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: shakilrom@yahoo.com

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.

References

Amy G, Pitt R, Singh R, Bradford WL, LaGrafti MB (1974) Water

quality management planning for urban runoff. US Environmental

Protection Agency, Washington, DC (EPA-440/9-75-004)

Arhounditsis G, Giourga C, Loumou A, Koulouri M (2002) Quan-

titative assessment of agricultural runoff and soil erosion using

mathematical modelling: application in the Mediterranean

Region. Environ Manag 30(3):434–453

ASCE (Task Committee on Definition of Criteria for Evaluation of

Watershed Models of the Watershed Management Committee,

Irrigation and Drainage Division) (1993) Criteria for evaluation

of watershed models. J Irrig Drain Eng 199(3)

Baddar B, Romhoo SA (2007) Modeling the non-point source

pollution in the Dal Lake catchment using geospatial tools.

J Environ Dev 2(1):21–30

Bagnolus F, Meher-Homji VM (1959) Bioclimatic types of southeast

Asia. Travaux de la Section Scientific at Technique Institut

Franscis de Pondicherry, 227

Burrough PA (1986) Principles of geographic information systems for

land resources assessment. Oxford Press, Oxford

EPA (2003a) Modelling report for Wissahickon Creek, Pennsylvania.

Siltation TDML Development Final Report. US Environmental

Protection Agency, Philadelphia, Pennsylvania

EPA (2003b) Nutrient and sediment TMDAL development for the

unnamed tributary to Bush run and upper portions of Bush Run

Allegheny and Washington counties. United States Environ-

mental Protection Agency, Philadelphia

Evans BM, Corradini KJ (2007) PRedICT Version 2.0: users guide

for the pollutant reduction impact comparison tool. Penn State

Institute of Energy and Environment, The Pennsylvania State

University, pp 44

Evans BM, Lehning DW, Corradini KJ, Petersen GW, Nizeyimana E,

Nizeyimana E, Hamlett JM, Robillard PD, Day RL (2002)

A comprehensive GIS-based modelling approach for predicting

nutrient load in watershed. Spat Hydrol 2(2):1–18

Evans BM, Lehning DW, Corradini KJ (2008) AVGWLF Version

7.1: users guide. Penn State Institute of Energy and Environ-

ment, The Pennsylvania State University, University Park, PA,

USA, pp 117

FAO (1998) Crop evapotranspiration: guidelines for computing crop

water requirements. FAO Irrigation and drainage paper 56, Rome

Frankenberger JR, Brooks ES, Walter MT, Walter MF, Steenhuis TS

(1999) A GIS-based variable source area hydrology model.

Hydrol Process 13:805–822

Ghosh AB, Bajaj JC, Hason R, Singh D (1983) Soil and water testing

methods: a laboratory manual. IARI, New Delhi

Haan CT (1972) A water yield model for small watersheds. Water

Resour Res 8(1):58–69

Haith DA (1987) Evaluation of daily rainfall erosivity model. Trans

Am Soc Agric Eng 30(1):90–93

Haith DA, Shoemaker LL (1987) Generalized watershed loading

functions for stream flow nutrients. Water Resour Bull

23(3):471–478

Haith DA, Mandel R, Shyan WR (1992) Generalized watershed

loading functions model: users manual. Ithaca, New York, USA,

pp 148–153

Hamon WR (1961) Estimating potential evapotranspiration. ASCE J

Hydraul Div 87(3HY):107–120

Hartkamp AD, White JW, Hoogenboom G (1999) Interfacing

geographic information system with agronomic modeling: a

review. Agron J 91:761–772

Hession CW, Shanholtz VO (1988) A geographic information system

for targeting nonpoint source agricultural pollution. J Soil Water

Conserv 43:264–266

Hinaman KC (1993) Use of geographic information systems to

assemble input-data sets for a finite difference model of ground

water flow. Water Resour Bull 29:401–405

Jeelani G, Shah AQ (2006) Geochemical characterization of water

and sediment from the Dal Lake, Kashmir Himalaya, India:

constraints on weathering and anthropogenic activity. Env Geol

50:12–23

Jeelani G, Shah AQ (2007) Hydrochemistry of Dal Lake of Kashmir

Valley. J Appl Geochem 9(1):120–134

Johnes PJ (1999) Understanding lake and catchment history as a tool

for integrated lake management. Hydrobiologia 396:41–60

Kaul V (1977) Limnological survey of Kashmir lakes with reference

to tropic status and conservation. Int J India Environ Sci 3:29–44

Kaul V (1979) Water characteristics of some fresh water bodies of

Kashmir. Curr Trends Life Sci 9:221–246

Kaul V, Handoo JK, Qadri BA (1977) Seasons of Kashmir. Geogr.

