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This research is about an integrated impact analysis of socioeconomic and biophysical processes at the watershed level on the current status of Dal Lake using multi-sensor and multi-temporal satellite data, simulation modelling together with field data verification. Thirteen watersheds (designated as ‘W1–W13’) were identified and investigated for land use/land cover change detection, quantification of erosion and sediment loads and socioeconomic analysis (total population, total households, literacy rate and economic development status).
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Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach Bazigha Badar & Shakil A. Romshoo & M. A. Khan Received: 3 May 2012 / Accepted: 4 December 2012 # Springer Science+Business Media Dordrecht 2013 Abstract Dal Lake, a cradle of Kashmiri civilization has strong linkage with socioeconomics of the state of Jammu and Kashmir. During last few decades, anthropo- genic pressures in Dal Lake Catchment have caused environmental deterioration impairing, inter-alia, sus- tained biotic communities and water quality. The present research was an integrated impact analysis of socioeco- nomic and biophysical processes at the watershed level on the current status of Dal Lake using multi-sensor and multi-temporal satellite data, simulation modelling to- gether with field data verification. Thirteen watersheds (designated as W1W13) were identified and investi- gated for land use/land cover change detection, quantifi- cation of erosion and sediment loads and socioeconomic analysis (total population, total households, literacy rate and economic development status). All the data for the respective watersheds was integrated into the GIS envi- ronment based upon multi-criteria analysis and knowledge-based weightage system was adopted for watershed prioritization based on its factors and after carefully observing the field situation. The land use/land cover change detection revealed significant changes with a uniform trend of decreased vegetation and increased impervious surface cover. Increased erosion and sediment loadings were recorded for the watersheds corresponding to their changing land systems, with bare and agriculture lands being the major contributors. The prioritization analysis revealed that W5>W2>W6>W8>W1 ranked highest in priority and W13>W3>W4>W11>W7 under medium priority. W12>W9>W10 belonged to low- priority category. The integration of the biophysical and the socioeconomic environment at the watershed level using modern geospatial tools would be of vital impor- tance for the conservation and management strategies of Dal Lake ecosystem. Keywords Dal Lake . Watershed . Remote sensing . Land use/land cover . GWLF . Prioritization Introduction Lakes are extremely fragile and sensitive ecosystems on earth that host rich aquatic biodiversity. Besides being the key components of our planets hydrological cycle, they provide important social and ecological functions (Ballatore and Muhandiki 2002). Despite the fact that freshwater bodies are very limited and sensitive resour- ces that need proper care and management, they are probably the most neglected and mismanaged natural Environ Monit Assess DOI 10.1007/s10661-012-3035-9 B. Badar (*) : S. A. Romshoo Department of Geology and Geophysics, University of Kashmir, Hazratbal, Srinagar, Kashmir 190006, India e-mail: [email protected] M. A. Khan Division of Environmental Science, Shere Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Kashmir 190006, India
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Page 1: Prioritizing Dal Watersheds

Integrating biophysical and socioeconomic informationfor prioritizing watersheds in a Kashmir Himalayan lake:a remote sensing and GIS approach

Bazigha Badar & Shakil A. Romshoo & M. A. Khan

Received: 3 May 2012 /Accepted: 4 December 2012# Springer Science+Business Media Dordrecht 2013

Abstract Dal Lake, a cradle of Kashmiri civilization hasstrong linkage with socioeconomics of the state ofJammu and Kashmir. During last few decades, anthropo-genic pressures in Dal Lake Catchment have causedenvironmental deterioration impairing, inter-alia, sus-tained biotic communities and water quality. The presentresearch was an integrated impact analysis of socioeco-nomic and biophysical processes at the watershed levelon the current status of Dal Lake using multi-sensor andmulti-temporal satellite data, simulation modelling to-gether with field data verification. Thirteen watersheds(designated as ‘W1–W13’) were identified and investi-gated for land use/land cover change detection, quantifi-cation of erosion and sediment loads and socioeconomicanalysis (total population, total households, literacy rateand economic development status). All the data for therespective watersheds was integrated into the GIS envi-ronment based upon multi-criteria analysis andknowledge-based weightage system was adopted for

watershed prioritization based on its factors and aftercarefully observing the field situation. The land use/landcover change detection revealed significant changes witha uniform trend of decreased vegetation and increasedimpervious surface cover. Increased erosion and sedimentloadings were recorded for the watersheds correspondingto their changing land systems, with bare and agriculturelands being the major contributors. The prioritizationanalysis revealed that W5>W2>W6>W8>W1 rankedhighest in priority andW13>W3>W4>W11>W7 undermedium priority. W12>W9>W10 belonged to low-priority category. The integration of the biophysical andthe socioeconomic environment at the watershed levelusing modern geospatial tools would be of vital impor-tance for the conservation and management strategies ofDal Lake ecosystem.

Keywords Dal Lake .Watershed . Remote sensing .

Land use/land cover . GWLF. Prioritization

Introduction

Lakes are extremely fragile and sensitive ecosystems onearth that host rich aquatic biodiversity. Besides beingthe key components of our planet’s hydrological cycle,they provide important social and ecological functions(Ballatore and Muhandiki 2002). Despite the fact thatfreshwater bodies are very limited and sensitive resour-ces that need proper care and management, they areprobably the most neglected and mismanaged natural

Environ Monit AssessDOI 10.1007/s10661-012-3035-9

B. Badar (*) : S. A. RomshooDepartment of Geology and Geophysics,University of Kashmir,Hazratbal,Srinagar, Kashmir 190006, Indiae-mail: [email protected]

M. A. KhanDivision of Environmental Science,Shere Kashmir University of Agricultural Sciencesand Technology of Kashmir,Shalimar,Srinagar, Kashmir 190006, India

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resources. While some problems originate in a lakeitself, the vast majority of problems originate from ac-tivities on the surrounding land (ILEC 2005). Resourcedevelopment, wise use and judicious conservation oflakes have been major challenges across the continents,particularly with regard to satisfying human needs with-in, and sometimes beyond, the lake basin. Lakes arelargely dependent on their watersheds for the energyand matter, with the nature of actions in these water-sheds driving the course of the reactions within thesewater bodies. Watershed deterioration mainly becauseof improper and unwise utilization of watershed resour-ces without any proper vision is a common phenomenonin most parts of the world (FAO 1985). Degraded water-sheds ultimately result in high nitrogen and phosphorusloads, algal bloom and toxicity, low oxygen and fishkills, loss of aquatic habitat, changes in communitystructure, loss of recreational amenity in these aquaticecosystems (Kira 1997; Dinar et al. 1995; Duker 2001;Jorgensen et al. 2003). Inflowing substances, includingsediments, minerals, nutrients and organic materials,coming from the watersheds tend to accumulate in thewater column or the lake bottom (World Lake VisionCommittee 2003), thereby, deteriorating these freshwa-ter ecosystems.

