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Research on Manasbal Lake
 

Research on Manasbal Lake

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This research makes use of the remote sensing, simulation modeling and field observations to assess the non-point source pollution load of a Himalayan lake from its catchment.

This research makes use of the remote sensing, simulation modeling and field observations to assess the non-point source pollution load of a Himalayan lake from its catchment.

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    Research on Manasbal Lake Research on Manasbal Lake Document Transcript

    • Environ Earth SciDOI 10.1007/s12665-011-0944-9 ORIGINAL ARTICLEGeospatial modeling for assessing the nutrient loadof a Himalayan lakeShakil Ahmad Romshoo • Mohammad MuslimReceived: 6 April 2009 / Accepted: 27 January 2011Ó Springer-Verlag 2011Abstract This research makes use of the remote sensing, Keywords Geospatial modeling Á Nutrient load Ásimulation modeling and field observations to assess the Remote sensing Á Watershed Á Digital elevation modelnon-point source pollution load of a Himalayan lake fromits catchment. The lake catchment, spread over an area ofabout 11 km2, is covered by different land cover types Introductionincluding wasteland (36%), rocky outcrops (30%), agri-culture (12%), plantation (12.2%), horticulture (6.2%) and The picturesque valley of Kashmir, located in the foothillsbuilt-up (3.1%) The GIS-based distributed modeling of the Himalaya, abounds in fresh water natural lakes thatapproach employed relied on the use of geospatial data sets have come into existence as a result of various geologicalfor simulating runoff, sediment, and nutrient (N and P) changes and also due to changes in the course of the Indusloadings from a watershed, given variable-size source River. These lakes categorized into glacial, Alpine andareas, on a continuous basis using daily time steps for valley lakes based on their origin, altitudinal situation andweather data and water balance calculations. The model nature of biota, provide an excellent opportunity forsimulations showed that the highest amount of nutrient studying the structure and functional process of an aquaticloadings are observed during wet season in the month of ecosystem system (Kaul 1977; Kaul et al. 1977; KhanMarch (905.65 kg of dissolved N, 10 kg of dissolved P, 2006; Trisal 1985; Zutshi et al. 1972). However, the10,386.81 kg of total N and 2,381.89 kg of total P). During unplanned urbanization, deforestation, soil erosion andthe wet season, the runoff being the highest, almost all the reckless use of pesticides for horticulture and agricultureexcess soil nutrients that are trapped in the soil are easily have resulted in heavy inflow of nutrients into these lakesflushed 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 thetions (1994–2004) showed that the main source areas of water quality, but also affect the aquatic life in the lakes, asnutrient pollution are agriculture lands and wastelands. On a result of which the process of aging of these lakes isan average basis, the source areas generated about hastened. As a consequence, most of the lakes in the3,969.66 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 oftion between the daily observed and predicted nutrient load Kashmir valley are characterized by excessive growth ofranged in value from 0.80 to 0.91 for both nitrogen and macrophytic vegetation, anoxic deep water layers, andphosphorus. 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 understandS. A. Romshoo (&) Á M. Muslim the hydrochemistry and hydrobiology of the KashmirDepartment 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 123
    • Environ Earth Scion modeling the pollution loads of lakes from the catch- Tim et al. 1992; Wong et al. 1997). These models provide ament areas in Kashmir Himalayas (Baddar and Romhoo deeper insight into the sources and impacts of pollution and2007; 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 practiceManasbal 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 andresidual 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 ais important to identify the pollution sources; both point spatial and temporal database; simulate nutrient pollutionand 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 nutrientet al. 1988, Tolson and Shoemaker 2007). The advance- productivity and contamination to the lake from thement in the field of geospatial modeling, data acquisition catchment. The research paper is organized into differentand computer technology facilitates the integrative analysis sections that provide information on the background of theof 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 toolsthat allow us to predict the hydrological and other landsurface processes and phenomena at different spatial and Study areatime scales (Frankenberger et al. 1999; Olivera andMaidment 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 longitudebrated and verified, can predict nutrient concentrations in 74°400 50.2200 to 74°430 53.8500 E. The climate of the studyspace 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 withFig. 1 Showing the study area123
