Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed

Morphometry and land cover based multi-criteria analysisfor assessing the soil erosion susceptibility of the westernHimalayan watershed

Sadaff Altaf & Gowhar Meraj &Shakil Ahmad Romshoo

Received: 4 February 2014 /Accepted: 15 August 2014# Springer International Publishing Switzerland 2014

Abstract Complex mountainous environments such asHimalayas are highly susceptibility to natural hazardsparticular those that are triggered by the action of watersuch as floods, soil erosion, mass movements and silta-tion of the hydro-electric power dams. Among all thenatural hazards, soil erosion is the most implicit and thedevastating hazard affecting the life and property of themillions of people living in these regions. Hence toreview and devise strategies to reduce the adverse im-pacts of soil erosion is of utmost importance to theplanners of watershed management programs in theseregions. This paper demonstrates the use of satellitebased remote sensing data coupled with the observa-tional field data in a multi-criteria analytical (MCA)framework to estimate the soil erosion susceptibility ofthe sub-watersheds of the Rembiara basin falling in thewestern Himalaya, using geographical information sys-tem (GIS). In this paper, watershed morphometry andland cover are used as an inputs to the MCA frameworkto prioritize the sub-watersheds of this basin on the basisof their different susceptibilities to soil erosion.Methodology included the derivation of a set of drain-age and land cover parameters that act as the indicatorsof erosion susceptibility. Further the output from theMCA resulted in the categorization of the sub-watersheds into low, medium, high and very high ero-sion susceptibility classes. A detailed prioritization map

for the susceptible sub-watersheds based on the com-bined role of land cover and morphometry is finallypresented. Besides, maps identifying the susceptiblesub-watersheds based on morphometry and land coveronly are also presented. The results of this study are partof the watershed management program in the study areaand are directed to instigate appropriate measures toalleviate the soil erosion in the study area.

Keywords Morphometry . Land cover . Soil erosionsusceptibility . ASTER . IRS LISS-III . Remote sensing .

GIS . Kashmir Himalaya

Introduction

Soil erosion is a major environmental and agriculturalproblem worldwide. Although soil erosion is a naturalprocess witnessed throughout the history, however inthe recent years, it has greatly intensified (Lal andStewart 1990; Morgan 2009). Each year, about 75 bil-lionmetric tons of soil is removed from the land bywindand water erosion, with most coming from agriculturalland (Myers 1993). This degradation of the arable landimposes immense threat to the economy of a region(Pimentel et al. 1995). The threat is even greater in areasof complex mountainous terrain such as Himalayaswhere it is the main driver behind several of the naturalhazards such as landslides and debris flows, often re-sponsible for huge loss of life and property (Huggel2004). Moreover, human activities such as deforesta-tion, overgrazing and land use changes greatly influence

Environ Monit AssessDOI 10.1007/s10661-014-4012-2

S. Altaf :G. Meraj : S. A. RomshooLand Surface Processes Group (LSPG) & Glaciology andClimate Change Group (GCCG), Remote Sensing Lab.Department of Earth Sciences, University of Kashmir,Hazratbal, Srinagar, Kashmir 190006, India

the rate of soil erosion when compared to the naturalagents (Pradhan 2010b). For understanding and manag-ing soil erosion, characteristics of the land which influ-ence erosion need to be studied. Topography, land cover(LC), amount and intensity of rainfall, and physico-chemical properties of soil are the main variables whichplay a significant role in the erosion of the landscape(Christopherson 1997; Romshoo et al. 2012; Kavianet al. 2013). Studying these variables over a large spatialextent is a cumbersome task and if timely and effectivestrategies at a decision-making scale are the objectives,watershed level management is the most opted solution.Watershed management involves proper utilization ofland, water, forest and soil resources of a watershed foroptimum production with minimum hazard to naturalresources (Biswas et al. 1999; Rashid et al. 2011; Merajet al. 2012). Moreover, satellite based remote sensingcoupled with GIS provides a platform for quick andefficient watershed management.

Many basins and sub-basins around the globe havebeen studied for drainage characteristics using conven-tional geomorphological techniques (Horton 1945;Strahler 1964; Leopold and Miller 1956; Morisawa1959; Krishnamurthy et al. 1996). Studies have focusedon drainage basins and their geometric characteristics(Abrahams 1984). In many studies, morphometric char-acteristics have been used to assess geomorphic process-es such as floods, sediment loads and estimation oferosion rates (Patton and Baker, 1976; Gardiner 1990;Bhat et al. 2013, 2014). In morphometric studies, themeasurement of various drainage network indices areused for assessing the impact of stream characteristicson the land surface processes occurring over a land-scape. Morphometric parameters are calculated fromthe analysis of various drainage characteristics such asstream orders, basin area, perimeter and length ofstreams (Kumar et al. 2000). Classification of drainagenetworks within the basins can be achieved using tradi-tional approaches such as using topographic maps andfrom field observations or alternatively with advancedmethods using remote sensing and GIS (Macka 2001;Maidment 2002). Moreover there have been studiescarried out which point out that it is quite common thatfield based drainage evaluation may omit many streamswhich have a capability to collect and transport water(Horton 1945; Melton 1957a, b; Lubowe 1964;Krumbein and Shreve 1970; Mark 1983).

In this context, digital elevation models (DEM) arethe most efficient means of deriving drainage networks

and basin boundaries (Ozdemir and Bird 2009).Software which derives drainage networks from theDEMs utilize flow of water from higher elevation tolower elevation assuming that the amount of water re-mains conserved within the channel and is not lost to anyother process such as evapo-transpiration etc. In recentyears, remote sensing and GIS has been extensively usedin the evaluation of land surface processes for mitigatinggeo-environmental risks, even some authors havecompletely defined the role of remote sensing in termsof its applicability in disaster mitigation (Verstappen1995; Skilodimou et al. 2003; Chen et al. 2009).

In the present study, morphometric parameters havebeen coupled with land cover classes to assess soilerosion susceptibility of different sub-watersheds ofthe Rembiara watershed in western Himalayan regionin Kashmir (Suresh et al. 2004; Arun et al. 2005;Ratnam et al. 2005; Bhat and Romshoo 2009; Javedet al. 2009). The drainage and land cover parameterswere generated keeping in view their relevance to thesusceptibility to soil erosion of a watershed.

