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.

1. Environ Earth Sci
DOI 10.1007/s12665-011-0944-9
ORIGINAL ARTICLE
Geospatial modeling for assessing the nutrient load
of a Himalayan lake
Shakil Ahmad Romshoo • Mohammad Muslim
Received: 6 April 2009 / Accepted: 27 January 2011
Ó Springer-Verlag 2011
Abstract This research makes use of the remote sensing, Keywords Geospatial modeling Á Nutrient load Á
simulation modeling and field observations to assess the Remote sensing Á Watershed Á Digital elevation model
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 Introduction
including wasteland (36%), rocky outcrops (30%), agri-
culture (12%), plantation (12.2%), horticulture (6.2%) and The picturesque valley of Kashmir, located in the foothills
built-up (3.1%) The GIS-based distributed modeling of the Himalaya, abounds in fresh water natural lakes that
approach employed relied on the use of geospatial data sets have come into existence as a result of various geological
for simulating runoff, sediment, and nutrient (N and P) changes and also due to changes in the course of the Indus
loadings from a watershed, given variable-size source River. These lakes categorized into glacial, Alpine and
areas, on a continuous basis using daily time steps for valley lakes based on their origin, altitudinal situation and
weather data and water balance calculations. The model nature of biota, provide an excellent opportunity for
simulations showed that the highest amount of nutrient studying the structure and functional process of an aquatic
loadings are observed during wet season in the month of ecosystem system (Kaul 1977; Kaul et al. 1977; Khan
March (905.65 kg of dissolved N, 10 kg of dissolved P, 2006; Trisal 1985; Zutshi et al. 1972). However, the
10,386.81 kg of total N and 2,381.89 kg of total P). During unplanned urbanization, deforestation, soil erosion and
the wet season, the runoff being the highest, almost all the reckless use of pesticides for horticulture and agriculture
excess soil nutrients that are trapped in the soil are easily have resulted in heavy inflow of nutrients into these lakes
flushed out and thus contribute to higher nutrient loading from the catchment areas (Baddar and Romhoo 2007).
into the lake during this time period. The 11-year simula- These anthropogenic influences not only deteriorate the
tions (1994–2004) showed that the main source areas of water quality, but also affect the aquatic life in the lakes, as
nutrient pollution are agriculture lands and wastelands. On a result of which the process of aging of these lakes is
an average basis, the source areas generated about hastened. As a consequence, most of the lakes in the
3,969.66 kg/year of total nitrogen and 817.25 kg/year of Kashmir valley are exhibiting eutrophication (Kaul 1979;
total phosphorous. Nash–Sutcliffe coefficients of correla- Khan 2008). It is now quite common that the lakes of
tion between the daily observed and predicted nutrient load Kashmir valley are characterized by excessive growth of
ranged in value from 0.80 to 0.91 for both nitrogen and macrophytic vegetation, anoxic deep water layers, and
phosphorus. 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
S. A. Romshoo (&) Á M. Muslim
the hydrochemistry and hydrobiology of the Kashmir
Department of Geology and Geophysics, University of Kashmir,
Hazratbal, Srinagar 190006, Kashmir, India Himalayan lakes (Jeelani and Shah 2007; Pandit 1998;
e-mail: shakilrom@yahoo.com Saini et al. 2008), very few studies, if at all, have focused
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2. Environ Earth Sci
on modeling the pollution loads of lakes from the catch- Tim et al. 1992; Wong et al. 1997). These models provide a
ment areas in Kashmir Himalayas (Baddar and Romhoo deeper insight into the sources and impacts of pollution and
2007; Muslim et al. 2008). The Manasbal watershed, the help to simulate alternate scenarios of water-quality con-
focus of this research, is the catchment area of the ditions under different land use and management practice
Manasbal Lake and drains the sewage and domestic in order to reduce the pollution impacts (Evans and Cor-
effluents from the new and expanding human settlements, radini 2007; Hartkamp et al. 1999; Kuo and Wu 1994;
and the runoff from fertilized agricultural land and the Lung 1986; Thomann and Mueller 1987; Thiemann and
residual insecticides and pesticides from the arable lands, Kaufmann 2000).