Kashmir Valley is known world over for its naturalbeauty, which comprises of some of the most beautifulmountains, forests, lakes and streams. The lakes ofKashmir identified as Glacial, Pine-forest and Valleylakes based on their origin, altitudinal situation and natureof biota, provide valuable research opportunities (Zutshiet al. 1972; Kaul 1977; Zutshi and Khan 1978; Pandit1996, p. 99). These lakes vary from being oligotrophic toeutrophic, while others are in the process of continuouschange towards eutrophication (Kaul 1979; Khan 2008).While these changes result in part from the natural courseof biotic, climatic and other environmental factors but inthe recent times these have been primarily because of thehuman interferences. Eutrophication and dwindling oflake ecosystems in Kashmir Himalayan lakes is a recentevent of the past 10–30 years, coinciding with a markedcivilization evolution in the lake drainage basins (Pandit1998). Since, there has not been much development asregards the industrialization in the Kashmir valley, themain contributors towards the eutrophication of the waterbodies are land use changes in the catchment, unplannedurbanization, increased sedimentation, flow of fertilisersand pesticides from the catchment (Pandit and Qadri1990; Badar and Romshoo 2007). Socioeconomic

activities and encroachment of the lake area by the lakedwellers has also contributed to the deterioration of theseonce pristine lakes.

With rapid socioeconomic changes and various en-vironmental perturbations during the last few decades,Dal Lake ecosystem has degraded significantly, result-ing in increased ecological vulnerability and hydrolog-ical disruption (Trisal 1987; Khan 1993a, b; 2000).During the last few decades, anthropogenic interven-tions in the catchment like unplanned urbanization, de-forestation, intensive grazing, stone quarrying etc. haveexerted tremendous pressures on the world famousfreshwater ecosystem. Increase in agricultural activityand the reduction of plant cover on the hillsides sur-rounding the lake with the consequential increase insurface erosion and leaching of soil nutrients have addedincreasing quantities of nutrient-rich runoff (Badar andRomshoo 2007). Increase in impervious surfaces likebarren, built-up and deforested areas of the Dal LakeCatchment has caused the peak flow to swell over theperiod of time (Amin and Romshoo 2007). Nearbyfarming practices have also added to the amount andrate of silt generated and added to the lake waters(Pandit and Fotedar 1982; Pandit and Qadri 1990).Further, interruptions to the internal flow of lake watercaused by weirs, islands, bunds, land between house-boats, etc. have reduced the capacity of the lake torespond to the stresses placed on it. The Dal Lakedrainage is characterised by a myriad of channels(Meerakshah, Nallah Amir Khan, Brari Nambal andChuntkul) which have been filled up during the lasttwo decades due to excessive siltation, sewage inflowand garbage dumping reducing their water holding ca-pacity and disrupting the ecological balance of the lake.The gradual reclamation of the lake to provide buildingand vegetable growing land and the increase in the areaof floating gardens have combined with natural process-es to reduce the area of open water within the lake area.A sizeable (20 %) portion of the lake is covered byfloating gardens reducing the open water area to(59 %) of the total Dal Lake area (Khan 2000).

Water quality degradation in Dal Lake is a majorconcern, and improving the ecological status of thislarge water body is now a regional and national priority.Although scientific knowledge concerning the causesand effects of stresses on the lake has grown rapidly,effective management policies have lagged in mostcases. The motivation for this study stems from the needfor simple and reliable information that could facilitate

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the participation of stakeholders and decision makers inthe implementation of water quality programs, thereby,improving the chances of the Dal Lake restoration. Thewatershed management concept recognizes the inter-relationships and linkages between various biophysicaland socioeconomic processes (Moore et al. 1977; FAO1985; Honore 1999) and has been identified as thefundamental unit for conservation and restoration pro-grammes. Earlier, integrated approach for watershedprioritization using remote sensing and GeographicalInformation System (GIS) data has been successfullyattempted by several workers (Prasad et al. 1997;Biswas et al. 1999; Khan et al. 2001; Gosain and Rao2004). Under this context, the study was carried out withthe objectives (1) to assess change in land use/land coverat watershed level, (2) to quantify the erosion and sed-iment loadings from the watersheds under changed landsystem conditions, (3) to assess the major socioeconom-ic parameters at watershed level, (4) to integrate thesocioeconomic and biophysical information for priori-tizing the watersheds.

Materials and methods

Study area

Dal Lake (34°02′ N latitude and 74°50′ E longitude)situated in Kashmir Himalayas, India functions as thecentral part of a large interconnected aquatic ecosystemand is the major surface water body of the KashmirValley. This lake has historically been the centre ofKashmiri civilization and has played a major role in theeconomy of the state of Jammu and Kashmir. It is ashallow, multi-basin drainage lake (Zutshi and Khan1978) covering an area of about 18 km2, with openwaterarea not more than 12 km2. The general relief of the lakecatchment is a basin and extends between altitudinalranges of 1,580–4,390 m. The flat areas are mostly usedfor cropland, horticulture and built up and more humanactivities have intensified during the last few decades.The mountainous areas are mostly covered by forest,grassland, scrublands, and the hilly regions consist ofnatural vegetation and barren land, respectively. Thecatchment area is dominated by the geological forma-tions of alluvium, Panjal traps and agglomerate slates.The Karewa deposits are quaternary fluvio-lacustrinedeposits which contain unconsolidated materials suchas light grey sand, dark grey clays, coarse- to fine-

grained sands, gravels, marls, silts, varved clays, brownloams, lignite, etc. (Wadia 1971; Varadan 1977; Data1983; Bhat 1989). A number of underground springsand streams feed the Dal Lake but the main source isthe Dachigam Creek, originating from the alpine MarsarLake. The catchment belongs to a Sub-Mediterraneantype climate with four seasons based on mean tempera-ture and precipitation (Bagnoulus and Meher-Homji1959). The catchment receives an average annual rainfallof 650 mm at Srinagar station and 870 mm at Dachigamstation. March, April and May are the wettest months ofthe year. The temperature varies between a monthlymean maximum of 31 °C in July and a minimum of−4 °C in January with an average of 11 °C. Thirteenwatersheds in the lake catchment, designated as ‘W1–W13’ were identified and taken up for the current study.Location of the study area is shown in Fig. 1.

Data sets used

For performing the change detection in land use and landcover, multi-date and multi-sensor satellite data in formof Landsat Thematic Mapper (TM) dated 15 October1992 and Indian Remote Sensing satellite data [IRS1D, Linear Imaging Self Scanning (LISS-III)] 19October, 2005 was used. Digital Elevation Model fromShuttle Radar Topographic Mission, with a spatial reso-lution of 1 arc-sec was used for generating the topo-graphic variables of the catchment for use in thegeospatial model (Rodriguez et al. 2006). A soil mapof the study areawas generated by using remotely sensedclassified data aided with extensive laboratory analysisof the soil samples followed by detailed ground truthing.A time series of hydro-meteorological data from thenearest observation station was used for input to thegeospatial model. Ancillary data related to the sedimentloadings was also used in this study. The Census dataprovided by the state Department was used as a source ofsocioeconomic data in the present research.

Geospatial modelling approach

Geospatial models are excellent tools for predictingvarious land surface processes and phenomena at differ-ent spatial and time scales (Young et al. 1987; Shamsi1996; Frankenberger et al. 1999; Romshoo 2003;Yuksel et al. 2008). For simulating the erosion andsediment loadings, a distributed/lumped parameter wa-tershed model Generalized Watershed Loading

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Function (GWLF) was used (Haith and Shoemaker1987). The model simulates runoff, erosion and sedi-ment loads from a watershed given variable-size sourceareas on a continuous basis and uses daily time steps forweather data and water balance calculations (Haith et al.1992; Lee et al. 2001; Evans et al. 2008). Monthlycalculations are made based on the daily water balanceaccumulated to monthly values. For the surface loading,the approach adopted is distributed in the sense that itallows multiple land use/land cover scenarios, but eacharea is assumed to be homogenous in regard to variousattributes considered by the model. For sub-surfaceloading, the model adopts a lumped parameter schemeusing a water balance approach. The model is particu-larly useful for application in regions where environ-mental data of all types is not available to assess thepoint and non-point source pollution from watershed(Evans et al. 2002; Strobe 2002).