Study area

Rembiara watershed is among the 24 watersheds of theJhelum basin. River Jhelum is one of the main tribu-taries of the upper Indus basin (UIB). Rembiara water-shed lies in the Pir Panjal range of the westernHimalaya. This watershed is socio-economically verysignificant catchment of the whole Jhelum basin. Thesoil of this watershed is very fertile for cultivation ofapple, one of the most important cash crops of theKashmir valley. About 664.61 km2 of land is under thiswatershed. It has an undulating topography and thusresponds well to different variables of erosion. TheRembiara stream which drains the watershed rises inthe Rupri ridge of the Pir Panjal range. Its headstreamsoriginate from Dhaklar Peer (4,660 m) and Bag Sarlake below Rupri pass (4,085 m) on one hand and thePir Panjal pass (3,494 m) and the Naba Pir pass(4,253 m) on the other (Fig. 1). The drainage patternof this watershed is, however, not uniform. The upperportion of the watershed shows dendritic drainage pat-tern while the lower portion shows more or less aparallel drainage pattern. The mountainous region be-longs to the Panjal trap formation and rest of the areabelongs to Karewas group of formations. Panjal trap lieson the top of the agglomerate slates and almost forms

Environ Monit Assess

the central axis of the Pir Panjal range where they attaina maximum width of about 100 m. (Raza et al. 1978).The watershed receives precipitation both in the form ofrain and snow. The analysis of the average monthlyrainfall of 10 years shows that the area receives highestrainfall in the month of March (148.83 mm) and lowestin the month of November (10.77 mm). The weather inthe area remains pleasant from April to October, but inrest of the year, particularly in winters, the study areaexperiences extreme cold and heavy snowfall.

Materials and methods

In the present study, different datasets have beenused which include (1) Advanced Space borneThermal Emission and Reflection Radiometer(ASTER) data, with 30 m spatial resolution fordrainage generation; (2) Indian Remote Sensing(IRS) LISS-III data with 23.5 m spatial resolution

(band 1, 2, 3 and 4) to generate land cover infor-mation of the study area using supervised classifi-cation and to verify drainage network derived fromthe ASTER; (3) topographic maps at 1:50,000scale to outline the natural drainage for DEMmanipulation. Additional information of the studyarea was collected through field survey.

In this study, we estimated morphometric parametersusing drainage generated using Advanced Space borneThermal Emission and Reflection Radiometer (ASTER)30 m digital elevation model (DEM) (Engelhardt et al.2012) with Arc Hydro package (ESRI v 9.3). Arc Hydrouses logical, efficient and consistent algorithm com-pared to the manual approach of drainage extraction. Adetailed procedure for extracting drainage networkusing Arc Hydro has been discussed by Youssef andPradhan (2011). It incorporates existing streams andlakes into the DEM for drainage analysis through aprocess termed as DEMmanipulation. For this purpose,in the present study, drainage on the topographic maps

Fig. 1 Study area map of Rembiara watershed, Western Himalayas, Kashmir, India


was digitized and used for DEM manipulation. Forproper determination of flow direction and flow accu-mulation, DEM sinks were also identified and filled.Boundaries of the sub-watersheds were derived by de-fining pour point, the location where water drained fromwhole of the watershed flows into the main river or lakeor an alluvial fan (Fig. 2). Rembiara watershed pos-sesses 10 major tributaries, the drainage systems (sub-watersheds) of which have been analyzed for soil ero-sion susceptibility. The morphometric analysis of all thesub-watersheds of these drainage systems are designat-ed as WS1 to WS10 and were outlined as shown inFig. 3a–j. Based on the cumulative number of the up-stream cells draining to each cell, stream networks in thesub-watershed were defined (O’Callaghan and Mark1984). We used a critical threshold 0.056, which repre-sents the 5.6 % area in the watershed, for definingstreams. The critical threshold is the minimum upstreamdrainage required to initiate a stream. Areas of the sub-watersheds were evaluated by calculating the geometryof the derived sub-watershed polygons. The length ofthe watershed was calculated by summing the length ofthe main stream channel and the distance from the top ofthe main channel to the watershed boundary (Altaf et al.2013). Strahler’s scheme for stream ordering has been

used, which was originally introduced by Horton andlater on modified by Strahler (1952b), Schumms (1956)and Singh (1980). The formulae used for the derivationof the relevant morphometric parameters pertaining tolinear, areal, and shape aspects of watershed are given inTable 1 (Meraj et al. 2013). The morphometric param-eters for all the sub-watersheds have been listed inTables 2 and 3.

For land cover classification of the study area, mul-tispectral Indian Remote Sensing (IRS) satellite, LinearImaging Self Scanner (LISS-III) data with 23.5m spatialresolution acquired on 21 October, 2008 was used.Geometric correction of the data was done at 1:50,000scale using 75 ground control points (GCP) with aRMSE of 0.45. The corrected satellite data was thenprocessed for the LC classification using maximumlikelihood supervised classification algorithm (Jensen1996; Tso and Mather 2001; Fu 1976; Mortan 2007).While choosing various training samples for knownland cover types, various image enhancement tech-niques were applied for homogeneity taking cognizanceof the ground truth information. The classification wasperformed in a way that the classes generated are thosewhich directly influence the soil erosion. Seven such LCclasses were identified, viz., agriculture, wasteland,

Fig. 2 Sub-watershed map of Rembiara watershed


impervious surface, forest, pasture, shrub and snow.Area under perennial water (alpine lakes and stream)was also classified. The generated LC was validated inthe field, using 305 field verification points. Kappa

coefficient for the final map was estimated using theformula shown below (Lo and Yeung Albert, 2002).Estimation of the accuracy of the classified map isessential to assess its authenticity and kappa coefficient

Fig. 3 Drainage maps of sub-watersheds WS1 (a) - WS10 (j) of Rembiara watershed


is one of the robust indicators of accuracy of the gener-ated LC (Foody 2002; Murtaza and Romshoo 2014).

k ¼ NX

i¼1

r

Xiið Þ−NX

i¼1

r

Xiþ :Xþið Þ( )

=N2−X

i¼1

r

Xiþ :Xþið Þ

Where,r is number of rows in error matrix; Xii is

number of observations in row i and column i(on the major diagonal); Xi+ is total of observa-tions in row i (shown as marginal total to right ofthe matrix); X+I I is total of observations in col-umn i (shown as marginal total at bottom of thematrix); and N is total number of observationsincluded in the matrix.