orchards and plantations into the lake. The objectives of this research were to identify the
For the management and conservation of water bodies, it critical source areas causing nutrient pollution; develop a
is important to identify the pollution sources; both point spatial and temporal database; simulate nutrient pollution
and non-point, and assess the pollution loads to the lakes at loading from the source areas in the catchment to the lake,
the catchment scale (Hession and Shanholtz 1988; Moore and to suggest a probable solution for reduction of nutrient
et al. 1988, Tolson and Shoemaker 2007). The advance- productivity and contamination to the lake from the
ment in the field of geospatial modeling, data acquisition catchment. The research paper is organized into different
and computer technology facilitates the integrative analysis sections that provide information on the background of the
of the geoinformation for pollution control programs study, study area, data sets used, simulation model, data
(Evans et al. 2002; Melesse et al. 2007; Olivieri et al. 1991; analysis, discussions and conclusions.
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 Study area
time scales (Frankenberger et al. 1999; Olivera and
Maidment 1999; Romshoo 2003; Shamsi 1996; Young Figure 1 shows the location of the Manasbal catchment,
et al. 1989; Yuksel et al. 2008; Zollweg et al. 1996). spread over an area of 11 km2 and lies between the lati-
Geospatial models, when suitably parameterized, cali- tudes 34°140 00.5900 to 34°160 53.4500 N and longitude
brated and verified, can predict nutrient concentrations in 74°400 50.2200 to 74°430 53.8500 E. The climate of the study
space and time when empirical sampling data are not area is characterized by warm summers and cold winters.
adequate (Evans and Corradini 2007; Hinaman 1993; Liao According to Bagnolus and Meher-Homji (1959), the cli-
and Tim 1997; Raterman et al. 2001; Sample et al. 2001; mate of Kashmir falls under sub-Mediterranean type with
Fig. 1 Showing the study area
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3. Environ Earth Sci
four seasons based on mean temperature and precipitation. Generalized Watershed Loading Function (GWLF) model
The study area receives an average annual precipitation of was used (Evans et al. 2002; Haith and Shoemaker 1987).
about 650 mm. The topography of the study area is The model simulates runoff, sediment, and nutrient (N and
undulating to flat with few steep slopes. The highest point, P) loadings from a watershed given variable-size source
in north eastern part of the catchment, rises to an elevation areas on a continuous basis and uses daily time steps for
of about 3,142 m. The topography gently drops in west and weather data and water balance calculations (Evans et al.
south west directions reaching its lowest at about 1,558 m 2008; Haith et al. 1992; Lee et al. 2001). It is also suitable
around the Manasbal Lake. The drainage pattern observed for calculating septic system loads, and allows for the
in study area is Trellis with the flow direction from east to inclusion of point source discharge data. Monthly calcula-
south west. Most of the streams are seasonal. Laar Kul, a tions are made for sediment and nutrient loads, based on the
main perennial stream, drains the catchment and discharges daily water balance accumulated to monthly values. For the
into the Manasbal Lake. The lake body has predominantly surface loading, the approach adopted is distributed in the
rural surroundings. The land use of the study area is mainly sense that it allows multiple land use/land cover scenarios,
agriculture and some of the main crops cultivated include but each area is assumed to be homogenous in regard to
rice and mustard. Large areas of barren and waste lands are various attributes considered by the model. The model does
also found in the catchment area. People are also involved not spatially distribute the source areas, i.e., there is no
in horticultural and plantation activities in the catchment. 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
Materials and methods balance approach. The model is particularly useful for
application in regions where environmental data of all types
Datasets used is not available to assess the point and non-point source
pollution from watershed (Evans et al. 2002; Strobe 2002).
