Prioritizing Dal Watersheds


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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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Prioritizing Dal Watersheds

  1. 1. Environ Monit AssessDOI 10.1007/s10661-012-3035-9Integrating biophysical and socioeconomic informationfor prioritizing watersheds in a Kashmir Himalayan lake:a remote sensing and GIS approachBazigha Badar & Shakil A. Romshoo & M. A. KhanReceived: 3 May 2012 / Accepted: 4 December 2012# Springer Science+Business Media Dordrecht 2013Abstract Dal Lake, a cradle of Kashmiri civilization has watershed prioritization based on its factors and afterstrong linkage with socioeconomics of the state of carefully observing the field situation. The land use/landJammu and Kashmir. During last few decades, anthropo- cover change detection revealed significant changes withgenic pressures in Dal Lake Catchment have caused a uniform trend of decreased vegetation and increasedenvironmental deterioration impairing, inter-alia, sus- impervious surface cover. Increased erosion and sedimenttained biotic communities and water quality. The present loadings were recorded for the watersheds correspondingresearch was an integrated impact analysis of socioeco- to their changing land systems, with bare and agriculturenomic and biophysical processes at the watershed level lands being the major contributors. The prioritizationon the current status of Dal Lake using multi-sensor and analysis revealed that W5>W2>W6>W8>W1 rankedmulti-temporal satellite data, simulation modelling to- highest in priority and W13>W3>W4>W11>W7 undergether with field data verification. Thirteen watersheds medium priority. W12>W9>W10 belonged to low-(designated as ‘W1–W13’) were identified and investi- priority category. The integration of the biophysical andgated for land use/land cover change detection, quantifi- the socioeconomic environment at the watershed levelcation of erosion and sediment loads and socioeconomic using modern geospatial tools would be of vital impor-analysis (total population, total households, literacy rate tance for the conservation and management strategies ofand economic development status). All the data for the Dal Lake ecosystem.respective watersheds was integrated into the GIS envi-ronment based upon multi-criteria analysis and Keywords Dal Lake . Watershed . Remote sensing .knowledge-based weightage system was adopted for Land use/land cover . GWLF . PrioritizationB. Badar (*) : S. A. RomshooDepartment of Geology and Geophysics, IntroductionUniversity of Kashmir,Hazratbal, Lakes are extremely fragile and sensitive ecosystems onSrinagar, Kashmir 190006, India earth that host rich aquatic biodiversity. Besides beinge-mail: the key components of our planet’s hydrological cycle,M. A. Khan they provide important social and ecological functionsDivision of Environmental Science, (Ballatore and Muhandiki 2002). Despite the fact thatShere Kashmir University of Agricultural Sciences freshwater bodies are very limited and sensitive resour-and Technology of Kashmir,Shalimar, ces that need proper care and management, they areSrinagar, Kashmir 190006, India probably the most neglected and mismanaged natural
  2. 2. Environ Monit Assessresources. While some problems originate in a lake activities and encroachment of the lake area by the lakeitself, the vast majority of problems originate from ac- dwellers has also contributed to the deterioration of thesetivities on the surrounding land (ILEC 2005). Resource once pristine lakes.development, wise use and judicious conservation of With rapid socioeconomic changes and various en-lakes have been major challenges across the continents, vironmental perturbations during the last few decades,particularly with regard to satisfying human needs with- Dal Lake ecosystem has degraded significantly, result-in, and sometimes beyond, the lake basin. Lakes are ing in increased ecological vulnerability and hydrolog-largely dependent on their watersheds for the energy ical disruption (Trisal 1987; Khan 1993a, b; 2000).and matter, with the nature of actions in these water- During the last few decades, anthropogenic interven-sheds driving the course of the reactions within these tions in the catchment like unplanned urbanization, de-water bodies. Watershed deterioration mainly because forestation, intensive grazing, stone quarrying etc. haveof improper and unwise utilization of watershed resour- exerted tremendous pressures on the world famousces without any proper vision is a common phenomenon freshwater ecosystem. Increase in agricultural activityin most parts of the world (FAO 1985). Degraded water- and the reduction of plant cover on the hillsides sur-sheds ultimately result in high nitrogen and phosphorus rounding the lake with the consequential increase inloads, algal bloom and toxicity, low oxygen and fish surface erosion and leaching of soil nutrients have addedkills, loss of aquatic habitat, changes in community increasing quantities of nutrient-rich runoff (Badar andstructure, loss of recreational amenity in these aquatic Romshoo 2007). Increase in impervious surfaces likeecosystems (Kira 1997; Dinar et al. 1995; Duker 2001; barren, built-up and deforested areas of the Dal LakeJorgensen et al. 2003). Inflowing substances, including Catchment has caused the peak flow to swell over thesediments, minerals, nutrients and organic materials, period of time (Amin and Romshoo 2007). Nearbycoming from the watersheds tend to accumulate in the farming practices have also added to the amount andwater column or the lake bottom (World Lake Vision rate of silt generated and added to the lake watersCommittee 2003), thereby, deteriorating these freshwa- (Pandit and Fotedar 1982; Pandit and Qadri 1990).ter ecosystems. Further, interruptions to the internal flow of lake water Kashmir Valley is known world over for its natural caused by weirs, islands, bunds, land between house-beauty, which comprises of some of the most beautiful boats, etc. have reduced the capacity of the lake tomountains, forests, lakes and streams. The lakes of respond to the stresses placed on it. The Dal LakeKashmir identified as Glacial, Pine-forest and Valley drainage is characterised by a myriad of channelslakes based on their origin, altitudinal situation and nature (Meerakshah, Nallah Amir Khan, Brari Nambal andof biota, provide valuable research opportunities (Zutshi Chuntkul) which have been filled up during the lastet al. 1972; Kaul 1977; Zutshi and Khan 1978; Pandit two decades due to excessive siltation, sewage inflow1996, p. 99). These lakes vary from being oligotrophic to and garbage dumping reducing their water holding ca-eutrophic, while others are in the process of continuous pacity and disrupting the ecological balance of the lake.change towards eutrophication (Kaul 1979; Khan 2008). The gradual reclamation of the lake to provide buildingWhile these changes result in part from the natural course and vegetable growing land and the increase in the areaof