For multi-criteria analysis we have used compoundvalue method for prioritizing the sub-watersheds forerosion susceptibility. This approach is based on the

principles of knowledge-driven modelling (Todorovskiand Džeroski 2006) and converts the qualitative under-standing of a phenomenon based on scientific knowl-edge into a quantitative estimation. However this meth-odology has some inherent disadvantages such as itassigns a lumped value for a parameter of an entity aswell as it can only be used in a comparison study as hasbeen done in this research. Further, it also impartssame weightage to all the parameters involved,which in some cases can exaggerate the final out-put. However in absence of a robust numericallyor physically based approaches, which often relieson the detailed estimation and parameterization ofthe processes involved, this method is one of thebest approaches to compare land surface processesbetween similar entities (such as watersheds). Dueto this reason it has been extensively used byvarious workers for sustainable planning and

Fig. 3 (continued)


management of sub-watersheds in regions of datascarcity (Chakraborti 1991; Adinarayana 2003; Ratnam

et al. 2005; Pandey et al. 2009; Javed et al. 2009; TMDL2010; Londhe et al. 2010; Chen et al. 2011; McCloskey

Table 1 Formulae adopted for computation of morphometric parameters

S. No. Morphometric Parameters Formulae Reference

1 Stream Order Hierarchical rank Strahler, 1964

2 Stream Length (Lu) Length of the stream Horton, 1945

3 Bifurcation Ratio (Rb) Rb=Nu/Nu+1 Schumms, 1956Where, Rb=Bifurcation ratio;

Nu=Total no. of stream segments of order ‘u’;

Nu+1=Number of segments of the next higher order

4 Mean Bifurcation Ratio (Rbm) Rbm=Average of bifurcation ratios of all orders Strahler, 1957

5 Drainage Density (D) D=Lu/A Horton, 1932Where, D=Drainage density;

Lu=Total stream length of all orders;

A=Area of the basin (km2)

6 Stream Frequency (Fs) Fs=Nu/A Horton, 1932Where, Fs=Stream frequency;

Nu=Total no. of streams of all orders;

A=Area of the basin (km2)

7 Drainage Texture (Rt) Rt=Nu/P Horton, 1945Where, Rt=Drainage texture;

Nu=Total no. of streams of all orders;

P=Perimeter (km)

8 Form Factor (Rf) Rf=A/Lb2 Horton, 1932Where, Rf=Form factor;

A=Area of the basin (km2);

Lb2=Square of basin length

9 Circularity Ratio (Rc) Rc=4*π *A/P2 Miller, 1953Where, Rc=Circularity ratio;

π = ‘Pi ’ value i.e. 3.14;


P=Perimeter (km)

10 Elongation Ratio (Re) Re=2/Lb sqrt (A/π ) Schumms, 1956Where, Re=Elongation ratio


π = ’Pi ’ value i.e. 3.14;

Lb=Basin length

11 Length of overland flow (Lg) Lg=1/D*2, Horton, 1945Where, Lg=Length of overland flow;

D=Drainage density

12 Shape index (Sw) Lb2/A Horton, 1945Where, Lb=Basin length;

A=Area of basin

13 Basin relief (H) H=Maximum relief – Minimum relief

14 Compactness coefficient (Cc) Cc=Pc/P u Suresh et al., 2004Where, Pc=Perimeter of watershed;

Pu=Perimeter of circle of watershed area


et al. 2011; Mosbahi et al. 2012; Trabucchi et al. 2013;Jang et al. 2013; Saghafian et al. 2013).

The prioritization in this method is based on the sub-watershed’s degree of erosion susceptibility using mor-phometric and land cover information (Javed et al.2009). In this method the total number of ranks assignedis based on the number of watersheds. Since there are 10sub-watersheds in the study area, ranks were assignedfrom 1 to 10. For both morphometry and land cover,rank 1 was assigned in a way that the value of theparameter represents maximum contribution to the ero-sion and rank 10 represents minimum contribution. Theaverage of the ranks of all the parameters for a particular

watershed is designated as compound value and repre-sents the collective impact of all the parameters onerosion susceptibility of a sub-watershed. It is denotedas Cp and is calculated from following formula.

Cp ¼ 1=nX

i¼1

n

R

Where,

Cp Compound value of a particular watershed.Ri Rank of a particular watershed for a parameter.n Number of parameters.

Table 2 Basin network characteristics of Rembiara sub watersheds

Sub-watershed

Basinarea(km2)

Maximumelevation(km)

Minimumelevation(km)

Perimeter(km)

Longestflow path(km)

Basinlength(km)

Perimeter of circleof watershed (km)

TotalNumber ofStreams

Total StreamLength (Km)

