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This research makes use of the remote sensing, simulation modeling and field observations to assess the non-point source pollution load of a Himalayan lake from its catchment.
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ORIGINAL ARTICLE
Geospatial modeling for assessing the nutrient loadof a Himalayan lake
Shakil Ahmad Romshoo • Mohammad Muslim
Received: 6 April 2009 / Accepted: 27 January 2011
� Springer-Verlag 2011
Abstract This research makes use of the remote sensing,
simulation modeling and field observations to assess the
non-point source pollution load of a Himalayan lake from
its catchment. The lake catchment, spread over an area of
about 11 km2, is covered by different land cover types
including wasteland (36%), rocky outcrops (30%), agri-
culture (12%), plantation (12.2%), horticulture (6.2%) and
built-up (3.1%) The GIS-based distributed modeling
approach employed relied on the use of geospatial data sets
for simulating runoff, sediment, and nutrient (N and P)
loadings from a watershed, given variable-size source
areas, on a continuous basis using daily time steps for
weather data and water balance calculations. The model
simulations showed that the highest amount of nutrient
loadings are observed during wet season in the month of
March (905.65 kg of dissolved N, 10 kg of dissolved P,
10,386.81 kg of total N and 2,381.89 kg of total P). During
the wet season, the runoff being the highest, almost all the
excess soil nutrients that are trapped in the soil are easily
flushed out and thus contribute to higher nutrient loading
into the lake during this time period. The 11-year simula-
tions (1994–2004) showed that the main source areas of
nutrient pollution are agriculture lands and wastelands. On
an average basis, the source areas generated about
3,969.66 kg/year of total nitrogen and 817.25 kg/year of
total phosphorous. Nash–Sutcliffe coefficients of correla-
tion between the daily observed and predicted nutrient load
ranged in value from 0.80 to 0.91 for both nitrogen and
phosphorus.
Keywords Geospatial modeling � Nutrient load �Remote sensing � Watershed � Digital elevation model
Introduction
The picturesque valley of Kashmir, located in the foothills
of the Himalaya, abounds in fresh water natural lakes that
have come into existence as a result of various geological
changes and also due to changes in the course of the Indus
River. These lakes categorized into glacial, Alpine and
valley lakes based on their origin, altitudinal situation and
nature of biota, provide an excellent opportunity for
studying the structure and functional process of an aquatic
ecosystem system (Kaul 1977; Kaul et al. 1977; Khan
2006; Trisal 1985; Zutshi et al. 1972). However, the
unplanned urbanization, deforestation, soil erosion and
reckless use of pesticides for horticulture and agriculture
have resulted in heavy inflow of nutrients into these lakes
from the catchment areas (Baddar and Romhoo 2007).
These anthropogenic influences not only deteriorate the
water quality, but also affect the aquatic life in the lakes, as
a result of which the process of aging of these lakes is
hastened. As a consequence, most of the lakes in the
Kashmir valley are exhibiting eutrophication (Kaul 1979;
Khan 2008). It is now quite common that the lakes of
Kashmir valley are characterized by excessive growth of
macrophytic vegetation, anoxic deep water layers, and
shallow marshy conditions along the peripheral regions
and have high loads of nutrients in their waters (Jeelani and
Shah 2006; Khan 2000; Koul et al. 1990). Though, quite a
number of studies have been conducted to understand
the hydrochemistry and hydrobiology of the Kashmir
Himalayan lakes (Jeelani and Shah 2007; Pandit 1998;
Saini et al. 2008), very few studies, if at all, have focused
S. A. Romshoo (&) � M. Muslim
Department of Geology and Geophysics, University of Kashmir,
Hazratbal, Srinagar 190006, Kashmir, India
e-mail: [email protected]
123
Environ Earth Sci
DOI 10.1007/s12665-011-0944-9
on modeling the pollution loads of lakes from the catch-
ment areas in Kashmir Himalayas (Baddar and Romhoo
2007; Muslim et al. 2008). The Manasbal watershed, the
focus of this research, is the catchment area of the
Manasbal Lake and drains the sewage and domestic
effluents from the new and expanding human settlements,
and the runoff from fertilized agricultural land and the
residual insecticides and pesticides from the arable lands,
orchards and plantations into the lake.
For the management and conservation of water bodies, it
is important to identify the pollution sources; both point
and non-point, and assess the pollution loads to the lakes at
the catchment scale (Hession and Shanholtz 1988; Moore
et al. 1988, Tolson and Shoemaker 2007). The advance-
ment in the field of geospatial modeling, data acquisition
and computer technology facilitates the integrative analysis
of the geoinformation for pollution control programs
(Evans et al. 2002; Melesse et al. 2007; Olivieri et al. 1991;
Prakash et al. 2000). Geospatial models are excellent tools
that allow us to predict the hydrological and other land
surface processes and phenomena at different spatial and
time scales (Frankenberger et al. 1999; Olivera and
Maidment 1999; Romshoo 2003; Shamsi 1996; Young
et al. 1989; Yuksel et al. 2008; Zollweg et al. 1996).