For accomplishing the research objectives, data from vari-
ous sources were used in this study. For generating the land Model structure and operation
use and land cover information, Indian Remote Sensing
Satellite data [IRS-ID, linear imaging self scanning (LISS- The GWLF model estimates dissolved liquid and solid
III) of 5 October 2004 with a spatial resolution of 23.5 m and phase nitrogen and phosphorous in stream flow from the
spectral resolution of 0.52–0.86 l was used in the study various sources as given in Eqs. 1 and 2 below (Haith and
(National Remote Sensing Agency 2003)]. Further, for Shoemaker 1987). Dissolved nutrient loads are transported
generating the topographic variables of the catchment for in runoff water and eroded soil from numerous source
use in the geospatial model, Digital Elevation Model (DEM) areas, each of which is considered uniform with respect to
from Shuttle Radar Topographic Mission (SRTM), having a soil and land cover.
spatial resolution of 90 m was used (Rodriguez et al. 2006).
A soil map of the study area, generated using remotely LDm ¼ DPm þ DRm þ DGm þ DSm ð1Þ
sensed data supported with extensive ground truthing and LSm ¼ SPm þ SRm þ SUm ð2Þ
lab analysis, was used in the simulation modeling. The
existing coarse soil map available for the study area was also where, LDm and LSm are the dissolved and solid phase
used for validation of the high-resolution soil map. A time nutrient load, respectively (kg), DPm and SPm are the point
series of hydro-meteorological data from the nearest source dissolved and solid phase nutrient load, respectively
observation station was used for input to the geospatial (kg), DRm and SRm are the rural runoff dissolved and solid
model. Some chemical parameters of water samples, viz; phase nutrient load, respectively (kg), DGm is the ground
nitrate, nitrite ammonia and total phosphorous were also water dissolved nutrient load (kg), DSm is the septic system
analyzed for validating the model simulations. Ancillary dissolved nutrient load (kg), SUm is the urban runoff
data on the dissolved nutrient concentration for the rural land nutrient load (kg).
(Haith 1987; Evans et al. 2002) was also used in this study. Dissolved loads from each source area are obtained by
multiplying runoff by dissolved concentration as given in
Geospatial modeling approach for estimating non-point Eq. 3.
source pollution X
dm
LDm ¼ 0:1 Cdk  Qkt  ARk ð3Þ
t¼1
For simulation of nutrient pollution from both point and
non-point sources and identification of critical source areas where LDm is monthly load from each source area,
at the watershed scale, a GIS-based distributed parameter Cdk , the nutrient concentration in runoff from source area
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4. Environ Earth Sci
k (mg/l), Qkt is the runoff from source area k on day t (cm), Nutrient load from ground water source DGm are esti-
ARk is area of source area k (ha), dm is number of days in mated with the equation given below:
month m. X
dm
The direct runoff is estimated from daily weather data DGm ¼ 0:1  Cg  AT  Gt ð8Þ
using Soil Conservation Services (SCS) curve number t¼1
equation given by Eq. 4.
where Cg is the nutrient concentration in ground water
ðRt þ Mt À 0:2DSkt Þ2 (mg/l), AT is the watershed area (ha) and Gt is the ground
Qkt ¼ : ð4Þ
Rt þ Mt þ 0:8DSkt water discharge to the stream on day t (cm).
Septic systems are classified according to four types:
Rainfall Rt (cm) and snowmelt Mt (cm of water) on the normal systems, ponding systems, short circulating systems
day t (cm), are estimated from daily precipitation and and direct discharge systems. Nutrient loads from septic
temperature data. DSkt is the catchment’s storage. systems are calculated by estimating the per capita daily
Catchment storage is estimated for each source area loads from each type of system and the number of people in
using CN values with the equation given below; the watershed served by each type. Monthly nutrient load
2; 540 from on-site septic system are estimated with equation
DSkt ¼ À 25:4 ð5Þ
CNkt given below;
where CNkt is the CN value for source area k, at time t. DSm ¼ NSm  SSm  PSm þ DDSm ð9Þ
Stream flow consists of surface runoff and sub-surface
where DSm is the total septic loads per month (m), NSm is
discharge from groundwater. The latter is obtained from a
the monthly (m) loads from normal septic system, SSm is
lumped parameter watershed water balance (Haan 1972).
