represents the 6 % area in the watershed, for defining streams. The critical threshold is the minimum upstream drainage required to initiate a stream. Areas of the watersheds were evaluated by calculating the geometry of the derived watershed polygons (Fig. 2a, b). The length of the watershed was calculated by summing the length of the main stream channel and the distance from the top of the main channel to the watershed boundary (Altaf et al. 2013). For stream ordering, Strahler’s scheme was used, which was originally introduced by Horton and later on modified by Strahler (1952), Schumm (1956), and Singh (1980). The formulae used for the derivation of the relevant morphometric parameters are given in Table 1. Some of the basic parameters used for the extraction of the morphometric parameters are shown in Table 2. 3.3 Land-cover (LC) classification Land-cover information is imperative for assessing several land surface processes including surface hydrology (Rashid and Romshoo 2012; Badar et al. 2013a, b). Landcover information was extracted from the IRS LISS III satellite data using maximum likelihood supervised classification algorithm (Fu 1976; Jensen 1996; Tso and Mather 2001; Mortan 2007; Murtaza and Romshoo 2014). While choosing the best training samples for known land-cover types, various image enhancement techniques were applied taking cognizance of the available ground truth information. A specific classification scheme was devised so that the classes generated could be functionally linked to the surface hydrological behavior. Seven such LC classes were identified, viz. impervious surface, wasteland, agriculture, forest, pasture, shrub, and snow. Perennial water (alpine lakes and stream) was also classified (Fig. 3a, b). The generated LC was validated in the field, using 400 field verification points (Table 3). Estimation of the assessment of accuracy of the classified map is essential to determine its authenticity. Kappa coefficient, being one of the best indicators of accuracy of LC (Foody 2002), was used for accuracy assessment and is mathematically represented as (Murtaza and Romshoo 2014).
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Nat Hazards b Fig. 2 Stream order map a Lidder watershed b Rembiara watershed. According to the Strahler’s stream ordering scheme, Lidder is a seventh order watershed and Rembiara is a sixth order watershed
Table 1 Formulae used for the calculation of morphometric parameters Parameters
Formulae
Reference
Stream order (U)
Hierarchical rank (Strahler scheme)
Horton (1945)
Stream length (Lu)
Length of the stream
Strahler (1964)
Bifurcation ratio (Rb)
Rb = Nu/Nu ? 1; where Rb = bifurcation ratio; Nu = total no. of stream segments of order ‘‘u’’; Nu ? 1 = number of segments of the next higher order
Schumm (1956)
Mean bifurcation ratio (Rbm)
Rbm = average of bifurcation ratios of all orders
Strahler (1957)
Drainage density (D)
D = Lu/A; where D = drainage density; Lu = total stream length of all orders; A = area of the watershed (km2)
Horton (1932)
Stream frequency (Fs)
Fs = Nu/A; where Fs = stream frequency; Nu = total no. of streams of all orders; A = area of the watershed (km2)
Horton (1932)
Drainage texture (Rt)
Rt = Nu/P; where Rt = drainage texture; Nu = total no. of streams of all orders; P = perimeter (km)
Horton (1945)
Elongation ratio (Re)
Re = 2/Lb sqrt (A/p); where Re = elongation ratio A = area of the watershed (km2); p = ‘‘Pi’’ value, i.e., 3.14; Lb = watershed length
Schumm (1956)
Length of overland flow (Lg)
Lg = 1/D 9 2; where Lg = length of overland flow; D = drainage density
Horton (1945)
Constant channel maintenance (C)
C = 1/D; where D = drainage density
Schumm (1956)
Shape index (Sw)
Sw = Lb2/A; where Lb = Watershed length; A = Area of watershed
Horton (1945)
Compactness coefficient (Cc)
Cc = P c/P u; where Pc = perimeter of watershed; Pu = perimeter of circle of watershed area
Suresh et al. (2004)
Table 2 Basic watershed characteristics used for the calculation of morphometric parameters
Parameter
Lidder
Rembiara
Basin area A (km2)
1,261.76
664.61
240.25
176.52
70.54
64.27
Basin perimeter P (km) Basin length Lb (km) Maximum stream order U
k¼
(
N
r X i¼1
7
6
Total number of streams Nu
7,759
307
Total stream length Lu (km)
3,689.161
665.80
) r r X X ðXii Þ % N ðXiþ ! Xþi Þ =N 2 % ðXiþ ! Xþi Þ i¼1
i¼1
where r is the number of rows in error matrix; Xii is the number of observations in row i and column i; Xi? is the total of observations in row i; X?II is the total of observations in column i; and N is the total number of observations included in the matrix.