The GWLF model computes the runoff by using theSoil Conservation Service Curve Number equation.Erosion is computed using the Universal Soil LossEquation and the sediment yield is the product oferosion and sediment delivery ratio. The yield in anymonth is proportional to the total transport capacity ofdaily runoff during the month.

The direct runoff is estimated from daily weatherdata using Soil Conservation Services (SCS) curvenumber method that is based on the area’s hydrologicsoil group, land use, treatment and hydrologic condi-tion given by Eq. 1.

Qkt ¼ Rt þMt � 0:2DSktÞ2Rt þMt þ 0:8DSkt

ð1Þ

Where Q is runoff (in centimetre), Rainfall Rt (incentimetre) and snowmelt Mt (in centimetre of water)

Fig. 1 Location map of the study area

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on the day t (in centimetre), are estimated from dailyprecipitation and temperature data. Precipitation isassumed to be rain when daily mean air temperatureis Tt (in degrees Celsius) is above 0 and snow fallotherwise. CN has a range from 30 to 100; lowernumbers indicate low runoff potential while largernumbers are for increasing runoff potential. The lowerthe curve number, the more permeable the soil is. DSktis the catchment’s storage. Catchment storage is esti-mated for each source area using CN values with theEq. 2 given below

DSkt ¼ 2; 540

CNkt� 25:4 ð2Þ

Where, CNkt is the CN value for source area k, attime t.

Stream flow consists of runoff and discharge fromgroundwater. The latter is obtained from a lumpedparameter watershed water balance (Haan 1972).Daily water balances are calculated for unsaturatedand shallow saturated zones. Infiltration to the unsat-urated and shallow saturated zones equals the excess,if any, of rainfall and snowmelt runoff. Percolationoccurs when unsaturated zone water exceeds fieldcapacity. The shallow saturated zone is modelled aslinear ground water reservoir. Daily evapotranspira-tion is given by the product of a cover factor andpotential evapotranspiration (Hamon 1961). The latteris estimated as a function of daily light hours, saturat-ed water vapour pressure and daily temperature.

Erosion is computed using the Universal Soil LossEquation (USLE) and the sediment yield is the productof erosion and sediment delivery ratio. The yield inany month is proportional to the total capacity of dailyrunoff during the month.

Erosion from source area (k) at time t, Xkt is esti-mated using the following equation:

Xkt ¼ 0:132� REt � Kk � LSð Þk � Ck � Pk � Rk ð3Þ

Where, Kk×(LS)k×Ck×P are the soil erodibility,topographic, cover and management and supportingpractice factor as specified by the USLE (Wischmeierand Smith 1978). REt is the rainfall erosivity on day t(megajoules-millimetre per hectare-hour).

Soil loss from stream bank erosion is based upon thefamiliar sediment transport function having the form

LER ¼ aQ 0:6f g ð4Þ

Where LER is the lateral erosion rate in metre/month which refers to the total distance that soil iseroded away from both banks along the entire lengthof a stream during a specified period of time, a is anempirically derived erosion potential factor, and Q ismean monthly stream flow in cubic metre per second.In this case, the value of 0.6 used based on a globalreview of stream bank erosion studies (Van Sickle andBeschta 1983; Lemke 1991; Rutherford 2000).

Preparation of input data

A variety of input parameters was required to run theGIS-based GWLF model for simulating different hydro-logical processes at watershed scale which include theland use/land cover data, digital topographic data, hydro-meteorological data, transport parameter data (hydrolog-ic and sediment) and nutrient parameter data. All thesedatasets were prepared with the procedures given below.

Land use and land cover data

Land use/land cover (LULC) information is very crit-ical for assessing a number of land surface processes.For identifying the change in LULC of the watershedsfrom 1992 to 2005, multi-date satellite imageries wereused. Supervised classification was performed on boththe images followed by the extensive field verificationand ground truthing of the identified land use classes.

Hydro-meteorological data

Daily precipitation and temperature data are requiredfor the simulation of hydrological processes by theGWLF model. The daily hydrometerlogical data fromthe Indian Metrological Department (IMD) compris-ing of daily precipitation and daily temperature (min-imum and maximum), with a time step of 28 years wasprepared as an input to the model. In addition, meandaylight hours for the catchment with latitude 34°Nwere obtained from literature (Haith et al. 1992; Evanset al. 2008). The study area receives an average rain-fall of about 650 mm with most of its precipitationbetween the months of March and May. January(−0.6 °C) is the coldest month while July (31.37 °C)is the hottest month. Maximum daylight is recordedfor the month of June (14.3 h) and July (14.1 h) andthe minimum daylight is received in the months ofDecember (9.7 h) and January (9.9 h).

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Transport parameters

Transport parameters including hydrologic, erosionand sediment of the catchment are those aspects thatinfluence the movement of the runoff and sedimentsfrom any given unit in the catchment down to the lake.Transport parameters calculated for different sourceareas in the catchment are given in Table 1, with thecomplete procedures for generating each of theseexplained as under

Hydrological parameters

The evapotranspiration (ET) cover coefficient is theratio of the water lost by evapotranspiration from theground and plants compared to what would be lost byevaporation from an equal area of standing water(Thuman et al. 2003). The ET cover coefficients de-pend upon the type of land use and time period withinthe growing season of a given field crop (FAO 1980;Haith 1987). Typical ET values ranged from 0.3 to1.00 for plantations depending upon the developmentstage. Values observed for the bare areas, urban surfa-ces, ploughed lands were 1.00, and 0.4 for agricultureand grasslands.

The SCS curve number is a parameter that deter-mines the amount of precipitation that infiltrates intothe ground or enters surface waters as runoff afteradjusting it to accommodate the antecedent soil mois-ture conditions based on total precipitation for the pre-ceding 5 days (EPA 2003a). A combination of factorssuch as land use/land cover, soil hydrological group,

hydrological conditions, soil moisture conditions andmanagement are used to determine the curve numbers(Arhounditsis et al. 2002). In GWLF model, the CNvalue is used to determine for each land use, the amountof precipitation that is assigned to the unsaturated zonewhere it may be lost through evapotranspiration and/orpercolation to the shallow saturated zone if storage inthe unsaturated zone exceeds soil water capacity. Inpercolation, the shallow saturated zone is considered tobe a linear reservoir that discharges to stream or losses todeep seepage, at a rate estimated by the product ofzone’s moisture storage and a constant rate coefficient(SCS 1986). The soil parameters of the catchment weredetermined by carrying out a comprehensive analysis ofthe soil samples in the laboratory. A total of 50 compos-ite soil samples, well distributed over various land useand land cover categories were collected from the lakecatchment. For the field sampling, similar soil unitswere delineated using the satellite imagery (Khan andRomshoo 2008). This was followed by laboratory anal-ysis of the samples for parameters like texture, organicmatter and water holding capacity. Soil texture wasdetermined by the International Pippeting Method(Piper 1966), field capacity of the samples was deter-mined by Veihmeyer and Hendricjson (1931) and thesoil organic matter/organic carbon was determined bythe rapid titration method (Walkley and Black 1934).Using the field and lab observations of the soil samples,soil texture was determined using the soil textural trian-gle (Toogood 1958). The spatial soil texture map(Fig. 2) and the soil organic matter map (Fig. 3) weredeveloped by stochastic interpolation method in GIS