biotic, climatic and other environmental factors but in of floating gardens have combined with natural process-the recent times these have been primarily because of the es to reduce the area of open water within the lake area.human interferences. Eutrophication and dwindling of A sizeable (20 %) portion of the lake is covered bylake ecosystems in Kashmir Himalayan lakes is a recent floating gardens reducing the open water area toevent of the past 10–30 years, coinciding with a marked (59 %) of the total Dal Lake area (Khan 2000).civilization evolution in the lake drainage basins (Pandit Water quality degradation in Dal Lake is a major1998). Since, there has not been much development as concern, and improving the ecological status of thisregards the industrialization in the Kashmir valley, the large water body is now a regional and national priority.main contributors towards the eutrophication of the water Although scientific knowledge concerning the causesbodies are land use changes in the catchment, unplanned and effects of stresses on the lake has grown rapidly,urbanization, increased sedimentation, flow of fertilisers effective management policies have lagged in mostand pesticides from the catchment (Pandit and Qadri cases. The motivation for this study stems from the need1990; Badar and Romshoo 2007). Socioeconomic for simple and reliable information that could facilitate
  3. 3. Environ Monit Assessthe participation of stakeholders and decision makers in grained sands, gravels, marls, silts, varved clays, brownthe implementation of water quality programs, thereby, loams, lignite, etc. (Wadia 1971; Varadan 1977; Dataimproving the chances of the Dal Lake restoration. The 1983; Bhat 1989). A number of underground springswatershed management concept recognizes the inter- and streams feed the Dal Lake but the main source isrelationships and linkages between various biophysical the Dachigam Creek, originating from the alpine Marsarand socioeconomic processes (Moore et al. 1977; FAO Lake. The catchment belongs to a Sub-Mediterranean1985; Honore 1999) and has been identified as the type climate with four seasons based on mean tempera-fundamental unit for conservation and restoration pro- ture and precipitation (Bagnoulus and Meher-Homjigrammes. Earlier, integrated approach for watershed 1959). The catchment receives an average annual rainfallprioritization using remote sensing and Geographical of 650 mm at Srinagar station and 870 mm at DachigamInformation System (GIS) data has been successfully station. March, April and May are the wettest months ofattempted by several workers (Prasad et al. 1997; the year. The temperature varies between a monthlyBiswas et al. 1999; Khan et al. 2001; Gosain and Rao mean maximum of 31 °C in July and a minimum of2004). Under this context, the study was carried out with −4 °C in January with an average of 11 °C. Thirteenthe objectives (1) to assess change in land use/land cover watersheds in the lake catchment, designated as ‘W1–at watershed level, (2) to quantify the erosion and sed- W13’ were identified and taken up for the current study.iment loadings from the watersheds under changed land Location of the study area is shown in Fig. 1.system conditions, (3) to assess the major socioeconom-ic parameters at watershed level, (4) to integrate the Data sets usedsocioeconomic and biophysical information for priori-tizing the watersheds. For performing the change detection in land use and land cover, multi-date and multi-sensor satellite data in form of Landsat Thematic Mapper (TM) dated 15 OctoberMaterials and methods 1992 and Indian Remote Sensing satellite data [IRS 1D, Linear Imaging Self Scanning (LISS-III)] 19Study area October, 2005 was used. Digital Elevation Model from Shuttle Radar Topographic Mission, with a spatial reso-Dal Lake (34°02′ N latitude and 74°50′ E longitude) lution of 1 arc-sec was used for generating the topo-situated in Kashmir Himalayas, India functions as the graphic variables of the catchment for use in thecentral part of a large interconnected aquatic ecosystem geospatial model (Rodriguez et al. 2006). A soil mapand is the major surface water body of the Kashmir of the study area was generated by using remotely sensedValley. This lake has historically been the centre of classified data aided with extensive laboratory analysisKashmiri civilization and has played a major role in the of the soil samples followed by detailed ground truthing.economy of the state of Jammu and Kashmir. It is a A time series of hydro-meteorological data from theshallow, multi-basin drainage lake (Zutshi and Khan nearest observation station was used for input to the1978) covering an area of about 18 km2, with open water geospatial model. Ancillary data related to the sedimentarea not more than 12 km2. The general relief of the lake loadings was also used in this study. The Census datacatchment is a basin and extends between altitudinal provided by the state Department was used as a source ofranges of 1,580–4,390 m. The flat areas are mostly used socioeconomic data in the present research.for cropland, horticulture and built up and more humanactivities have intensified during the last few decades. Geospatial modelling approachThe mountainous areas are mostly covered by forest,grassland, scrublands, and the hilly regions consist of Geospatial models are excellent tools for predictingnatural vegetation and barren land, respectively. The various land surface processes and phenomena at differ-catchment area is dominated by the geological forma- ent spatial and time scales (Young et al. 1987; Shamsitions of alluvium, Panjal traps and agglomerate slates. 1996; Frankenberger et al. 1999; Romshoo 2003;The Karewa deposits are quaternary fluvio-lacustrine Yuksel et al. 2008). For simulating the erosion anddeposits which contain unconsolidated materials such sediment loadings, a distributed/lumped parameter wa-as light grey sand, dark grey clays, coarse- to fine- tershed model Generalized Watershed Loading
  4. 4. Environ Monit AssessFig. 1 Location map of the study areaFunction (GWLF) was used (Haith and Shoemaker The GWLF model computes the runoff by using the1987). The model simulates runoff, erosion and sedi- Soil Conservation Service Curve Number equation.ment loads from a watershed given variable-size source Erosion is computed using the Universal Soil Lossareas on a continuous basis and uses daily time steps for Equation and the sediment yield is the product ofweather data and water balance calculations (Haith et al. erosion and sediment delivery ratio. The yield in any1992; Lee et al. 2001; Evans et al. 2008). Monthly month is proportional to the total transport capacity ofcalculations are made based on the daily water balance daily runoff during the month.accumulated to monthly values. For the surface loading, The direct runoff is estimated from daily weatherthe approach adopted is distributed in the sense that it data using Soil Conservation Services (SCS) curveallows multiple land use/land cover scenarios, but each number method that is based on the area’s hydrologicarea is assumed to be homogenous in regard to various soil group, land use, treatment and hydrologic condi-attributes considered by the model. For sub-surface tion given by Eq. 1.loading, the model adopts a lumped parameter schemeusing a water balance approach. The model is particu- Rt þ Mt À 0:2DSkt Þ2larly useful for application in regions where environ- Qkt ¼ ð1Þ Rt þ Mt þ 0:8DSktmental data of all types is not available to assess thepoint and non-point source pollution from watershed Where Q is runoff (in centimetre), Rainfall Rt (in(Evans et al. 2002; Strobe 2002). centimetre) and snowmelt Mt (in centimetre of water)