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Nat Hazards b Fig. 3 Hydrological land-cover classes map a Lidder watershed b Rembiara watershed. A agriculture, F forest, IS impervious surface, P pasture, S snow, Sh shrub, W water, WL wasteland
Table 3 Accuracy assessment of land cover for both Lidder and Rembiara watersheds Reference data A
W
F
IS
S
P
WL
Row total
User’s accuracy
Classification data A
101
W F
3
3 46
1
1
68 111
IS S
Column total Producer’s accuracy
1 20
1
95.28
47
75
97.87
90.67
112 99.11
16 100.00
106
95.28
48
95.83
72
94.44
114
97.37
18
88.89
21
95.24
19
21
90.48
21
23
400
95.24
82.61
2 106
2
16
2
P WL
2 1
Overall accuracy = [(101 ? 46 ? 68 ? 111 ? 16 ? 20 ? 19)/400] 9 100 = 95.25 % A agriculture, W water, F forest, IS impervious surface, S shrub, P pasture, WL wasteland Bold numbers here gives emphasis on the diagonal total, that is used to calculate the overall accuracy of the land cover generated
3.4 Slope analysis Topographical information was generated using ASTER 30 m GDEM (Tarboton 1989). In order to determine the significance of the slopes in aiding the flow of water, we have adopted a standard slope classification (NRCC 1998). This classification has scientifically established the relationship between runoff and slope of a given area. The areas of both the watersheds were categorized into the following 10 slope classes: Level (0"–0.3"), nearly level (0.3"–1.1"), very gentle slope (1.1"–3.0"), gentle slope (3.0"–5.0"), moderate slope (5.0"–8.5"), strong slope (8.5"–16.5"), very strong slope (16.5"–24"), extreme slope (24"– 35"), steep slope (35"–45"), and very steep slope (45"–90") (Fig. 4a, b). 3.5 Comparative evaluation of runoff potential of the two watersheds For assessing the combined role of all the parameters of morphometry, land cover, and slope on the hydrological behavior at watershed scale, we devised a runoff score methodology. This method is based on the principles of knowledge-driven modelling and converts the established scientific knowledge-based understanding of a phenomenon into a quantitative estimation (Todorovski and Dzˇeroski 2006). However, it has certain disadvantages, such as it assigns a lumped value for a parameter and can only be used in a comparison study, similar to what has been done in this research. It also imparts equal weightage to all the involved parameters, which can possibly, in some cases, exaggerate the results. However, in the absence of robust numerically or physically based approaches that often rely on the detailed estimation and parameterization of the processes, this
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Nat Hazards b Fig. 4 Hydrological slope classes map a Lidder watershed b Rembiara watershed. NRCC 1998 slope categories: Ll (0o-0.3o), NLI (0.3o-1.1o), VGS (1.1o-3.0o), GS (3.0o-5.0o), MS (5.0o-8.5o), SS (8.5o-16.5o), VSS (16.5o-24o), ES (24o-35o), SS (35o-45o) and VStS (45o-90o)
Table 4 Knowledge-driven evaluation of total runoff (TR) from all the parameters of morphometry, slope, and LC Parameters
Lidder
Runoff score
Rembiara
Runoff score
Morphometry (values) Drainage density
2.92
2
1.00
1
Stream frequency
5.34
2
0.46
1
Mean bifurcation ratio
4.74
2
3.59
1
28.06
2
1.73
1
Length of overland flow
0.17
2
0.50
1
Elongation ratio
0.57
2
0.42
1
Constant channel maintenance
0.25
2
0.14
1
Compactness coefficient
1.91
2
1.93
1
Shape index
3.94
2
7.14
1
Agriculture
16.35
2
35.83
1
Impervious surface
19.97
2
5.55
1
Forest
34.04
1
20.85
2
Drainage texture
Land cover (% area)
Wasteland
4.22
2
1.68
1
Pasture
2.13
2
1.08
2
Shrub
13.98
1
15.71
1
Snow
8.40
1
15.89
2
Slope (% area) Level (0"–0.3")
0.04
2
0.1
1
Nearly level (0.3"–1.1")
0.72
2
1.73
1
Very gentle slope (1.1"–3.0")
4.19
2
10.13
1
Gentle slope (3.0"–5.0")
5.58
2
13.41
1 1
8.65
2
19.22
Strong slope (8.5"–16.5")
Moderate slope (5.0"–8.5")
13.98
1
20.69
2
Very strong slope (16.5"–24")
14.41
2
11.98
1
Extreme slope (24"–35")
24.60
2
13.66
1
Steep slope (35"–45")
18.42
2
6.92
1
9.42
2
2.17
1
Very steep slope (45"–90") Total runoff score (TR)
48
30
Total runoff score summation of both the watersheds = 78 % TR of Lidder = 61.53 % TR of Rembiara = 38.46 % difference in the TR between Lidder and Rembiara = 23.07
method is one of the best to compare land surface processes between watersheds (Altaf et al. 2014). Due to this reason, it has been extensively used by various workers for sustainable planning and management of sub-watersheds in regions of data scarcity
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(Chakraborti 1991; Adinarayana 2003; Ratnam et al. 2005; Javed et al. 2009; Londhe et al. 2010; Chen et al. 2011; Mosbahi et al. 2012; Saghafian et al. 2013; Altaf et al. 2014). Each runoff score reflects the comparative runoff potential of the two watersheds. For morphometry, LC, and slope parameters, score 2 was assigned when the parameter depicts maximum runoff potential among the two watersheds and score 1 was assigned when the parameter depicts minimum runoff potential. The sum of the scores of all the parameters of a watershed represents the collective impact of all the parameters on the runoff potential of that watershed (Table 4). It is denoted as total runoff (TR) and mathematically can be represented as TR ¼
n X
S
i¼1
where TR = total runoff score of the watershed, Si = score of a particular watershed for a parameter, n = number of parameters.