WS1 55.23 2.16 1.58 43.62 26.08 19.26 4.19 28 58.95

WS2 34.61 3.34 2.06 39.41 19.63 15.05 3.32 16 32.27

WS3 107.29 4.61 2.58 53.64 23.84 17.88 5.85 62 95.58

WS4 116.07 4.69 2.57 53.85 24.08 20.44 6.08 68 104.82

WS5 4.28 4.23 2.54 12.42 5.00 4.36 1.17 8 8.04

WS6 1.22 3.58 2.56 5.31 1.75 1.48 0.62 5 3.95

WS7 10.40 4.04 2.47 15.30 6.56 5.78 1.82 10 14.62

WS8 16.99 3.09 2.13 28.99 3.82 3.28 2.33 11 11.16

WS9 128.04 3.05 1.61 73.48 41.54 31.54 6.39 60 162.33

WS10 70.84 2.16 1.59 55.72 22.27 17.43 4.75 34 84.90

Table 3 Quantitative morphometry results of Rembiara sub watersheds

Sub-watershed H Rb Rbm D Fs Rf Rc Lg Rt Re Sw Cc

I/II II/III III/IV

WS1 0.58 5.75 4.00 – 4.88 1.07 0.51 0.15 0.36 0.47 0.64 0.44 6.71 1.66

WS2 1.27 4.00 3.00 – 3.50 0.93 0.46 0.15 0.28 0.54 0.41 0.44 6.54 1.89

WS3 2.03 5.56 4.50 2.00 4.02 0.89 0.58 0.34 0.47 0.56 1.16 0.65 2.98 1.46

WS4 2.12 5.40 3.33 3.00 3.91 0.90 0.59 0.28 0.50 0.55 1.26 0.59 3.60 1.41

WS5 1.69 7.00 – – 7.00 1.88 1.87 0.22 0.35 0.27 0.64 0.54 4.45 1.69

WS6 1.02 4.00 – – 4.00 3.22 4.08 0.56 0.55 0.16 0.94 0.85 1.78 1.36

WS7 1.57 3.50 2.00 – 2.75 1.41 0.96 0.31 0.56 0.36 0.65 0.63 3.22 1.34

WS8 0.96 10.00 – – 10.00 0.66 0.65 1.58 0.25 0.76 0.38 1.42 0.63 1.98

WS9 1.44 4.09 3.67 3.00 3.59 1.27 0.47 0.13 0.30 0.39 0.82 0.40 7.77 1.83

WS10 0.57 4.50 6.00 – 5.25 1.20 0.48 0.23 0.29 0.42 0.61 0.55 4.29 1.87


Results and discussion

Establishing the level of erosion susceptibility usingmorphometric parameters

Watershed morphometry provides a complete descrip-tion of the linkages between different land surface pro-cesses and different components of the land system suchas geomorphology, hydrology, geology and LC (Astrasand Soulankellis 1992; Ifabiyi and Eniolorunda 2012).Moreover the characteristic drainage system of a water-shed has a strong impact on its infiltration capacity andrunoff (Sharma et al. 1985). Some morphometric pa-rameters directly serve as indicators of soil erosionintensity and have been termed as ‘erosion risk assess-ment parameters’. These include the linear morphomet-ric parameters such as drainage density, stream frequen-cy, mean bifurcation ratio, drainage texture, length ofoverland flow and areal morphometric parameter likebasin relief. These have a direct relationship with erod-ibility i.e. greater the values of these parameters; more isthe erosion severity in the region and vice-versa.Whereas some of the shape morphometric parameterssuch as elongation ratio, circulatory ratio, form factor,basin shape and compactness coefficient have an inverserelation with erodibility (Nooka Ratnam et al. 2005).Based on these direct relationships for the linear andshape parameters, the highest value of a morphometricparameter was given rank 1; the immediate higher valuewas ranked 2, and so on. Whereas for the shape param-eters, the lowest value of a morphometric parameter wasgiven rank 1; the value lower than this was ranked 2 andso on. After assigning ranks to every parameter, anaverage of all the ranks was calculated to arrive atcompound value (Cp). The upper portion of the water-shed shows dendritic drainage pattern while as lowerportion shows more or less parallel drainage pattern.The smallest sub-watershed of the Rembiara watershedhas an area 1.22 km2 whereas the largest has an area of128.04 km2. The stream order of the sub-watersheds ofRembiara watershed ranges from 2 to 4 as is shown inFig. 3 a–j, Tables 2 and 3. The ranking of sub-watersheds on the basis of morphometry is discussedbelow (Table 4).

Drainage density (D)

The lower drainage density of any watershed indicatesthat it has permeable subsurface material, good

vegetation cover and low relief and vice versa (Luo1900; Harlin and Wijeyawickrema 1985). In theRembiara watershed, lowest drainage density was ob-served in WS8 (0.66/km) which indicated that it has thegreatest permeability among the other sub-watershedsor conversely it has the greatest tendency to withstanderosion if only D is taken as a criterion for erosionsusceptibility. Since least erosion susceptibility couldbe construed in WS8 it was assigned a rank 10. Thenext higher D was observed in WS3 (0.89/km), and wasfollowed by WS4 (0.90/km), WS2 (0.93/km), WS1(1.07/km), WS10 (1.20/km), WS9 (1.27/km) WS7(1.41/km) and WS5 (1.88/km). These were assignedranks from 9 to 2 respectively. Since the highest drain-age density was observed in WS6 (3.22/km) whichindicated that it has the lowest permeability and thushighest erosion susceptibility in terms of D, it wasassigned the lowest rank 1.

Stream frequency (Fs)

Stream frequency is inversely related to permeability,infiltration capacity and directly related to the relief ofwatersheds (Montgomery and Dietrich 1989, 1992).High Fs thus indicates that the watershed has rockyterrain and very low infiltration capacity which contrib-utes towards more erosion and vice versa. Among thesub-watersheds of Rembiara, highest stream frequencywas observed in WS6 (4.08/km2) which indicated that ithas the least infiltration capacity and thus highest ero-sion susceptibility in terms of Fs only. Hence it wasassigned rank 1. Fs was observed lowest in WS2(0.46/km2) which indicated it possesses least erosionsusceptibility and was thus assigned rank 10. The othersub-watersheds which followed in decreasing order ofFs were WS5 (1.87/km2), WS7 (0.96/km2), WS8 (0.65/km2), WS4 (0.59/km2), WS3 (0.58/km2), WS1(0.51 km2), WS10 (0.48/km2) and WS9 (0.47/km2)were assigned ranks 2 to 9 respectively.

Mean bifurcation ratio (Rbm)

Mean bifurcation ratio is an indicator of structural com-plexity and permeability of the terrain and is thus neg-atively correlated with the permeability of a watershed.High Rbm indicates early hydrograph peak with a po-tential for flash flooding during the storm events whichresults in degradation of top soil (Howard 1990; Rakeshet al. 2000). The relationship of Rbm with erosion


susceptibility of an area is same as is imparted bydrainage density and stream frequency. Therefore, theranking in this case was same as was given in case of Dand Fs. The mean bifurcation ratio of all the sub-watersheds is very high which indicates that all thesub-watersheds are structurally complex and have lowpermeability. WS8 shows highest Rbm of 10.00 andthus possesses lowest permeability among other sub-watersheds and was thus assigned rank 1. Sub-watersheds WS5 (7.00), WS10 (5.25), WS1 (4.88),WS3 (4.02), WS6 (4), WS4 (3.91), WS9 (3.59), WS2(3.50), WS7 (2.75) were assigned ranks 2 to 10respectively.

Drainage texture (Rt)

Drainage texture is greatly influenced by infiltrationcapacity (Horton 1945). Regions of low infiltration ca-pacity will give rise to higher Rt and thus will lead tomore erosion. Therefore, the watershed with highest Rtwhich in this watershed is WS4 (1.26) was given rank 1indicating that it is most susceptible to erosion due to

low infiltration capacity. Accordingly, the sub-watershed with lowest Rt i.e. WS8 (0.38) was assignedrank 10, which indicated that it has least susceptibility toerosion. Sub-watersheds WS3 (1.16), WS6 (0.94), WS9(0.82), WS7 (0.65), WS5 (0.64), WS1 (0.64), WS10(0.61), WS2 (0.41) andWS8 (0.38) were assigned ranksfrom 2 to 9 respectively.

Length of overland flow (Lg)

Length of the overland flow is one of the most importantindependent variable affecting both hydrologic andphysiographic development of drainage basins (Horton1932). Lg will be less for steeper slopes and longer forgentle slopes and is thus directly related to average slopeof the channel. The sub-watershed with highest Lg,WS8 (0.76) was assigned rank 1 which indicated thatit has the highest potential to erode the land in a singlestretch and has the highest susceptibility to erosion. Thelowest Lg was observed in WS6 (0.16), making it leastsusceptible to erosion as for as Lg is concerned. Othersub-watersheds of the study area WS3 (0.56), WS4

Table 4 Morphometry based watershed prioritization for erosion susceptibility of Rembiara sub-watersheds

Sub-watershed Morphometry Cp value Erosion susceptibility

Linear parameters Shape parameters Relief parameter

D Fs Rbm Rt Lg Rc Re Rf Cc Sw H

WS1 1.07 0.51 4.88 0.64 0.47 0.36 0.44 0.15 1.66 6.71 0.58 6.20 MediumRank 6 7 4 7 5 6 2 2 5 9 9

WS2 0.93 0.46 3.50 0.41 0.54 0.28 0.44 0.15 1.89 6.54 1.27 7.00 LowRank 7 10 9 9 4 2 3 3 9 8 6