Table 1 Transport parameters used for different source areas in GWLF model

Source areas Hydrological conditions LS C P K WCN WDET WGET ET coefficient

Agriculture Fair 2.609 0.42 0.52 0.169 82 0.3 1.0 0.4

Horticulture Fair 3.206 0.05 0.1 0.186 87 0.3 1.0 0.6

Forest Fair 46.33 1 1 0.226 68 0.3 1.0 0.7

Hay/pasture Fair 59.38 0.03 0.74 0.255 63 0.3 1.0 0.5

Built up N/A 0.488 0.08 0.2 0.13 94 1 1.0 1.0

Bare land Poor 42.66 0.8 0.8 0.15 89 1.0 0.3 1.0

Good hydrological condition refers to the areas that are protected from grazing and cultivation so that the litter and shrubs cover the soil;fair conditions refer to intermediate conditions, i.e. areas not fully protected from grazing and the poor hydrological conditions refer toareas that are heavily grazed or regularly cultivated so that the litter, wild woody plants and bushes are destroyed

LS slope length and steepness factor, C cover factor, P management factor, K soil erodibility value, WCN weighted curve numbervalues, WDET weighted average dormant season evapotranspiration, WGET weighted average growing season evapotranspiration, ETevapotranspiration coefficient

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environment (Burrough 1986). The soil hydrologicalgroups for all the soil units in the catchment werederived from the soil texture and permeability properties(Fig. 4 and Table 2).

Sediment yield parameters

Several soil and topographic parameters are requiredfor simulating the soil erosion using the GWLFmodel. The LS factor used as a combination of slopelength and slope steepness parameters determinesthe effect of topography on soil erosion and wasderived from the Digital Elevation Model of thestudy area (Arhounditsis et al. 2002). The soil erod-ibility factor (K) of the catchment was generatedfrom the soil texture and soil organic matter contentmaps which were prepared as described above(Steward et al. 1975). The rainfall erosivity factor

(RE) was estimated from the product of the stormenergy (E) and the maximum 30-min rainfall inten-sity (I30) data collected for that period. Erosivitycoefficient for the dry season (May–Sep) was esti-mated to be 0.01 and coefficient for wet season wasestimated to be 0.034 (Montanarella et al. 2000).The crop management factor (C) related to soilprotection cover (Wischmeier and Smith 1978) andthe conservation practice factor (P) that reflects soilconservation measures (Pavanelli and Bigi 2004)were determined from the land use and land covercharacteristics (Haith et al. 1992; EPA 2003b). TheGWLF model estimates the sediment yield by mul-tiplying sediment delivery ratio (SDR) with the es-timated erosion. Use of the logarithmic graph basedon the catchment area (Vanoni 1975; Haith et al.1992; Evans et al. 2008) was made for determiningthe SDR.

Fig. 2 Soil texture map of the study area

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Socioeconomic analysis

Socioeconomic data regarding the various parameterssuch as total population, total households, literacy rateand economic development status for all the watershedsof Dal Lake Catchment was collected from the CensusDepartment, Government of India. The data was thendigitised and converted into GIS format for integrationwith other geospatial data. Figure 5 shows the socioeco-nomic boundaries with respect to different watersheds.

Integrated impact analysis and watershed prioritization

Considering the importance of watershed development forrestoration of aquatic ecosystems, it is necessary to plan theactivities on priority basis for achieving tangible results,which also facilitate addressing the critical source areas toarrive at proper solutions. Once all the data about theLULC change, erosion, sediment and socioeconomic

variables at watershed level was generated, it was thenintegrated into the GIS environment based upon multi-criteria analysis. Multi-criteria evaluation is primarilyconcerned with how to combine the information fromseveral criteria to form a single index of evaluation. Inthe present study, knowledge-based weightage systemwasadopted for watershed prioritization based on its factorsand after carefully observing the field situation. Keeping inview the role of such variables in the deterioration of lakesin general, and Dal Lake in particular, different weightageswere given to each of these parameters depending upontheir importance and relevance to assess their cumulativeimpacts in each of these watersheds (Table 3). A scale of10 was set and weightage of 4 was assigned to land use/land cover change of watersheds. A weightage of 3 wasgiven to erosion and sediment. Socioeconomic variableswere also given a weightage of 3. The basis for assigningweightage to different themes was according to the relativeimportance to each parameter in the study area.

Fig. 3 Soil organic matter map of the study area

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Results

Land use/land cover change detection

The LULC of the watersheds has undergone signifi-cant changes from 1992 to 2005 as depicted by thespatial distribution of these classes in the study areafor the respective years with the change being more

prominent in certain watersheds (Figs. 6 and 7). It wasobserved from Table 4 that some of the classes werefound to be completely absent in certain watershedsfor both the years, while some marked their presenceas a result of the changing land system. From Table 4,it is observed that turf/golf course was present in W3only covering an area of 0.51 km2 in 2005. Snowcover was found to be absent in W1, W3, W4, W5

Fig. 4 Soil hydrological group map of the study area

Table 2 Dominant soil hydrological groups used in the GWLF model (Haith et al. 1992)

Dominanthydrological group

Soil texture Soil runoff potential and permeability properties

A Sand, loamy sand, sandy loam Low surface runoff potential

B Silt loam, loam Moderately course soils with intermediate rates of water transmission

C Sandy clay loam Moderately fine texture soils with slow rates of water transmission

D Clay loam, silty clay loam, sandy clay,silty clay, clay

High surface runoff potential

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and W6 for both the years. In the rest of watersheds, itrecorded the maximum change in W7 with an increaseof 2.35 km2 followed by W13 (+1.58 km2), W11(+0.49 km2), W8 (+0.43 km2), W2 (+ 0.12 km2), W9(+0.05 km2) and W10 (+0.01 km2). Decrease in snowcover was observed for W12 only (−0.14 km2).

Water bodies showed an increase in their area in W1(+0.21 km2), W2 (+0.16 km2) and W4 (+0.13 km2). Adecrease by 0.01 and 0.08 km2 was respectivelyrecorded for W3 and W13. The rest of the watershedsW4, W5, W6, W7, W8, W9, W10, W11 and W12 didnot record the presence of water. Water channel areas

Fig. 5 Distribution of socioeconomic boundaries with respect to watersheds in Dal Lake Catchment

Table 3 Details of parameters used for watershed prioritization

Parameter Data source Factors Weightage

Land use/land coverchange

Derived from satellite imagerieswith extensive field validation

More the decrease in vegetation cover, higherthe priority

4 4

Erosion GIS-based hydrological model Higher the erosion, more the priority 2 3Sediment GIS-based hydrological model Higher the sediment loading, more the priority 1

Total population Census data, Government of India Higher the population, more the priority 1 3Total households Census data, Government of India Higher the number of households, more the priority 0.5

Literacy rate Census data, Government of India Lower the literacy, higher the priority 0.5

Economic developmentstatus

Census data, Government of India Lower the economic development status, higherthe priority

1

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were found to be absent for W1, W2, W3 and W4 forboth 1992 and 2005. W7 marked a decrease in the areaby 0.06 km2. In the rest of the watersheds, water chan-nels showed an increase with the maximum in W10(+1.2 km2) followed by W6 (+0.18 km2), W11(+0.09 km2), W12 (+0.08 km2), W13 (+0.05 km2),W5 (+0.02 km2) and W8 (+0.01 km2).