the monthly (m) loads from short-circuited septic system,
Daily water balances are calculated for unsaturated and
PSm is the monthly (m) loads from ponded septic system,
shallow saturated zones. Infiltration to the unsaturated and
DDSm is the monthly (m) loads from direct discharge
shallow saturated zones equals the excess, if any, of rainfall
system.
and snowmelt runoff. Percolation occurs when unsaturated
SUm , the urban nutrient load, assumed to be entirely
zone water exceeds field capacity. The shallow saturated
solid phase, are modeled by exponential accumulation and
zone is modeled as linear ground water reservoir. Daily
wash-off function proposed by Amy et al. (1974) and
evapotranspiration is given by the product of a cover factor
Sartor and Boyd (1972). Nutrients accumulate on urban
and potential evapotranspiration (Hamon 1961). The latter
surfaces over time and are washed off by runoff events.
is estimated as a function of daily light hours, saturated
water vapor pressure and daily temperature.
Input data preparation
Monthly solid phase nutrient load are estimated using
Eq. 6 given below. The solid phase rural nutrient loads are
The GIS-based GWLF model requires various types of
given by the product of the monthly sediment yield and
input data for simulating the nutrient loads at the watershed
average sediment nutrient concentration.
level viz., land use/land cover data, digital topographic
SRm ¼ 0:001  Cs  Ym ð6Þ data, hydro-meteorological data, transport parameter data
(hydrologic and sediment) and nutrient parameter data. The
where SRm is the solid phase rural nutrient load, Cs is the
procedure for the generation of the input data and their use
average sediment nutrient concentration (mg/l), Ym water-
shed sediment yield (mg). Erosion is computed using the in simulating nutrient loads is given in the following
paragraphs.
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 Land use and land cover data
of daily runoff during the month.
Erosion from source area (k) at time t, Xkt is estimated The catchment is primarily rural, and the main land use/
using the following equation: land cover (also referred to as runoff sources) are agri-
cultural, plantation, horticultural, wasteland and built-up
Xkt ¼ 0:132  REt  Kk  ðLSÞk  Ck  Pk  ARk ð7Þ
area. Identification of these critical source areas in the
where Kk ; ðLSÞk ; Ck and Pk are the soil erodibility, topo- catchment required the use of latest available satellite
graphic, cover and management and supporting practice image depicting current land use in the study area. In order
factor as specified by the USLE (Wischmeier and Smith to determine the area covered by various land use types,
1978). REt is the rainfall erosivity on day t (MJ mm/ both supervised and unsupervised classification of the
ha h y). satellite data was performed (Schowengerdt 1983). A
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5. Environ Earth Sci
combination of both the techniques was used to develop a for the catchment with latitude 34°N were obtained from
hybrid approach. This was followed by creation of field the literature (Evans et al. 2008; Haith et al. 1992). The
classes (land use types), which were then verified during study area receives an average annual rainfall of about
field assessment and ground truthing. The 11 km2 catch- 650 mm. From the analysis of the data, it is observed that
ment mainly consists of 12% agriculture, 12.2% plantation, the catchment receives most of its precipitation between
6.2% horticulture, 36% wasteland, 30% bare rock and 3.1% the months of July and March. Particularly, March, July,
built-up. Table 1 shows the accuracy assessment matrix of September and November are the wettest months of the
the classified map. The overall accuracy of the classifica- year and May–June is driest period with very little rains.
tion was found to be 92% with over all Kappa statistics January is the coldest month in the year with the average
equal to 0.89. Figure 2 shows the classified land use/land minimum temperature dipping up to -2°C and the July is
cover map of the study area. the hottest month with average maximum temperature
soaring up to 31°C. Maximum daylight is observed in June
Hydro-meteorological data (14.2 h) and July (14 h) and the minimum daylight is
received in the months of December (9.8 h) January (10 h).