WS3 0.89 0.58 4.02 1.16 0.56 0.47 0.65 0.34 1.46 2.98 2.03 5.60 HighRank 9 6 5 2 2 7 8 8 4 3 2

WS4 0.90 0.59 3.91 1.26 0.55 0.50 0.59 0.28 1.41 3.60 2.12 5.30 Very highRank 8 5 7 1 3 8 6 6 3 5 1

WS5 1.88 1.87 7.00 0.64 0.27 0.35 0.54 0.22 1.69 4.45 1.69 5.00 Very highRank 2 2 2 6 9 5 4 4 6 7 3

WS6 3.22 4.08 4.00 0.94 0.16 0.55 0.85 0.56 1.36 1.78 1.02 5.90 HighRank 1 1 6 3 10 9 9 9 2 2 7

WS7 1.41 0.96 2.75 0.65 0.36 0.56 0.63 0.31 1.34 3.22 1.57 6.20 MediumRank 3 3 10 5 8 10 7 7 1 4 4

WS8 0.66 0.65 10.00 0.38 0.76 0.25 1.42 1.58 1.98 0.63 0.96 6.60 LowRank 10 4 1 10 1 1 10 10 10 1 8

WS9 1.27 0.47 3.59 0.82 0.39 0.30 0.40 0.13 1.83 7.77 1.44 6.00 HighRank 4 9 8 4 7 4 1 1 7 10 5

WS10 1.20 0.48 5.25 0.61 0.42 0.29 0.55 0.23 1.87 4.29 0.57 6.70 LowRank 5 8 3 8 6 3 5 5 8 6 10


(0.55), WS2 (0.54), WS1 (0.47), WS10 (0.42), WS9(0.39), WS7 (0.36) andWS5 (0.27) were assigned ranks2 to 9 respectively.

Basin relief/ total relief (H)

The elevation difference between the highest and thelowest points on the valley floor of a sub-watershed isreferred to as total relief. There is a strong correlationbetween hydrological characteristics and the H of adrainage basin (Schumms 1956). It is an index of overallsteepness of a drainage basin as well as of the intensityof erosion processes operating on the slopes of a basin.The ranking system adopted in this case was similar toadopted in case of D, Fs and Rbm since they all impartthe same erosional characteristics in any landscape. Inthe Rembiara watershed, lowest basin relief was ob-served inWS10 (0.57) which indicated its low steepnessand thus low susceptibility to erosion. Hence it wasassigned rank 10. The highest basin relief was observedinWS4 (2.12), which was thus assigned rank 1 due to itshigh steepness and thus higher susceptibility to erosion.Sub-watersheds, WS1 (0.58), WS8 (0.96), WS6 (1.02),WS2 (1.27), WS9 (1.44), WS7 (1.57), WS5 (1.69) andWS3 (2.03) were assigned ranks 9 to 2 respectively.Lower basin relief for some sub-watersheds indicatedthe presence of sub-surface rocks that are exposed in theform of small ridges and mounds having a lower degreeof slope whereas higher values of basin relief indicatedsteep slope and high relief in those sub-watersheds.’

Elongation ratio (Re)

Elongation ratio generally varies from 0.6 to 1.0 and isassociated with a wide variety of climate and geology.Values close to 1.0 are typical of regions with very lowrelief whereas that of 0.6 to 0.8 are associated with highrelief and steep ground slope (Dar et al. 2013). Thewatershed with highest Re (WS8) (1.42) was assignedrank 10 indicating that it has least susceptibility toerosion. Whereas, WS9 (0.40) was assigned rank 1and indicated highest susceptibility. The watershedsWS1 (0.44), WS2 (0.44), WS5 (0.54), WS10 (0.55),WS4 (0.59), WS7 (0.63), WS3 (0.65) and WS6 (0.85)were assigned ranks 2 to 9 respectively. Elongation ratiofor different sub-watersheds of Rembiara watershedimplies that except WS6 and WS8, which have ovaland circular shape characteristics respectively, all othersub-watersheds are less elongated (Re<0.7).

Circularity ratio (Rc)

Circulatory ratio is influenced by many of the basincharacteristics such as length and frequency of streams,geological structures, LULC, climate, relief and slope ofthe basin. Higher Rc is indicative of circular shape of thewatershed and of the moderate to high relief and perme-able surface. Low Rc indicates elongated, low relief andimpermeable surface. Sub-watershed WS8 (0.25)showed the lowest Rc and was ranked 1 due to its verylow infiltration capacity and resulting more erosionsusceptibility. Similarly the sub-watershed WS7 (0.56)showed the highest Rc and was assigned rank 10 as itindicates it possesses low relief and higher infiltrationcapacity and resulting lower susceptibility. The other sub-watersheds,WS2 (0.28),WS10 (0.29),WS9 (0.30),WS5(0.35), WS1 (0.36), WS3 (0.47), WS4 (0.50) and WS6(0.55) were assigned ranks 2 to 9 respectively.

Form factor (Rf)

All the sub-watersheds of Rembiara have lower toslightly higher values of form factor, ranging from0.13 in WS9 to 1.58 in WS8 and were thus assignedrank 1 and 10 respectively (Javed et al. 2009). Sub-watersheds WS1 (0.15), WS2 (0.15), WS5 (0.22),WS10 (0.23), WS4 (0.28), WS7 (0.31), WS3 (0.34)and WS6 (0.56) were given ranks 2 to 9 respectively.

Basin shape/ shape index (Sw)

Rate of water and sediment yield along the length andrelief of the drainage basin is largely affected by itsshape. Therefore, in terms of response to erosion, basinshape behaves similar to form factor. Hence the rankingadopted was same as in case of form factor. Among allthe sub-watersheds of Rembiara watershed, the lowestbasin shape value of 0.63 was observed inWS8 and wasthus assigned rank 1 due to its highest contribution toerosion. The highest value of 7.77 was observed in caseof WS9 which was assigned rank 10 indicating this sub-watershed is least prone to erosion. Sub-watershedsWS6, WS3, WS7, WS4, WS10, WS5, WS2, WS1 andWS9 were assigned ranks 2 to 9 respectively.

Compactness coefficient (Cc)

The Compactness coefficient of a watershed directlycorresponds to the infiltration capacity of the watershed.


Therefore the ranking adopted was similar to Rf and Sw.The lowest Cc was observed in WS7 which indicated ithas lowest infiltration capacity and was thus assignedrank 1. The highest Cc was observed in WS8 (1.98)indicating it has highest infiltration capacity and wasthus assigned rank 10. Sub-watersheds, WS6 (1.36),WS4 (1.41), WS3 (1.46), WS1 (1.66), WS5 (1.69),WS9 (1.83), WS10 (1.87) and WS2 (1.89) wereassigned ranks from 2 to 9 respectively.