Bare land class was recorded in all the watershedsand showed an increasing trend in each watershed. Themaximum increase was recorded for W5 (+3.26 km2),followed by W4 (+2.34 km2), W3 (+1.98 km2), W2(+1.97 km2), W13 (+1.83 km2), W11 (+1.35 km2),W8 (+1.1 km2) and W6 (+1.05 km2). The remainingwatersheds namely W7 (+0.57 km2), W9 (+0.5 km2),W1 (+0.11 km2) and W10 (+0.06 km2) showed slightincrease in the bare land area. Bare exposed rocks weremostly confined to the upper watersheds but markedtheir presence down the mountain reaches as well.Increase in area is observed for W11 (+1.11 km2), fol-lowed by W13 (+0.65 km2), W11 (+ 0.15 km2), W2

(+0.01 km2) andW3 (+0.01 km2). Decline in the area ofbare exposed rocks was recorded for W8 (−0.05 km2),W10 (−0.04 km2), W9 (−0.03 km2), W7 (−0.01 km2)and W5 (−0.01 km2). Table 4 reveals that the built-upclass was absent in W7, W11, W12 and W13. Increasein area was observed for W1 (+3.65 km2), followed byW2 (+3.23 km2), W3 (+1.99 km2), W5 (+ 1.53 km2),W4 (+ 1.41 km2), W6 (+0.91 km2), W8, (+0.17 km2),W10 (+0.12 km2) and W9 (+0.01 km2).

Agriculture class was found to be absent in W9,W10, W11 and W12. In the remaining watersheds,both increasing as well as a decreasing trend wasobserved. W8 (−1.45 km2) followed by W7(0.48 km2) and W6 (0.15 km2) showed an increasein agriculture area as shown in Table 4, whereas, adecline in area was observed for W5 (−1.29 km2), W3(−0.78 km2) and W4 (−0.52 km2). Analysis of thestatistics for horticulture revealed a decrease in areain W1 (−3.9 km2), followed by W3 (−2.21 km2), W8(−0.74 km2), W5 (−0.72 km2) and W4 (−0.49 km2).

Fig. 6 Land use/land cover map of the watersheds in 1992

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W2 and W7 showed slight increase in area, while inremaining watersheds the class was found to be absentfor both the years. Fallow land class was found to bepredominantly absent in most of the watersheds of DalLake Catchment. It was found to cover very small areaand at the same time showed a decreasing trend in areawith W2 (−0.22 km2), W3 (−0.22 km2) followed byW4 (−0.17 km2) and W5 (−0.13 km2) being the onlywatersheds recording the presence of fallow land.

The results for coniferous forest class revealed a de-cline in majority of the watersheds and was found to beabsent in W1 for both 1992 and 2005. Maximum declinewas recorded in W3 (−1.82 km2) followed by W12(−1.06 km2), W13 (−0.65 km2), W11 (−0.55 km2), W7(−0.54 km2), W10 (−0.39 km2), W9 (−0.36 km2), W8(−0.35 km2), W5 (−0.26 km2) and W2 (−0.07 km2). Aslight increase was observed forW4 (+0.36 km2) andW6(+0.01 km2). A similar trend was observed for deciduousforests with W3 recording a decline (−1.3 km2) followed

by W12 (−1.21 km2), W10 (−1.04 km2), W2(−0.98 km2), W8 (−0.92 km2), W13 (−0.65 km2), W9(−0.60 km2), W6 (−0.35 km2), W5 (−0.27 km2), W11(−0.41 km2) andW7 (−0.18 km2). The only increase wasobserved for W4 where an increase by 0.92 km2 wasrecorded. Sparse forest class was found to be present inall the watersheds for both 1992 and 2005 with the samedecreasing trend as that of the other forest classes. It wasobserved from Table 4 that W3 (+2.91 km2), W11(+2.72 km2) andW4 (+0.28 km2) are the only watershedswhere increase in area was recorded for the respectiveyears. For the remaining watersheds, sparse forests de-creased in area with the major decline in W13 (−1.86Km2) followed by W6 (−1.32 km2), W7 (−0.92 km2),W2 (−0.88 km2), W5 (−0.63 km2), W8 (−0.52 km2),W12 (−0.52 km2), W10 (−0.23 km2), W1 (−0.04 km2)and W9 (−0.03 km2).

Grasslands/pasturelands showed a declining trend inmajority of the watersheds. The decrease in area was

Fig. 7 Land use/land cover map of the watersheds in 2005

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Tab

le4

Chang

ein

theland

use/land

coverpattern

inwatersheds(199

2–20

05)

Sam

ple

no.