Geospatial modeling approach adopted here for the esti-
mation of nutrient load requires daily precipitation and Transport parameters
temperature data. The daily hydro-meteorological data,
precipitation, temperature (minimum and maximum), Transport parameters are those aspects of the catchment
rainfall intensity, of the last 25 years, from the Indian that influence the movement of the runoff and sediments
Meteorological Department (IMD), was thus prepared for from any given cell in the catchment down to the lake.
the input into the model. In addition, mean daylight hours 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
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6. Environ Earth Sci
Fig. 2 Land use/land cover 74°39'30"E 74°40'0"E 74°40'30"E 74°41'0"E 74°41'30"E 74°42'0"E 74°42'30"E 74°43'0"E 74°43'30"E
classified map of the Manasbal 34°17'0"N
catchment Legend
Agriculture
Barrenrock
34°16'30"N
Builtup
Horticulture
Plantation
Wasteland
34°16'0"N
Water
34°15'30"N
34°15'0"N
MANASBAL LAKE
34°14'30"N
Kilometers
34°14'0"N 0 0.25 0.5 1 1.5
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
different source areas in the catchment. The detailed pro- coefficient. The values of the ET coefficient vary from the
cedures for generating these parameters are described highest 1.00 for the bare areas, urban surfaces, ploughed
below. lands; 0.4 for agriculture and grasslands. For plantations,
the ET coefficient varied from 0.3 to 1.00 depending upon
Parameters for hydrological characterization the development stage.
The SCS curve number is a parameter that determines
The evapotranspiration (ET) cover coefficient is the ratio of the amount of precipitation that infiltrates into the ground
the water lost by evapotranspiration from the ground and or enters surface waters as runoff after adjusting it to
plants compared to what would be lost by evaporation from accommodate the antecedent soil moisture conditions
an equal area of standing water (Thuman et al. 2003). The based on total precipitation for the preceeding 5 days (EPA
ET cover coefficient vary by land use type and time period 2003a). It is based on combination of factors such as land
within the growing season of a given field crop (FAO 1998; use/land cover, soil hydrological group, hydrological con-
Haith 1987). Therefore, the identification of the develop- ditions, soil moisture conditions and management
ment stages of the standing crop in the study area was done (Arhounditsis et al. 2002). In GWLF, the CN value is used
during the field surveys for accurate allocation of the ET to determine for each land use, the amount of precipitation
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7. Environ Earth Sci
that is assigned to the unsaturated zone where it may be lost Parameters for sediment yield estimation
through evapotranspiration and/or percolation to the shal-
low saturated zone if storage in the unsaturated zone For simulating the soil erosion using GWLF model, a
exceeds soil water capacity. In percolation, the shallow number of soil and topographic parameters are required.
saturated zone is considered to be a linear reservoir that The slope length and slope steepness parameters, together
discharges to stream or losses to deep seepage, at a rate designated as LS factor, determine the effect of topography
estimated by the product of zone’s moisture storage and a on soil erosion. LS factor was estimated from the Digital
constant rate coefficient (SCS 1986). The soil parameters Elevation Model of the watershed (Arhounditsis et al.
for the catchment were obtained by analyzing the soil 2002). For determining the soil erodibility factor (K) on a
samples in the laboratory. In all, 33 composite soil sam- given unit of land, the soil texture and soil organic matter
ples, well distributed over various land use and land cover content maps generated, as described above, were used
types, were collected from the catchment. Satellite image (Steward et al. 1975). The rainfall erosivity factor (RE) was
was used to delineate similar soil units for field sampling estimated from the product of the storm energy (E) and the
(Khan and Romshoo 2008). The soil composite samples maximum 30-min rainfall intensity (I30) data collected for
were analyzed for texture, soil organic matter and water that period. Erosivity coefficient for the dry season (May–
holding capacity. Soil texture analysis was carried out by September) was estimated to be 0.01 and coefficient for
‘‘Feel method’’ (Ghosh et al. 1983), field capacity of the wet season was estimated to be 0.034 (Montanrella et al.