    • Environ Earth Scifour seasons based on mean temperature and precipitation. Generalized Watershed Loading Function (GWLF) modelThe 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 andundulating to flat with few steep slopes. The highest point, P) loadings from a watershed given variable-size sourcein north eastern part of the catchment, rises to an elevation areas on a continuous basis and uses daily time steps forof 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 suitablearound the Manasbal Lake. The drainage pattern observed for calculating septic system loads, and allows for thein 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 themain perennial stream, drains the catchment and discharges daily water balance accumulated to monthly values. For theinto the Manasbal Lake. The lake body has predominantly surface loading, the approach adopted is distributed in therural 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 torice and mustard. Large areas of barren and waste lands are various attributes considered by the model. The model doesalso found in the catchment area. People are also involved not spatially distribute the source areas, i.e., there is noin 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 waterMaterials and methods balance approach. The model is particularly useful for application in regions where environmental data of all typesDatasets 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 operationuse and land cover information, Indian Remote SensingSatellite data [IRS-ID, linear imaging self scanning (LISS- The GWLF model estimates dissolved liquid and solidIII) of 5 October 2004 with a spatial resolution of 23.5 m and phase nitrogen and phosphorous in stream flow from thespectral 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 transportedgenerating the topographic variables of the catchment for in runoff water and eroded soil from numerous sourceuse in the geospatial model, Digital Elevation Model (DEM) areas, each of which is considered uniform with respect tofrom 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 ð1Þsensed data supported with extensive ground truthing and LSm ¼ SPm þ SRm þ SUm ð2Þlab analysis, was used in the simulation modeling. Theexisting coarse soil map available for the study area was also where, LDm and LSm are the dissolved and solid phaseused for validation of the high-resolution soil map. A time nutrient load, respectively (kg), DPm and SPm are the pointseries of hydro-meteorological data from the nearest source dissolved and solid phase nutrient load, respectivelyobservation station was used for input to the geospatial (kg), DRm and SRm are the rural runoff dissolved and solidmodel. Some chemical parameters of water samples, viz; phase nutrient load, respectively (kg), DGm is the groundnitrate, nitrite ammonia and total phosphorous were also water dissolved nutrient load (kg), DSm is the septic systemanalyzed for validating the model simulations. Ancillary dissolved nutrient load (kg), SUm is the urban runoffdata 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 inGeospatial modeling approach for estimating non-point Eq. 3.source pollution X dm LDm ¼ 0:1 Cdk  Qkt  ARk ð3Þ t¼1For simulation of nutrient pollution from both point andnon-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 123
    • Environ Earth Scik (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 ð8Þusing Soil Conservation Services (SCS) curve number t¼1equation 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 groundQkt 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 systemsday t (cm), are estimated from daily precipitation and and direct discharge systems. Nutrient loads from septictemperature data. DSkt is the catchment’s storage. systems are calculated by estimating the per capita dailyCatchment storage is estimated for each source area loads from each type of system and the number of people inusing 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 equationDSkt 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 isdischarge from groundwater. The latter is obtained from a the monthly (m) loads from normal septic system, SSm islumped 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 dischargeshallow 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 entirelyzone water exceeds field capacity. The shallow saturated solid phase, are modeled by exponential accumulation andzone is modeled as linear ground water reservoir. Daily wash-off function proposed by Amy et al. (1974) andevapotranspiration is given by the product of a cover factor Sartor and Boyd (1972). Nutrients accumulate on urbanand 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, saturatedwater vapor pressure and daily temperature. Input data preparation Monthly solid phase nutrient load are estimated usingEq. 