Geospatial models, when suitably parameterized, cali-
brated and verified, can predict nutrient concentrations in
space and time when empirical sampling data are not
adequate (Evans and Corradini 2007; Hinaman 1993; Liao
and Tim 1997; Raterman et al. 2001; Sample et al. 2001;
Tim et al. 1992; Wong et al. 1997). These models provide a
deeper insight into the sources and impacts of pollution and
help to simulate alternate scenarios of water-quality con-
ditions under different land use and management practice
in order to reduce the pollution impacts (Evans and Cor-
radini 2007; Hartkamp et al. 1999; Kuo and Wu 1994;
Lung 1986; Thomann and Mueller 1987; Thiemann and
Kaufmann 2000).
The objectives of this research were to identify the
critical source areas causing nutrient pollution; develop a
spatial and temporal database; simulate nutrient pollution
loading from the source areas in the catchment to the lake,
and to suggest a probable solution for reduction of nutrient
productivity and contamination to the lake from the
catchment. The research paper is organized into different
sections that provide information on the background of the
study, study area, data sets used, simulation model, data
analysis, discussions and conclusions.
Study area
Figure 1 shows the location of the Manasbal catchment,
spread over an area of 11 km2 and lies between the lati-
tudes 34�14000.5900 to 34�16053.4500N and longitude
74�40050.2200 to 74�43053.8500E. The climate of the study
area is characterized by warm summers and cold winters.
According to Bagnolus and Meher-Homji (1959), the cli-
mate of Kashmir falls under sub-Mediterranean type with
Fig. 1 Showing the study area
Environ Earth Sci
123
four seasons based on mean temperature and precipitation.
The study area receives an average annual precipitation of
about 650 mm. The topography of the study area is
undulating to flat with few steep slopes. The highest point,
in north eastern part of the catchment, rises to an elevation
of about 3,142 m. The topography gently drops in west and
south west directions reaching its lowest at about 1,558 m
around the Manasbal Lake. The drainage pattern observed
in study area is Trellis with the flow direction from east to
south west. Most of the streams are seasonal. Laar Kul, a
main perennial stream, drains the catchment and discharges
into the Manasbal Lake. The lake body has predominantly
rural surroundings. The land use of the study area is mainly
agriculture and some of the main crops cultivated include
rice and mustard. Large areas of barren and waste lands are
also found in the catchment area. People are also involved
in horticultural and plantation activities in the catchment.
Materials and methods
Datasets used
For accomplishing the research objectives, data from vari-
ous sources were used in this study. For generating the land
use and land cover information, Indian Remote Sensing
Satellite data [IRS-ID, linear imaging self scanning (LISS-
III) of 5 October 2004 with a spatial resolution of 23.5 m and
spectral resolution of 0.52–0.86 l was used in the study
(National Remote Sensing Agency 2003)]. Further, for
generating the topographic variables of the catchment for
use in the geospatial model, Digital Elevation Model (DEM)
from Shuttle Radar Topographic Mission (SRTM), having a
spatial resolution of 90 m was used (Rodriguez et al. 2006).
A soil map of the study area, generated using remotely
sensed data supported with extensive ground truthing and
lab analysis, was used in the simulation modeling. The
existing coarse soil map available for the study area was also
used for validation of the high-resolution soil map. A time
series of hydro-meteorological data from the nearest
observation station was used for input to the geospatial
model. Some chemical parameters of water samples, viz;
nitrate, nitrite ammonia and total phosphorous were also
analyzed for validating the model simulations. Ancillary
data on the dissolved nutrient concentration for the rural land
(Haith 1987; Evans et al. 2002) was also used in this study.
Geospatial modeling approach for estimating non-point
source pollution
For simulation of nutrient pollution from both point and
non-point sources and identification of critical source areas
at the watershed scale, a GIS-based distributed parameter
Generalized Watershed Loading Function (GWLF) model
was used (Evans et al. 2002; Haith and Shoemaker 1987).
The model simulates runoff, sediment, and nutrient (N and
P) loadings from a watershed given variable-size source
areas on a continuous basis and uses daily time steps for
weather data and water balance calculations (Evans et al.
2008; Haith et al. 1992; Lee et al. 2001). It is also suitable
for calculating septic system loads, and allows for the
inclusion of point source discharge data. Monthly calcula-
tions are made for sediment and nutrient loads, based on the
daily water balance accumulated to monthly values. For the
surface loading, the approach adopted is distributed in the
sense that it allows multiple land use/land cover scenarios,
but each area is assumed to be homogenous in regard to
various attributes considered by the model. The model does
not spatially distribute the source areas, i.e., there is no
spatial routing, but simply aggregates the loads from each
area into a watershed total. For sub-surface loading, the
model acts as a lumped parameter model using a water
balance approach. The model is particularly useful for
application in regions where environmental data of all types
is not available to assess the point and non-point source
pollution from watershed (Evans et al. 2002; Strobe 2002).