4 Results and discussion 4.1 Morphometric analysis Watershed morphometry shows lumped or semi-distributed characteristics of the watershed. Watershed hydrology is greatly affected by its morphometry (Tucker and Bras 1998). Infiltration and runoff characteristics of a watershed are the governing factors in shaping its drainage pattern (Sharma et al. 1985; Dar et al. 2013). Runoff potential has a direct relationship with many of the morphometric parameters such as drainage density, stream frequency, mean bifurcation ratio, drainage texture, and elongation ratio (i.e., greater the values of these parameters greater is, the runoff potential of the watershed and vice versa). Whereas some parameters such as length of overland flow, circulatory ratio, form factor, basin shape, and compactness coefficient have an inverse relation with runoff potential. In order to investigate the collective role of different morphometric parameters on the surface hydrology, between the two watersheds, the higher value of a morphometric parameter, which has a direct relationship with runoff potential, was assigned score 2 and corresponding lower value of the other watershed was assigned score 1. Whereas those parameters that have an inverse relationship with the runoff potential, the lower value was assigned score 2 and higher value was assigned score 1. In view of the morphometry results, Lidder watershed scored 2 and Rembiara watershed scored 1 for all the morphometric parameters (Table 4). Morphometry of Lidder and Rembiara watersheds is shown in Table 4, and its hydrological importance is discussed in some detail below. 4.1.1 Stream order (U) The main stride in the drainage basin analysis is the description of the stream orders (Horton 1945). According to the Strahler (1964) ordering scheme, Lidder is seventh-order watershed and Rembiara is fifth-order watershed. Since higher stream order is associated with more discharge and higher velocity of the stream flow, it is deduced that the surface runoff and sediment load from Lidder is more as compared to Rembiara watershed.
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4.1.2 Mean bifurcation ratio (Rbm) The possibility of variations in watershed geometry and lithology introduces unequal bifurcation ratio from one order to the next and tends to be constant throughout the succession (Romshoo et al. 2012). Rbm of Lidder watershed is 4.74 and that of Rembiara watershed is 3.59. Higher Rbm in case of Lidder specifies early hydrograph peak during the storm events for this watershed compared to Rembiara (Rakesh et al. 2000). 4.1.3 Drainage density (Dd) Hydrology of a watershed fluctuates considerably in response to the changes in the drainage density (Yildiz 2009). Drainage density of Lidder watershed is higher (2.92 km/km2) than that of Rembiara watershed (1.00 km/km2). Higher Dd of Lidder watershed insinuates impermeable subsurface material, sparse vegetation, and mountainous relief for this watershed, and lower Dd for Rembiara watershed reveals that it has permeable subsurface material, good vegetation cover, and low relief. Consequently, high Dd in case of Lidder reflects that it has a highly dichotomized drainage basin with a quite rapid hydrological response to rainfall events compared to Rembiara watershed (Melton 1957a, b). 4.1.4 Stream frequency (Fs) Stream frequency is highly correlated to permeability, infiltration capacity, and relief of watersheds (Montgomery and Dietrich 1989, 1992). Fs of Lidder watershed is very high (5.34) compared to Rembiara watershed (0.46). High Fs of Lidder specifies that it has rocky terrain and thus has very low infiltration capacity compared to Rembiara, and hence, it is associated with early discharge peak that could result in flashfloods as compared to Rembiara watershed that shall take some time to peak because of the lower runoff rates. 4.1.5 Length of overland flow (Lg) Length of overland flow affects the hydrological and geomorphic development of drainage basins (Horton, 1932). Among the two watersheds, length of overland flow is highest in Rembiara watershed (0.50) compared to Lidder watershed (0.17). Higher Lg of Rembiara watershed indicates that it has gentler slopes and longer flow paths compared to Lidder watershed. In other words, it means that in Lidder watershed, surface runoff will take less time to reach the outlet, thereby making it more vulnerable to the flooding compared to the Rembiara watershed. 4.1.6 Elongation ratio (Re) Elongation ratio of Lidder and Rembiara watersheds is 0.56 and 0.45, respectively. The results indicate that Rembiara watershed is comparatively more elongated than Lidder watershed (Strahler 1964). Hence, Lidder will comparatively attain hydrograph peak quicker than Rembiara watershed and thus increase its vulnerability to flooding in the event of stormy rainfall. 4.1.7 Shape index (Sw) Shape of a watershed essentially affects the water and sediment yield from the drainage basin. Shape index of Lidder (3.94) is lower than that of Rembiara (7.14), which specifies
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that Lidder will exhibit shorter basin lag time as compared to Rembiara watershed, thereby attaining peak flow in comparatively shorter duration of time, making Lidder downstream more vulnerable to flooding than Rembiara. 4.1.8 Compactness coefficient (Cc) Compactness coefficient of Lidder and Rembiara is 1.91 and 2.05, respectively. Compactness coefficient expresses the relationship of a basin with that of a circular basin having the same area. Circular basin yields the shortest time of concentration before peak flow occurs in the basin. If Cc = 1, then the basin completely behaves as a circular basin. Cc [ 1 indicates that the basin has more deviation from the circular nature. Higher values of Cc for Rembiara watershed indicate more deviation from the circular nature than Lidder. Therefore, Rembiara will have the longest time of concentration before attaining peak flow compared to Lidder watershed, making the latter more susceptible to flooding. 