6 given below. The solid phase rural nutrient loads are The GIS-based GWLF model requires various types ofgiven by the product of the monthly sediment yield and input data for simulating the nutrient loads at the watershedaverage sediment nutrient concentration. level viz., land use/land cover data, digital topographicSRm ¼ 0:001  Cs  Ym ð6Þ data, hydro-meteorological data, transport parameter data (hydrologic and sediment) and nutrient parameter data. Thewhere SRm is the solid phase rural nutrient load, Cs is the procedure for the generation of the input data and their useaverage 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 sedimentyield 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 dataof 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-upXkt ¼ 0:132  REt  Kk  ðLSÞk  Ck  Pk  ARk ð7Þ area. Identification of these critical source areas in thewhere Kk ; ðLSÞk ; Ck and Pk are the soil erodibility, topo- catchment required the use of latest available satellitegraphic, cover and management and supporting practice image depicting current land use in the study area. In orderfactor 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 theha h y). satellite data was performed (Schowengerdt 1983). A123
    • Environ Earth Scicombination of both the techniques was used to develop a for the catchment with latitude 34°N were obtained fromhybrid approach. This was followed by creation of field the literature (Evans et al. 2008; Haith et al. 1992). Theclasses (land use types), which were then verified during study area receives an average annual rainfall of aboutfield assessment and ground truthing. The 11 km2 catch- 650 mm. From the analysis of the data, it is observed thatment mainly consists of 12% agriculture, 12.2% plantation, the catchment receives most of its precipitation between6.2% 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 thethe 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 averageequal to 0.89. Figure 2 shows the classified land use/land minimum temperature dipping up to -2°C and the July iscover map of the study area. the hottest month with average maximum temperature soaring up to 31°C. Maximum daylight is observed in JuneHydro-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 parameterstemperature data. The daily hydro-meteorological data,precipitation, temperature (minimum and maximum), Transport parameters are those aspects of the catchmentrainfall intensity, of the last 25 years, from the Indian that influence the movement of the runoff and sedimentsMeteorological 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 forTable 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 TotalAgriculture 22 0 0 0 0 1 23Bare rock 1 55 4 0 0 0 60Wasteland 1 5 71 0 0 1 78Horticulture 1 0 0 10 0 0 11Built-up 1 0 0 0 2 0 3Plantation 1 0 0 0 0 24 25Total 27 60 75 10 2 26 184Accuracy totals (overall classification accuracy = 92.00%)Class names Producers accuracy (%) Users accuracy (%)Agriculture 81.48 95.65Bare rock 91.67 91.67Horticulture 100.00 90.91Built-up 100.00 66.67Plantation 92.31 96.00Wasteland 94.67 91.03Kappa (j^) statistics (overall kappa statistics = 0.8903)Class name KappaAgriculture 0.9497Bare rock 0.8810Wasteland 0.8564Horticulture 0.9043Built-up 0.6633Plantation 0.9540 123
    • Environ Earth SciFig. 2 Land use/land cover 74°3930"E 74°400"E 74°4030"E 74°410"E 74°4130"E 74°420"E 74°4230"E 74°430"E 74°4330"Eclassified map of the Manasbal 34°170"Ncatchment Legend Agriculture Barrenrock 34°1630"N Builtup Horticulture Plantation Wasteland 34°160"N Water 34°1530"N 34°150"N MANASBAL LAKE 34°1430"N Kilometers 34°140"N 0 0.25 0.5 1 1.5Table 2 Summary of transport parameters used for the GWLF modelSource areas Area in hectare Hydrologic conditions LS C P K WCN WDET WGET ET coefficientAgriculture 132.653 Fair 2.063 0.5 0.5 0.210 82 0.3 1.0 0.4Bare rock 334.195 Poor 19.617 1.0 1.0 0.410 98 0.3 0.3 1.0Waste land 398.822 Poor 23.791 1.0 1.0 0.330 68 1.0 1.0 1.0Built-up 68.371 N/A 2.063 1.0 1.0 0.410 86 1.0 1.0 1.0Plantation 1.094 Fair 2.359 0.5 0.5 0.080 65 0.3 1.0 0.7Horticulture 34.272 Fair 3.416 0.5 0.5 0.080 65 0.3 1.0 0.6Good hydrological condition refers to the areas that are protected from grazing and cultivation so that the litter and shrubs cover the soil; fairconditions refer to intermediate conditions, i.e., areas not fully protected from grazing and the poor hydrological conditions refer to areas that areheavily grazed or regularly cultivated so that the litter, wild woody plants and bushes are destroyedK 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 evapotranspirationdifferent source areas in the catchment. The detailed pro- coefficient. The