soil samples was determined using the methodology 2000). The crop management factor (C) related to soil
adapted by Veihmeyer and Hendricjson (1931) and the soil protection cover (Wischmeier and smith 1978) and the
organic carbon/organic matter percent was determined by conservation practice factor (P) that reflects soil conser-
rapid titration method (Walkley and Black 1934). Using the vation measures (Pavanelli and Bigi 2004) were deter-
field and lab observations of the soil samples, it was pos- mined from the land use and land cover characteristics
sible to determine the soil texture using the soil textural (EPA 2003b; Haith et al. 1992). In the GWLF model, the
triangle (Toogood 1958). The spatial soil texture map, as sediment yield is estimated by multiplying sediment
shown in Fig. 3 and the soil organic carbon map, shown in delivery ratio (SDR) with the estimated erosion. Therefore,
Fig. 4, was generated using stochastic interpolation method the SDR was determined through the use of the logarithmic
in GIS environment (Burrough 1986). The texture and graph based on the catchment area (Evans et al. 2008;
permeability properties of the soils were used to determine Haith et al. 1992; Vanori 1975). For the Manasbal catch-
the soil hydrological groups for all the soil units in the ment with an area of about 11 km2, a sediment delivery
catchment (Table 3). ratio of 0.23 was observed.
Fig. 3 Soil textural map of the 74°39'0"E 74°39'30"E 74°40'0"E 74°40'30"E 74°41'0"E 74°41'30"E 74°42'0"E 74°42'30"E 74°43'0"E 74°43'30"E 74°44'0"E
study area
34°17'0"N
Legend
Loam
34°16'30"N Sandyclay
Sandyclayloam
Sandyloam
Siltloam
34°16'0"N
34°15'30"N
34°15'0"N
MANASBAL LAKE
34°14'30"N
34°14'0"N
Kilometers
0 0.35 0.7 1.4 2.1
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8. Environ Earth Sci
Fig. 4 Soil organic matter 74°39'0"E 74°39'30"E 74°40'0"E 74°40'30"E 74°41'0"E 74°41'30"E 74°42'0"E 74°42'30"E 74°43'0"E 74°43'30"E 74°44'0"E
content of the study area
34°17'0"N
Legend
0.5
34°16'30"N
4.202
4.538
4.84
34°16'0"N 4.908
34°15'30"N
34°15'0"N
MANASBAL LAKE
34°14'30"N
34°14'0"N
Kilometers
0 0.35 0.7 1.4 2.1
Table 3 Soil hydrological
Hydrological Soil permeability (and runoff potential) Soil texture
groups used in the GWLF
group characteristics
model
A Soil exhibiting low surface runoff potential Sand, loamy sand, Sandy loam
B Moderately course soil with intermediate rates Silty loam, loam
of water transmission
C Moderately fine texture soils with slow rates Sandy clay loam
of water transmission
D Soils with high surface runoff potential Clay loam, silty loam,
Sandy clay, silty clay, clay
Nutrient parameters period and during this period, all stream flow is made up of
base flow. Figure 5 shows that March, July, September and
Collection of runoff from various field crops for assessment November are the wettest months of the year with the mean
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- Fig. 5 Showing the mean monthly simulated hydrological output and
tion. It is clear from the figure that May–June is driest the observed rainfall
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9. Environ Earth Sci
monthly rainfall of about 12.2, 11.98, 10.2, and 10.6 cm, the 11-year simulation period to determine the relationship
respectively. During this period, surface runoff, stream (Fig. 8). The analysis of the precipitation data reveals that
flow and groundwater flow are substantially high with the lowest amount of rainfall was received in 1997 (5.38 cm)
peak flows reached in March. and highest in 2004 (11.2 cm). From the data, years 1994,
1997 and 1998 can be considered to be relatively dry years,
Temporal variability of nutrient loading whereas the years 2002, 2003 and 2004 can be considered
as relatively wet years.