Model structure and operation
The GWLF model estimates dissolved liquid and solid
phase nitrogen and phosphorous in stream flow from the
various sources as given in Eqs. 1 and 2 below (Haith and
Shoemaker 1987). Dissolved nutrient loads are transported
in runoff water and eroded soil from numerous source
areas, each of which is considered uniform with respect to
soil and land cover.
LDm ¼ DPm þ DRm þ DGm þ DSm ð1ÞLSm ¼ SPm þ SRm þ SUm ð2Þ
where, LDm and LSm are the dissolved and solid phase
nutrient load, respectively (kg), DPm and SPm are the point
source dissolved and solid phase nutrient load, respectively
(kg), DRm and SRm are the rural runoff dissolved and solid
phase nutrient load, respectively (kg), DGm is the ground
water dissolved nutrient load (kg), DSm is the septic system
dissolved nutrient load (kg), SUm is the urban runoff
nutrient load (kg).
Dissolved loads from each source area are obtained by
multiplying runoff by dissolved concentration as given in
Eq. 3.
LDm ¼ 0:1Xdm
t¼1
Cdk � Qkt � ARk ð3Þ
where LDm is monthly load from each source area,
Cdk, the nutrient concentration in runoff from source area
Environ Earth Sci
123
k (mg/l), Qkt is the runoff from source area k on day t (cm),
ARk is area of source area k (ha), dm is number of days in
month m.
The direct runoff is estimated from daily weather data
using Soil Conservation Services (SCS) curve number
equation given by Eq. 4.
Qkt ¼Rt þMt � 0:2DSktð Þ2
Rt þMt þ 0:8DSkt: ð4Þ
Rainfall Rt (cm) and snowmelt Mt (cm of water) on the
day t (cm), are estimated from daily precipitation and
temperature data. DSkt is the catchment’s storage.
Catchment storage is estimated for each source area
using CN values with the equation given below;
DSkt ¼2; 540
CNkt� 25:4 ð5Þ
where CNkt is the CN value for source area k, at time t.
Stream flow consists of surface runoff and sub-surface
discharge from groundwater. The latter is obtained from a
lumped parameter watershed water balance (Haan 1972).
Daily water balances are calculated for unsaturated and
shallow saturated zones. Infiltration to the unsaturated and
shallow saturated zones equals the excess, if any, of rainfall
and snowmelt runoff. Percolation occurs when unsaturated
zone water exceeds field capacity. The shallow saturated
zone is modeled as linear ground water reservoir. Daily
evapotranspiration is given by the product of a cover factor
and potential evapotranspiration (Hamon 1961). The latter
is estimated as a function of daily light hours, saturated
water vapor pressure and daily temperature.
Monthly solid phase nutrient load are estimated using
Eq. 6 given below. The solid phase rural nutrient loads are
given by the product of the monthly sediment yield and
average sediment nutrient concentration.
SRm ¼ 0:001� Cs � Ym ð6Þ
where SRm is the solid phase rural nutrient load, Cs is the
average sediment nutrient concentration (mg/l), Ym water-
shed sediment yield (mg). Erosion is computed using the
Universal Soil Loss Equation (USLE) and the sediment
yield is the product of erosion and sediment delivery ratio.
The yield in any month is proportional to the total capacity
of daily runoff during the month.
Erosion from source area (k) at time t, Xkt is estimated
using the following equation:
Xkt ¼ 0:132� REt � Kk � ðLSÞk � Ck � Pk � ARk ð7Þ
where Kk; ðLSÞk;Ck and Pk are the soil erodibility, topo-
graphic, cover and management and supporting practice
factor as specified by the USLE (Wischmeier and Smith
1978). REt is the rainfall erosivity on day t (MJ mm/
ha h y).
Nutrient load from ground water source DGm are esti-
mated with the equation given below:
DGm ¼ 0:1� Cg � AT�Xdm
t¼1
Gt ð8Þ
where Cg is the nutrient concentration in ground water
(mg/l), AT is the watershed area (ha) and Gt is the ground
water discharge to the stream on day t (cm).
Septic systems are classified according to four types:
normal systems, ponding systems, short circulating systems
and direct discharge systems. Nutrient loads from septic
systems are calculated by estimating the per capita daily
loads from each type of system and the number of people in
the watershed served by each type. Monthly nutrient load
from on-site septic system are estimated with equation
given below;
DSm ¼ NSm � SSm � PSm þ DDSm ð9Þ
where DSm is the total septic loads per month (m), NSm is
the monthly (m) loads from normal septic system, SSm is
the monthly (m) loads from short-circuited septic system,
PSm is the monthly (m) loads from ponded septic system,
DDSm is the monthly (m) loads from direct discharge
system.