values of the ET coefficient vary from thecedures for generating these parameters are described highest 1.00 for the bare areas, urban surfaces, ploughedbelow. lands; 0.4 for agriculture and grasslands. For plantations, the ET coefficient varied from 0.3 to 1.00 depending uponParameters for hydrological characterization the development stage. The SCS curve number is a parameter that determinesThe evapotranspiration (ET) cover coefficient is the ratio of the amount of precipitation that infiltrates into the groundthe water lost by evapotranspiration from the ground and or enters surface waters as runoff after adjusting it toplants compared to what would be lost by evaporation from accommodate the antecedent soil moisture conditionsan equal area of standing water (Thuman et al. 2003). The based on total precipitation for the preceeding 5 days (EPAET cover coefficient vary by land use type and time period 2003a). It is based on combination of factors such as landwithin 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 managementment stages of the standing crop in the study area was done (Arhounditsis et al. 2002). In GWLF, the CN value is usedduring the field surveys for accurate allocation of the ET to determine for each land use, the amount of precipitation123
    • Environ Earth Scithat is assigned to the unsaturated zone where it may be lost Parameters for sediment yield estimationthrough evapotranspiration and/or percolation to the shal-low saturated zone if storage in the unsaturated zone For simulating the soil erosion using GWLF model, aexceeds 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, togetherdischarges to stream or losses to deep seepage, at a rate designated as LS factor, determine the effect of topographyestimated by the product of zone’s moisture storage and a on soil erosion. LS factor was estimated from the Digitalconstant 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 asamples in the laboratory. In all, 33 composite soil sam- given unit of land, the soil texture and soil organic matterples, well distributed over various land use and land cover content maps generated, as described above, were usedtypes, were collected from the catchment. Satellite image (Steward et al. 1975). The rainfall erosivity factor (RE) waswas 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 forwere 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 soiladapted by Veihmeyer and Hendricjson (1931) and the soil protection cover (Wischmeier and smith 1978) and theorganic 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 characteristicssible to determine the soil texture using the soil textural (EPA 2003b; Haith et al. 1992). In the GWLF model, thetriangle (Toogood 1958). The spatial soil texture map, as sediment yield is estimated by multiplying sedimentshown 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 logarithmicin 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 deliverycatchment (Table 3). ratio of 0.23 was observed.Fig. 3 Soil textural map of the 74°390"E 74°3930"E 74°400"E 74°4030"E 74°410"E 74°4130"E 74°420"E 74°4230"E 74°430"E 74°4330"E 74°440"Estudy area 34°170"N Legend Loam 34°1630"N Sandyclay Sandyclayloam Sandyloam Siltloam 34°160"N 34°1530"N 34°150"N MANASBAL LAKE 34°1430"N 34°140"N Kilometers 0 0.35 0.7 1.4 2.1 123
    • Environ Earth SciFig. 4 Soil organic matter 74°390"E 74°3930"E 74°400"E 74°4030"E 74°410"E 74°4130"E 74°420"E 74°4230"E 74°430"E 74°4330"E 74°440"Econtent of the study area 34°170"N Legend 0.5 34°1630"N 4.202 4.538 4.84 34°160"N 4.908 34°1530"N 34°150"N MANASBAL LAKE 34°1430"N 34°140"N Kilometers 0 0.35 0.7 1.4 2.1Table 3 Soil hydrological Hydrological Soil permeability (and runoff potential) Soil texturegroups used in the GWLF group characteristicsmodel 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, clayNutrient parameters period and during this period, all stream flow is made up of base flow. Figure 5 shows that March, July, September andCollection of runoff from various field crops for assessment November are the wettest months of the year with the meanof nutrient concentration was one of the greatest challengesof the study and because of the resource and time con-straints, this research made use of the values estimated byHaith (1987) for different source areas which are more orless representative of rural catchments and are assumed tobe same for the study area.ResultsCatchment hydrological conditionsThe model simulations were run for 11 years from April toMarch of the next year on monthly basis. Figure 5 showsthe mean monthly hydrological model simulations for11 years (1994–2004) along with the observed precipita- Fig. 5 Showing the mean monthly simulated hydrological output andtion. It is clear from the figure that May–June is driest the observed rainfall123