Class

names

DW1

DW2

DW3

DW4

DW5

DW6

DW7

DW8

DW9

DW10

DW11

DW12

DW13

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1992

2005

1Turf

0.00

0.00

0.00

0.00

0.00

0.51

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2Snow

0.00

0.00

0.00

0.12

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2.35

0.00

0.43

0.00

0.05

0.00

0.01

0.84

1.33

0.15

0.01

0.52

2.10

3Water

bodies

0.38

0.59

0.59

0.43

0.05

0.04

0.00

0.13

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.49

0.41

4Water

channel

area

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.03

0.11

0.29

0.12

0.06

0.01

0.02

0.00

0.01

0.00

1.20

0.24

0.15

0.02

0.10

0.62

0.67

5Bareland

0.00

0.11

0.88

2.85

0.12

2.10

0.29

2.63

2.67

5.93

0.73

1.78

1.31

1.88

0.27

1.37

0.17

0.67

0.31

0.37

3.16

4.51

1.06

1.81

5.18

7.01

6Bareexposed

rocks

0.00

0.00

0.00

0.01

0.00

0.01

0.00

0.00

0.02

0.01

0.01

0.01

1.73

1.72

0.42

0.37

0.19

0.16

0.14

0.10

2.81

3.92

0.87

1.02

7.29

7.94

7Builtup

2.15

5.80

6.07

9.30

0.08

2.07

0.11

1.52

0.12

1.65

0.07

0.98

0.00

0.00

0.02

0.19

0.00

0.01

0.00

0.12

0.00

0.00

0.00

0.00

0.00

0.00

8Agriculture

0.41

0.16

1.31

0.55

1.01

0.23

0.99

0.47

6.05

4.77

3.62

3.77

0.09

0.57

0.74

2.19

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.11

9Horticulture

5.28

1.38

0.74

0.76

3.00

0.79

1.57

1.08

9.54

8.82

5.37

5.06

0.00

0.05

2.03

1.29

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

10Fallow

0.04

0.00

0.22

0.00

0.22

0.00

0.13

0.00

0.17

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

11Grasslands

0.00

0.02

0.12

0.01

1.40

0.02

0.21

0.01

1.37

0.06

0.83

0.33

1.03

0.33

2.24

2.37

5.04

5.84

4.70

4.08

2.67

1.89

5.44

5.37

4.44

3.87

12Coniferous

forest

0.00

0.00

0.10

0.03

5.05

3.23

0.86

0.50

1.08

0.82

1.97

1.98

6.93

6.39

5.36

5.01

6.10

5.74

8.79

8.40

3.13

2.58

12.11

11.05

2.86

2.21

13Deciduous

forest

0.00

0.00

1.72

0.74

9.03

7.73

2.42

1.50

3.34

3.07

3.80

3.45

4.89

4.71

12.41

11.49

12.00

11.38

10.50

9.46

4.48

4.07

7.81

6.60

5.85

5.20

14Sparseforest

0.04

0.00

1.10

0.22

0.53

3.44

1.12

1.40

4.35

3.72

3.07

1.84

1.92

1.00

2.20

1.68

0.60

0.57

1.04

0.81

3.94

1.22

1.19

0.67

3.98

2.12

15Scrubland

0.00

0.01

0.01

0.00

0.00

0.73

0.00

0.01

0.02

0.30

0.01

0.22

0.02

0.33

0.01

0.57

0.00

0.46

0.01

0.92

1.63

4.96

0.44

2.65

1.37

4.09

16Aquatic

vegetatio

n0.71

3.62

0.31

0.65

0.00

0.00

0.00

0.10

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

17Plantation

9.40

6.72

13.63

11.13

4.20

3.80

6.10

4.45

3.26

2.81

1.51

1.39

1.65

0.31

2.59

1.32

0.80

0.01

0.01

0.03

1.80

0.07

0.21

0.02

3.21

0.11

Total

18.41

18.41

26.8

26.8

24.7

24.7

13.8

13.8

32.0

32.0

21.1

21.1

19.7

19.7

28.3

28.3

24.9

24.9

25.5

25.5

24.7

24.7

29.3

29.3

35.84

35.84

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found to be highest inW3 (−1.38 km2), followed byW5(−1.31 km2), W11 (−0.78 km2), W10 (−0.62 km2), W7(−0.70 km2), W13 (−0.57 km2), W6 (−0.50 km2), W4(−0.20 km2) and W2 (−0.11 km2). W9 (+0.8 km2) fol-lowed by W8 (+ 0.13 km2) and W1 (+0.02 km2) are theonly watersheds that showed an increase in the

grassland cover. Scrublands revealed an increasing trendin all the watersheds except for W2 which showed adecline by 0.01 km2. The highest change was witnessedfor W11 (+3.33 km2) followed by W13 (+2.72 km2),W12 (+2.21 km2), W10 (+0.91 km2), W3 (+0.73 km2),W8 (+0.56 km2), W9 (+0.46 km2), W7 (+0.31 km2),

Table 5 Classification accuracy of the land use and land cover of the study area

Class name Referencetotals

Classifiedtotals

Numbercorrect

Producer’saccuracy (%)

Users’accuracy (%)

Kappastatistics

Built up 10 9 8 80 88.90 0.8851

Agriculture 5 6 5 100 83.33 0.8305

Horticulture 10 9 9 90 100.00 1

Coniferous forest 24 24 22 91.67 91.67 0.9094

Deciduous forest 32 33 28 87.5 84.85 0.8304

Sparse forest 10 9 8 80.00 88.89 0.8851

Grasslands 14 12 12 85.71 100.00 1

Scrubland 5 6 5 100 83.33 0.8305

Plantation 14 15 12 85.71 80.0 0.7902

Aquatic vegetation 2 3 2 100 66.67 0.6644

Barren 14 12 11 78.57 91.67 0.9126

Bare exposed rocks 5 6 4 80.00 66.67 0.6610

Water 6 6 6 100.00 100.00 1

Snow 2 3 2 100.00 66.67 0.6644

Totals 300 300 281 0.91314

Overall accuracy=93.67 %

Table 6 Watershed contributionto erosion and sediment loadunder changed land use/landcover

Watershed ID Erosion (tons/year) Sediment (tons/year)

1992 2005 Change 1992 2005 Change

W1 11.74 50.89 39.15 2.32 8.37 6.05

W2 41.99 67.05 25.06 8.29 15.86 7.57

W3 53.66 92.67 39.01 19.09 31.39 12.3

W4 26.81 45.39 18.58 5.24 9.3 4.06

W5 269.29 505.22 236.93 43.6 80.3 36.7

W6 125.04 201.53 76.49 22.52 32.76 10.42

W7 44.17 78.95 34.78 10.21 17.11 6.9

W8 44.26 100.42 56.16 12.08 27.28 15.2

W9 7.8 20.62 12.82 2.18 8.24 6.06

W10 0.04 10.08 10.04 0.01 0.95 0.94

W11 161.68 216.23 54.55 30.2 36.85 6.65

W12 40.87 81.71 40.84 7.94 10.76 2.82

W13 474.94 482.9 7.96 68.78 75.48 6.7

Total 1,302.29 1,953.66 651.37 232.45 354.65 122.2

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W5 (+0.28 km2), W6 (+0.21 km2), W4 (+0.01 km2) andW1 (+0.01 km2).

It was also observed that the plantation covershowed a declining trend from 1992 to 2005. Themajor changes were recorded in W13 (−3.1 km2) fol-lowed by W1 (−2.68 km2), W2 (−2.5 km2), W11(−1.73 km2), W4 (−1.65 km2), W7 (−1.34 km2), W8(−1.27 km2), W9 (−0.79 km2), W5 (−0.45 km2), W3(−0.40 km2), W12 (−0.19 km2) and W6 (−0.12 km2).The only increase in plantation cover was observed forW10 (+0.02 km2). The statistics for aquatic vegetationrevealed that this class was restricted in its occurrenceand recorded an increase in W1 (+2.91 km2) followedby W2 (+0.34 km2), W4 (+0.10 km2) and W5(+0.01 km2).

The overall accuracy of the classified land use/landcover data was observed to be 93.67 % (Table 5) witha kappa coefficient of 0.913.

Model simulation results

The results of the model simulations for erosion andsediment loadings revealed an increasing trend in allwatersheds (Table 6). The spatial distribution of the in-creasing trend of the watersheds is given in Figs. 8 and 9.It was observed that maximum increase in the erosionyield was recorded for W5 with (236.93 t/year) followedby W6 (76.49 t/year), W8 (56.16 t/year) and W11(54.55 t/year). Watersheds namely W9 (12.82 t/year),W10 (10.04 t/year) and W13 (7.96 t/year) recorded leastincrease. Similarly, the highest increase in sediment load-ings was recorded for W5 (36.7 t/year) followed by W8(15.2 t/year), W3 (12.3 t/year) andW6 (10.42 t/year) andW2 (7.57 t/year). Whereas, W4 (4.06 t/year), W12(2.82 t/year) andW10 (0.94 t/year) showed less increase.

Source area (land use/land cover) contributions forerosion and sediment yields (Table 7) revealed that bare

Fig. 8 Watershed wise increased erosion loading under changed land use/land cover

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lands followed by agriculture, forests and hay/pastureexperienced the maximum loadings. Horticulture, high-

intensity and low-intensity developed areas recorded neg-ligible contributions. Further analysis of the data in

Fig. 9 Watershed wise increased sediment loading under changed land use/land cover

Table 7 Source area contribution to erosion and sediment loads under changed land use/land cover

Source Erosion (tons/year) Sediment (tons/year)

1992 2005 Change 1992 2005 Change

Hay/pasture 11.41 57.55 46.14 3.2 26.19 22.99

Agriculture 94.25 117.50 23.25 30.1 49.68 19.58

Forest 25.16 27.88 2.72 1.3 8.64 7.34

Horticulture 0.133 0.15 0.02 0.0 0.01 0.01

Turf/golf course − 0.02 0.02 − 0.00 0.00

Bare land 1,171.16 1,750.06 578.9 90.7 121.31 30.61

Low-intensity development 0.018 0.05 0.03 0.9 0.00 0.9

High-intensity development 0.164 0.44 0.27 0.0 0.02 0.02

Stream bank 106.2 148.80 42.6

Totals 1,302.295 1,953.66 651.37 232.4 354.65 122.25

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Table 7 showed that the major increase in erosion load-ings was recorded for bare lands (578.9 t/year) followedby hay/pastures (46.14 t/year), agriculture (23.3 t/year)and forests (2.72 t/year). Increase in sediment loadswas observed to be highest for the stream banks(42.6 t/year) followed by bare lands (30.61 t/year),pasture/grasslands (22.9 t/year) and agriculture(19.58 t/year). Horticulture, high-intensity and

low-intensity developed areas again recorded insig-nificant changes in the sediment loadings.