SUm, the urban nutrient load, assumed to be entirely
solid phase, are modeled by exponential accumulation and
wash-off function proposed by Amy et al. (1974) and
Sartor and Boyd (1972). Nutrients accumulate on urban
surfaces over time and are washed off by runoff events.
Input data preparation
The GIS-based GWLF model requires various types of
input data for simulating the nutrient loads at the watershed
level viz., land use/land cover data, digital topographic
data, hydro-meteorological data, transport parameter data
(hydrologic and sediment) and nutrient parameter data. The
procedure for the generation of the input data and their use
in simulating nutrient loads is given in the following
paragraphs.
Land use and land cover data
The catchment is primarily rural, and the main land use/
land cover (also referred to as runoff sources) are agri-
cultural, plantation, horticultural, wasteland and built-up
area. Identification of these critical source areas in the
catchment required the use of latest available satellite
image depicting current land use in the study area. In order
to determine the area covered by various land use types,
both supervised and unsupervised classification of the
satellite data was performed (Schowengerdt 1983). A
Environ Earth Sci
123
combination of both the techniques was used to develop a
hybrid approach. This was followed by creation of field
classes (land use types), which were then verified during
field assessment and ground truthing. The 11 km2 catch-
ment mainly consists of 12% agriculture, 12.2% plantation,
6.2% horticulture, 36% wasteland, 30% bare rock and 3.1%
built-up. Table 1 shows the accuracy assessment matrix of
the classified map. The overall accuracy of the classifica-
tion was found to be 92% with over all Kappa statistics
equal to 0.89. Figure 2 shows the classified land use/land
cover map of the study area.
Hydro-meteorological data
Geospatial modeling approach adopted here for the esti-
mation of nutrient load requires daily precipitation and
temperature data. The daily hydro-meteorological data,
precipitation, temperature (minimum and maximum),
rainfall intensity, of the last 25 years, from the Indian
Meteorological Department (IMD), was thus prepared for
the input into the model. In addition, mean daylight hours
for the catchment with latitude 34�N were obtained from
the literature (Evans et al. 2008; Haith et al. 1992). The
study area receives an average annual rainfall of about
650 mm. From the analysis of the data, it is observed that
the catchment receives most of its precipitation between
the months of July and March. Particularly, March, July,
September and November are the wettest months of the
year and May–June is driest period with very little rains.
January is the coldest month in the year with the average
minimum temperature dipping up to -2�C and the July is
the hottest month with average maximum temperature
soaring up to 31�C. Maximum daylight is observed in June
(14.2 h) and July (14 h) and the minimum daylight is
received in the months of December (9.8 h) January (10 h).
Transport parameters
Transport parameters are those aspects of the catchment
that influence the movement of the runoff and sediments
from any given cell in the catchment down to the lake.
Table 2 shows the transport parameters calculated for
Table 1 Error matrix and classification accuracy of the land use and land cover of the study area
Agriculture Bare rock Wasteland Horticulture Built-up Plantation Total
Agriculture 22 0 0 0 0 1 23
Bare rock 1 55 4 0 0 0 60
Wasteland 1 5 71 0 0 1 78
Horticulture 1 0 0 10 0 0 11
Built-up 1 0 0 0 2 0 3
Plantation 1 0 0 0 0 24 25
Total 27 60 75 10 2 26 184
Accuracy totals (overall classification accuracy = 92.00%)
Class names Producers accuracy (%) Users accuracy (%)
Agriculture 81.48 95.65
Bare rock 91.67 91.67
Horticulture 100.00 90.91
Built-up 100.00 66.67
Plantation 92.31 96.00
Wasteland 94.67 91.03
Kappa (j^) statistics (overall kappa statistics = 0.8903)
Class name Kappa
Agriculture 0.9497
Bare rock 0.8810
Wasteland 0.8564
Horticulture 0.9043
Built-up 0.6633
Plantation 0.9540
Environ Earth Sci
123
different source areas in the catchment. The detailed pro-
cedures for generating these parameters are described
below.
Parameters for hydrological characterization
The evapotranspiration (ET) cover coefficient is the ratio of
the water lost by evapotranspiration from the ground and
plants compared to what would be lost by evaporation from
an equal area of standing water (Thuman et al. 2003). The
ET cover coefficient vary by land use type and time period
within the growing season of a given field crop (FAO 1998;
Haith 1987). Therefore, the identification of the develop-
ment stages of the standing crop in the study area was done
during the field surveys for accurate allocation of the ET
coefficient. The values of the ET coefficient vary from the
highest 1.00 for the bare areas, urban surfaces, ploughed
lands; 0.4 for agriculture and grasslands. For plantations,
the ET coefficient varied from 0.3 to 1.00 depending upon
the development stage.