    • Environ Earth Scimonthly rainfall of about 12.2, 11.98, 10.2, and 10.6 cm, the 11-year simulation period to determine the relationshiprespectively. During this period, surface runoff, stream (Fig. 8). The analysis of the precipitation data reveals thatflow 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 tothe lake for 11-year simulation period. The figure shows Spatial variability of nutrient loadingthat the lowest amount of loading is received between Apriland June. The graph further reveals that after June, the rise Table 4 details the annual nutrient loadings from the sourcein 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 catchmentincreases from 95.53 kg in August to 905.65 kg in March generates about 1,191.1 kg/year of dissolved nitrogen andfor the dissolved nitrogen, and from 1.83 kg in August to 2,674.12 kg/year of particulate nitrogen, with a total10 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 dissolvedMarch (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 fromFigure 7a–d shows the annual nutrient loading for the the catchment. From the table, it is also evident that annualManasbal 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 is75.276 kg/year for total phosphorous. On the other hand, normally associated with high erosion in the wasteland andyear 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 becausetotal 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 sourceThe mean annual nutrient model estimates, as shown in areas towards the total nutrient loading to the lake, theFig. 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 nutrientload for dissolved and total Nand P (c) (d) 123
    • Environ Earth SciFig. 7 a–d Showing the annual (a) (b)nutrient load for dissolved andtotal N and P during thesimulation 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’’, betweenis shown in Fig. 9a–b for the total N and total P, respec- observed and predicted values for mean annual nitrogen andtively. The agriculture areas contribute to maximum load- phosphorous loads the Nash–Sutcliffe statistical measureing (both N and P) to the lake followed by wasteland (for recommended by ASCE (1993) for hydrological studiestotal N) and bare rock (for total P) (Table 4). was used. With the Nash–Sutcliffe measure, R2 coefficientTable 4 11-year simulated annual average of the sediment yield, erosion, runoff and nutrient loading from the source areasSource 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 35.47Agriculture 128 7.66 3,974.74 874.44 291.97 3,041.08 11.76 699.04Plantation/Horticulture 197 2.34 17.95 3.95 8.97 21.38 0.28 3.38Bare rock 335 7.34 184.77 23.22 9.68 18.29 1.61 77.96Built-up 36 13.01 0 0 0 8.43 0 1.40Total 1,082 – – – 1,191.1 3,969.66 49.12 817.25Dis. N dissolved nitrogen, Tot. N total nitrogen, Dis. P dissolved phosphorous, Tot. P total phosphorous123
    • 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 covertypes for nitrogen and phosphorous loading to the lake Discussion Knowledge about the hydrological conditions of the catchment is important, because it provides the basis forTable 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 catchmentMonths 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 theMay 0.950 0.939 catchment during this period of the year, it can be deducedJune 0.930 0.87 that most of the nutrient loading reaching the ManasbalJuly 0.919 0.87 Lake from its catchment are transported through streamAugust 0.903 0.89 flow and base flow during this period. Further, storm eventsSeptember 0.972 NA are normally associated with the transport of nutrientsOctober 0.974 NA through overland flow or percolation to groundwaterNovember 0.981 NA (Johnes 1999); it is expected that the nutrient loading to theDecember 0.990 NA lake will reach maximum levels during wetter spillsJanuary 0.980 0.96 observed during March, July, September and NovemberFebruary 1.0 0.8 The lowest mean monthly nutrient loading from April toMarch 0.980 1 June could be attributed to the fact that the catchmentNA 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 soilis calculated. Model predictions and observations for total nutrients that are trapped in the soil are easily flushed outphosphorous (mg/l) and dissolved nitrogen (mg/l) are and thus contribute to the higher nutrient loading into thecompared in Figs. 10 and 11, respectively. A quantitative lake during this time period. It is therefore concluded fromsummary of the comparison between the predictions and the the observations that the nutrients that accumulate in 123