It is observed that no single parameter alone can beused to explain the erosion susceptibility of any sub-watershed. Therefore after assigning ranks to every soilerosion risk morphometric parameter, Compound value(Cp) was derived by calculating the average of ranksassigned to the individual parameters. The average isused as an index denoting sub-watershed’s erosion sus-ceptibility. The sub-watershed with the lowest Cp value

is most susceptible to erosion and needs highest priorityfor soil conservation measures. Based on Cp values, thesub-watersheds were categorized into four prioritygroups-very high priority (5.00–5.50), high priority(5.51–6.00), medium priority (6.01–6.50) and low pri-ority (6.51–7.00). Out of 10 sub-watersheds, WS4 andWS5 fall in very high priority; WS3, WS6 andWS9 fallin high priority; WS1 and WS7 fall in medium priority;whereas WS2, WS8 and WS10 fall in low prioritycategory (Table 4). Fig. 4 shows priority of sub-watersheds based on morphometric parameters.

Establishing the level of erosion susceptibility usingland cover parameters

Land cover (LC) has a significant impact on the drain-age patterns of a watershed, and the difference in its

Fig. 4 Morphometry based watershed prioritization map of Rembiara sub-watersheds


coverage significantly affects erosion susceptibilityof sub-watersheds (Rosenqvist and Birkett 2002).Moreover, the differences in the vegetal cover stronglyaffect the soil moisture, infiltration, evapo-transpirationand interception process of watersheds (Choudharyet al. 1996, Arthur et al. 2003; Rashid and Romshoo2012; Romshoo and Rashid 2012 ). The land that isimpervious either by natural composition or by humansettlement heavily contributes to runoff due to the ob-struction of infiltration process (Dams et al. 2013).Moreover it also plays a pivotal role in shaping drainagepattern in the watershed (Fohrer et al. 2001; Quilbe et al.2006). Higher percentage of plant cover and largeamounts of root biomass are greatly effective in slowingthe rates of erosion (Carlos et al. 2012; Badar et al.2013).Moreover tree canopies and the foliage of smallerplants intercept the rain water and thus also help inpreventing erosion (Mustard and Sunshine 1999; Yuet al. 2012; Romshoo et al. 2012).

The LC classification was done on the rationale thatclasses identified in the study area strongly affect soilerosion. The classes generated were agriculture, waste-land, impervious surface, forest, pasture, shrub andsnow (Fig. 5). The classified LC was followed by ex-tensive ground verification wherein a total of 305

ground samples were taken. After incorporating thepost-field corrections, the overall accuracy of the LCgenerated was 96.39 % (Table 5). The calculatedkappa coefficient of the LC was 0.90. The classifi-cation accuracy achieved was quite encouragingand has improved the quality of the assessment ofsoil erosion using LC (Foody 2002). Table 6 sum-marizes the comparative area statistics of LC of thesub-watersheds. The analysis reveals that the sameLC class with significantly varying proportions ispresent in all the sub-watersheds.

On the basis of response of LC towards erosion,prioritization ranking was performed. Percent area ofthe class was used as an index for assigning ranks.Among all the sub-watersheds, the watershed havingthe maximum percentage of a class which directlycauses erosion (e.g. in case of wasteland) was assignedrank 1, and the minimum percentage of the same classwas assigned rank 10. Similarly, if a class restrictserosion (e.g. in case of pasture), it was assigned rankin a way that among all the sub-watersheds, one whichhas the lowest percentage of such a class was assignedrank 1 and the watershed which possesses the maximumpercentage of the same class was assigned rank 10.Accordingly the ranks between 1 and 10 were

Fig. 5 Supervised Land cover classification map of Rembiara watershed


assigned to the watersheds based on the LC’scontribution to the erosion. Finally, a single value(compound value, Cp) was generated by averaging

the ranks for each watershed. The area descriptionof LC and the ranking criteria for individual clas-ses is discussed below (Table 7).

Table 6 Land cover area statistics of Rembiara sub-watersheds

Sub- watershed Area Land cover categories Total

Agriculture Snow Impervious surface Water Forest Waste land Pasture Shrub

WS1 km2 35.11 0.01 2.68 6.92 10.69 0.03 0.00 0.02 55.45

% 63.31 0.02 4.83 12.47 19.27 0.05 0.00 0.04 100

WS2 km2 4.25 0.01 0.28 2.77 21.51 0.06 0.80 5.34 35.01

% 12.15 0.02 0.79 7.92 61.44 0.16 2.27 15.25 100

WS3 km2 0.00 54.71 11.92 2.45 11.56 0.04 0.35 25.03 106.06

% 0.00 51.6 11.23 2.31 10.90 0.04 0.33 23.60 100

WS4 km2 0.00 48.71 14.68 0.91 11.82 3.86 0.90 31.36 112.25

% 0.00 43.4 13.08 0.81 10.53 3.44 0.81 27.94 100

WS5 km2 0.01 0.82 1.21 0.03 0.58 0.49 0.14 1.14 4.42

% 0.14 18.5 27.37 0.66 13.19 11.08 3.25 25.82 100

WS6 km2 0.00 0.02 0.03 0.00 0.05 0.16 0.39 0.62 1.28

% 0.00 1.5 2.29 0.22 3.64 12.87 30.86 48.63 100

WS7 km2 0.00 1.62 1.86 0.06 1.70 0.51 0.78 4.01 10.54

% 0.04 15.3 17.69 0.59 16.17 4.79 7.37 38.02 100

WS8 km2 1.79 0.00 0.02 0.74 1.45 1.49 2.29 9.48 17.26

% 10.37 0.00 0.14 4.30 8.42 8.60 13.27 54.90 100

WS9 km2 50.34 0.00 0.31 2.21 46.88 3.96 1.57 23.46 128.73

% 39.10 0.00 0.24 1.72 36.42 3.08 1.22 18.23 100

WS10 km2 56.47 0.00 1.55 5.62 7.45 0.20 0.01 0.22 71.53

% 78.94 0.00 2.17 7.86 10.42 0.27 0.02 0.31 100

Table 5 Accuracy assessment of supervised land cover classification of Rembiara watershed

Reference data

IS W A Sh P WL S F Row total User’s accuracy

Classification data

IS 67 3 70 95.71

W 3 27 30 90

A 70 5 75 93.33

Sh 24 24 100

P 15 15 100

WL 30 30 100

S 4 4 100

F 57 57 100

Column total 70 30 70 24 20 30 4 57 305

Producer’s accuracy 95.71 90 100 100 75 100 100 100

Overall accuracy=[(67+27+70+24+15+30+4+57)/305]×100=96.39 %

IS Impervious surface, W Water, A Agriculture, Sh Shrub, P Pasture, WLWasteland, S Snow, F Forest


Agriculture

Agricultural land is the first dominant category inthe study area and covers about 36.03 % of thetotal watershed. Land under agriculture is topo-graphically flat and as a result water gets moretime to infiltrate. Moreover, in such lands theupper soil layer is strongly grasped by the rootbiomass of crops. These two combined effectsmake these lands less susceptible to erosion. Insub-watersheds, the maximum area under agricul-tural land was observed in WS10 (78.94 %) and itwas thus ranked 10 indicating that it has the leasterosion susceptibility. Lowest percentage of agri-cultural land cover was found in WS3 and thus isthe most susceptible to erosion. Sub-watershedsWS1 (63.31 %), WS9 (39.10 %) and WS2(12.15 %) were ranked 9, 8 and 7 respectively.WS8 (10.37 %) was assigned rank 6, while asWS5 (0.14 %) and WS7 (0.04 %) were assignedrank 5 and 4 respectively. Small covers of agricul-tural land were found in WS4 and WS6 whichwere given lower ranks of 3 and 2 respectively.