Figure 6a–d shows the mean monthly nutrient loading to
the lake for 11-year simulation period. The figure shows Spatial variability of nutrient loading
that the lowest amount of loading is received between April
and June. The graph further reveals that after June, the rise Table 4 details the annual nutrient loadings from the source
in the amount of loading almost coincides with the increase areas in the catchment for the 11-year’s simulation period.
in runoff from July (Fig. 5). The mean monthly loading The simulations reveal that on an average, the catchment
increases from 95.53 kg in August to 905.65 kg in March generates about 1,191.1 kg/year of dissolved nitrogen and
for the dissolved nitrogen, and from 1.83 kg in August to 2,674.12 kg/year of particulate nitrogen, with a total
10 kg in March for the dissolved phosphorus. The highest nitrogen load of 3,969.66 kg/year. As given in the table,
amount of nutrient loading is observed in the month of the catchment generates about 49.12 kg/year of dissolved
March (905.65 kg of dissolved N, 10 kg of dissolved P, phosphorous and 768.13 kg/year of particulate phospho-
10,386.81 kg of total N and 2,381.89 kg of total P). rous with a total phosphorous load of 817.25 kg/year from
Figure 7a–d shows the annual nutrient loading for the the catchment. From the table, it is also evident that annual
Manasbal catchment during the 11-year simulation period. surface runoff is highest in the wasteland areas (27.66 cm)
The figure shows that the lowest nutrient loading to the followed by the built-up (13.01 cm). The simulation val-
lake in 1997 with 291.402 kg/year for total nitrogen and ues, as shown in the table, further reveal that high runoff is
75.276 kg/year for total phosphorous. On the other hand, normally associated with high erosion in the wasteland and
year 2004 produces higher amount of nutrient loading with agriculture areas (81,717.85 kg/year and 3,974.74 kg/year,
1,171.31 kg/year for total nitrogen and 229.37 kg/year for respectively). There is no erosion in the urban area because
total phosphorous. of the concrete nature of the landscape. Higher the erosion,
higher is the amount of sediments generated from a par-
Nutrient loading and precipitation patterns ticular source area as shown in the table. In order to
appreciate the contributions made by the different source
The mean annual nutrient model estimates, as shown in areas towards the total nutrient loading to the lake, the
Fig. 7a–d, were compared with the annual precipitation for relative contribution of the source areas (by land use types)
Fig. 6 a–d Showing the (a) (b)
10-year mean monthly nutrient
load for dissolved and total N
and P
(c) (d)
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10. Environ Earth Sci
Fig. 7 a–d Showing the annual (a) (b)
nutrient load for dissolved and
total N and P during the
simulation period (1994–2004)
(c)
(d)
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-
Fig. 8 Showing the annual precipitation observed in the catchment
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
is shown in Fig. 9a–b for the total N and total P, respec- observed and predicted values for mean annual nitrogen and
tively. The agriculture areas contribute to maximum load- phosphorous loads the Nash–Sutcliffe statistical measure
ing (both N and P) to the lake followed by wasteland (for recommended by ASCE (1993) for hydrological studies
total N) and bare rock (for total P) (Table 4). was used. With the Nash–Sutcliffe measure, R2 coefficient
Table 4 11-year simulated annual average of the sediment yield, erosion, runoff and nutrient loading from the source areas
Source areas Area Runoff Erosion Sediment Dis. N Tot. N Dis. P Tot. P
(ha) (cm) (kg/year) (kg/year) (kg/year) (kg/year) (kg/year) (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
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11. Environ Earth Sci
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
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.