  5. 5. Environ Monit Assesson the day t (in centimetre), are estimated from daily Where LER is the lateral erosion rate in metre/precipitation and temperature data. Precipitation is month which refers to the total distance that soil isassumed to be rain when daily mean air temperature eroded away from both banks along the entire lengthis Tt (in degrees Celsius) is above 0 and snow fall of a stream during a specified period of time, a is anotherwise. CN has a range from 30 to 100; lower empirically derived erosion potential factor, and Q isnumbers indicate low runoff potential while larger mean monthly stream flow in cubic metre per second.numbers are for increasing runoff potential. The lower In this case, the value of 0.6 used based on a globalthe curve number, the more permeable the soil is. DSkt review of stream bank erosion studies (Van Sickle andis the catchment’s storage. Catchment storage is esti- Beschta 1983; Lemke 1991; Rutherford 2000).mated for each source area using CN values with theEq. 2 given below Preparation of input data 2; 540DSkt ¼ À 25:4 ð2Þ A variety of input parameters was required to run the CNkt GIS-based GWLF model for simulating different hydro- Where, CNkt is the CN value for source area k, at logical processes at watershed scale which include thetime t. land use/land cover data, digital topographic data, hydro- Stream flow consists of runoff and discharge from meteorological data, transport parameter data (hydrolog-groundwater. The latter is obtained from a lumped ic and sediment) and nutrient parameter data. All theseparameter watershed water balance (Haan 1972). datasets were prepared with the procedures given below.Daily water balances are calculated for unsaturatedand shallow saturated zones. Infiltration to the unsat- Land use and land cover dataurated and shallow saturated zones equals the excess,if any, of rainfall and snowmelt runoff. Percolation Land use/land cover (LULC) information is very crit-occurs when unsaturated zone water exceeds field ical for assessing a number of land surface processes.capacity. The shallow saturated zone is modelled as For identifying the change in LULC of the watershedslinear ground water reservoir. Daily evapotranspira- from 1992 to 2005, multi-date satellite imageries weretion is given by the product of a cover factor and used. Supervised classification was performed on bothpotential evapotranspiration (Hamon 1961). The latter the images followed by the extensive field verificationis estimated as a function of daily light hours, saturat- and ground truthing of the identified land use classes.ed water vapour pressure and daily temperature. Erosion is computed using the Universal Soil Loss Hydro-meteorological dataEquation (USLE) and the sediment yield is the productof erosion and sediment delivery ratio. The yield in Daily precipitation and temperature data are requiredany month is proportional to the total capacity of daily for the simulation of hydrological processes by therunoff during the month. GWLF model. The daily hydrometerlogical data from Erosion from source area (k) at time t, Xkt is esti- the Indian Metrological Department (IMD) compris-mated using the following equation: ing of daily precipitation and daily temperature (min- imum and maximum), with a time step of 28 years wasXk t ¼ 0:132  REt  Kk  ðLSÞk  Ck  Pk  Rk ð3Þ prepared as an input to the model. In addition, mean daylight hours for the catchment with latitude 34°N Where, Kk ×(LS)k ×Ck ×P are the soil erodibility, were obtained from literature (Haith et al. 1992; Evanstopographic, cover and management and supporting et al. 2008). The study area receives an average rain-practice factor as specified by the USLE (Wischmeier fall of about 650 mm with most of its precipitationand Smith 1978). REt is the rainfall erosivity on day t between the months of March and May. January(megajoules-millimetre per hectare-hour). (−0.6 °C) is the coldest month while July (31.37 °C) Soil loss from stream bank erosion is based upon the is the hottest month. Maximum daylight is recordedfamiliar sediment transport function having the form for the month of June (14.3 h) and July (14.1 h) and the minimum daylight is received in the months ofLER ¼ aQf0:6g ð4Þ December (9.7 h) and January (9.9 h).
  6. 6. Environ Monit AssessTransport parameters hydrological conditions, soil moisture conditions and management are used to determine the curve numbersTransport parameters including hydrologic, erosion (Arhounditsis et al. 2002). In GWLF model, the CNand sediment of the catchment are those aspects that value is used to determine for each land use, the amountinfluence the movement of the runoff and sediments of precipitation that is assigned to the unsaturated zonefrom any given unit in the catchment down to the lake. where it may be lost through evapotranspiration and/orTransport parameters calculated for different source percolation to the shallow saturated zone if storage inareas in the catchment are given in Table 1, with the the unsaturated zone exceeds soil water capacity. Incomplete procedures for generating each of these percolation, the shallow saturated zone is considered toexplained as under be a linear reservoir that discharges to stream or losses to deep seepage, at a rate estimated by the product ofHydrological parameters zone’s moisture storage and a constant rate coefficient (SCS 1986). The soil parameters of the catchment wereThe evapotranspiration (ET) cover coefficient is the determined by carrying out a comprehensive analysis ofratio of the water lost by evapotranspiration from the the soil samples in the laboratory. A total of 50 compos-ground and plants compared to what would be lost by ite soil samples, well distributed over various land useevaporation from an equal area of standing water and land cover categories were collected from the lake(Thuman et al. 2003). The ET cover coefficients de- catchment. For the field sampling, similar soil unitspend upon the type of land use and time period within were delineated using the satellite imagery (Khan andthe growing season of a given field crop (FAO 1980; Romshoo 2008). This was followed by laboratory anal-Haith 1987). Typical ET values ranged from 0.3 to ysis of the samples for parameters like texture, organic1.00 for plantations depending upon the development matter and water holding capacity. Soil