4.1.9 Drainage texture (Rt) Drainage texture of Lidder is very high (28.05) as compared to Rembiara (1.74). It is because of the very high number of streams in case of Lidder watershed (7,759) than in Rembiara watershed (307). Infiltration capacity of a watershed determines its Rt. Authors have classified Rt into five classes such as: very coarse (\2), coarse (2–4), moderate (4–6), fine (6–8), and very fine ([8) (Altaf et al. 2013). According to this classification, Lidder watershed falls in very fine Rt class, whereas Rembiara has very coarse drainage texture. Since very coarse texture watersheds have large basin lag time followed by coarse, fine, and very fine texture classes, it clearly indicates that Lidder watershed has shorter basin response time as compared to the Rembiara watershed, making its downstream vulnerable to flooding. 4.1.10 Constant channel maintenance (C) The reciprocal of the drainage density (D) is the constant of channel maintenance (C), and it signifies how much drainage area is required to maintain a unit length of stream. Constant channel maintenance (C) for Lidder and Rembiara is 0.34 and 1.00, respectively. Lower C in case of Lidder watershed indicates that it has weak or very low-resistant soils, sparse vegetation, and mountainous terrain. Higher C for Rembiara watershed shows that it is associated with resistant soils, good vegetation, and comparably plain terrain. It again reaffirms that for Lidder basin, lag time is less compared to Rembiara and thus making its downstream comparatively more vulnerable to flooding. 4.2 Land cover (LC) The type and distribution of land cover has profound impact on the surface hydrology and other land surface processes (Romshoo et al. 2012). The differences in the extent and type of LC shall considerably affect flooding patterns and magnitude in the two watersheds (Matheussen et al. 2000; Fohrer et al. 2001; Quilbe et al. 2006). Land cover was verified by extensive ground truth of 400 samples for post-field correction. The overall accuracy and kappa coefficient of the LC was 95.25 % and 0.89, respectively (Table 3). In order to relate LC with surface hydrology, percent area of the LC class was used as an indicator for determining and assigning scores. Between the two watersheds, the watershed having the
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maximum percentage of a class which has a direct relationship with runoff (e.g., impervious surface) was assigned score 2, and the minimum percentage of the same class in the other watershed was assigned score 1. Similarly, if the presence of a class delays runoff (e.g., agriculture), lower percentage of such a class was assigned score 2 and the watershed which possesses the higher percentage of the same class was assigned score 1 (Table 4). The percent area description of LC and the scoring criteria for individual classes is discussed in some more details below. Seven hydrologically important classes were identified and classified, viz. impervious surface, wasteland, agriculture, forest, pasture, shrub, and snow. In impervious surface, LC class soil is either altogether absent (e.g., exposed rock) or the soil is anthropogenically shielded as in the case of settlements. The contributing runoff potential from this category is highest. Lidder watershed with comparatively higher percentage of impervious surface (19.97 %) was assigned score 2 and the Rembiara watershed was assigned score 1 for relatively low impervious LC. Wasteland is any unutilized/degraded piece of land. Such lands contribute significantly toward surface runoff due to the soil compaction. Therefore, score 2 was assigned to Lidder watershed due to higher percentage of wasteland (4.22 %), and score 1 was assigned to Rembiara watershed with only 1.68 % of area under this LC category. Agricultural lands are topographically flat and have very high infiltration capacity or conversely very low runoff potential. Also, infiltration is further enhanced due to crop roots that act as pathways to the movement of water during infiltration. These two factors are collectively responsible for very low runoff potential from areas under agriculture LC. Lidder watershed with lower percentage of agricultural LC of about 16.35 % was assigned score 2, and Rembiara watershed with 35.83 % of agricultural LC was assigned score 1. Land under forests also acts as retarder for surface runoff. Lidder watershed comparatively has a higher percentage of land under forests (34.04 %); therefore, it was assigned score 1, and Rembiara watershed with 20.85 % of land under forests was assigned score 2. Pasture lands act as mat on the land surface and enhance infiltration and decrease surface runoff. Both the watersheds have almost equal but low percentage of land under this category (2.13 % in case of Lidder and 1.08 % in case of Rembiara), and therefore, both the watersheds were assigned same score, i.e., 2. Shrub lands cover the land surface and therefore delay runoff from the watershed. Since both the watersheds had almost equal and higher percentage of land under this category (13.98 % in case of Lidder and 15.19 % in case of Rembiara), hence both the watersheds were assigned same score, i.e., 1. The snow in both watersheds was classified from the October satellite image and thus is perennial snow cover. Since snow is the source of perennial surface runoff, Lidder watershed with lower percentage of about 8.40 % was assigned score 1 and Rembiara watershed with higher percentage of about 15.89 % under this category was assigned score 2 (Table 4). 4.3 Slope analysis Watershed hydrology is strongly influenced by the hill slope processes (Tucker and Bras 1998). Both the watersheds have been categorized into 10 slope classes. The slope classes were specifically chosen for assessing the impact of slope on runoff rates. Both the watersheds have considerable proportions of area under all the designated slope classes. In Lidder watershed, maximum area of 309.12 km2 falls in slope range of 24"–35" while as the minimum area of 0.51 km2 falls in the slope range of 0"–0.3", which indicates that only 0.04 % of total area of the basin is falling in lowest slope category and 24.6 % of total area of the basin falls under the highest slope categories. In contrast, for Rembiara watershed,