Rev. India. 41(2):123–130

Khan MA (2000) Anthropogenic eutrophication and red tide outbreak

in lacustrine systems of the Kashmir Himalaya. Acta Hydrochim

Hydrobiol (Weinheim) 28:95–101

Khan MA (2008) Chemical environment and nutrient fluxes in a flood

plain wetland ecosystem, Kashmir Himalayas, India. Indian For

134(4):505–514

Khan S, Romshoo SA (2008) Integrated analysis of geomorphic,

pedologic and remote sensing data for digital soil mapping.

J Himal Ecol Sustain Dev 3(1):39–50

Koul VK, Davis W, Zutshi DP (1990) Calcite super-saturation in

some subtropical Kashmir Himalayan lakes. Hydrobiologia

192(2–3):215–222

Kuo JT, Wu JH (1994) A nutrient model for a lake with time variable

volumes. Water Sci Technol 24(6):133–139

Lee KY, Fisher T, Rochelle NE (2001) Modeling the hydrochemistry

of the Choptank River basin using GWLF and Arc/Info:2. Model

validation and application. Biochemistry 56(3):311–348

Liao H, Tim US (1997) An interactive modeling environment for

nonpoint source pollution control. J Am Water Resour Assoc

(JAWRA) 33(3):591–603

Environ Earth Sci

123

Lung WS (1986) Assessing phosphorous control in the James River

Basin. J Environ Eng 112(1):44–60

Melesse AM, Weng Q, Prasad S, Thenkabail PS, Senay GB (2007)

Remote sensing sensors and applications in environmental

resource mapping and modeling. Sensor 7(12):3209–3241

Mitish WJ, Day JW, Gilliam JW (2001) Reducing nitrogen loading to

the Gulf of Mexico from the Mississippi River Basin: strategies to

counter a persistent ecological problem. Biosciences 5(5):373–388

Montanrella L, Jones RJ, Knijff JM (2000) Soil Erosion Risk

Assessment in Europe. The European Soil Bureau

Moore ID, Grayson RB, Brursch GJ (1988) A contour-based

topographic model for hydrological and ecological applications.

Earth Surf Proc Landf 13:305–320

Muslim M, Romshoo SA, Bhat SA (2008) Modelling the pollution

load of Manasbal Lake using remote sensing and GIS. In:

Proceedings of the 4th annual symposium of the Indian Society

of Geomatics (Geomatica 2008), Bhopal, India, 18–20 Feb, 2008

Nakamura F, Kameyama S, Mizugaki S (2004) Rapid shrinkage of

Kushiro Mire, the largest mire in Japan, due to increased

sedimentation associated with land-use development in the

catchment. Catena 55(2):213–229

National Remote Sensing Agency (2003) IRS-P6 data user’s hand-

book. NRSA Report No. IRS-P6/NRSA/NDC/HB-10/03

Olivera F, Maidment DR (1999) Geographic information systems

(GIS)-based spatially distributed model for runoff routing. Water

Resour Res 35(4):1155–1164

Olivieri LJ, Schaal GM, Logan TJ, Elliot WJ, Motch B (1991)

Generating AGNPS input using remote sensing and GIS. Paper

91–2622. American Society of Agricultural Engineers, St.

Joseph, Michigan

Omernik JM, Abermathy AR, Male LM (1981) Stream nutrient levels

and proximity of agriculture and forest land to streams: some

relationships. J Soil Water Conserv 36(4):227–231

Osborne LL, Wiley MJ (1988) Empirical relationship between land

use/cover and stream water quality in an agricultural watershed.

J Environ Manag 26:9–27

Pandit AK (1998) Trophic evolution of lakes in Kashmir Himalayas:

conservation of lakes in Kashmir Himalayas. In: Pandit AK (ed)

Natural resources in Kashmir Himalayas. Valley book House,

Srinagar, Kashmir, pp 178–214

Pavanelli D, Bigi A (2004) Indirect analysis method to estimate

suspended sediment concentration: reliability and relationship of

turbidity and settleable solids. Biosyst Eng 83:463–468

Perry J, Vanderklein E (1996) Water quality management of a natural

resource. Blackwell Science, Cambridge, USA, p 639

Prakash B, Teeter LD, Lockay BG, Flynn KM (2000) The use of

remote sensing and GIS in watershed level analyses of non-point

source pollution problems. For Ecol Manag 128:65–73

Raterman B, Schaars FW, Giffioen M (2001) GIS and MATLAB

integrated for ground water modeling. In: 21st annual ESRI

international user conference, San Diego, California. Available at

http://gis.esri.com/library/userconf/proc01/professional/papers/pa

p600/p600.htm. Accessed on 19 October 2004

Rodriguez E, Morris CS, Belz JE (2006) A global assessment of

SRTM performance. Photogramm Eng Remote Sens 72:249–260

Romshoo SA (2003) Radar remote sensing for monitoring of dynamic

ecosystem processes related to the biogeochemical exchanges in

tropical peatlands. Vis Geosci 8:63–82

Saini RK, Swain S, Patra A, Geelani G, Gupta H, Purushothaman P,

Chakrapani GJ (2008) Water chemistry of three Himalayan

lakes: Dal (Jammu and Kashmir), Khajjiar (Himachal Pradesh)