    • Environ Earth SciTable 7 Coefficient of Constituents Monthly means Coefficient ofdetermination (R2) for the determination (r2)predicted and observed values Predicted Observedfor 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 phosphorouswith 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. ConclusionsFig. 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 approachcultivated land due to fertilization during drier periods are for the quantification of mean annual nutrient loads, runofflater flushed out during periods of high rainfall. The lowest and erosion rates provided reliable estimates over variablenutrient loading observed during 1997 relates well with source areas in the lake catchment. Higher nutrient loadingthe low amount of precipitation for the year and similarly, was observed during the wet periods as against low nutrientthe highest nutrient loading observed during 2004 due to the loading during the drier periods. It is therefore concludedhighest precipitation received for that year. Both, mean that the precipitation has a significant influence on themonthly and annual pattern of the loading, are showing timing and amounts of nutrient exports from crop fields togood 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 andhydrology and nutrient concentration has also been reported soil erosion rates vary for different land use classes. Thein some other studies (Mitish et al. 2001; Nakamura et al. highest nutrient load (total N and total P) are observed from2004). However, Young et al. 2008 reported that there is no agriculture, followed by the wastelands. The runoff andclear correlation between the river discharge and the nutri- erosion rates are highest for the wastelands found in theent concentration. Similar relationship has been observed in catchment. The validation studies of the water-quality data123
    • Environ Earth Scishowed good agreement between the predictions and the Evans BM, Lehning DW, Corradini KJ (2008) AVGWLF Versionobservations 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,torily simulated the variations in nutrient loads on monthly, USA, pp 117seasonal and annual time basis. The validation of the model FAO (1998) Crop evapotranspiration: guidelines for computing cropsimulations with the stream discharge data, if available, water requirements. FAO Irrigation and drainage paper 56, Romecould have enhanced the credibility of the simulation results. Frankenberger JR, Brooks ES, Walter MT, Walter MF, Steenhuis TS (1999) A GIS-based variable source area hydrology model. The estimation of nutrient loads, runoff and erosion from Hydrol Process 13:805–822the source areas shall facilitate prioritization of the source Ghosh AB, Bajaj JC, Hason R, Singh D (1983) Soil and water testingareas for remedial measures to control the pollution and methods: a laboratory manual. IARI, New Delhieutrophication in the lake. It would be useful to check the Haan CT (1972) A water yield model for small watersheds. Water Resour Res 8(1):58–69viability of constructing riparian zones and artificial wet- Haith DA (1987) Evaluation of daily rainfall erosivity model. Translands as the effective sinks for nutrient in an agricultural Am Soc Agric Eng 30(1):90–93watershed before runoff reaches the water body. A certain Haith DA, Shoemaker LL (1987) Generalized watershed loadingamount of control needs to be exercised on the excessive use functions for stream flow nutrients. Water Resour Bull 23(3):471–478of fertilizers in the agricultural fields in the catchment area. Haith DA, Mandel R, Shyan WR (1992) Generalized watershed loading functions model: users manual. Ithaca, New York, USA,Acknowledgments This study was funded by the Space Applica- pp 148–153tions Center (SAC), Indian Space Research Organization (ISRO), Hamon WR (1961) Estimating potential evapotranspiration. ASCE JIndia, under the National Wetland Inventory and Assessment project. Hydraul Div 87(3HY):107–120The authors express gratitude to the anonymous reviewers and the Hartkamp AD, White JW, Hoogenboom G (1999) Interfacingeditor for their valuable comments and suggestions on the earlier geographic information system with agronomic modeling: amanuscript version that improved the content and structure of this review. Agron J 91:761–772manuscript. 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 toReferences assemble input-data sets for a finite difference model of ground water flow. Water Resour Bull 29:401–405Amy G, Pitt R, Singh R, Bradford WL, LaGrafti MB (1974) Water Jeelani G, Shah AQ (2006) Geochemical characterization of water quality management planning for urban runoff. US Environmental and sediment from the Dal Lake, Kashmir Himalaya, India: Protection Agency, Washington, DC (EPA-440/9-75-004) constraints on weathering and anthropogenic activity. Env GeolArhounditsis G, Giourga C, Loumou A, Koulouri M (2002) Quan- 50:12–23 titative assessment of agricultural runoff and soil erosion using Jeelani G, Shah AQ (2007) Hydrochemistry of Dal Lake of Kashmir mathematical modelling: application in the Mediterranean Valley. J Appl Geochem 9(1):120–134 Region. Environ Manag 30(3):434–453 Johnes PJ (1999) Understanding lake and catchment history as a toolASCE (Task Committee on Definition of Criteria for Evaluation of for integrated lake management. Hydrobiologia 396:41–60 Watershed Models of the Watershed Management Committee, Kaul V (1977) Limnological survey of Kashmir lakes with reference Irrigation and Drainage Division) (1993) Criteria for evaluation to tropic status and conservation. Int J India Environ Sci 3:29–44 of watershed models. J Irrig Drain Eng 199(3) Kaul V (1979) Water characteristics of some fresh water bodies ofBaddar B, Romhoo SA (2007) Modeling the non-point source Kashmir. Curr Trends Life Sci 9:221–246 pollution in the Dal Lake catchment using geospatial tools. Kaul V, Handoo JK, Qadri BA (1977) Seasons of Kashmir. Geogr. J Environ Dev 2(1):21–30 Rev. India. 