Sengupta, M. and Dalwani, R. (Editors). 2008 Proceedings of Taal2007: The 12th World Lake Conference: 668-679

Assessing the Pollution Load of Dal Lake Using Geospatial Tools *1Bazigha Badar and *2Shakil Ahmad Romshoo Remote Sensing and GIS Laboratory, P.G. Department of Geology and Geophysics, University of Kashmir, Srinagar-190006, J & K, India E-mail: 1 punkmail@rediffmail.com 2 shakilrom@yahoo.com

ABSTRACT The rate at which we deplete and degrade our fresh aquatic resources poses a great threat to our future life support system. The rise in human population exploits more of natural resources and this is met through the growth of industries specifically chemicals and petrochemicals, urbanization, deforestation and intensive agricultural practices. Degraded watersheds ultimately result in high nitrogen and phosphorus loads, algal blooms and toxicity, low oxygen and fish kills, loss of aquatic habitat, changes in community structure, loss of recreational amenity, loss of recreational and commercial fisheries and loss of water supply (or high treatment costs). The water quality of Dal Lake has deteriorated considerably in the last few decades. The main environmental issues are excessive weed growth, reduction in water clarity, enrichment of waters and high microbial activity. Contaminants enter the lake through direct point sources, diffuse agricultural sources and diffuse urban sources.

Non-point source pollution, which is basically storm / urban water runoff that has been polluted by land use, is still not well understood. It is difficult to quantify loadings produced by non-point source pollution and to predict water quality responses of water bodies due to these loadings. Urban watersheds are particularly vulnerable to non-point pollution as a result of runoff from the surrounding landscape. Several parameters influence the type and extent of pollution. Remote sensing and geographic information system (GIS) have been involved in water quality assessment since decades. Water quality models of volume, sediment loads, water quality and flow are therefore heavily dependent on geographic data sources which are provided in GIS.

In the present study, one of the models of BASINS (Better Assessment Science Integrating Point and Non-point Sources) i.e. PLOAD (Pollution Load) was used. PLOAD runs in the ArcView interface. The main objectives of the study were to define pollutant levels coming from various land uses throughout the catchment (area – 316 km2) into the lake and using modeling to assess the effect of various management alternatives (land use changes) on water quality and to provide a technically sound tool to assess the current and future water quality conditions. The datasets used in the study included Landsat 7 Enhanced Thematic Mapper (ETM) image, 40 m digital elevation model (DEM), ancillary data including reports and publications, pollutant loading rate data (export coefficient values in lb/ac/yr) and field data. Though PLOAD model can take many data inputs, only land use / land cover, watershed and export coefficient data was used to execute the model. The export coefficient values for various pollutants like biological oxygen demand (BOD), total suspended soilds (TSS), total dissolved solids (TDS), ammonia (NH3), total phosphorus (TP) and total nitrogen (TN) were collected from the literature (Rast and Lee 1978; Loehr et al. 1989; Baldys et al. 1998; McFarland and Hauck 2001; Line et al. 2002).

The land use / land cover map of the catchment depicted 13 land use classes, whereas the watershed map depicted 14 watersheds in Dal lake catchment. Chemical analysis of the water samples associated with each land use class was carried out in order to validate the export coefficient values. The model was run as a final step. The PLOAD model gives the pollutant loadings by watershed in pounds/year (lb/yr) and watershed area in pounds/acre/year (lb/ac/yr). Among the 14 watersheds, watersheds number 7 and 14 witnessed the highest annual loads of almost all the pollutants. The reason for this may be attributed to the fact that these watersheds are under direct biotic interference. Also the maximum number of land use/land cover classes are present in these watersheds.

At the end of this study it was observed that loading rates vary for different pollutants in different watersheds. Different watersheds have a maximum loading of different pollutants which is dependent upon different land use/ land covers .The highest loads were witnessed for those watersheds (watershed numbers 7 and 14) in the catchment in which built up (urban), agriculture, aquatic vegetation and stream (Telbal Nallah) were the dominant land use / land covers. The different pollutants coming from these watersheds are contributing towards the steady degradation of this beautiful lake. As a result, the Dal is dying an unsung death. It is concluded that these watersheds in the catchment of the Dal lake need to be prioritized and managed on pollutant basis.

There was also non availability of data such as that of the best management practices and point source location data which are used as inputs to the PLOAD model. The research can be taken forward by incorporating more data inputs required by the model to calculate precise pollution loads.

Keywords: Eutrophication, Watershed, Landsat, Digital Elevation Model, Land use, Land cover.

INTRODUCTION One of the most beautiful lakes of India, Dal lake, is a Himalayan urban lake surrounded by mountains on its three sides. This lake with its multi-faceted eco-system and grandeur has been inviting the attention of national and international tourists for centuries. The water quality of Dal Lake has deteriorated considerably in the last few decades. The main environmental issues are excessive weed growth, reduction in water clarity, enrichment of waters and high microbial activity. Contaminants enter the lake through direct point sources, diffuse agricultural sources and diffuse urban sources.