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Nat Hazards Fig. 5 a Villages vulnerable to flooding in Lidder watershed. The model-based floodplain (MBF) in both c the watersheds is shown as magenta color on the Landsat ETM ? image of September 10, 2014. The blue tones in the image are the flood-inundated areas as of September 10, 2014. On this date, majority of the flood waters from these villages had moved downstream towards the central Kashmir region (Srinagar). b Villages vulnerable to flooding in Rembiara watershed. The September 10, 2014 Landsat ETM ? image shows considerable number of villages, still under flooding and within the model-based floodplain (MDF) of this watershed
major area of 137.48 km2 falls in slope range of 8.5"–16.5" while an area of 0.65 km2 falls in slope range of 0"–0.3" indicating that only 0.01 % of total area of the basin is falling in level slope category of the catchment and 20.69 % of total area of the basin falls under the very steep slope category (Table 4). The steep slope zones are indication of the quick runoff during rains or storm events (Tucker and Bras 1998). There is a significant relationship between the slope and the contributing runoff area (Willgoose 1996). As in case of land cover, for assessing the impact of slope on flooding, the scoring was adopted on the basis of percent area in the specific slope class. It was also assumed that runoff in a particular slope class remains same in both the watersheds. Moreover, the watershed which has the maximum percent area in slope categories below 8.5" has more plainer areas (as is case in Rembiara watershed) or conversely it will possess lower runoff potential and thus was assigned score 1. The watershed which has the maximum percent area in slope categories above 8.5" was assigned score 2. The complete scoring scheme for all the parameters is shown in Table 4. 4.4 Assessing downstream flood vulnerability of the Lidder and Rembiara watersheds Flood vulnerability assessment of the two watersheds was performed by the summation of the runoff scores of all the influencing parameters related to morphometry, land cover, and slope. Analysis of the runoff scores revealed that for Lidder watershed, TR is 48. For Rembiara, it is 30, i.e., 23.07 % lesser than Lidder (Table 4). From runoff score analysis, it is clear that Lidder will have the quickest hydrological response to a rainfall or storm event as compared to Rembiara. On the basis of this analysis, it is implicit that for a same intensity storm event, Lidder will comparatively show quick surface runoff than Rembiara or in other words basin lag time is lesser in Lidder than Rembiara. Lesser basin lag time is an indicator of the higher flood vulnerability of the downstream areas of the watershed. It is therefore concluded that the downstream areas in Lidder are more vulnerable to flooding than those of Rembiara. The study reveals that about 140,822 persons falling in 37 villages in the downstream areas of Lidder are under high flood risk compared to only 79,399 persons inhabiting 51 villages downstream of Rembiara watershed (Census of India, Bureau: J&K 2011) (Fig. 5a, b). This suggests that, besides increased physical vulnerability, Lidder watershed is also socially more vulnerable to flooding than Rembiara (Zhang and You 2014). Further, the model-based floodplains (MBF) of the two watersheds, as shown in Fig. 5a, b, were completely inundated, during the September 2014 devastating floods observed in Kashmir valley, thus validating the floodplain extents vis-a-vis the villages identified and delineated in this research. As shown in Fig. 6, the blue tones in the middle of the September 10, 2014 Landsat ETM? image of the Jhelum basin are the floodinundated areas. However, several parts of the MBF in North Kashmir region could not be validated, as the 2014 floodwaters did not reach thus far because of the numerous breaches of the Jhelum River upstream, inundating vast areas of the South and Central Kashmir to greater depths.
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Fig. 6 Satellite image of the Landsat ETM ? dated September 10, 2014 showing the flood-inundated areas of the Kashmir valley. The blue tones are the flood waters. Model-based floodplain (MBF) is shown as black outline surrounding the flooded areas. The Lidder and Rembiara watersheds are shown as red outline. Many prominent flooded landmarks are indicated as yellow dot callouts. River Jhelum’s normal course is shown as green line in the middle of the MBF of the Jhelum basin
In light of the research findings from this research, it is suggested that the reckless and unplanned urbanization of the floodplains and conversion of wetlands in the Jhelum basin need to be stopped forthwith. This practice is the single most important reason responsible for enhanced extreme flooding event of the September 2014 in Jhelum. There is an urgent need for developing a robust strategy for the restoration of wetlands in the basin, dredging of the Jhelum River and construction of an alternate flood channel to divert the extreme flood waters to Wular Lake. Further since, the runoff score approach for assessing the hydrological response is a qualitative approach and thus needs to be validated by simulating runoff regimes at watershed scale using physically based hydrological models and then correlating the model simulations with the TR score generated for that watershed. However, the lack of the appropriate hydrometeorological observation stations all over the Kashmir Himalaya and particularly in the studied watersheds hinders the quantification of various hydrological processes and their validation. The availability of the discharge data is vital for validating
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and calibrating the hydrological simulation models. It is therefore of utmost importance that a network of hydrometeorological and river discharge stations is established all over the basin to promote better prediction of the flooding mechanism. Till the observation network is established in this mountainous region, the integrated analysis of the morphometric, land cover, and topographic analysis for characterizing the hydrological behavior of the Kashmir Himalayan watersheds, as demonstrated in this study, might be the sensible alternative.