and Nainital (Uttarakhand). Himal Geol 29(1):63–72

Sample DJ, Heaney JP, Wright LT, Koustas R (2001) Geographic

information systems, decision support systems, and urban storm

water management. J Water Resour Plan Manag 127(3):155–161

Sartor JD, Boyd GB (1972) Water pollution aspects of street surface

contaminants. EPA-R2/72-081, US Environmental Protection

Agency, Washington, DC

Schowengerdt R (1983) Techniques for image processing and

classification in remote sensing. Academic, New York

SCS (1986) Urban hydrology for small watersheds. Soil Conservation

Services. 55 (2)

Shamsi UM (1996) Storm-water management implementation

through modeling and GIS. J Water Resour Plan Manag

122(2):114–127

Steward BA, Woolhiser DA, Wischmeier WH, Carol JH, Frere MH

(1975) Control of water pollution from cropland. US Environ-

mental Protection Agency, Washington

Strobe RO (2002) Water quality monitoring network design method-

ology for the identification of critical sampling points. PhD

thesis, Department of Agriculture and Biological Engineering,

The Pennsylvania State University, Pennsylvania

Thiemann S, Kaufmann H (2000) Determination of chlorophyll

content and trophic state of lakes using field spectrometer and

IRS-IC satellite data in the Mecklenburg Lake District, Ger-

many. Remote Sens Environ 73:235–277

Thomann RV, Mueller JA (1987) Principles of surface water quality

modelling and control. Harper and Row, New York, p 644

Thuman OE, Andrew, Rees TA (2003) Watershed and water quality

modeling analytical report, Triad Engineering Incorporated,

Indianapolis, Indaina, 46219

Tim US, Mostaghimi S, Shanholtz VO (1992) Identification of critical

nonpoint pollution source areas using geographic information

systems and water quality modeling. Water Resour Bull

28:877–887

Tolson BA, Shoemaker CA (2007) Cannonsville reservoir watershed

SWAT2000 model development, calibration and validation.

J Hydrol 337(1–2):68–86

Toogood JA (1958) A simplified textural classification diagram. Can J

Soil Sci 38:54–55

Trisal CL (1985) Trophic status of Kashmir Valley lakes. Geobios

Spl. Vol-I.17179. In: Mishra SD, Sen DN, Ahmed I (eds) Proc

Nat Sympos Evalua Environ, Jodhpur, India

Vanori VA (1975) Sediment engineering. American Society of Civil

Engineers, New York

Veihmeyer FJ, Hendricjson AH (1931) The moisture equivalent as a

measure of the field capacity of soils. Soil Sci 32:181–194

Walkley A, Black IA (1934) An estimation of Degtijareff method for

determination of soil organic matter and a proposed modification

of the chronic acid titration method. Soil Sci 34:29–38

Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses

a guide to conservation planning. US Department of Agriculture,

Washington, DC

Wong KM, Strecker EW, Strenstrom MK (1997) GIS to estimate storm-

water pollutant mass loadings. J Environ Eng 123(8):737–745

Young RA, Onstad CA, Bosch DD, Anderson WP (1989) AGNPS, a

nonpoint source pollution model for evaluating agricultural

watersheds. J Soil Water Conserv 44:168–173

Young SA, Nakamura F, Mizugaki S (2008) Hydrology, suspended

sediment dynamics and nutrient loading in Lake Takkobu, a

degrading lake ecosystem in Kushiro Mire, Northern Japan.

Environ Monit Assess 145:267–281

Yuksel A, Akay AE, Gundogan R, Reis M, Cetiner M (2008)

Application of GeoWEPP for determining sediment yield and

runoff in the Orcan Creek watershed in Kahramanmaras, Turkey.

Sensors 8(2):1222–1236

Zollweg JA, Gburek WJ, Steenhuis TS (1996) SMoRMOD: a GIS

integrated rainfall-runoff model. Trans ASAE 39:1299–1307

Zutshi DP, Kaul V, Vass KK (1972) Limnological studies of high

altitude Kashmir lakes. Verh Inter Verin Limnol 118:599–604

Environ Earth Sci

123