The SCS curve number is a parameter that determines
the amount of precipitation that infiltrates into the ground
or enters surface waters as runoff after adjusting it to
accommodate the antecedent soil moisture conditions
based on total precipitation for the preceeding 5 days (EPA
2003a). It is based on combination of factors such as land
use/land cover, soil hydrological group, hydrological con-
ditions, soil moisture conditions and management
(Arhounditsis et al. 2002). In GWLF, the CN value is used
to determine for each land use, the amount of precipitation
MANASBAL LAKE
74°43'30"E74°43'0"E74°42'30"E74°42'0"E74°41'30"E74°41'0"E74°40'30"E74°40'0"E74°39'30"E
34°17'0"N
34°16'30"N
34°16'0"N
34°15'30"N
34°15'0"N
34°14'30"N
34°14'0"N 0 0.5 1 1.50.25Kilometers
Legend
Agriculture
Barrenrock
Builtup
Horticulture
Plantation
Wasteland
Water
Fig. 2 Land use/land cover
classified map of the Manasbal
catchment
Table 2 Summary of transport parameters used for the GWLF model
Source areas Area in hectare Hydrologic conditions LS C P K WCN WDET WGET ET coefficient
Agriculture 132.653 Fair 2.063 0.5 0.5 0.210 82 0.3 1.0 0.4
Bare rock 334.195 Poor 19.617 1.0 1.0 0.410 98 0.3 0.3 1.0
Waste land 398.822 Poor 23.791 1.0 1.0 0.330 68 1.0 1.0 1.0
Built-up 68.371 N/A 2.063 1.0 1.0 0.410 86 1.0 1.0 1.0
Plantation 1.094 Fair 2.359 0.5 0.5 0.080 65 0.3 1.0 0.7
Horticulture 34.272 Fair 3.416 0.5 0.5 0.080 65 0.3 1.0 0.6
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
K soil erodibility value, LS slope length and steepness factor, C cover factor, P management factor, WCN weighted curve number values,
WGET weighted average growing season evapotranspiration, WDET weighted average dormant season evapotranspiration
Environ Earth Sci
123
that is assigned to the unsaturated zone where it may be lost
through evapotranspiration and/or percolation to the shal-
low saturated zone if storage in the unsaturated zone
exceeds soil water capacity. In percolation, the shallow
saturated zone is considered to be a linear reservoir that
discharges to stream or losses to deep seepage, at a rate
estimated by the product of zone’s moisture storage and a
constant rate coefficient (SCS 1986). The soil parameters
for the catchment were obtained by analyzing the soil
samples in the laboratory. In all, 33 composite soil sam-
ples, well distributed over various land use and land cover
types, were collected from the catchment. Satellite image
was used to delineate similar soil units for field sampling
(Khan and Romshoo 2008). The soil composite samples
were analyzed for texture, soil organic matter and water
holding capacity. Soil texture analysis was carried out by
‘‘Feel method’’ (Ghosh et al. 1983), field capacity of the
soil samples was determined using the methodology
adapted by Veihmeyer and Hendricjson (1931) and the soil
organic carbon/organic matter percent was determined by
rapid titration method (Walkley and Black 1934). Using the
field and lab observations of the soil samples, it was pos-
sible to determine the soil texture using the soil textural
triangle (Toogood 1958). The spatial soil texture map, as
shown in Fig. 3 and the soil organic carbon map, shown in
Fig. 4, was generated using stochastic interpolation method
in GIS environment (Burrough 1986). The texture and
permeability properties of the soils were used to determine
the soil hydrological groups for all the soil units in the
catchment (Table 3).
Parameters for sediment yield estimation
For simulating the soil erosion using GWLF model, a
number of soil and topographic parameters are required.
The slope length and slope steepness parameters, together
designated as LS factor, determine the effect of topography
on soil erosion. LS factor was estimated from the Digital
Elevation Model of the watershed (Arhounditsis et al.
2002). For determining the soil erodibility factor (K) on a
given unit of land, the soil texture and soil organic matter
content maps generated, as described above, were used
(Steward et al. 1975). The rainfall erosivity factor (RE) was
estimated from the product of the storm energy (E) and the
maximum 30-min rainfall intensity (I30) data collected for
that period. Erosivity coefficient for the dry season (May–
September) was estimated to be 0.01 and coefficient for
wet season was estimated to be 0.034 (Montanrella et al.
2000). The crop management factor (C) related to soil
protection cover (Wischmeier and smith 1978) and the
conservation practice factor (P) that reflects soil conser-
vation measures (Pavanelli and Bigi 2004) were deter-
mined from the land use and land cover characteristics
(EPA 2003b; Haith et al. 1992). In the GWLF model, the
sediment yield is estimated by multiplying sediment
delivery ratio (SDR) with the estimated erosion. Therefore,
the SDR was determined through the use of the logarithmic
graph based on the catchment area (Evans et al. 2008;
Haith et al. 1992; Vanori 1975). For the Manasbal catch-
ment with an area of about 11 km2, a sediment delivery
ratio of 0.23 was observed.