5 Conclusions This study assessed the impact of morphometry, land cover, and slope on flood vulnerability in the downstream areas of Lidder and Rembiara watersheds of the Jhelum basin. Morphometry indicated that Lidder watershed attains peak discharges quicker than Rembiara watershed. Since there is a close relationship between the morphometric parameters and the mean annual flood (Carlston 1963), it is safely concluded that Lidder watershed is more prone to flooding compared to the Rembiara watershed. Moreover, from land-cover analysis, it is concluded that the Lidder watershed, with comparatively lower percentage of infiltrationfavorable land-cover types, will generate more surface runoff compared to the Rembiara watershed. Further, Lidder watershed has more precipitous and topographically rugged terrain compared to the Rembiara watershed that has extensive areas with flat or near flat terrain. This topographic situation has tremendous influence on the flow regimes of the two watersheds making Lidder more vulnerable to flooding. Consequently, Lidder shows lesser basin lag time compared to Rembiara. From the analysis of these results, it is concluded that during heavy rain spells, downstream of Lidder watershed should be more vulnerable to flooding than the downstream of the Rembiara watershed. Moreover, the differential topographic attribution in the two watersheds is going to have differing influences on the transport of water and sediments. As a result, the impacts on the flooding shall also vary and shall depend among other things, on the amount and pattern of the precipitation. This research demonstrates that the differential geomorphologic, morphometric, topographic, and land-cover characteristics of these two watersheds have a strong influence on the hydrological functionality and response. In the absence of adequate instrumentation and field data, these are unswerving and reliable indicators to infer hydrological information including flooding and flood vulnerability at the watershed scale (Patton 1988). Acknowledgments This research work has been accomplished under a research grant provided by the Department of Science and Technology, Government of India (DST-GOI) for the project titled ‘‘Integrated Flood vulnerability Assessment for Flood Risk Management and Disaster Mitigation.’’ The authors express their gratitude to the funding agency for the financial assistance. The authors further express gratefulness to the anonymous reviewers for their valuable comments and suggestions on the earlier version of the manuscript that greatly improved its content and structure.
References Adinarayana J (2003) Spatial decision support system for identifying priority sites for watershed management schemes. In: First interagency conference on research in the watersheds (ICRW), Benson, pp 405–408 Agarwal A, Narain S (eds) (1996) Floods, floodplains and environmental myths. State of India’s environment: a citizen report. Centre for Science and Environment, New Delhi
123
Nat Hazards Altaf F, Meraj G, Romshoo SA (2013) Morphometric analysis to infer hydrological behaviour of Lidder watershed, western Himalaya, India. Geogr J 2013, Article ID 178021, 2013 (178021), 14 pages Altaf S, Meraj G, Romshoo SA (2014) Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed. Environ Monit Assess. doi:10.1007/s10661014-4012-2 Arnell N (2002) Hydrology and global environmental change. Prentice-Hall, Harlow Badar B, Romshoo SA, Khan MA (2013a) Modeling the catchment hydrological response in a Himalayan lake as a function of changing land system. Earth Syst Sci 112(2):434–450 Badar B, Romshoo SA, Khan MA (2013b) Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach. Environ Monit Assess 185:6419–6445 Barredo JI, Engelen G (2010) Land use scenario modeling for flood risk mitigation. Sustainability 2(5):1327–1344 Barry RG, Chorley RJ (1998) Atmosphere, weather and climate, 7th edn. Routledge, London/New York, p 409 Bates PD, De Roo APJ (2000) A simple raster-based model for flood inundation simulation. J Hydrol 236:54–77 Berz G, Kron W, Loster T, Rauch E, Schimetschek J, Schmieder J et al (2001) World map of natural hazards—a global view of the distribution and intensity of significant exposures. Nat Hazards 23(2–3):443–465 Bhat SA, Romshoo SA (2009) Digital elevation model based watershed characteristics of upper watersheds of Jhelum basin. J Appl Hydrol XXI(2):23–34 Bhat SA, Meraj G, Yaseen S, Bhat AR, Pandit AK (2013) Assessing the impact of anthropogenic activities on spatiotemporal variation of water quality in Anchar lake, Kashmir Himalayas. Int J Environ Sci 3(5):1625–1640 Bhat SA, Meraj G, Yaseen S, Pandit AK (2014) Statistical assessment of water quality parameters for pollution source identification in Sukhnag stream: an inflow stream of lake Wular (Ramsar Site), Kashmir Himalaya. J Ecosyst 2014, Article ID 898054, 18 pages Bosch JM, Hewlett JD (1982) A review of catchment experiments to determine the effects of vegetation changes on water yield and evapotranspiration. J Hydrol 55:3–23 Brasington J, Richards K (1998) Interactions between model predictions, parameters and DTM scales for TOPMODEL. Comput Geosci 24:299–314 Carlston CW (1963) Drainage density and stream flow; US Geological Survey Professional Report No. 422C Chakraborti AK (1991) Sediment yield prediction and prioritization of watershed using remote sensing data. In: Proceedings of the 12th Asian conference on remote sensing, Singapore Chaponnie`re A, Boulet G, Chehbouni A, Aresmouk M (2008) Understanding