Impervious surface

About 5.58 % of the Rembiara watershed is coveredwith impervious surface. Impervious surface class is thatland where the soil is either altogether absent (e.g.,exposed rock) or where soil surface is obstructed likein the case of settlements. The erosion susceptibility ofsuch a land was assumed to be zero, because there wasno soil to get eroded. In a multi-criteria methodologyused in this study, such areas shall contribute least toerosion susceptibility to the overall susceptibility of agiven watershed. Therefore, the watershed which hasthe highest percentage of this class has the lowest con-tribution towards erosion susceptibility and the water-shed which has the lowest percentage under this classcontributes highest towards susceptibility to erosion.Thus, rank 10 was assigned to the watershed withhighest percentage of impervious surface, whereas thewatershed having lowest percentage of impervious sur-face was assigned rank 1. In the sub-watersheds, themaximum impervious surface was present in WS5(27.4 %) and was assigned rank 10 indicating it hasleast erosion possibilities. Minimum impervious surface

Table 7 Land cover based watershed prioritization for erosion susceptibility of Rembiara sub-watersheds

Sub- watershed Land cover categories Cp value Erosion Susceptibility

Agriculture Impervious Surface Forest Waste land Pastures Shrub Snow

WS1 63.31 4.83 19.27 0.05 0.00 0.04 0.02 5.43 HighRank 9 6 8 9 1 1 4

WS2 12.15 0.79 61.44 0.16 2.27 15.25 0.02 6.00 MediumRank 7 3 10 8 6 3 5

WS3 0.00 11.23 10.90 0.04 0.33 23.60 39.58 5.86 MediumRank 1 7 5 10 3 5 10

WS4 0.00 13.08 10.53 3.44 0.81 27.94 33.30 5.71 MediumRank 3 8 4 5 4 7 9

WS5 0.14 27.37 13.19 11.08 3.25 25.82 14.19 6.29 LowRank 5 10 6 2 7 6 8

WS6 0.00 2.29 3.64 12.87 30.86 48.63 1.14 4.86 Very highRank 2 5 1 1 10 9 6

WS7 0.04 17.69 16.17 4.79 7.37 38.02 11.76 6.71 LowRank 4 9 7 4 8 8 7

WS8 10.37 0.14 8.42 8.60 13.27 54.90 0.00 4.86 Very highRank 6 1 2 3 9 10 3

WS9 39.10 0.24 36.42 3.08 1.22 18.23 0.00 5.29 HighRank 8 2 9 6 5 4 3

WS10 78.94 2.17 10.42 0.27 0.02 0.31 0.00 4.43 Very highRank 10 4 3 7 2 2 3


was present inWS8 (0.14%) which was assigned rank 1indicating it has maximum possibilities of erosion due toincreased soil-water/wind interactions. The other sub-watersheds WS7 (17.7 %) and WS4 (13.1 %) wereassigned rank 9 and 8 respectively. While as WS3(11.23 %), WS1 (4.83 %), WS6 (2.29 %), WS10(2.17 %), WS2 (0.79 %) and WS9 (0.24 %) wereassigned ranks 7 to 2 respectively.

Forest

Forests are the second dominant category in the Rembiarawatershed is about 20.98 % of its total area and comprisesdense and moderately dense forests and plantation. Forestsare among those LC classes which act against the forces ofsoil erosion. Therefore rank 10, was assigned to watershedhaving the highest percentage of forest cover and rank 1was assigned to the watershed having the lowest

percentage of forest cover. The highest land under forestwas present in the WS2 (61.4 %), and was assigned rank10 and has least erosion susceptibility.While theminimumforest cover was present inWS6 (3.6 %) and was assignedrank 1 indicating it has maximum susceptibilities to ero-sion. Sub-watersheds, WS9 (36.42 %), WS1 (19.27 %),WS7 (16.17 %), WS5 (13.19 %), WS3 (10.90 %), WS4(10.53 %), WS10 (10.42 %) and WS8 (8.42 %) wereassigned ranks 9 to 2 respectively.

Wasteland

Wasteland is any unutilized/ degraded piece of land. Itwas assumed that such areas are prone to wind and watererosion. Therefore, the watershed having lowest per-centage of the wasteland was assigned rank 10, andthe watershed with highest percentage of the wastelandwas assigned rank 1. Among the 10 sub-watersheds, the

Fig. 6 Land cover based watershed prioritization map of Rembiara sub-watersheds


maximum area of wasteland was found inWS6 (12.9%)which was assigned rank 1 indicating highest erosion.The minimum wasteland area was found in WS3(0.04 %) and was thus assigned rank 10 indicating leastwind erosion. Sub-watersheds WS5 (11.1 %), WS8(8.60 %), WS7 (4.79 %), WS4 (3.44 %), WS9(3.08 %), WS10 (0.27 %), WS2 (0.16 %) and WS1(0.05 %) were assigned ranks from 2 to 9 respectively.

Pasture

Pastures due to their dense root structure are highlyefficient in holding the soil particles together. They alsodecrease the rate of water movement over the landsurface hence providing enough time for the water toinfiltrate. The watersheds with good percentage of pas-tures are thus least susceptible to erosion. Therefore rank10 was assigned to the watershed with highest percent-age of the pasture, and rank 1 was assigned to thewatershed with lowest percentage of the pasture. InRembiara watershed only 1.09% is covered by pastures,and among all the sub-watersheds the coverage is max-imum in WS6 (30.9 %). It was thus assigned rank 10.WS1 was assigned rank 1, since there was no pasturepresent in this sub-watershed. The sub-watersheds WS8(13.27 %), WS7 (7.37 %), WS2 (2.27 %), WS9(1.22 %), WS4 (0.81 %), WS3 (0.33 %) and WS10(0.02 %) were assigned ranks from 9 to 2 respectively.