MANASBAL LAKE
74°44'0"E74°43'30"E74°43'0"E74°42'30"E74°42'0"E74°41'30"E74°41'0"E74°40'30"E74°40'0"E74°39'30"E74°39'0"E
34°17'0"N
34°16'30"N
34°16'0"N
34°15'30"N
34°15'0"N
34°14'30"N
34°14'0"N
0 0.7 1.4 2.10.35Kilometers
LegendLoamSandyclaySandyclayloamSandyloamSiltloam
Fig. 3 Soil textural map of the
study area
Environ Earth Sci
123
Nutrient parameters
Collection of runoff from various field crops for assessment
of nutrient concentration was one of the greatest challenges
of the study and because of the resource and time con-
straints, this research made use of the values estimated by
Haith (1987) for different source areas which are more or
less representative of rural catchments and are assumed to
be same for the study area.
Results
Catchment hydrological conditions
The model simulations were run for 11 years from April to
March of the next year on monthly basis. Figure 5 shows
the mean monthly hydrological model simulations for
11 years (1994–2004) along with the observed precipita-
tion. It is clear from the figure that May–June is driest
period and during this period, all stream flow is made up of
base flow. Figure 5 shows that March, July, September and
November are the wettest months of the year with the mean
MANASBAL LAKE
74°44'0"E74°43'30"E74°43'0"E74°42'30"E74°42'0"E74°41'30"E74°41'0"E74°40'30"E74°40'0"E74°39'30"E74°39'0"E
34°17'0"N
34°16'30"N
34°16'0"N
34°15'30"N
34°15'0"N
34°14'30"N
34°14'0"N
0 0.7 1.4 2.10.35Kilometers
Legend
0.5
4.202
4.538
4.84
4.908
Fig. 4 Soil organic matter
content of the study area
Table 3 Soil hydrological
groups used in the GWLF
model
Hydrological
group
Soil permeability (and runoff potential)
characteristics
Soil texture
A Soil exhibiting low surface runoff potential Sand, loamy sand, Sandy loam
B Moderately course soil with intermediate rates
of water transmission
Silty loam, loam
C Moderately fine texture soils with slow rates
of water transmission
Sandy clay loam
D Soils with high surface runoff potential Clay loam, silty loam,
Sandy clay, silty clay, clay
Fig. 5 Showing the mean monthly simulated hydrological output and
the observed rainfall
Environ Earth Sci
123
monthly rainfall of about 12.2, 11.98, 10.2, and 10.6 cm,
respectively. During this period, surface runoff, stream
flow and groundwater flow are substantially high with the
peak flows reached in March.
Temporal variability of nutrient loading
Figure 6a–d shows the mean monthly nutrient loading to
the lake for 11-year simulation period. The figure shows
that the lowest amount of loading is received between April
and June. The graph further reveals that after June, the rise
in the amount of loading almost coincides with the increase
in runoff from July (Fig. 5). The mean monthly loading
increases from 95.53 kg in August to 905.65 kg in March
for the dissolved nitrogen, and from 1.83 kg in August to
10 kg in March for the dissolved phosphorus. The highest
amount of nutrient loading is observed in the month of
March (905.65 kg of dissolved N, 10 kg of dissolved P,
10,386.81 kg of total N and 2,381.89 kg of total P).
Figure 7a–d shows the annual nutrient loading for the
Manasbal catchment during the 11-year simulation period.
The figure shows that the lowest nutrient loading to the
lake in 1997 with 291.402 kg/year for total nitrogen and
75.276 kg/year for total phosphorous. On the other hand,
year 2004 produces higher amount of nutrient loading with
1,171.31 kg/year for total nitrogen and 229.37 kg/year for
total phosphorous.
Nutrient loading and precipitation patterns
The mean annual nutrient model estimates, as shown in
Fig. 7a–d, were compared with the annual precipitation for
the 11-year simulation period to determine the relationship
(Fig. 8). The analysis of the precipitation data reveals that
lowest amount of rainfall was received in 1997 (5.38 cm)
and highest in 2004 (11.2 cm). From the data, years 1994,
1997 and 1998 can be considered to be relatively dry years,
whereas the years 2002, 2003 and 2004 can be considered
as relatively wet years.
Spatial variability of nutrient loading
Table 4 details the annual nutrient loadings from the source
areas in the catchment for the 11-year’s simulation period.
The simulations reveal that on an average, the catchment
generates about 1,191.1 kg/year of dissolved nitrogen and
2,674.12 kg/year of particulate nitrogen, with a total
nitrogen load of 3,969.66 kg/year. As given in the table,
the catchment generates about 49.12 kg/year of dissolved
phosphorous and 768.13 kg/year of particulate phospho-
rous with a total phosphorous load of 817.25 kg/year from
the catchment. From the table, it is also evident that annual
surface runoff is highest in the wasteland areas (27.66 cm)
followed by the built-up (13.01 cm). The simulation val-
ues, as shown in the table, further reveal that high runoff is
normally associated with high erosion in the wasteland and
agriculture areas (81,717.85 kg/year and 3,974.74 kg/year,
respectively). There is no erosion in the urban area because
of the concrete nature of the landscape. Higher the erosion,
higher is the amount of sediments generated from a par-
ticular source area as shown in the table. In order to
appreciate the contributions made by the different source
areas towards the total nutrient loading to the lake, the
relative contribution of the source areas (by land use types)
(a) (b)
(d)(c)
Fig. 6 a–d Showing the
10-year mean monthly nutrient
load for dissolved and total N
and P
Environ Earth Sci
123
is shown in Fig. 9a–b for the total N and total P, respec-
tively. The agriculture areas contribute to maximum load-
ing (both N and P) to the lake followed by wasteland (for
total N) and bare rock (for total P) (Table 4).
Validation
The model simulations were validated by comparing the
predictions with measured nutrient load from the Manasbal
catchment to the lake body for 1 year period (April 2003–
March 2004). In all, 58 well-distributed samples were col-
lected from the lake for validating the simulated nutrient
load in the lake. The samples taken once a month (6–8) were
mixed before analyzing for the nutrient concentration.
However, no sampling was done for the of September,
October, November and December months. The compari-
sons between the observed and predicted dissolved N (mg/l)
and total P (mg/l) are given in the Tables 5, 6 respectively.
To assess the correlation, or ‘‘goodness of fit’’, between
observed and predicted values for mean annual nitrogen and
phosphorous loads the Nash–Sutcliffe statistical measure
recommended by ASCE (1993) for hydrological studies
was used. With the Nash–Sutcliffe measure, R2 coefficient
(a) (b)
(d)(c)
Fig. 7 a–d Showing the annual
nutrient load for dissolved and
total N and P during the
simulation period (1994–2004)
Fig. 8 Showing the annual precipitation observed in the catchment
Table 4 11-year simulated annual average of the sediment yield, erosion, runoff and nutrient loading from the source areas
Source areas Area
(ha)
Runoff
(cm)
Erosion
(kg/year)
Sediment
(kg/year)
Dis. N
(kg/year)
Tot. N
(kg/year)
Dis. P
(kg/year)
Tot. P
(kg/year)
Wasteland 386 27.66 81,717.85 17,977.85 880.48 880.48 35.47 35.47
Agriculture 128 7.66 3,974.74 874.44 291.97 3,041.08 11.76 699.04
Plantation/Horticulture 197 2.34 17.95 3.95 8.97 21.38 0.28 3.38
Bare rock 335 7.34 184.77 23.22 9.68 18.29 1.61 77.96
Built-up 36 13.01 0 0 0 8.43 0 1.40
Total 1,082 – – – 1,191.1 3,969.66 49.12 817.25
Dis. N dissolved nitrogen, Tot. N total nitrogen, Dis. P dissolved phosphorous, Tot. P total phosphorous
Environ Earth Sci
123
is calculated. Model predictions and observations for total
phosphorous (mg/l) and dissolved nitrogen (mg/l) are
compared in Figs. 10 and 11, respectively. A quantitative
summary of the comparison between the predictions and the
observations is given in Table 7. The Nash–Sutcliffe coef-
ficient (coefficient of determination R2) derived for the
validation of nutrient loads in Manasbal catchment are very
good, and ranged in value from 0.8 to 0.91 for dissolved
nitrogen and total phosphorous, respectively.
Discussion
Knowledge about the hydrological conditions of the
catchment is important, because it provides the basis for
comprehending the behavior of nutrient fluxes that even-
tually end up in the lake. The little rain that the catchment
receives during the dry period (May–June) is lost through
evapotranspiration observed to maximum during this per-
iod. Since there is almost negligible runoff from the
catchment during this period of the year, it can be deduced
that most of the nutrient loading reaching the Manasbal
Lake from its catchment are transported through stream
flow and base flow during this period. Further, storm events
are normally associated with the transport of nutrients
through overland flow or percolation to groundwater
(Johnes 1999); it is expected that the nutrient loading to the
lake will reach maximum levels during wetter spills
observed during March, July, September and November
The lowest mean monthly nutrient loading from April to
June could be attributed to the fact that the catchment
receives low rainfall and subsequently low amounts of
stream flow. During the wetter time period (March and
August), the runoff being highest, almost all the excess soil
nutrients that are trapped in the soil are easily flushed out
and thus contribute to the higher nutrient loading into the
lake during this time period. It is therefore concluded from
the observations that the nutrients that accumulate in
Fig. 9 a–b Showing the relative contribution of land use/land cover
types for nitrogen and phosphorous loading to the lake
Table 5 Comparison of the model predictions and observations for
dissolved nitrogen
Months Predicted Observed
April 0.980 0.950
May 0.950 0.939
June 0.930 0.87
July 0.919 0.87
August 0.903 0.89
September 0.972 NA
October 0.974 NA
November 0.981 NA
December 0.990 NA
January 0.980 0.96
February 1.0 0.8
March 0.980 1
NA not available
Table 6 Comparison of the model predictions and observations for
total phosphorous
Months Predicted Observed
April 0.046 0.05
May 0.118 0.121
June 0.77 0.57
July 0.627 0.62
August 0.072 0.069
September 0.97 NA
October 0.97 NA
November 0.98 NA
December 0.99 NA
January 0.002 0.0012
February 0.017 0.011
March 0.172 0.279
NA not available
Environ Earth Sci
123
cultivated land due to fertilization during drier periods are
later flushed out during periods of high rainfall. The lowest
nutrient loading observed during 1997 relates well with
the low amount of precipitation for the year and similarly,
the highest nutrient loading observed during 2004 due to the
highest precipitation received for that year. Both, mean
monthly and annual pattern of the loading, are showing
good relation with the hydrology observed in the Manasbal
catchment. Similarly strong relationship between the
hydrology and nutrient concentration has also been reported
in some other studies (Mitish et al. 2001; Nakamura et al.
2004). However, Young et al. 2008 reported that there is no
clear correlation between the river discharge and the nutri-
ent concentration. Similar relationship has been observed in
Mississippi River Basin (Mitish et al. 2001). It is therefore
concluded from these observations that the precipitation has
a great influence on the timing and amounts of nutrient
exports from crop fields to the catchment outlet.
The results show that the Manasbal Lake receives large
amounts of nutrients from its catchment area and are
dependent upon the land use and land cover types. The
maximum nutrient loading from agriculture, wastelands
and built-up areas is partly related to higher runoff gener-
ated from the agriculture lands due to faulty agriculture
practices (Omernik et al. 1981), high runoff and low
infiltration from rocky (wastelands and bare lands) and
concrete (built-up) land cover types (Osborne and Wiley
1988). Therefore, for reducing the pollution load to the
lake, it is vital to know various source areas in the catch-
ment that contribute nutrients to the lake so that remedial
measures are taken to arrest the pollution to the lake (Perry
and Vanderklein 1996; Prakash et al. 2000).
Validation studies showed that, overall, there is quite
good correlation between the observations and predictions
with the wet period showing better correlation compared to
the dry months. The suitable values for the Nash–Sutcliffe
coefficient from 0.8 to 0.91 indicate that the model satis-
factorily simulates the variations in nutrient loads on
monthly, seasonal and annual basis.
Conclusions
The studies have established that the Manasbal Lake situ-
ated in rural Kashmir is showing definite and progressive
signs of eutrophication. The GIS-based modeling approach
for the quantification of mean annual nutrient loads, runoff
and erosion rates provided reliable estimates over variable
source areas in the lake catchment. Higher nutrient loading
was observed during the wet periods as against low nutrient
loading during the drier periods. It is therefore concluded
that the precipitation has a significant influence on the
timing and amounts of nutrient exports from crop fields to
the catchment outlet.
It has been observed that the nutrient loading, runoff and
soil erosion rates vary for different land use classes. The
highest nutrient load (total N and total P) are observed from
agriculture, followed by the wastelands. The runoff and
erosion rates are highest for the wastelands found in the
catchment. The validation studies of the water-quality data
Table 7 Coefficient of
determination (R2) for the
predicted and observed values
for the nutrient parameters
Constituents Monthly means Coefficient of
determination (r2)Predicted Observed
Dissolved nitrogen (mg/l) 0.963 0.909 0.80
Total phosphorous (mg/l) 0.477 0.227 0.91
Fig. 10 Showing the validation of the simulated total phosphorous
with the observed total phosphorous
Fig. 11 Showing the validation of the simulated dissolved nitrogen
with the observed dissolved nitrogen
Environ Earth Sci
123
showed good agreement between the predictions and the
observations at the catchment scale and the model satisfac-
torily simulated the variations in nutrient loads on monthly,
seasonal and annual time basis. The validation of the model
simulations with the stream discharge data, if available,
could have enhanced the credibility of the simulation results.
The estimation of nutrient loads, runoff and erosion from
the source areas shall facilitate prioritization of the source
areas for remedial measures to control the pollution and
eutrophication in the lake. It would be useful to check the
viability of constructing riparian zones and artificial wet-
lands as the effective sinks for nutrient in an agricultural
watershed before runoff reaches the water body. A certain
amount of control needs to be exercised on the excessive use
of fertilizers in the agricultural fields in the catchment area.
Acknowledgments This study was funded by the Space Applica-
tions Center (SAC), Indian Space Research Organization (ISRO),
India, under the National Wetland Inventory and Assessment project.
The authors express gratitude to the anonymous reviewers and the
editor for their valuable comments and suggestions on the earlier
manuscript version that improved the content and structure of this
manuscript.
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