hydrological processes with scarce data in a mountain environment. Hydrol Process 22(12):1908–1921 Chen L, Qian X, Shi Y (2011) Critical area identification of potential soil loss in a typical watershed of the three Gorges reservoir region. Water Resour Manage 25(13):3445–3463 Chow VT (1964) Hand book of applied hydrology. McGraw-Hill, New York Clague JJ, Evans SG (1994) Formation and failure of natural dams in the Canadian cordillera. Geol Surv Can Bull 464:35 Costa JE (1988) Floods from dam failures. Flood geomorphology. Wiley, New York, pp 439–463 Costa JE, Schuster RL (1988) The formation and failure of natural dams. Geol Soc Am Bull 7:1054–1068 Dar RA, Chandra R, Romshoo SA (2013) Morphotectonic and Lithostratigraphic analysis of Intermontane Karewa basin of Kashmir Himalayas, India. J Mt Sci 10(1):1–15 Deepika B, Kumar A, Katihalli SJ (2014) Impact of estuarine processes and hydro-meteorological forcing on landform changes: a remote sensing, GIS and statistical approach. Arab J Geosci. doi:10.1007/ s12517-014-1264-7 Diakakis M (2011) A method for flood hazard mapping based on basin morphometry: application in two catchments in Greece. Nat Hazards 56(3):803–814 Dutto F, Mortara G (1992) Rischi conessi con la dinamica glaciale nelle Alpi Italiane. Geografia Fisica Dinamica Quaternaria 15:85–99 Ebi KL, Woodruff R, von Hildebrand A, Corvalan C (2007) Climate change-related health impacts in the Hindu Kush-Himalayas. EcoHealth 4(3):264–270 Engelhardt BM, Weisberg PJ, Chambers JC (2012) Influences of watershed geomorphology on extent and composition of riparian vegetation. J Veg Sci 23:127–139 Fohrer N, Haverkamp S, Eckhardt K, Frede HG (2001) Hydrologic response to land use changes on the catchment scale. Phys Chem Earth (B) 26:577–582
123
Nat Hazards Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201 Fu KS (1976) Pattern recognition in remote sensing of the Earth resources. IEEE Trans Geosci Electron 14(1):10–18 Haeberli W, Alean JC, Mu¨ller P, Funk M (1989) Assessing the risks from glacier hazards in high mountain regions: some experiences in the Swiss Alps. Ann Glaciol 13:77–101 Horton RE (1932) Drainage basin characteristics. Trans Am Geophys Union 13:350–361 Horton RE (1945) Erosional development of streams and their drainage basins: hydrological approach to quantitative geomorphology. Bull Geol Soc Am 56:275–370 Hudson PF, Colditz RR (2003) Flood delineation in a large and complex alluvial valley, lower Panuco basin, Mexico. J Hydrol 280:229–245 Husain M (ed) (1998) Geography of Jammu and Kashmir, 2nd edn. Rajesh Publication, New Delhi Inter-governmental Panel on Climate Change (IPCC) (2007) Climate Change, impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, et al (eds) Contribution Group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge Ives JD (2004) Himalayan perceptions: environmental change and the well-being of mountain peoples. Routledge (Taylor and Francis Group), London Javed A, Khanday MY, Ahmed R (2009) Prioritization of subwatersheds based on morphometric and landuse analysis using remote sensing and GIS techniques. J Indian Soc Remote Sens 37:261–274 Jensen J (1996) Introduction to digital image processing: a remote sensing perspective, 1st edn. Prentice Hall, New York Korup O, Clague JJ (2009) Natural hazards, extreme events, and mountain topography. Quatern Sci Rev 28(11):977–990 Londhe S, Nathawat MS, Subudhi AP (2010) Erosion susceptibility zoning and prioritization of mini watersheds using Geomatics approach. Int J Geomat Geosci 1(3):511–528 Matheussen B, Kirschbaum RL, Goodman IA et al (2000) Effects of land cover change on stream flow in the interior Columbia river basin (USA and Canada). Hydrol Process 14(5):867–885 McBean G (2004) Climate change and extreme weather: a basis for action. Nat Hazards 31(1):177–190 Melton MA (1957a) Geometric properties of mature drainage systems and their representation in an E4 phase space. J Geol 66:35–54 Melton MA (1957b) An analysis of the relations among elements of climate, surface, properties and geomorphology, technical report 11. Columbia University, Department of Geology, ONR, Geography Branch, New York Meraj G, Romshoo SA, Yousuf AR (2012) Geoinformatics approach to qualitative forest density loss estimation and protection cum conservation strategy- a case study of Pir Panjal range, J&K, India. Int J Curr Res Rev 04(16):47–61 Meraj G, Yousuf AR, Romshoo SA (2013) Impacts of the Geo-environmental setting on the flood vulnerability at watershed scale in the Jhelum basin; M Phil dissertation, University of Kashmir, India http://dspaces.uok.edu.in/jspui//handle/1/1362 Mirza MMQ (2005) Hydrologic modeling approaches for climate impact assessment in South Asia. Climate Change and Water Resources in South Asia, Balkema Press, Leiden, pp 23–54 Montgomery DR, Dietrich WE (1989) Channel initiation and the problem of landscape scale. Science 255(5046):826–830 Montgomery DR, Dietrich WE (1992) Source areas, drainage density, and channel initiation. Water Resour Res 25(8):1907–1918 Mortan JC (2007) Image analysis, classification and change detection in remote sensing, with algorithms for ENVI/IDL. CRC press, Taylor & Francis Group, London Mosbahi M, Benabdallah S, Boussema MR (2012) Assessment of soil erosion risk using SWAT model. Arab J Geosci. doi:10.1007/s12517-012-0658-7 Murtaza KO, Romshoo SA (2014) Determining the suitability and accuracy of various statistical algorithms for satellite data classification. Int J Geomat Geosci 4(4):585–599 National Research Council Canada, Canada. Agriculture & Agri-Food Canada, Research Branch (NRCC) (1998) The Canadian system of soil classification. Canadian Agricultural Services Coordinating Committee. In: Soil Classification Working Group (ed), NRC Research Press, p 149 O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput Vis Graph Image Process 28:323–344 Patton PC (1988) Drainage basin morphometry and floods. In: Baker VR, Kochel RC, Patton PC (eds) Flood geomorphology. Wiley, New York, pp 51–64