Fig. 9 a–b Showing the relative contribution of land use/land cover
types for nitrogen and phosphorous loading to the lake
Discussion
Knowledge about the hydrological conditions of the
catchment is important, because it provides the basis for
Table 5 Comparison of the model predictions and observations for comprehending the behavior of nutrient fluxes that even-
dissolved nitrogen tually end up in the lake. The little rain that the catchment
Months Predicted Observed receives during the dry period (May–June) is lost through
evapotranspiration observed to maximum during this per-
April 0.980 0.950
iod. Since there is almost negligible runoff from the
May 0.950 0.939
catchment during this period of the year, it can be deduced
June 0.930 0.87 that most of the nutrient loading reaching the Manasbal
July 0.919 0.87 Lake from its catchment are transported through stream
August 0.903 0.89 flow and base flow during this period. Further, storm events
September 0.972 NA are normally associated with the transport of nutrients
October 0.974 NA through overland flow or percolation to groundwater
November 0.981 NA (Johnes 1999); it is expected that the nutrient loading to the
December 0.990 NA lake will reach maximum levels during wetter spills
January 0.980 0.96 observed during March, July, September and November
February 1.0 0.8 The lowest mean monthly nutrient loading from April to
March 0.980 1 June could be attributed to the fact that the catchment
NA not available 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
is calculated. Model predictions and observations for total nutrients that are trapped in the soil are easily flushed out
phosphorous (mg/l) and dissolved nitrogen (mg/l) are and thus contribute to the higher nutrient loading into the
compared in Figs. 10 and 11, respectively. A quantitative lake during this time period. It is therefore concluded from
summary of the comparison between the predictions and the the observations that the nutrients that accumulate in
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12. Environ Earth Sci
Table 7 Coefficient of
Constituents Monthly means Coefficient of
determination (R2) for the
determination (r2)
predicted and observed values Predicted Observed
for the nutrient parameters
Dissolved nitrogen (mg/l) 0.963 0.909 0.80
Total phosphorous (mg/l) 0.477 0.227 0.91
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-
Fig. 10 Showing the validation of the simulated total phosphorous
with the observed total phosphorous 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
Fig. 11 Showing the validation of the simulated dissolved nitrogen The studies have established that the Manasbal Lake situ-
with the observed dissolved nitrogen ated in rural Kashmir is showing definite and progressive
signs of eutrophication. The GIS-based modeling approach
cultivated land due to fertilization during drier periods are for the quantification of mean annual nutrient loads, runoff
later flushed out during periods of high rainfall. The lowest and erosion rates provided reliable estimates over variable
nutrient loading observed during 1997 relates well with source areas in the lake catchment. Higher nutrient loading
the low amount of precipitation for the year and similarly, was observed during the wet periods as against low nutrient
the highest nutrient loading observed during 2004 due to the loading during the drier periods. It is therefore concluded
highest precipitation received for that year. Both, mean that the precipitation has a significant influence on the
monthly and annual pattern of the loading, are showing timing and amounts of nutrient exports from crop fields to
good relation with the hydrology observed in the Manasbal the catchment outlet.
catchment. Similarly strong relationship between the It has been observed that the nutrient loading, runoff and
hydrology and nutrient concentration has also been reported soil erosion rates vary for different land use classes. The
in some other studies (Mitish et al. 2001; Nakamura et al. highest nutrient load (total N and total P) are observed from
2004). However, Young et al. 2008 reported that there is no agriculture, followed by the wastelands. The runoff and
clear correlation between the river discharge and the nutri- erosion rates are highest for the wastelands found in the
ent concentration. Similar relationship has been observed in catchment. The validation studies of the water-quality data
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13. Environ Earth Sci
showed good agreement between the predictions and the Evans BM, Lehning DW, Corradini KJ (2008) AVGWLF Version
observations at the catchment scale and the model satisfac- 7.1: users guide. Penn State Institute of Energy and Environ-
ment, The Pennsylvania State University, University Park, PA,
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viability of constructing riparian zones and artificial wet- Haith DA (1987) Evaluation of daily rainfall erosivity model. Trans
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tions Center (SAC), Indian Space Research Organization (ISRO), Hamon WR (1961) Estimating potential evapotranspiration. ASCE J
India, under the National Wetland Inventory and Assessment project. Hydraul Div 87(3HY):107–120
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