41(2):123–130Bagnolus F, Meher-Homji VM (1959) Bioclimatic types of southeast Khan MA (2000) Anthropogenic eutrophication and red tide outbreak Asia. Travaux de la Section Scientific at Technique Institut in lacustrine systems of the Kashmir Himalaya. Acta Hydrochim Franscis de Pondicherry, 227 Hydrobiol (Weinheim) 28:95–101Burrough PA (1986) Principles of geographic information systems for Khan MA (2008) Chemical environment and nutrient fluxes in a flood land resources assessment. Oxford Press, Oxford plain wetland ecosystem, Kashmir Himalayas, India. Indian ForEPA (2003a) Modelling report for Wissahickon Creek, Pennsylvania. 134(4):505–514 Siltation TDML Development Final Report. US Environmental Khan S, Romshoo SA (2008) Integrated analysis of geomorphic, Protection Agency, Philadelphia, Pennsylvania pedologic and remote sensing data for digital soil mapping.EPA (2003b) Nutrient and sediment TMDAL development for the J Himal Ecol Sustain Dev 3(1):39–50 unnamed tributary to Bush run and upper portions of Bush Run Koul VK, Davis W, Zutshi DP (1990) Calcite super-saturation in Allegheny and Washington counties. United States Environ- some subtropical Kashmir Himalayan lakes. Hydrobiologia mental Protection Agency, Philadelphia 192(2–3):215–222Evans BM, Corradini KJ (2007) PRedICT Version 2.0: users guide Kuo JT, Wu JH (1994) A nutrient model for a lake with time variable for the pollutant reduction impact comparison tool. Penn State volumes. Water Sci Technol 24(6):133–139 Institute of Energy and Environment, The Pennsylvania State Lee KY, Fisher T, Rochelle NE (2001) Modeling the hydrochemistry University, pp 44 of the Choptank River basin using GWLF and Arc/Info:2. ModelEvans BM, Lehning DW, Corradini KJ, Petersen GW, Nizeyimana E, validation and application. Biochemistry 56(3):311–348 Nizeyimana E, Hamlett JM, Robillard PD, Day RL (2002) Liao H, Tim US (1997) An interactive modeling environment for A comprehensive GIS-based modelling approach for predicting nonpoint source pollution control. J Am Water Resour Assoc nutrient load in watershed. Spat Hydrol 2(2):1–18 (JAWRA) 33(3):591–603 123
    • Environ Earth SciLung WS (1986) Assessing phosphorous control in the James River Sartor JD, Boyd GB (1972) Water pollution aspects of street surface Basin. J Environ Eng 112(1):44–60 contaminants. EPA-R2/72-081, US Environmental ProtectionMelesse AM, Weng Q, Prasad S, Thenkabail PS, Senay GB (2007) Agency, Washington, DC Remote sensing sensors and applications in environmental Schowengerdt R (1983) Techniques for image processing and resource mapping and modeling. Sensor 7(12):3209–3241 classification in remote sensing. Academic, New YorkMitish WJ, Day JW, Gilliam JW (2001) Reducing nitrogen loading to SCS (1986) Urban hydrology for small watersheds. Soil Conservation the Gulf of Mexico from the Mississippi River Basin: strategies to Services. 55 (2) counter a persistent ecological problem. Biosciences 5(5):373–388 Shamsi UM (1996) Storm-water management implementationMontanrella L, Jones RJ, Knijff JM (2000) Soil Erosion Risk through modeling and GIS. J Water Resour Plan Manag Assessment in Europe. The European Soil Bureau 122(2):114–127Moore ID, Grayson RB, Brursch GJ (1988) A contour-based Steward BA, Woolhiser DA, Wischmeier WH, Carol JH, Frere MH topographic model for hydrological and ecological applications. (1975) Control of water pollution from cropland. US Environ- Earth Surf Proc Landf 13:305–320 mental Protection Agency, WashingtonMuslim M, Romshoo SA, Bhat SA (2008) Modelling the pollution Strobe RO (2002) Water quality monitoring network design method- load of Manasbal Lake using remote sensing and GIS. In: ology for the identification of critical sampling points. PhD Proceedings of the 4th annual symposium of the Indian Society thesis, Department of Agriculture and Biological Engineering, of Geomatics (Geomatica 2008), Bhopal, India, 18–20 Feb, 2008 The Pennsylvania State University, PennsylvaniaNakamura F, Kameyama S, Mizugaki S (2004) Rapid shrinkage of Thiemann S, Kaufmann H (2000) Determination of chlorophyll Kushiro Mire, the largest mire in Japan, due to increased content and trophic state of lakes using field spectrometer and sedimentation associated with land-use development in the IRS-IC satellite data in the Mecklenburg Lake District, Ger- catchment. Catena 55(2):213–229 many. Remote Sens Environ 73:235–277National Remote Sensing Agency (2003) IRS-P6 data user’s hand- Thomann RV, Mueller JA (1987) Principles of surface water quality book. NRSA Report No. IRS-P6/NRSA/NDC/HB-10/03 modelling and control. Harper and Row, New York, p 644Olivera F, Maidment DR (1999) Geographic information systems Thuman OE, Andrew, Rees TA (2003) Watershed and water quality (GIS)-based spatially distributed model for runoff routing. Water modeling analytical report, Triad Engineering Incorporated, Resour Res 35(4):1155–1164 Indianapolis, Indaina, 46219Olivieri LJ, Schaal GM, Logan TJ, Elliot WJ, Motch B (1991) Tim US, Mostaghimi S, Shanholtz VO (1992) Identification of critical Generating AGNPS input using remote sensing and GIS. Paper nonpoint pollution source areas using geographic information 91–2622. American Society of Agricultural Engineers, St. systems and water quality modeling. Water Resour Bull Joseph, Michigan 28:877–887Omernik JM, Abermathy AR, Male LM (1981) Stream nutrient levels Tolson BA, Shoemaker CA (2007) Cannonsville reservoir watershed and proximity of agriculture and forest land to streams: some SWAT2000 model development, calibration and validation. relationships. J Soil Water Conserv 36(4):227–231 J Hydrol 337(1–2):68–86Osborne LL, Wiley MJ (1988) Empirical relationship between land Toogood JA (1958) A simplified textural classification diagram. Can J use/cover and stream water quality in an agricultural watershed. Soil Sci 38:54–55 J Environ Manag 26:9–27 Trisal CL (1985) Trophic status of Kashmir Valley lakes. GeobiosPandit AK (1998) Trophic evolution of lakes in Kashmir Himalayas: Spl. Vol-I.17179. In: Mishra SD, Sen DN, Ahmed I (eds) Proc conservation of lakes in Kashmir Himalayas. In: Pandit AK (ed) Nat Sympos Evalua Environ, Jodhpur, India Natural resources in Kashmir Himalayas. Valley book House, Vanori VA (1975) Sediment engineering. American Society of Civil Srinagar, Kashmir, pp 178–214 Engineers, New YorkPavanelli D, Bigi A (2004) Indirect analysis method to estimate Veihmeyer FJ, Hendricjson AH (1931) The moisture equivalent as a suspended sediment concentration: reliability and relationship of measure of the field capacity of soils. Soil Sci 32:181–194 turbidity and settleable solids. Biosyst Eng 83:463–468 Walkley A, Black IA (1934) An estimation of Degtijareff method forPerry J, Vanderklein E (1996) Water quality management of a natural determination of soil organic matter and a proposed modification resource. Blackwell Science, Cambridge, USA, p 639 of the chronic acid titration method. Soil Sci 34:29–38Prakash B, Teeter LD, Lockay BG, Flynn KM (2000) The use of Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses remote sensing and GIS in watershed level analyses of non-point a guide to conservation planning. US Department of Agriculture, source pollution problems. For Ecol Manag 128:65–73 Washington, DCRaterman B, Schaars FW, Giffioen M (2001) GIS and MATLAB Wong KM, Strecker EW, Strenstrom MK (1997) GIS to estimate storm- integrated for ground water modeling. In: 21st annual ESRI water pollutant mass loadings. J Environ Eng 123(8):737–745 international user conference, San Diego, California. Available at Young RA, Onstad CA, Bosch DD, Anderson WP (1989) AGNPS, a http://gis.esri.com/library/userconf/proc01/professional/papers/pa nonpoint source pollution model for evaluating agricultural p600/p600.htm. Accessed on 19 October 2004 watersheds. J Soil Water Conserv 44:168–173Rodriguez E, Morris CS, Belz JE (2006) A global assessment of Young SA, Nakamura F, Mizugaki S (2008) Hydrology, suspended SRTM performance. Photogramm Eng Remote Sens 72:249–260 sediment dynamics and nutrient loading in Lake Takkobu, aRomshoo SA (2003) Radar remote sensing for monitoring of dynamic degrading lake ecosystem in Kushiro Mire, Northern Japan. ecosystem processes related to the biogeochemical exchanges in Environ Monit Assess 145:267–281 tropical peatlands. Vis Geosci 8:63–82 Yuksel A, Akay AE, Gundogan R, Reis M, Cetiner M (2008)Saini RK, Swain S, Patra A, Geelani G, Gupta H, Purushothaman P, Application of GeoWEPP for determining sediment yield and Chakrapani GJ (2008) Water chemistry of three Himalayan runoff in the Orcan Creek watershed in Kahramanmaras, Turkey. lakes: Dal (Jammu and Kashmir), Khajjiar (Himachal Pradesh) Sensors 8(2):1222–1236 and Nainital (Uttarakhand). Himal Geol 29(1):63–72 Zollweg JA, Gburek WJ, Steenhuis TS (1996) SMoRMOD: a GISSample DJ, Heaney JP, Wright LT, Koustas R (2001) Geographic integrated rainfall-runoff model. Trans ASAE 39:1299–1307 information systems, decision support systems, and urban storm Zutshi DP, Kaul V, Vass KK (1972) Limnological studies of high water management. J Water Resour Plan Manag 127(3):155–161 altitude Kashmir lakes. Verh Inter Verin Limnol 118:599–604123