texture wasstage. Values observed for the bare areas, urban surfa- determined by the International Pippeting Methodces, ploughed lands were 1.00, and 0.4 for agriculture (Piper 1966), field capacity of the samples was deter-and grasslands. mined by Veihmeyer and Hendricjson (1931) and the The SCS curve number is a parameter that deter- soil organic matter/organic carbon was determined bymines the amount of precipitation that infiltrates into the rapid titration method (Walkley and Black 1934).the ground or enters surface waters as runoff after Using the field and lab observations of the soil samples,adjusting it to accommodate the antecedent soil mois- soil texture was determined using the soil textural trian-ture conditions based on total precipitation for the pre- gle (Toogood 1958). The spatial soil texture mapceding 5 days (EPA 2003a). A combination of factors (Fig. 2) and the soil organic matter map (Fig. 3) weresuch as land use/land cover, soil hydrological group, developed by stochastic interpolation method in GISTable 1 Transport parameters used for different source areas in GWLF modelSource areas Hydrological conditions LS C P K WCN WDET WGET ET coefficientAgriculture Fair 2.609 0.42 0.52 0.169 82 0.3 1.0 0.4Horticulture Fair 3.206 0.05 0.1 0.186 87 0.3 1.0 0.6Forest Fair 46.33 1 1 0.226 68 0.3 1.0 0.7Hay/pasture Fair 59.38 0.03 0.74 0.255 63 0.3 1.0 0.5Built up N/A 0.488 0.08 0.2 0.13 94 1 1.0 1.0Bare land Poor 42.66 0.8 0.8 0.15 89 1.0 0.3 1.0Good 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 destroyedLS 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
  7. 7. Environ Monit AssessFig. 2 Soil texture map of the study areaenvironment (Burrough 1986). The soil hydrological (RE) was estimated from the product of the stormgroups for all the soil units in the catchment were energy (E) and the maximum 30-min rainfall inten-derived from the soil texture and permeability properties sity (I30) data collected for that period. Erosivity(Fig. 4 and Table 2). coefficient for the dry season (May–Sep) was esti- mated to be 0.01 and coefficient for wet season wasSediment yield parameters estimated to be 0.034 (Montanarella et al. 2000). The crop management factor (C) related to soilSeveral soil and topographic parameters are required protection cover (Wischmeier and Smith 1978) andfor simulating the soil erosion using the GWLF the conservation practice factor (P) that reflects soilmodel. The LS factor used as a combination of slope conservation measures (Pavanelli and Bigi 2004)length and slope steepness parameters determines were determined from the land use and land coverthe effect of topography on soil erosion and was characteristics (Haith et al. 1992; EPA 2003b). Thederived from the Digital Elevation Model of the GWLF model estimates the sediment yield by mul-study area (Arhounditsis et al. 2002). The soil erod- tiplying sediment delivery ratio (SDR) with the es-ibility factor (K) of the catchment was generated timated erosion. Use of the logarithmic graph basedfrom the soil texture and soil organic matter content on the catchment area (Vanoni 1975; Haith et al.maps which were prepared as described above 1992; Evans et al. 2008) was made for determining(Steward et al. 1975). The rainfall erosivity factor the SDR.
  8. 8. Environ Monit AssessFig. 3 Soil organic matter map of the study areaSocioeconomic analysis variables at watershed level was generated, it was then integrated into the GIS environment based upon multi-Socioeconomic data regarding the various parameters criteria analysis. Multi-criteria evaluation is primarilysuch as total population, total households, literacy rate concerned with how to combine the information fromand economic development status for all the watersheds several criteria to form a single index of evaluation. Inof Dal Lake Catchment was collected from the Census the present study, knowledge-based weightage system wasDepartment, Government of India. The data was then adopted for watershed prioritization based on its factorsdigitised and converted into GIS format for integration and after carefully observing the field situation. Keeping inwith other geospatial data. Figure 5 shows the socioeco- view the role of such variables in the deterioration of lakesnomic boundaries with respect to different watersheds. in general, and Dal Lake in particular, different weightages were given to each of these parameters depending uponIntegrated impact analysis and watershed prioritization their importance and relevance to assess their cumulative impacts in each of these watersheds (Table 3). A scale ofConsidering the importance of watershed development for 10 was set and weightage of 4 was assigned to land use/restoration of aquatic ecosystems, it is necessary to plan the land cover change of watersheds. A weightage of 3 wasactivities on priority basis for achieving tangible results, given to erosion and sediment. Socioeconomic variableswhich also facilitate addressing the critical source areas to were also given a weightage of 3. The basis for assigningarrive at proper solutions. Once all the data about the weightage to different themes was according to the relativeLULC change, erosion, sediment and socioeconomic importance to each parameter in the study area.
  9. 9. Environ Monit AssessFig. 4 Soil hydrological group map of the study areaResults prominent in certain watersheds (Figs. 6 and 7). It was observed from Table 4 that some of the classes wereLand use/land cover change detection found to be completely absent in certain watersheds for both the years, while some marked their presenceThe LULC of the watersheds has undergone signifi- as a result of the changing land system. From Table 4,cant changes from 1992 to 2005 as depicted by the it is observed that turf/golf course was present in W3spatial distribution of these classes in the study area only covering an area of 0.51 km2 in 2005. Snowfor the respective years with the change being more cover was found to be absent in W1, W3, W4, W5Table 2 Dominant soil hydrological groups used in the GWLF model (Haith et al. 1992)Dominant Soil texture Soil runoff potential and permeability propertieshydrological groupA Sand, loamy sand, sandy loam Low surface runoff potentialB Silt loam, loam Moderately course soils with intermediate rates of water transmissionC Sandy clay loam Moderately fine texture soils with slow rates of water transmissionD Clay loam, silty clay loam, sandy clay, High surface runoff potential silty clay, clay
  10. 10. Environ Monit AssessFig. 5 Distribution of socioeconomic boundaries with respect to watersheds in Dal Lake Catchmentand W6 for both the years. In the rest of watersheds, it Water bodies showed an increase in their area in W1recorded the maximum change in W7 with an increase (+0.21 km2), W2 (+0.16 km2) and W4 (+0.13 km2). Aof 2.35 km2 followed by W13 (+1.58 km2), W11 decrease by 0.01 and 0.08 km 2 was respectively(+0.49 km2), W8 (+0.43 km2), W2 (+ 0.12 km2), W9 recorded for W3 and W13. The rest of the watersheds(+0.05 km2) and W10 (+0.01 km2). Decrease in snow W4, W5, W6, W7, W8, W9, W10, W11 and W12 didcover was observed for W12 only (−0.14 km2). not record the presence of water. Water channel areasTable 3 Details of parameters used for watershed prioritizationParameter Data source Factors WeightageLand use/land cover Derived from satellite imageries More the decrease in vegetation cover, higher 4 4 change with extensive field validation the priorityErosion GIS-based hydrological model Higher the erosion, more the priority 2 3Sediment GIS-based hydrological model Higher the sediment loading, more the priority 1Total 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 0.5Literacy rate Census data, Government of India Lower the literacy, higher the priority 0.5Economic development Census data, Government of India Lower the economic development status, higher 1 status the priority
  11. 11. Environ Monit AssessFig. 6 Land use/land cover map of the watersheds in 1992were found to be absent for W1, W2, W3 and W4 for (+0.01 km2) and W3 (+0.01 km2). Decline in the area ofboth 1992 and 2005. W7 marked a decrease in the area bare exposed rocks was recorded for W8 (−0.05 km2),by 0.06 km2. In the rest of the watersheds, water chan- W10 (−0.04 km2), W9 (−0.03 km2), W7 (−0.01 km2)nels showed an increase with the maximum in W10 and W5 (−0.01 km2). Table 4 reveals that the built-up(+1.2 km 2 ) followed by W6 (+0.18 km 2 ), W11 class was absent in W7, W11, W12 and W13. Increase(+0.09 km2), W12 (+0.08 km2), W13 (+0.05 km2), in area was observed for W1 (+3.65 km2), followed byW5 (+0.02 km2) and W8 (+0.01 km2). W2 (+3.23 km2), W3 (+1.99 km2), W5 (+ 1.53 km2), Bare land class was recorded in all the watersheds W4 (+ 1.41 km2), W6 (+0.91 km2), W8, (+0.17 km2),and showed an increasing trend in each watershed. The W10 (+0.12 km2) and W9 (+0.01 km2).maximum increase was recorded for W5 (+3.26 km2), Agriculture class was found to be absent in W9,followed by W4 (+2.34 km2), W3 (+1.98 km2), W2 W10, W11 and W12. In the remaining watersheds,(+1.97 km2), W13 (+1.83 km2), W11 (+1.35 km2), both increasing as well as a decreasing trend wasW8 (+1.1 km2) and W6 (+1.05 km2). The remaining observed. W8 (−1.45 km 2 ) followed by W7watersheds namely W7 (+0.57 km2), W9 (+0.5 km2), (0.48 km2) and W6 (0.15 km2) showed an increaseW1 (+0.11 km2) and W10 (+0.06 km2) showed slight in agriculture area as shown in Table 4, whereas, aincrease in the bare land area. Bare exposed rocks were decline in area was observed for W5 (−1.29 km2), W3mostly confined to the upper watersheds but marked (−0.78 km2) and W4 (−0.52 km2). Analysis of thetheir presence down the mountain reaches as well. statistics for horticulture revealed a decrease in areaIncrease in area is observed for W11 (+1.11 km2), fol- in W1 (−3.9 km2), followed by W3 (−2.21 km2), W8lowed by W13 (+0.65 km2), W11 (+ 0.15 km2), W2 (−0.74 km2), W5 (−0.72 km2) and W4 (−0.49 km2).
  12. 12. Environ Monit AssessFig. 7 Land use/land cover map of the watersheds in 2005W2 and W7 showed slight increase in area, while in by W12 (−1.21 km 2 ), W10 (−1.04 km 2 ), W2remaining watersheds the class was found to be absent (−0.98 km2), W8 (−0.92 km2), W13 (−0.65 km2), W9for both the years. Fallow land class was found to be (−0.60 km2), W6 (−0.35 km2), W5 (−0.27 km2), W11predominantly absent in most of the watersheds of Dal (−0.41 km2) and W7 (−0.18 km2). The only increase wasLake Catchment. It was found to cover very small area observed for W4 where an increase by 0.92 km2 wasand at the same time showed a decreasing trend in area recorded. Sparse forest class was found to be present inwith W2 (−0.22 km2), W3 (−0.22 km2) followed by all the watersheds for both 1992 and 2005 with the sameW4 (−0.17 km2) and W5 (−0.13 km2) being the only decreasing trend as that of the other forest classes. It waswatersheds recording the presence of fallow land. observed from Table 4 that W3 (+2.91 km2), W11 The results for coniferous forest class revealed a de- (+2.72 km2) and W4 (+0.28 km2) are the only watershedscline in majority of the watersheds and was found to be where increase in area was recorded for the respectiveabsent in W1 for both 1992 and 2005. Maximum decline years. For the remaining watersheds, sparse forests de-was recorded in W3 (−1.82 km2) followed by W12 creased in area with the major decline in W13 (−1.86(−1.06 km2), W13 (−0.65 km2), W11 (−0.55 km2), W7 Km2) followed by W6 (−1.32 km2), W7 (−0.92 km2),(−0.54 km2), W10 (−0.39 km2), W9 (−0.36 km2), W8 W2 (−0.88 km2), W5 (−0.63 km2), W8 (−0.52 km2),(−0.35 km2), W5 (−0.26 km2) and W2 (−0.07 km2). A W12 (−0.52 km2), W10 (−0.23 km2), W1 (−0.04 km2)slight increase was observed for W4 (+0.36 km2) and W6 and W9 (−0.03 km2).(+0.01 km2). A similar trend was observed for deciduous Grasslands/pasturelands showed a declining trend inforests with W3 recording a decline (−1.3 km2) followed majority of the watersheds. The decrease in area was
  13. 13. Environ Monit AssessTable 4 Change in the land use/land cover pattern in watersheds (1992–2005)Sample Class names DW1 DW2 DW3 DW4 DW5 DW6 DW7 DW8 DW9 DW10 DW11 DW12 DW13no. 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 20051 Turf 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 0.002 Snow 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 2.103 Water 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 0.414 Water channel 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 area5 Bare land 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 7.016 Bare exposed 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 rocks7 Built up 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 0.008 Agriculture 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 0.119 Horticulture 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 0.0010 Fallow 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 0.0011 Grasslands 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 3.8712 Coniferous 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 forest13 Deciduous 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 forest14 Sparse forest 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 2.1215 Scrubland 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 4.0916 Aquatic 0.71 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 vegetation17 Plantation 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
  14. 14. Environ Monit AssessTable 5 Classification accuracy of the land use and land cover of the study areaClass name Reference Classified Number Producer’s Users’ Kappa totals totals correct accuracy (%) accuracy (%) statisticsBuilt up 10 9 8 80 88.90 0.8851Agriculture 5 6 5 100 83.33 0.8305Horticulture 10 9 9 90 100.00 1Coniferous forest 24 24 22 91.67 91.67 0.9094Deciduous forest 32 33 28 87.5 84.85 0.8304Sparse forest 10 9 8 80.00 88.89 0.8851Grasslands 14 12 12 85.71 100.00 1Scrubland 5 6 5 100 83.33 0.8305Plantation 14 15 12 85.71 80.0 0.7902Aquatic vegetation 2 3 2 100 66.67 0.6644Barren 14 12 11 78.57 91.67 0.9126Bare exposed rocks 5 6 4 80.00 66.67 0.6610Water 6 6 6 100.00 100.00 1Snow 2 3 2 100.00 66.67 0.6644Totals 300 300 281 0.91314Overall accuracy=93.67 %found to be highest in W3 (−1.38 km2), followed by W5 grassland cover. Scrublands revealed an increasing trend(−1.31 km2), W11 (−0.78 km2), W10 (−0.62 km2), W7 in all the watersheds except for W2 which showed a(−0.70 km2), W13 (−0.57 km2), W6 (−0.50 km2), W4 decline by 0.01 km2. The highest change was witnessed(−0.20 km2) and W2 (−0.11 km2). W9 (+0.8 km2) fol- for W11 (+3.33 km2) followed by W13 (+2.72 km2),lowed by W8 (+ 0.13 km2) and W1 (+0.02 km2) are the W12 (+2.21 km2), W10 (+0.91 km2), W3 (+0.73 km2),only watersheds that showed an increase in the W8 (+0.56 km2), W9 (+0.46 km2), W7 (+0.31 km2),Table 6 Watershed contributionto erosion and sediment load Watershed ID Erosion (tons/year) Sediment (tons/year)under changed land use/landcover 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
  15. 15. Environ Monit AssessW5 (+0.28 km2), W6 (+0.21 km2), W4 (+0.01 km2) and Model simulation resultsW1 (+0.01 km2). It was also observed that the plantation cover The results of the model simulations for erosion andshowed a declining trend from 1992 to 2005. The sediment loadings revealed an increasing trend in allmajor changes were recorded in W13 (−3.1 km2) fol- watersheds (Table 6). The spatial distribution of the in-lowed by W1 (−2.68 km2), W2 (−2.5 km2), W11 creasing trend of the watersheds is given in Figs. 8 and 9.(−1.73 km2), W4 (−1.65 km2), W7 (−1.34 km2), W8 It was observed that maximum increase in the erosion(−1.27 km2), W9 (−0.79 km2), W5 (−0.45 km2), W3 yield was recorded for W5 with (236.93 t/year) followed(−0.40 km2), W12 (−0.19 km2) and W6 (−0.12 km2). by W6 (76.49 t/year), W8 (56.16 t/year) and W11The only increase in plantation cover was observed for (54.55 t/year). Watersheds namely W9 (12.82 t/year),W10 (+0.02 km2). The statistics for aquatic vegetation W10 (10.04 t/year) and W13 (7.96 t/year) recorded leastrevealed that this class was restricted in its occurrence increase. Similarly, the highest increase in sediment load-and recorded an increase in W1 (+2.91 km2) followed ings was recorded for W5 (36.7 t/year) followed by W8by W2 (+0.34 km 2 ), W4 (+0.10 km 2 ) and W5 (15.2 t/year), W3 (12.3 t/year) and W6 (10.42 t/year) and(+0.01 km2). W2 (7.57 t/year). Whereas, W4 (4.06 t/year), W12 The overall accuracy of the classified land use/land (2.82 t/year) and W10 (0.94 t/year) showed less increase.cover data was observed to be 93.67 % (Table 5) with Source area (land use/land cover) contributions fora kappa coefficient of 0.913. erosion and sediment yields (Table 7) revealed that bareFig. 8 Watershed wise increased erosion loading under changed land use/land cover
  16. 16. Environ Monit AssessFig. 9 Watershed wise increased sediment loading under changed land use/land coverlands followed by agriculture, forests and hay/pasture intensity and low-intensity developed areas recorded neg-experienced the maximum loadings. Horticulture, high- ligible contributions. Further analysis of the data inTable 7 Source area contribution to erosion and sediment loads under changed land use/land coverSource Erosion (tons/year) Sediment (tons/year) 1992 2005 Change 1992 2005 ChangeHay/pasture 11.41 57.55 46.14 3.2 26.19 22.99Agriculture 94.25 117.50 23.25 30.1 49.68 19.58Forest 25.16 27.88 2.72 1.3 8.64 7.34Horticulture 0.133 0.15 0.02 0.0 0.01 0.01Turf/golf course − 0.02 0.02 − 0.00 0.00Bare land 1,171.16 1,750.06 578.9 90.7 121.31 30.61Low-intensity development 0.018 0.05 0.03 0.9 0.00 0.9High-intensity development 0.164 0.44 0.27 0.0 0.02 0.02Stream bank 106.2 148.80 42.6Totals 1,302.295 1,953.66 651.37 232.4 354.65 122.25
  17. 17. Environ Monit AssessTable 8 Ward wise socioeco-nomic characterization Ward no. Total Total Population Literacy Economic households population density rate develop 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 67,805.00Table 7 showed that the major increase in erosion load- low-intensity developed areas again recorded insig-ings was recorded for bare lands (578.9 t/year) followed nificant changes in the sediment hay/pastures (46.14 t/year), agriculture (23.3 t/year)and forests (2.72 t/year). Increase in sediment loads Socioeconomic characterizationwas observed to be highest for the stream banks(42.6 t/year) followed by bare lands (30.61 t/year), The results for socioeconomic characterisation given inpasture/grasslands (22.9 t/year) and agriculture Tables 8 and 9 revealed that almost half the number of(19.58 t/year). Horticulture, high-intensity and watersheds in the catchment are uninhabited because ofTable 9 Watershed wise socio-economic characterization ID Watershed name Total Total Literacy rate Economic development population households status 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 − − − −− uninhabited
  18. 18. Environ Monit Assesstheir high altitude, dense forested and remote nature. of the eastern portion of the lake catchment including theFigures 10 and 11 show the spatial distribution of total Dachigam National Park. The highest literacy ratepopulation and number of households respectively for the (Fig. 12) was found for W2 (69.64) followed by W6different watersheds. Among the populated watersheds, (57.25), W8 (56.37), W3, W4 (55.64) and W5 (55.54).DW2 recorded the highest population (65,228 individu- The lowest literacy was recorded for W1 (52.63).als) and the highest number of households (9,562). This Watersheds were categorised into high, medium and lowwatershed mainly comprised of the congested Srinagar as per their economic development status (Fig. 13). W2city west and the south. This was followed by W1 (28,463 belonged to the highest, W1, W5, W6 to the medium andindividuals and 3,921 households) again comprising of W3, W4, W8 belonged to the low category.the Srinagar city west. It was found that W4 (17,473individuals and 2,578 households) included eastern parts Integrated impact analysis and watershed prioritizationof the catchment comprising of the areas of Nishat,Shalimar, etc. W6 (14,980 individuals and 2,169 house- On the basis of priority and cumulative weightageholds) and W5 (14,672 individuals and 3,921 households) assigned to each thematic map, all 13 watersheds werecomprised of the northern parts of the city in Dal Lake grouped into three categories: high, medium and lowCatchment. W3 (10,519 individuals and 1,555 house- priority shown in Table 10. Figures 14 and 15 show theholds) includes the city east side. W8 (1,731 individuals spatial distribution of the prioritized watersheds. It wasand 243 households) recorded the lowest population and observed that five (5) watersheds namely W5>W2>W6lowest number of households. This watershed comprised >W8>W1 ranked highest in the overall weightage andFig. 10 Spatial distribution of total population in the watersheds
  19. 19. Environ Monit AssessFig. 11 Spatial distribution of total households in the watershedshence are considered under high priority. Of the remain- agriculture, horticulture, built up, bare lands, grasslands,ing eight watersheds, five watersheds namely W13>W3 scrublands and forests. These changes are largely attrib->W4>W11>W7 were considered under the medium- utable to the activities of man as land use/land cover ispriority category. The remaining three watersheds, i.e. among the most evident impacts of human activities onW12>W9>W10, fell under low-priority category. natural resources (Lundqvist 1998), and can be observed using current and archived remotely sensed data with the potential scientific value for the study of human–Discussion environment interaction and aid in ascertaining the im- pact of land use on the amount of pollution (Tekle andLand use/land cover change analysis at watershed level Hedlund 2000; Tong and Chen 2002; Tang et al. 2005;in Dal Lake Catchment for the 15-year time period Tong et al. 2008). Understanding the land use/land cover(1992–2005) revealed significant changes. The type characteristics at the watershed level is essential as suchand distribution of land use/land cover substantially properties determine the erosion and the pollution po-affects a number of hydrological processes such as tential of the watersheds.runoff, erosion and sediment loadings that in turn pro- Agriculture and horticulture classes showed a de-foundly affect lake ecosystems (Matheussen et al. 2000; cline with a progressive increase in the built-up area.Fohrer et al. 2001; Quilbe et al. 2008). During the study Increased population and congestion in the old cityperiod, considerable changes were observed for almost have resulted in the conversion of large peripheralall the land use/land cover classes particularly areas that were essentially used for agro-horticultural
  20. 20. Environ Monit AssessFig. 12 Spatial distribution of literacy rate in the watershedspurposes into built up mostly for residential purposes. dwindling grasslands as well as the sparse forests in theAccelerated nutrient enrichment of the Dal Lake due watersheds. Decline in the coniferous, deciduous andto incoming effluents from these watersheds resulted sparse forest of the study area was found to be the resultin the proficient and luxuriant growth of macrophytes of large-scale deforestation, both within the Dachigamthat was revealed by the increased area of aquatic National Park as well as outside it particularly along thevegetation. In the later parts of the year, the surface higher reaches of the catchment. Increase in the area ofwaters remain covered by the decomposed thick mats bare lands during study period at both the higher anddisrupting the ecological balance of the lake (Khan lower elevations of the Dal Lake Catchment was ob-2000; Pandit 1999). served. It was found that the overgrazed grasslands and Large-scale decline in grassland area revealed tremen- deforested areas have paved the way for creation of barrendous pressures on this ecologically and socioeconomically area. This land is very much vulnerable to increasedimportant land cover attributed to the biotic interference in erosion and sediment yields as well increased runoffand around the Dachigam National Park including clear- (Shah and Bhat 2004).ing of the grasslands at the low altitudes for cultivation, The erosion and sediment loadings varied for differ-exploitation for medicinal plants and other activities. ent watersheds depending on the topography, land use/Several decades of grazing and that too beyond the carry- land cover, soil type as these are the principal factorsing capacity have resulted in the creation of denuded and influencing contaminant transport in a watershed (Vieuxsemi-denuded patches in these grasslands (Bhat et al. and Farajalla 1994; Barnes 1997). The increase, al-2002). Increased scrubland area may be attributed to the though small in certain watersheds, was by and large
  21. 21. Environ Monit AssessFig. 13 Spatial distribution of economic development status in the watershedsTable 10 Results of prioritization carried out for watersheds in reflective of the changing biophysical charcteristics ofDal Lake Catchment these watersheds attributable mostly to the increasedS. No Watershed name Priority result Priority rank anthropogenic pressures. The increased erosion and sediment loadings were in particular observed for those1 W1 High PZ5 watersheds where the stress on the vegetation was the2 W2 High PZ2 maximum, namely W5, W13, W11, W6 and W8. In3 W3 Medium PZ7 addition, various agro-horticultural activities carried out4 W4 Medium PZ8 particularly in W5, W6 and W8 accelerate the potential5 W5 High PZ1 for the processes of surface runoff and soil erosion6 W6 High PZ3 (Stoate et al. 2001; Van Rompaey et al. 2001; Hansen7 W7 Medium PZ10 et al. 2004). Biotic interferences like overgrazing of8 W8 High PZ4 grasslands beyond the carrying capacity, clearing of9 W9 Low PZ12 forest areas for contruction and agricultural purposes10 W10 Low PZ13 has led to the creation of denuded patches accelerating11 W11 Medium PZ9 the erosion (Bhat et al. 2002). Moreover, increased12 W12 Low PZ11 barren and scrubland surfaces also contributed largely to runoff without much infiltration capacity. Such water-13 W13 Medium PZ6 sheds were also found to have fairly good area underPZ priority zone steep and very steep slope classes indicating quick
  22. 22. Environ Monit AssessFig. 14 Watershed prioritization map of Dal Lake Catchmentrunoff during rainfall or storm water events (Tucker and the anthropogenic pressures, thereby, preventing theBras 1998). Stone quarring in W8, although banned loss of vegetative and canopy cover (Rishi 1982;now, resulted in largely degraded and defaced moun- Guerra et al. 1998; Janetos and Justice 2000).tains posing serious threats of soil erosion and land- Bare lands, hay/pastures and agriculture were theslides. The subsequent sediment loss, carried major source area contributors for erosion and sedi-downslope pollutes the waters of Dal lake (Shah and ment loads as these are more erodible than the vege-Bhat 2004). Watersheds namely W12 and W7 recorded tated areas (Singh and Prakash 1985). Higher rates ofno major changes in the land use/land cover because of soil and sediment loss have been reported from else-negligible anthropogenic pressures/activities and hence where from cultivated areas (Dunne et al. 1978;minimal increase in erosion and sediment yields. Brown 1984; Ouyang and Bartholic 2001). IncreasedVegetation changes are often the result of anthropogenic scrublands primarily due to the degradation of grass-pressures (Janetos and Justice 2000). lands has also resulted in increased loads of sediment W3, W2, W4 and W1 because of their urbanised and erosion. Forests, horticulture and developed areasenvironment and impervious nature and flat slopes were the least contributors because of their vegetativeprovided minimum probabilities of erosion and sedi- and impervious nature respectively (Mkhonta 2000).ment loss, even though subject to high runoff. W9 and The sediment/silt generated from various land use/W10 were the least contributors owing to their highly land cover categories in the watersheds finally flowsforested nature and thick vegetative cover. Besides, into the lake largely through the Telbal Stream result-being alpine in nature makes them inaccesssible to ing in decreased depth and volume of water and lake
  23. 23. Environ Monit AssessFig. 15 Spatial distribution of watershed priority zonesageing (Zutshi and Yousuf 2000). Owing to the inad- to the lakes (Loeb 1988). These watersheds can beequate land use management in the catchment, Dal taken up to develop a robust strategy for mitiga-Lake receives large amounts of eroded soil that has tion and control of the lake deterioration on adisrupted the ecological balance of the lake. sustainable basis with immediate effect to prevent Socioeconomic GIS integrated with the biophys- the further degradation of the Dal Lake. For theseical remote sensing has emerged as a new and watersheds, a detailed survey for soil and waterpromising field that provides insights into the so- conservation measures, water resources develop-cioeconomic aspects of environmental and physical ment, scientific land use planning for preservationproblems and could be used as a useful aid for of eco-diversity, integrated study for developmentlinking the environmental problems to communi- of natural as well as social resources, etc., toties (Buckle et al. 2006). High- and medium- accelerate the rehabilitation and to generate a de-prioritized watersheds suggested that the changes tailed database in each natural resources theme, isin the biophysical environment and the behaviour a pre-requisite for formulation of watershed planof different land surface processes are reflective of for its sustainable development and management.the different socioeconomic pressures (Moldan et The low-prioritized watersheds may be taken upal. 1997; Peters and Maybeck 2000). Alteration of for development and management plans in athe landscape and other human-caused disturbances phased manner (Vittala et al. 2008). Since thishave been shown to be important factors affecting approach is considered to be ideal in maintainingmass transport (loading) of erosion and sediment the ecological balance (Sahai 1988), it shall,
  24. 24. Environ Monit Assessgreatly help in devising the conservation and man- immediate effect. The research methodology establishedagement strategies for the restoration of the lake during the present study should help in the effectiveecosystem (Prasad et al. 1997; Biswas et al. 1999; conservation and management of other threatened la-Khan et al. 2001; Gosain and Rao 2004). custrine ecosystems of Kashmir Himalaya. Acknowledgments The authors are thankful to the Indian Meteorological Department and Division of Agronomy,Conclusion Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar for providing hydrome-During the present research, an integrated approach trological data for this study.based 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 different Referenceswatershed scale processes (land use/land cover changedetection analysis, quantification of erosion, sediment Amin, A. & Romshoo, S.A. (2007). Assessing the hydrologicand socioeconomic analysis) in all the 13 watersheds characteristics of Dal Lake catchment using GIS. In:of the Dal Lake Catchment and quantify their impacts Proceedings of TAAL 2007: the 12th World Lake Conferenceon Dal Lake. With the help of this integrated method- (pp. 659–667). Arhounditsis, G., Giourga, C., Loumou, A., & Koulouri, M.ology, remote sensing data was used to generate up-to- (2002). Quantitative assessment of agricultural runoff anddate information about different parameters, simula- soil erosion using mathematical modeling: application intion models and geospatial techniques were used to the Mediterranean region. Environmental Management, 30simulate the hydrological, sediment, erosion process- (3), 434–453. Badar, B. & Romshoo, S.A. (2007). Assessing the pollution loades. This contemporary approach was fully aided by the of Dal Lake using geospatial tools. In: Proceedings of TAALextensive field surveys carried out for ground truthing 2007: the 12th World Lake Conference (pp. 668–679).of the remote sensing data as well as for the sampling Bagnolus, F. & Meher-Homji, V.M. (1959). Bio-climatic typespurposes that aided in an on spot investigation of the of south East Asia. Travaux de la Section Scientific at Technique Institut Franscis de Pondicherry. (p. 227).study area. As a result of this integrated approach a Ballatore, T. J., & Muhandiki, V. S. (2002). The case for a worldcollective understanding of the critical source areas in lake vision. Hydrological Processes, 16(11), 2079–2089.the lake catchment has been possible that would be Barnes, P.L. (1997). Row crop pollution in North-East Kansas,helpful in addressing the watershed problems affecting Kansas State University, Kansas. GISdevelopment > Proceedings > ACRS > 1997. Dal Lake ecosystem at the root cause level. The Accessed 14 Dec 2008.limitation of this study was the non-availability of the Bhat, D. K. (1989). Geology of Karewa basin (p. 122). Kashmir:latest socioeconomic data at the watershed level that Geological Survey of India Records.could help in better identification and assessment of Bhat, G. A., Qadri, M., & Zutshi, D. P. (2002). An ecological survey of Dachigam National Park, Kashmir with emphasis on grass-socioeconomic pressures. The current study made use lands. In A. K. Pandit (Ed.), Natural resources of Westernof the 2001 Census data. Besides the GWLF model, Himalaya (pp. 341–376). Hazratbal: Valley Book House.simulations can be improved upon by incorporating Biswas, S., Sudhakar, S., & Desai, V. R. (1999). Prioritization ofmore surface processes data (nutrient runoff, point subwatersheds based on morphometric analysis of drainage basin—a remote sensing and GIS approach. Journal ofsource data etc.) that was not available at the time of Indian Society of Remote Sensing, 27, 155–156.the study. Brown, L. R. (1984). Conserving soils. In L. R. Brown (Ed.), The integration of the biophysical and the socioeco- State of the world (pp. 53–75). New York: Norton.nomic environment taken up at the watershed level Buckle, P., Mars, G. and Samle, S. (2006). New approaches to assessing vulnerability and resilience. Australian Journalduring the current study shall aid in developing and of Emergency Management (Winter): 8–14.designing the conservation and management plans vis- Burrough, P. A. (1986). Principles of geographic informationà-vis water quality restoration programme of the Dal systems for land resources assessment. Oxford: OxfordLake ecosystem. The watershed prioritization, in partic- Press. Data, N.K. (1983). Geology, evolution and hydrocarbon prospec-ular, shall facilitate the development of a robust strategy tus of Kashmir valley. Petroleum Asia Journal, 176– the critically impaired watersheds for the control of Dinar, A., Seidl, S., Olem, H., Jordan, V., Duda, A., & Johnson,pollution and conservation and management plans with R. (1995). Restoring and protecting the World’s Lakes and
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