Apart from the conventional techniques, remote sensing and geographic information system (GIS) modeling have been widely used in water quality studies. Water quality models of volume, sediment loads, water quality and flow are therefore heavily dependent on geographic data sources which are provided in (GIS) (Usery et al. 2004). Various mathematical models have been developed and applied on streams, lakes and estuaries (Lung 1986; Thomann and Mueller 1987; Kuo and Wu 1991; Kuo et al. 1994). Modeling of non-point source pollution is still in its early stages. However, model such as the Unites States Environmental Protection Agency’s (USEPA) BASINS (Better Assessment Science Integrating Point and Non-point Sources) is beginning to bring a uniform approach (CH2M HILL USEPA 2001). BASINS is a multi-purpose environmental analysis system for use

by regional, state, and local agencies in performing watershed and water quality based studies. BASINS contain many models such as HSPF (Hydrologic Simulation Program Fortran), SWAT (Soil and Water Assessment Tool) and PLOAD (GIS Pollutant Load Application).

In the present study, a GIS-based model PLOAD (Pollution Load) was used to estimate the annual pollutant loadings coming into the lake from the watersheds within the catchment. The main objective of the study was to define pollutant levels coming from various land uses in the catchment into the lake. This study assumes great significance keeping in view the ecological and aesthetic importance of the lake. Because heavy pollutant loads may adversely affect the lake functions, estimating the increase in pollutant load due to the development may be of interest for regulatory and planning purposes. STUDY AREA

The study area is the Dal lake catchment (Figure 1), which is situated between the geographical coordinates of 34°02´ – 34°13´ N latitude and 74°50´ – 75°09´ E longitude. The catchment has an area of 321 km², nearly half of which comprises the Dachigam National Park. The Dal lake is situated in the heart of the Srinagar at an elevation of 1587 m a.m.s.l and has surface area of 11.5 km².

Figure 1: Location map of the study area. The Dal lake is a multi-basin lake (Figure 2) having both inflow and outflow channels and is believed to be fed by a number of underground springs, but the main source is the Telbal Nallah that enters into the lake on

the northern side after originating from Marsar Lake high up in the mountains and draining the Dachigam Reserve enroute. On the southwest side the surplus water flows via Dalgate into Tsunthkul - a tributary to

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the river Jhelum. There is another exit also i.e., Nallah Amir Khan connecting the Nagin basin with Anchar lake.

Figure 2: Map showing the Dal lake basins.

The Dal lake catchment is fan shaped and broadens in the westward direction. The western portion of the watershed is a flatter area, whereas the northern and eastern sides rise high. The Dal lake catchment exhibits a varied topography with altitudinal range of 1580-4360 meters. The climate of the area is sub-humid temperate. The average monthly temperature is 11ºC and the average annual rainfall is 650 mm at Srinagar and 870 mm at Dachigam. Data Sources In the study, a variety of data sources including Landsat Enhanced Thematic Mapper (ETM) satellite image acquired on 30 September 2001, 40 m digital elevation model (DEM) (Figure 3), ancillary data of the export coefficient values for various pollutants (Rast and Lee 1978; Loehr et al. 1989; Baldys et al. 1998; McFarland and Hauck 2001; Line et al. 2002) and journals like Oriental Science and Journal of Research and Development published by Center of Research for Development, University of Kashmir, chemical parameters data of the water samples viz. nitrite, nitrate, total phosphorus, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonia, total dissolved soils (TDS) and total suspended solids (TSS) and ground truth data were used.

Figure 3: DEM of the Dal lake catchment. METHODOLOGY The details of the methodology are described in Figure 4. The research applied an integrated approach by using simulation modeling, remote sensing and laboratory / field data. The methodology shall be briefly discussed under the following headings: Pollution Modeling A GIS-based model PLOAD was used in this study. PLOAD is a simplified geospatial model developed by CH2M HILL (USEPA, 2001) for calculating pollutant loads from watersheds. PLOAD estimates non-point loads (NPS) of pollution on an annual average basis, for any user-specified pollutant. The user may calculate the NPS loads using either the export coefficient or the EPA’s Simple Method approach. The various requirements of the model include:

• Generating of land use / land cover map in a vector format.

• Watershed map of the area. • Export coefficient values (in lb/ac/yr)

Optionally, best management practices (BMPs), which serve to reduce NPS loads, and point source loads may also be included in computing total watershed loads. Finally, there are several product alternatives that may be specified to show the NPS pollution results as maps and tabular lists.

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Landsat ETM Satellite Image

DEM

Figure 4: Flow-chart showing the methodology adopted. Generating Model Input Parameters Delineation of Catchment Area and Watersheds: A 40m DEM was used for topographic analysis. The topographic details were useful in understanding the different land use classes of the area, as the terrain determines the spatial variability of the land use classes. It was also used in the delineation of catchment and watersheds and for

generating a three-dimensional view of the terrain, helping to better understand the three-dimensional nature of the real world. The catchment area and the watersheds were identified from DEM and then digitized in GIS to a vector layer along with associated attribute database.