Socioeconomic characterization

The results for socioeconomic characterisation given inTables 8 and 9 revealed that almost half the number ofwatersheds in the catchment are uninhabited because of

Table 8 Ward wise socioeco-nomic characterization Ward no. Total

householdsTotalpopulation

Populationdensity

Literacyrate

Economicdevelop status

01 6,008 40,632 28.92 55.64 66, 042.20

02 3,427 24,067 34.85 65.50 53, 896.29

03 2,027 17,755 7.87 70.94 25, 645.94

04 6,398 41,715 360.73 69.12 2,56,113.30

05 7,159 50,293 655.97 65.12 3,48,646.80

06 4,700 35,507 346.38 70.82 21,44,414.90

07 7,307 68,103 554.95 63.51 5,28,950.30

08 9,107 66,586 280.41 51.62 3,09,802.10

09 6,274 44,905 296.42 59.43 2,34,953.50

10 2,658 19,505 66.42 62.73 50,518.20

11 4,252 30,107 49.09 8.61 63,889.00

12 5,710 38,432 38.21 59.02 64,569.10

13 4,443 32,020 17.33 57.11 38,260.10

14 7,504 53,295 68.99 50.49 1,25,303.20

15 6,011 41,928 45.63 57.86 86,360.50

22 5,572 38,369 108.64 52.64 1,17,591.70

30 372 5,599 39.74 95.80 32,105.90

CB 3,074 18,923 58.36 75.66 67,805.00

Table 9 Watershed wise socio-economic characterization

− uninhabited

ID Watershed name Totalpopulation

Totalhouseholds

Literacy rate Economic developmentstatus

1 W1 28,463 3,921 52.63 Low

2 W2 65,228 9,562 69.64 High

3 W3 10,519 1,555 55.64 Low

4 W4 17,473 2,578 55.64 Low

5 W5 14,672 2,067 55.54 Medium

6 W6 14,980 2,169 57.25 Medium

7 W7 − − − −8 W8 1,731 243 56.37 Low

9 W9 − − − −10 W10 − − − −11 W11 − − − −12 W12 − − − −13 W13 − − − −

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their high altitude, dense forested and remote nature.Figures 10 and 11 show the spatial distribution of totalpopulation and number of households respectively for thedifferent watersheds. Among the populated watersheds,DW2 recorded the highest population (65,228 individu-als) and the highest number of households (9,562). Thiswatershed mainly comprised of the congested Srinagarcity west and the south. This was followed byW1 (28,463individuals and 3,921 households) again comprising ofthe Srinagar city west. It was found that W4 (17,473individuals and 2,578 households) included eastern partsof the catchment comprising of the areas of Nishat,Shalimar, etc. W6 (14,980 individuals and 2,169 house-holds) andW5 (14,672 individuals and 3,921 households)comprised of the northern parts of the city in Dal LakeCatchment. W3 (10,519 individuals and 1,555 house-holds) includes the city east side. W8 (1,731 individualsand 243 households) recorded the lowest population andlowest number of households. This watershed comprised

of the eastern portion of the lake catchment including theDachigam National Park. The highest literacy rate(Fig. 12) was found for W2 (69.64) followed by W6(57.25), W8 (56.37), W3, W4 (55.64) and W5 (55.54).The lowest literacy was recorded for W1 (52.63).Watersheds were categorised into high, medium and lowas per their economic development status (Fig. 13). W2belonged to the highest, W1, W5, W6 to the medium andW3, W4, W8 belonged to the low category.

Integrated impact analysis and watershed prioritization

On the basis of priority and cumulative weightageassigned to each thematic map, all 13 watersheds weregrouped into three categories: high, medium and lowpriority shown in Table 10. Figures 14 and 15 show thespatial distribution of the prioritized watersheds. It wasobserved that five (5) watersheds namelyW5>W2>W6>W8>W1 ranked highest in the overall weightage and

Fig. 10 Spatial distribution of total population in the watersheds

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hence are considered under high priority. Of the remain-ing eight watersheds, five watersheds namelyW13>W3>W4>W11>W7 were considered under the medium-priority category. The remaining three watersheds, i.e.W12>W9>W10, fell under low-priority category.

Discussion

Land use/land cover change analysis at watershed levelin Dal Lake Catchment for the 15-year time period(1992–2005) revealed significant changes. The typeand distribution of land use/land cover substantiallyaffects a number of hydrological processes such asrunoff, erosion and sediment loadings that in turn pro-foundly affect lake ecosystems (Matheussen et al. 2000;Fohrer et al. 2001; Quilbe et al. 2008). During the studyperiod, considerable changes were observed for almostall the land use/land cover classes particularly

agriculture, horticulture, built up, bare lands, grasslands,scrublands and forests. These changes are largely attrib-utable to the activities of man as land use/land cover isamong the most evident impacts of human activities onnatural resources (Lundqvist 1998), and can be observedusing current and archived remotely sensed data withthe potential scientific value for the study of human–environment interaction and aid in ascertaining the im-pact of land use on the amount of pollution (Tekle andHedlund 2000; Tong and Chen 2002; Tang et al. 2005;Tong et al. 2008). Understanding the land use/land covercharacteristics at the watershed level is essential as suchproperties determine the erosion and the pollution po-tential of the watersheds.

Agriculture and horticulture classes showed a de-cline with a progressive increase in the built-up area.Increased population and congestion in the old cityhave resulted in the conversion of large peripheralareas that were essentially used for agro-horticultural

Fig. 11 Spatial distribution of total households in the watersheds

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purposes into built up mostly for residential purposes.Accelerated nutrient enrichment of the Dal Lake dueto incoming effluents from these watersheds resultedin the proficient and luxuriant growth of macrophytesthat was revealed by the increased area of aquaticvegetation. In the later parts of the year, the surfacewaters remain covered by the decomposed thick matsdisrupting the ecological balance of the lake (Khan2000; Pandit 1999).

Large-scale decline in grassland area revealed tremen-dous pressures on this ecologically and socioeconomicallyimportant land cover attributed to the biotic interference inand around the Dachigam National Park including clear-ing of the grasslands at the low altitudes for cultivation,exploitation for medicinal plants and other activities.Several decades of grazing and that too beyond the carry-ing capacity have resulted in the creation of denuded andsemi-denuded patches in these grasslands (Bhat et al.2002). Increased scrubland area may be attributed to the

dwindling grasslands as well as the sparse forests in thewatersheds. Decline in the coniferous, deciduous andsparse forest of the study area was found to be the resultof large-scale deforestation, both within the DachigamNational Park as well as outside it particularly along thehigher reaches of the catchment. Increase in the area ofbare lands during study period at both the higher andlower elevations of the Dal Lake Catchment was ob-served. It was found that the overgrazed grasslands anddeforested areas have paved the way for creation of barrenarea. This land is very much vulnerable to increasederosion and sediment yields as well increased runoff(Shah and Bhat 2004).