Shrub

As with forests and pastures, shrubs also act to reduce soilerosion. Therefore, the ranking scheme adopted in case offorests, pasture and agriculture was also applied to shrubs.About 15.19 % of Rembiara watershed is covered byshrubs and is maximum in WS8 (54.9 %) which wasassigned rank 10. Minimum shrub cover was found inWS1 (0.04 %) and was thus assigned rank 1 indicatingmaximum erosion possibilities when taking only shrubclass into consideration. Sub-watersheds WS6 (48.6 %),WS7 (38.02 %), WS4 (27.94 %), WS5 (25.82 %), WS3(23.60 %), WS9 (18.23 %), WS2 (15.25 %) and WS10(0.31 %) were thus assigned ranks 9 to 2 respectively.

Snow

The total percentage of perennial snow in Rembiarawatershed is 15.99 %. It was assumed that impervioussurfaces and areas under snowwill have same impact on T

able8

Watershed

prioritizationforerosionsusceptib

ility

ofRem

biarasub-watershedsbasedon

thecombinedinfluenceof

land

coverandmorphom

etry

Sub-

watershed

Morphom

etricparameters

Landcover

Cp

value

Erosion

Susceptib

ility

Linearparameters

Shape

parameters

Relief

parameter

Agriculture

Impervious

surface

ForestWaste

land

Pastures

Shrubs

Snow

DFs

Rbm

Rt

Lg

Rc

Re

Rf

Cc

Sw

H

WS1

67

47

56

22

59

99

68

91

14

5.88

7

WS2

710

99

42

33

98

67

310

86

35

6.59

10

WS3

96

52

27

88

43

21

75

103

510

5.71

4

WS4

85

71

38

66

35

13

84

54

79

5.47

1

WS5

22

26

95

44

67

35

106

27

68

5.53

3

WS6

11

63

109

99

22

72

51

110

96

5.47

2

WS7

33

105

810

77

14

44

97

48

87

6.41

9

WS8

104

110

11

1010

101

86

12

39

103

5.88

8

WS9

49

84

74

11

710

58

29

65

43

5.71

5

WS1

05

83

86

35

58

610

104

37

22

35.76

6


the erosion susceptibility in an area. Therefore, theranking scheme adopted in case of snow clad surfaceswas the same as adopted for impervious surfaces. Themaximum coverage was found in WS3 (51.6 %) andwas assigned rank 10 due to its least soil-water/windinteractions. The sub-watersheds at lower elevations likeWS8,WS9 andWS10 have no perennial snow and weretherefore together assigned rank 3. The sub-watershedsWS4 (43.4 %), WS5 (14.19 %), WS7 (11.76 %), WS6(1.14 %), WS2 (0.02 %) and WS1 (0.02 %) wereassigned ranks 9 to 4 respectively.

As seen in case of morphometry, no single class canexplain the erosion susceptibility of a sub-watershed.Therefore after assigning ranks to each LC class, acompound value (Cp) was calculated by averaging ofranks of individual LC categories for each sub-water-shed. Using compound value, the sub-watersheds werecategorized into four priority groups based on the

urgency for the soil management strategies - very highpriority (4.43–5.00), high priority (5.01–5.57), mediumpriority (5.58–6.14) and low priority (6.15–6.71). Outof the 10 sub-watersheds WS6, WS8 and WS10 fall invery high priority; WS1 andWS9 in high priority; WS2,WS3 and WS4 in medium priority; whereas WS5 andWS7 fall under the low priority category (Table 7).Fig. 6 shows priority of sub-watersheds on the basis ofLC analysis.

Establishing the level of erosion susceptibility, basedon the combined influence of LC and Morphometry

In this study, two components of land system viz, mor-phometry and LC were assessed for their roles in mak-ing an area susceptible to erosion. In order to find asingle answer to their collective contribution towardsthe erosion susceptibility, we finally integrated the

Fig. 7 Watershed prioritization ranking map Rembiara sub-watersheds based on the combined impact of morphometry and land cover


impact of morphometry and LC to identify the water-shed (s) that is most susceptible to erosion. For priori-tizing watersheds on the basis of the combined effect ofLC and morphometry on erosion susceptibility of thewatersheds, the individual rankings obtained were aver-aged for each watershed to get a common compoundvalue (Cp). As shown in Table 8, average of LC andmorphometry rankings was used to get a common com-pound value (Cp) for each sub-watershed. The resultsshow that the highest priority must be given to WS4followed by WS6, WS5 and WS3 (Fig. 7). The resultsalso reveal that the least priority could be given to WS2as among other erosion risk parameters, stream frequen-cy in this case is least as well as it is mainly covered byforests. Moreover, the individual prioritization results ofLC and morphometry supports this observation.According to LC based prioritization, WS4 and WS2shows medium priority while as from the results ofmorphometry based prioritization these two sub-watersheds are very high and low priority watershedsrespectively (Table 8).

Conclusion

The contemporary approaches of remote sensing andGIS used for morphometric and land cover evaluationsin this study were found to be very appropriate incomparison to the conventional approaches. The presentstudy was aimed at prioritizing the watersheds on thebasis of their susceptibility to soil erosion so that appro-priate measures could be suggested for conservation ofthe land and water resources. The identification of crit-ical areas is a pre-requisite for developing andimplementing the best management practices of landand water conservation in the mountainous Himalaya.The research was conducted in the Rembiara watershedin the western Himalaya, which has been facing humanpressures in the form of deforestation, boulder extrac-tions, intensification in improper agricultural practicesand reckless urbanization. These practices have, in turnresulted in degradation of the land resources in thisregion. The result of prioritization on the basis of mor-phometric analysis reveals that sub-watersheds WS4,WS5, and WS9 fall under very high priority group i.e.,they are highly susceptible to erosion of the top soil,whereas on the basis of LC analysis WS6, WS8, WS9and WS10 sub-watersheds are highly susceptible andthus fall under very high priority category. However,

both LC and morphometry show a common high prior-ity for WS9. Sub-watersheds, WS4, WS5, WS6, WS8,WS9 and WS10, which were found to be in very highpriority group, must be taken up immediately for im-plementation of soil and water conservation measures.The conservation strategies recommended includebuilding up of check dams, contour farming, conversionof wastelands to agriculture, afforestation and reforesta-tion under social forestry program, and putting a stop tothe overgrazing of the pastures falling under these sub-watersheds.

Acknowledgments The research work was conducted as part ofthe DST, Govt. of India, New Delhi sponsored research project onIntegrated Flood vulnerability Assessment for Flood Risk Man-agement and Disaster Mitigation and the financial assistance re-ceived under the project to accomplish this research is thankfullyacknowledged. The authors express gratitude to the anonymousreviewers for their valuable comments and suggestions on theearlier version of the manuscript that greatly improved the contentand structure of this manuscript.

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Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed