Prioritizing Dal Watersheds

Environ Monit Assess
DOI 10.1007/s10661-012-3035-9

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 watershed prioritization based on its factors and after
strong linkage with socioeconomics of the state of carefully observing the field situation. The land use/land
Jammu and Kashmir. During last few decades, anthropo- cover change detection revealed significant changes with
genic pressures in Dal Lake Catchment have caused a uniform trend of decreased vegetation and increased
environmental deterioration impairing, inter-alia, sus- impervious surface cover. Increased erosion and sediment
tained biotic communities and water quality. The present loadings were recorded for the watersheds corresponding
research was an integrated impact analysis of socioeco- to their changing land systems, with bare and agriculture
nomic and biophysical processes at the watershed level lands being the major contributors. The prioritization
on the current status of Dal Lake using multi-sensor and analysis revealed that W5>W2>W6>W8>W1 ranked
multi-temporal satellite data, simulation modelling to- highest in priority and W13>W3>W4>W11>W7 under
gether 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 and
gated for land use/land cover change detection, quantifi- the socioeconomic environment at the watershed level
cation 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 of
and 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 . Prioritization

B. Badar (*) : S. A. Romshoo
Department of Geology and Geophysics, Introduction
University of Kashmir,
Hazratbal, Lakes are extremely fragile and sensitive ecosystems on
Srinagar, Kashmir 190006, India
earth that host rich aquatic biodiversity. Besides being
e-mail: badarbazigha@gmail.com
the key components of our planet’s hydrological cycle,
M. A. Khan they provide important social and ecological functions
Division of Environmental Science, (Ballatore and Muhandiki 2002). Despite the fact that
Shere 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 are
Srinagar, Kashmir 190006, India probably the most neglected and mismanaged natural


resources. While some problems originate in a lake activities and encroachment of the lake area by the lake
itself, the vast majority of problems originate from ac- dwellers has also contributed to the deterioration of these
tivities 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. have
of improper and unwise utilization of watershed resour- exerted tremendous pressures on the world famous
ces without any proper vision is a common phenomenon freshwater ecosystem. Increase in agricultural activity
in 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 in
loads, algal bloom and toxicity, low oxygen and fish surface erosion and leaching of soil nutrients have added
kills, loss of aquatic habitat, changes in community increasing quantities of nutrient-rich runoff (Badar and
structure, loss of recreational amenity in these aquatic Romshoo 2007). Increase in impervious surfaces like
ecosystems (Kira 1997; Dinar et al. 1995; Duker 2001; barren, built-up and deforested areas of the Dal Lake
Jorgensen et al. 2003). Inflowing substances, including Catchment has caused the peak flow to swell over the
sediments, minerals, nutrients and organic materials, period of time (Amin and Romshoo 2007). Nearby
coming from the watersheds tend to accumulate in the farming practices have also added to the amount and
water column or the lake bottom (World Lake Vision rate of silt generated and added to the lake waters
Committee 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 to
mountains, forests, lakes and streams. The lakes of respond to the stresses placed on it. The Dal Lake
Kashmir identified as Glacial, Pine-forest and Valley drainage is characterised by a myriad of channels
lakes based on their origin, altitudinal situation and nature (Meerakshah, Nallah Amir Khan, Brari Nambal and
of biota, provide valuable research opportunities (Zutshi Chuntkul) which have been filled up during the last
et al. 1972; Kaul 1977; Zutshi and Khan 1978; Pandit two decades due to excessive siltation, sewage inflow
1996, 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 building
While these changes result in part from the natural course and vegetable growing land and the increase in the area
of 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 by
lake ecosystems in Kashmir Himalayan lakes is a recent floating gardens reducing the open water area to
event 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 major
1998). Since, there has not been much development as concern, and improving the ecological status of this
regards 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 causes
bodies 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 most
and pesticides from the catchment (Pandit and Qadri cases. The motivation for this study stems from the need
1990; Badar and Romshoo 2007). Socioeconomic for simple and reliable information that could facilitate


the participation of stakeholders and decision makers in grained sands, gravels, marls, silts, varved clays, brown
the implementation of water quality programs, thereby, loams, lignite, etc. (Wadia 1971; Varadan 1977; Data
improving the chances of the Dal Lake restoration. The 1983; Bhat 1989). A number of underground springs
watershed management concept recognizes the inter- and streams feed the Dal Lake but the main source is
relationships and linkages between various biophysical the Dachigam Creek, originating from the alpine Marsar
and socioeconomic processes (Moore et al. 1977; FAO Lake. The catchment belongs to a Sub-Mediterranean
1985; 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-Homji
grammes. Earlier, integrated approach for watershed 1959). The catchment receives an average annual rainfall
prioritization using remote sensing and Geographical of 650 mm at Srinagar station and 870 mm at Dachigam
Information System (GIS) data has been successfully station. March, April and May are the wettest months of
attempted by several workers (Prasad et al. 1997; the year. The temperature varies between a monthly
Biswas et al. 1999; Khan et al. 2001; Gosain and Rao mean maximum of 31 °C in July and a minimum of
2004). Under this context, the study was carried out with −4 °C in January with an average of 11 °C. Thirteen
the 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 used
socioeconomic 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 October
Materials and methods 1992 and Indian Remote Sensing satellite data [IRS
1D, Linear Imaging Self Scanning (LISS-III)] 19
Study 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 the
central part of a large interconnected aquatic ecosystem geospatial model (Rodriguez et al. 2006). A soil map
and is the major surface water body of the Kashmir of the study area was generated by using remotely sensed
Valley. This lake has historically been the centre of classified data aided with extensive laboratory analysis
Kashmiri 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 the
shallow, multi-basin drainage lake (Zutshi and Khan nearest observation station was used for input to the
1978) covering an area of about 18 km2, with open water geospatial model. Ancillary data related to the sediment
area not more than 12 km2. The general relief of the lake loadings was also used in this study. The Census data
catchment is a basin and extends between altitudinal provided by the state Department was used as a source of
ranges 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 human
activities have intensified during the last few decades. Geospatial modelling approach
The mountainous areas are mostly covered by forest,
grassland, scrublands, and the hilly regions consist of Geospatial models are excellent tools for predicting
natural 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; Shamsi
tions 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 and
deposits 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


Fig. 1 Location map of the study area

Function (GWLF) was used (Haith and Shoemaker The GWLF model computes the runoff by using the
1987). 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 Loss
areas on a continuous basis and uses daily time steps for Equation and the sediment yield is the product of
weather data and water balance calculations (Haith et al. erosion and sediment delivery ratio. The yield in any
1992; Lee et al. 2001; Evans et al. 2008). Monthly month is proportional to the total transport capacity of
calculations 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 weather
the approach adopted is distributed in the sense that it data using Soil Conservation Services (SCS) curve
allows multiple land use/land cover scenarios, but each number method that is based on the area’s hydrologic
area 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 scheme
using a water balance approach. The model is particu- Rt þ Mt À 0:2DSkt Þ2
larly useful for application in regions where environ- Qkt ¼ ð1Þ
Rt þ Mt þ 0:8DSkt
mental data of all types is not available to assess the
point 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)


on 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 is
assumed to be rain when daily mean air temperature eroded away from both banks along the entire length
is Tt (in degrees Celsius) is above 0 and snow fall of a stream during a specified period of time, a is an
otherwise. CN has a range from 30 to 100; lower empirically derived erosion potential factor, and Q is
numbers 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 global
the curve number, the more permeable the soil is. DSkt review of stream bank erosion studies (Van Sickle and
is the catchment’s storage. Catchment storage is esti- Beschta 1983; Lemke 1991; Rutherford 2000).
mated for each source area using CN values with the
Eq. 2 given below Preparation of input data
2; 540
DSkt 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 the
time 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 these
parameter watershed water balance (Haan 1972). datasets were prepared with the procedures given below.
Daily water balances are calculated for unsaturated
and shallow saturated zones. Infiltration to the unsat- Land use and land cover data
urated 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 watersheds
linear ground water reservoir. Daily evapotranspira- from 1992 to 2005, multi-date satellite imageries were
tion is given by the product of a cover factor and used. Supervised classification was performed on both
potential evapotranspiration (Hamon 1961). The latter the images followed by the extensive field verification
is 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 data
Equation (USLE) and the sediment yield is the product
of erosion and sediment delivery ratio. The yield in Daily precipitation and temperature data are required
any month is proportional to the total capacity of daily for the simulation of hydrological processes by the
runoff 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 was
Xk t ¼ 0:132 Â REt Â Kk Â ðLSÞk Â Ck Â Pk Â 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; Evans
topographic, 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 precipitation
and 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 recorded
familiar 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 of
LER ¼ aQf0:6g ð4Þ December (9.7 h) and January (9.9 h).


Transport parameters hydrological conditions, soil moisture conditions and
management are used to determine the curve numbers
Transport parameters including hydrologic, erosion (Arhounditsis et al. 2002). In GWLF model, the CN
and sediment of the catchment are those aspects that value is used to determine for each land use, the amount
influence the movement of the runoff and sediments of precipitation that is assigned to the unsaturated zone
from any given unit in the catchment down to the lake. where it may be lost through evapotranspiration and/or
Transport parameters calculated for different source percolation to the shallow saturated zone if storage in
areas in the catchment are given in Table 1, with the the unsaturated zone exceeds soil water capacity. In
complete procedures for generating each of these percolation, the shallow saturated zone is considered to
explained as under be a linear reservoir that discharges to stream or losses to
deep seepage, at a rate estimated by the product of
Hydrological parameters zone’s moisture storage and a constant rate coefficient
(SCS 1986). The soil parameters of the catchment were
The evapotranspiration (ET) cover coefficient is the determined by carrying out a comprehensive analysis of
ratio 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 use
evaporation 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 units
pend upon the type of land use and time period within were delineated using the satellite imagery (Khan and
the 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, organic
1.00 for plantations depending upon the development matter and water holding capacity. Soil texture was
stage. Values observed for the bare areas, urban surfa- determined by the International Pippeting Method
ces, 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 by
mines 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 map
ceding 5 days (EPA 2003a). A combination of factors (Fig. 2) and the soil organic matter map (Fig. 3) were
such as land use/land cover, soil hydrological group, developed 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 to
areas 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 number
values, WDET weighted average dormant season evapotranspiration, WGET weighted average growing season evapotranspiration, ET
evapotranspiration coefficient


Fig. 2 Soil texture map of the study area

environment (Burrough 1986). The soil hydrological (RE) was estimated from the product of the storm
groups 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 was
Sediment yield parameters estimated to be 0.034 (Montanarella et al. 2000).
The crop management factor (C) related to soil
Several soil and topographic parameters are required protection cover (Wischmeier and Smith 1978) and
for simulating the soil erosion using the GWLF the conservation practice factor (P) that reflects soil
model. 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 cover
the effect of topography on soil erosion and was characteristics (Haith et al. 1992; EPA 2003b). The
derived 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 based
from 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.


Fig. 3 Soil organic matter map of the study area

Socioeconomic 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 primarily
such as total population, total households, literacy rate concerned with how to combine the information from
and economic development status for all the watersheds several criteria to form a single index of evaluation. In
of Dal Lake Catchment was collected from the Census the present study, knowledge-based weightage system was
Department, Government of India. The data was then adopted for watershed prioritization based on its factors
digitised and converted into GIS format for integration and after carefully observing the field situation. Keeping in
with other geospatial data. Figure 5 shows the socioeco- view the role of such variables in the deterioration of lakes
nomic boundaries with respect to different watersheds. in general, and Dal Lake in particular, different weightages
were given to each of these parameters depending upon
Integrated impact analysis and watershed prioritization their importance and relevance to assess their cumulative
impacts in each of these watersheds (Table 3). A scale of
Considering 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 was
activities on priority basis for achieving tangible results, given to erosion and sediment. Socioeconomic variables
which also facilitate addressing the critical source areas to were also given a weightage of 3. The basis for assigning
arrive at proper solutions. Once all the data about the weightage to different themes was according to the relative
LULC change, erosion, sediment and socioeconomic importance to each parameter in the study area.


Fig. 4 Soil hydrological group map of the study area

Results prominent in certain watersheds (Figs. 6 and 7). It was
observed from Table 4 that some of the classes were
Land use/land cover change detection found to be completely absent in certain watersheds
for both the years, while some marked their presence
The 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 W3
spatial distribution of these classes in the study area only covering an area of 0.51 km2 in 2005. Snow
for the respective years with the change being more cover was found to be absent in W1, W3, W4, W5

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

Dominant Soil texture Soil runoff potential and permeability properties
hydrological group

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, High surface runoff potential
silty clay, clay


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

and W6 for both the years. In the rest of watersheds, it Water bodies showed an increase in their area in W1
recorded the maximum change in W7 with an increase (+0.21 km2), W2 (+0.16 km2) and W4 (+0.13 km2). A
of 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 did
cover was observed for W12 only (−0.14 km2). not record the presence of water. Water channel areas

Table 3 Details of parameters used for watershed prioritization

Parameter Data source Factors Weightage

Land use/land cover Derived from satellite imageries More the decrease in vegetation cover, higher 4 4
change with extensive field validation the priority
Erosion GIS-based hydrological model Higher the erosion, more the priority 2 3
Sediment 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 3
Total 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 development Census data, Government of India Lower the economic development status, higher 1
status the priority


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

were found to be absent for W1, W2, W3 and W4 for (+0.01 km2) and W3 (+0.01 km2). Decline in the area of
both 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 by
W5 (+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 was
W8 (+1.1 km2) and W6 (+1.05 km2). The remaining observed. W8 (−1.45 km 2 ) followed by W7
watersheds namely W7 (+0.57 km2), W9 (+0.5 km2), (0.48 km2) and W6 (0.15 km2) showed an increase
W1 (+0.11 km2) and W10 (+0.06 km2) showed slight in agriculture area as shown in Table 4, whereas, a
increase in the bare land area. Bare exposed rocks were decline in area was observed for W5 (−1.29 km2), W3
mostly confined to the upper watersheds but marked (−0.78 km2) and W4 (−0.52 km2). Analysis of the
their presence down the mountain reaches as well. statistics for horticulture revealed a decrease in area
Increase in area is observed for W11 (+1.11 km2), fol- in W1 (−3.9 km2), followed by W3 (−2.21 km2), W8
lowed by W13 (+0.65 km2), W11 (+ 0.15 km2), W2 (−0.74 km2), W5 (−0.72 km2) and W4 (−0.49 km2).


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

W2 and W7 showed slight increase in area, while in by W12 (−1.21 km 2 ), W10 (−1.04 km 2 ), W2
remaining watersheds the class was found to be absent (−0.98 km2), W8 (−0.92 km2), W13 (−0.65 km2), W9
for both the years. Fallow land class was found to be (−0.60 km2), W6 (−0.35 km2), W5 (−0.27 km2), W11
predominantly absent in most of the watersheds of Dal (−0.41 km2) and W7 (−0.18 km2). The only increase was
Lake Catchment. It was found to cover very small area observed for W4 where an increase by 0.92 km2 was
and at the same time showed a decreasing trend in area recorded. Sparse forest class was found to be present in
with W2 (−0.22 km2), W3 (−0.22 km2) followed by all the watersheds for both 1992 and 2005 with the same
W4 (−0.17 km2) and W5 (−0.13 km2) being the only decreasing trend as that of the other forest classes. It was
watersheds 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 watersheds
cline in majority of the watersheds and was found to be where increase in area was recorded for the respective
absent 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 in
forests with W3 recording a decline (−1.3 km2) followed majority of the watersheds. The decrease in area was


Table 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 DW13
no.
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

1 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 0.00
2 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 2.10
3 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 0.41
4 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
area
5 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 7.01
6 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
rocks
7 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 0.00
8 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 0.11
9 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 0.00
10 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 0.00
11 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 3.87
12 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
forest
13 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
forest
14 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 2.12
15 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 4.09
16 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
vegetation
17 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


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

Class name Reference Classified Number Producer’s Users’ Kappa
totals totals correct accuracy (%) accuracy (%) statistics

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 %

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 contribution
to erosion and sediment load Watershed ID Erosion (tons/year) Sediment (tons/year)
under changed land use/land
cover 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


W5 (+0.28 km2), W6 (+0.21 km2), W4 (+0.01 km2) and Model simulation results
W1 (+0.01 km2).
It was also observed that the plantation cover The results of the model simulations for erosion and
showed a declining trend from 1992 to 2005. The sediment loadings revealed an increasing trend in all
major 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 W11
The 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 least
revealed 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 W8
by 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 for
a kappa coefficient of 0.913. erosion and sediment yields (Table 7) revealed that bare

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


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

lands 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 in

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


Table 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 67,805.00

Table 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 loadings.
by hay/pastures (46.14 t/year), agriculture (23.3 t/year)
and forests (2.72 t/year). Increase in sediment loads Socioeconomic characterization
was 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 in
pasture/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 of

Table 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


their high altitude, dense forested and remote nature. of the eastern portion of the lake catchment including the
Figures 10 and 11 show the spatial distribution of total Dachigam National Park. The highest literacy rate
population and number of households respectively for the (Fig. 12) was found for W2 (69.64) followed by W6
different 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 low
watershed mainly comprised of the congested Srinagar as per their economic development status (Fig. 13). W2
city west and the south. This was followed by W1 (28,463 belonged to the highest, W1, W5, W6 to the medium and
individuals and 3,921 households) again comprising of W3, W4, W8 belonged to the low category.
the Srinagar city west. It was found that W4 (17,473
individuals and 2,578 households) included eastern parts Integrated impact analysis and watershed prioritization
of 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 weightage
holds) and W5 (14,672 individuals and 3,921 households) assigned to each thematic map, all 13 watersheds were
comprised of the northern parts of the city in Dal Lake grouped into three categories: high, medium and low
Catchment. W3 (10,519 individuals and 1,555 house- priority shown in Table 10. Figures 14 and 15 show the
holds) includes the city east side. W8 (1,731 individuals spatial distribution of the prioritized watersheds. It was
and 243 households) recorded the lowest population and observed that five (5) watersheds namely W5>W2>W6
lowest number of households. This watershed comprised >W8>W1 ranked highest in the overall weightage and

Fig. 10 Spatial distribution of total population in the watersheds


Fig. 11 Spatial distribution of total households in the watersheds

hence 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 is
priority category. The remaining three watersheds, i.e. among the most evident impacts of human activities on
W12>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 and
Land 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 such
and 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 city
period, considerable changes were observed for almost have resulted in the conversion of large peripheral
all the land use/land cover classes particularly areas that were essentially used for agro-horticultural


Fig. 12 Spatial distribution of literacy rate in the watersheds

purposes into built up mostly for residential purposes. dwindling grasslands as well as the sparse forests in the
Accelerated nutrient enrichment of the Dal Lake due watersheds. Decline in the coniferous, deciduous and
to incoming effluents from these watersheds resulted sparse forest of the study area was found to be the result
in the proficient and luxuriant growth of macrophytes of large-scale deforestation, both within the Dachigam
that was revealed by the increased area of aquatic National Park as well as outside it particularly along the
vegetation. In the later parts of the year, the surface higher reaches of the catchment. Increase in the area of
waters remain covered by the decomposed thick mats bare lands during study period at both the higher and
disrupting 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 barren
dous pressures on this ecologically and socioeconomically area. This land is very much vulnerable to increased
important land cover attributed to the biotic interference in erosion and sediment yields as well increased runoff
and 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 factors
ing capacity have resulted in the creation of denuded and influencing contaminant transport in a watershed (Vieux
semi-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


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

Table 10 Results of prioritization carried out for watersheds in reflective of the changing biophysical charcteristics of
Dal Lake Catchment these watersheds attributable mostly to the increased
S. No Watershed name Priority result Priority rank
anthropogenic pressures. The increased erosion and
sediment loadings were in particular observed for those
1 W1 High PZ5 watersheds where the stress on the vegetation was the
2 W2 High PZ2 maximum, namely W5, W13, W11, W6 and W8. In
3 W3 Medium PZ7 addition, various agro-horticultural activities carried out
4 W4 Medium PZ8 particularly in W5, W6 and W8 accelerate the potential
5 W5 High PZ1 for the processes of surface runoff and soil erosion
6 W6 High PZ3 (Stoate et al. 2001; Van Rompaey et al. 2001; Hansen
7 W7 Medium PZ10 et al. 2004). Biotic interferences like overgrazing of
8 W8 High PZ4 grasslands beyond the carrying capacity, clearing of
9 W9 Low PZ12 forest areas for contruction and agricultural purposes
10 W10 Low PZ13 has led to the creation of denuded patches accelerating
11 W11 Medium PZ9
the erosion (Bhat et al. 2002). Moreover, increased
12 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 under
PZ priority zone steep and very steep slope classes indicating quick


Fig. 14 Watershed prioritization map of Dal Lake Catchment

runoff during rainfall or storm water events (Tucker and the anthropogenic pressures, thereby, preventing the
Bras 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 the
slides. 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 of
no 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). Increased
Vegetation 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 areas
environment and impervious nature and flat slopes were the least contributors because of their vegetative
provided 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 flows
forested 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


Fig. 15 Spatial distribution of watershed priority zones

ageing (Zutshi and Yousuf 2000). Owing to the inad- to the lakes (Loeb 1988). These watersheds can be
equate 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 a
disrupted 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 these
ical remote sensing has emerged as a new and watersheds, a detailed survey for soil and water
promising field that provides insights into the so- conservation measures, water resources develop-
cioeconomic aspects of environmental and physical ment, scientific land use planning for preservation
problems and could be used as a useful aid for of eco-diversity, integrated study for development
linking the environmental problems to communi- of natural as well as social resources, etc., to
ties (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, is
in the biophysical environment and the behaviour a pre-requisite for formulation of watershed plan
of 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 up
al. 1997; Peters and Maybeck 2000). Alteration of for development and management plans in a
the landscape and other human-caused disturbances phased manner (Vittala et al. 2008). Since this
have been shown to be important factors affecting approach is considered to be ideal in maintaining
mass transport (loading) of erosion and sediment the ecological balance (Sahai 1988), it shall,


greatly help in devising the conservation and man- immediate effect. The research methodology established
agement strategies for the restoration of the lake during the present study should help in the effective
ecosystem (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-temporal
satellite data, GIS simulation model (GWLF) together
with extensive field observations was used for the first
time to conduct an in-depth investigation of different References
watershed scale processes (land use/land cover change
detection analysis, quantification of erosion, sediment Amin, A. & Romshoo, S.A. (2007). Assessing the hydrologic
and 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 Conference
on 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 and
date information about different parameters, simula- soil erosion using mathematical modeling: application in
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