123
Nat Hazards Peduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Nat Hazards Earth Syst Sci 9(4):1149–1159 Quilbe R, Rousseau AN, Duchemin M, Poulin A, Gangbazo G, Villeneuve JP (2006) Selecting a calculation method to estimate sediment and nutrient loads in streams: application to the Beaurivage River (Quebec, Canada). J Hydrol 326:295–310 Rakesh K, Lohani AK, Sanjay K, Chattered C, Nema RK (2000) GIS based morphometric analysis of Ajay river basin up to Srarath gauging site of South Bihar. J Appl Hydrol 14(4):45–54 Rashid I, Romshoo SA (2012) Impact of anthropogenic activities on water quality of Lidder River in Kashmir Himalayas. Environ Monit Assess 185:4705–4719 Rashid M, Lone M, Romshoo SA (2011) Geospatial tools for assessing land degradation in Budga district, Kashmir Himalaya. Indian J Earth Syst Sci 120(3):423–433 Ratnam NK, Srivastava YK, Rao VV, Amminedu E, Murthy KSR (2005) Checkdam positioning by prioritization micro-watersheds using SYI model and morphometric analysis—remote sensing and GIS perspective. J India Soc Remote Sens 33(1):25–38 Raza M, Ahmad A, Mohammad A (1978) The valley of Kashmir: a geographical interpretation. Vikas Publishing House Pvt. Ltd, New Delhi Romshoo SA, Rashid I (2014) Assessing the impacts of changing land cover and climate on Hokersar wetland in Indian Himalayas. Arab J Geosci 7(1):143–160 Romshoo SA, Bhat SA, Rashid I (2012) Geoinformatics for assessing the morphometric control on hydrological response at watershed scale in the Upper Indus basin. J Earth Syst Sci 121(3):659–686 Saghafian B, Golian S, Elmi M, Akhtari R (2013) Monte Carlo analysis of the effect of spatial distribution of storms onprioritization of flood source areas. Nat Hazards 66:1059–1071 Schumm SA (1956) The evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull Geol Soc Am 67:597–646 Sen D (2010) Flood Hazards in India and management strategies. In natural and anthropogenic. Springer, The Netherlands Sepu´lveda SA, Padilla C (2008) Rain-induced debris and mudflow triggering factors assessment in the Santiago cordilleran foothills, Central Chile. Nat Hazards 47(2):201–215 Sharma R, Sahai B, Karale RL (1985) Identification of erosion-prone areas in a part of the Ukai catchment. In: Proceedings, sixth Asian conference on remote sensing, National Remote Sensing Agency, Hyderabad, pp 121–126 Simonovic SP, Li L (2003) Methodology for assessment of climate change impacts on large-scale flood protection system. J Water Resour Plan Manag 129(5):361–371 Singh OP (1980) Geomorphology of drainage basins in Palamau upland. In: Ram Bhadur Mandal, Vishwa Nath Prasad (eds) Recent trends and concepts in geography. Concept Publishing, New Delhi, pp 229–247 Singh O, Kumar M (2013) Flood events, fatalities and damages in India from 1978 to 2006. Nat Hazards 69(3):1815–1834 Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Bull Geol Soc Am 63:1117–1142 Strahler AN (1957) Quantitative analysis of watershed geomorphology. Trans Am Geophys Union 38:913–920 Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks, Sec. 4–11. In: Chow VT (ed) Handbook of applied hydrology. McGraw-Hill, New-York Suresh M, Sudhakara S, Tiwari KN, Chowdary VM (2004) Prioritization of watersheds using morphometric parameters and assessment of surface water potential using remote sensing. J Indian Soc Remote Sens 32(3):249–259 Tarboton DG (1989) The analysis of river basins and channel networks using digital terrain data, Sc. D. Thesis, M.I.T., Cambridge Todorovski L, Dzˇeroski S (2006) Integrating knowledge-driven and data-driven approaches to modeling. Ecol Model 194(1):3–13 Tso B, Mather PM (2001) Classification methods for remotely sensed data. Taylor and Francis, London, pp 186–229 Tucker GE, Bras RL (1998) Hill slope processes, drainage density and landscape morphology. Water Resour Res 34(10):2751–2764 Van De Wiel MJ, Coulthard TJ, Macklin MG, Lewin J (2011) Modelling the response of river systems to environmental change: progress, problems and prospects for palaeo-environmental reconstructions. Earth Sci Rev 104(1):167–185 Wadia DN (1979) Geology of India, 4th edn. Tata McGraw-Hill Publishing Co, New Delhi Ward RC, Robinson M (2000) Principles of hydrology, 4th edn. McGraw-Hill, Maidenhead
123
Nat Hazards Watson RT, Haeberli W (2004) Environmental threats, mitigation strategies and high mountain areas. Mountain areas: a global resource. Ambio 13:2–10 Willgoose G (1996) A statistic for testing the elevation characteristics of landscape simulation models. J Geophys Res 99(13):987–996 Yildiz O (2009) An investigation of the effect of drainage density on hydrologic response. Turk J Eng Environ Sci 28(2):85–94 Youssef AM, Pradhan B (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62(3):611–623 Zaz S, Romshoo SA (2012) Assessing the geoindicators of land degradation in the Kashmir Himalayan region, India. Nat Hazards 64:1219–1245 Zhang YL, You WJ (2014) Social vulnerability to floods: a case study of Huaihe River Basin. Nat Hazards 71(3):2113–2125
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