The erosion and sediment loadings varied for differ-ent watersheds depending on the topography, land use/land cover, soil type as these are the principal factorsinfluencing contaminant transport in a watershed (Vieuxand Farajalla 1994; Barnes 1997). The increase, al-though small in certain watersheds, was by and large

Fig. 12 Spatial distribution of literacy rate in the watersheds

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reflective of the changing biophysical charcteristics ofthese watersheds attributable mostly to the increasedanthropogenic pressures. The increased erosion andsediment loadings were in particular observed for thosewatersheds where the stress on the vegetation was themaximum, namely W5, W13, W11, W6 and W8. Inaddition, various agro-horticultural activities carried outparticularly in W5, W6 and W8 accelerate the potentialfor the processes of surface runoff and soil erosion(Stoate et al. 2001; Van Rompaey et al. 2001; Hansenet al. 2004). Biotic interferences like overgrazing ofgrasslands beyond the carrying capacity, clearing offorest areas for contruction and agricultural purposeshas led to the creation of denuded patches acceleratingthe erosion (Bhat et al. 2002). Moreover, increasedbarren and scrubland surfaces also contributed largelyto runoff without much infiltration capacity. Such water-sheds were also found to have fairly good area understeep and very steep slope classes indicating quick

Fig. 13 Spatial distribution of economic development status in the watersheds

Table 10 Results of prioritization carried out for watersheds inDal Lake Catchment

S. No Watershed name Priority result Priority rank

1 W1 High PZ5

2 W2 High PZ2

3 W3 Medium PZ7

4 W4 Medium PZ8

5 W5 High PZ1

6 W6 High PZ3

7 W7 Medium PZ10

8 W8 High PZ4

9 W9 Low PZ12

10 W10 Low PZ13

11 W11 Medium PZ9

12 W12 Low PZ11

13 W13 Medium PZ6

PZ priority zone

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runoff during rainfall or storm water events (Tucker andBras 1998). Stone quarring in W8, although bannednow, resulted in largely degraded and defaced moun-tains posing serious threats of soil erosion and land-slides. The subsequent sediment loss, carrieddownslope pollutes the waters of Dal lake (Shah andBhat 2004). Watersheds namely W12 and W7 recordedno major changes in the land use/land cover because ofnegligible anthropogenic pressures/activities and henceminimal increase in erosion and sediment yields.Vegetation changes are often the result of anthropogenicpressures (Janetos and Justice 2000).

W3, W2, W4 and W1 because of their urbanisedenvironment and impervious nature and flat slopesprovided minimum probabilities of erosion and sedi-ment loss, even though subject to high runoff. W9 andW10 were the least contributors owing to their highlyforested nature and thick vegetative cover. Besides,being alpine in nature makes them inaccesssible to

the anthropogenic pressures, thereby, preventing theloss of vegetative and canopy cover (Rishi 1982;Guerra et al. 1998; Janetos and Justice 2000).

Bare lands, hay/pastures and agriculture were themajor source area contributors for erosion and sedi-ment loads as these are more erodible than the vege-tated areas (Singh and Prakash 1985). Higher rates ofsoil and sediment loss have been reported from else-where from cultivated areas (Dunne et al. 1978;Brown 1984; Ouyang and Bartholic 2001). Increasedscrublands primarily due to the degradation of grass-lands has also resulted in increased loads of sedimentand erosion. Forests, horticulture and developed areaswere the least contributors because of their vegetativeand impervious nature respectively (Mkhonta 2000).The sediment/silt generated from various land use/land cover categories in the watersheds finally flowsinto the lake largely through the Telbal Stream result-ing in decreased depth and volume of water and lake

Fig. 14 Watershed prioritization map of Dal Lake Catchment

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ageing (Zutshi and Yousuf 2000). Owing to the inad-equate land use management in the catchment, DalLake receives large amounts of eroded soil that hasdisrupted the ecological balance of the lake.

Socioeconomic GIS integrated with the biophys-ical remote sensing has emerged as a new andpromising field that provides insights into the so-cioeconomic aspects of environmental and physicalproblems and could be used as a useful aid forlinking the environmental problems to communi-ties (Buckle et al. 2006). High- and medium-prioritized watersheds suggested that the changesin the biophysical environment and the behaviourof different land surface processes are reflective ofthe different socioeconomic pressures (Moldan etal. 1997; Peters and Maybeck 2000). Alteration ofthe landscape and other human-caused disturbanceshave been shown to be important factors affectingmass transport (loading) of erosion and sediment

to the lakes (Loeb 1988). These watersheds can betaken up to develop a robust strategy for mitiga-tion and control of the lake deterioration on asustainable basis with immediate effect to preventthe further degradation of the Dal Lake. For thesewatersheds, a detailed survey for soil and waterconservation measures, water resources develop-ment, scientific land use planning for preservationof eco-diversity, integrated study for developmentof natural as well as social resources, etc., toaccelerate the rehabilitation and to generate a de-tailed database in each natural resources theme, isa pre-requisite for formulation of watershed planfor its sustainable development and management.The low-prioritized watersheds may be taken upfor development and management plans in aphased manner (Vittala et al. 2008). Since thisapproach is considered to be ideal in maintainingthe ecological balance (Sahai 1988), it shall,

Fig. 15 Spatial distribution of watershed priority zones

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greatly help in devising the conservation and man-agement strategies for the restoration of the lakeecosystem (Prasad et al. 1997; Biswas et al. 1999;Khan et al. 2001; Gosain and Rao 2004).

Conclusion

During the present research, an integrated approachbased on the use of multi-sensor and multi-temporalsatellite data, GIS simulation model (GWLF) togetherwith extensive field observations was used for the firsttime to conduct an in-depth investigation of differentwatershed scale processes (land use/land cover changedetection analysis, quantification of erosion, sedimentand socioeconomic analysis) in all the 13 watershedsof the Dal Lake Catchment and quantify their impactson Dal Lake. With the help of this integrated method-ology, remote sensing data was used to generate up-to-date information about different parameters, simula-tion models and geospatial techniques were used tosimulate the hydrological, sediment, erosion process-es. This contemporary approach was fully aided by theextensive field surveys carried out for ground truthingof the remote sensing data as well as for the samplingpurposes that aided in an on spot investigation of thestudy area. As a result of this integrated approach acollective understanding of the critical source areas inthe lake catchment has been possible that would behelpful in addressing the watershed problems affectingthe Dal Lake ecosystem at the root cause level. Thelimitation of this study was the non-availability of thelatest socioeconomic data at the watershed level thatcould help in better identification and assessment ofsocioeconomic pressures. The current study made useof the 2001 Census data. Besides the GWLF model,simulations can be improved upon by incorporatingmore surface processes data (nutrient runoff, pointsource data etc.) that was not available at the time ofthe study.

The integration of the biophysical and the socioeco-nomic environment taken up at the watershed levelduring the current study shall aid in developing anddesigning the conservation and management plans vis-à-vis water quality restoration programme of the DalLake ecosystem. The watershed prioritization, in partic-ular, shall facilitate the development of a robust strategyin the critically impaired watersheds for the control ofpollution and conservation and management plans with

immediate effect. The researchmethodology establishedduring the present study should help in the effectiveconservation and management of other threatened la-custrine ecosystems of Kashmir Himalaya.

Acknowledgments The authors are thankful to the IndianMeteorological Department and Division of Agronomy,Sher-e-Kashmir University of Agricultural Sciences andTechnology of Kashmir, Shalimar for providing hydrome-trological data for this study.

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