Water Sampling

Image Processing

Land Use/ Land Cover Classification

Ground Truthing

Final Land Use/ Land Cover Map

Catchment Area

Watershed Map

Chemical Parameter Estimation

Export Coefficient

Table

PLOAD Model

Pollutant Loading (lbs/ac/yr)

Literature Review

Validation

Pollutant Loading (lbs/yr)

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Image Processing and Land Use / Land Cover Classification: Before using the satellite imagery for classification, the image was pre-processed and processed in ERDAS Imagine. The image was geometrically corrected in ERDAS Imagine and referenced to Lat-Long coordinate system with WGS 84 datum. Different contrast enhancements were used to enhance the interpretability of the image.

To generate the LULC map of the study area, Landsat 7 ETM+ image with three channels of information was selected. Appropriate scene (area of the study) was clipped from the image. A false colour composite (FCC) having a band combination 4:3:2 (IR:R:G) was used. Supervised classification was carried out on the image using maximum likelihood classifier. Ground validation was carried out for all the land use classes identified in the study area. The necessary changes were incorporated and the final map was prepared.

An accuracy assessment of the classification was carried out taking random samples over the whole study area to cross validate the classification. By selecting random and field sampled ground truth data, 250 data points were collected and were utilized in the analysis. Table 1. Methodology for chemical parameter estimation. Sl. Parameter Methodology 1 Biochemical Oxygen

Demand (mg/l) CSIR (1974)

2

Total Phosphorous (μg/l)

Spectrophotometrically by Stannous Chloride Method (A.P.H.A., 1998)

3 Nitrate- nitrogen (μg/l)

Spectrophotometrically by Salicylate Method (CSIR, 1974)

4 Nitrite – nitrogen (μg/l)

Sulphanilimide method (Golterman and Clymo, 1969)

5 Ammonical Nitrogen (μg/l)

Spectrophotometrically by Phenate Method (A.P.H.A., 1998)

6 Total Dissolved Solids (mg/l) A.P.H.A. (1998)

7 Total Suspended Solids (mg/l) A.P.H.A. (1998)

Estimation of Chemical Parameters: Field visits to the area were conducted and water samples were collected for chemical analysis. Water samples were collected from different land use land covers throughout the catchment by dipping 1 litre polyethylene bottles just below the surface of water. Analysis of the samples was carried out in the Limnology Laboratory of the Centre of Research for

Development (CORD), University of Kashmir. The parameters analyzed were TDS, TSS, BOD, Total Phosphorus, Total Nitrogen and Ammonia. Analysis of the chemical parameters was done in accordance to the methodology adopted by Golterman and Clymo (1969), CSIR (1974) and A.P.H.A. (1998) (Table 1). Pollution Data and Export Coefficient Table: In the study, the data about the export coefficient values for various pollutants were taken from secondary sources (Rast and Lee 1978; Loehr et al. 1989; Baldys et al. 1998; McFarland and Hauck 2001; Line et al. 2002) and journals like Oriental Science and Journal of Research and Development published by Center of Research for Development, University of Kashmir were also reviewed to get information about the pollutants. The values were validated by the results obtained from the analysis of the water samples, and the resultant data was used in the generation of export coefficient table. Model Simulations PLOAD model was used to simulate the pollutant loads into the Dal lake. Among GIS data, land use and watershed data were generated in the present study. Best Management Practices (BMP) data being optional were not generated due to unavailability of data. Among tabular data, export coefficient table was generated. Impervious terrain factor data tables were not developed, since they are used only when using simple method for pollutant loading calculation, whereas export coefficient method was used in this study. Pollutant reduction BMP data tables and point source facility locations and loads being optional were not generated due to unavailability of data.

For calculating pollutant loading into the Dal lake, export coefficient method was used, since the application of the simple method is limited to small drainage areas of less than one square mile. After the generation of all the required inputs, the PLOAD was executed and the model results showing the pollutant loading were represented as maps and tables. RESULTS Land Use / Land Cover Map 13 different classes viz. forest, boulder, meadow, water, agriculture, plantation, barren, aquatic vegetation, scrub, built-up, moist bare rock, cloud and shadow were observed in the study area (Figure 5). The satellite derived land use / land cover information was verified in the field. The overall classification accuracy was found to be 89.60% with overall Kappa statistics equal to 0.8780.

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Figure 5: Land use / land cover map of the study area.

Plantation dominates the study area covering 26.32 % of the total study area (Table 2). Forest ranks second with 17.59 %, whereas barren comes next with 11.78 %. The least representative of the land use is moist bare rock covering just 0.59 % of the area.The land use map helped in having an understanding of various pollution loads coming from different points in the catchment, as the pollution load is directly related to the land use/land cover type. Table 2. Percentage-wise distribution of land use classes.

S. No. Class Name Area in Sq. Miles

Percentage (%) Area

1 Water 4.762 3.67 2 Clouds 0.572 0.44 3 Meadows 4.349 3.35 4 Barren 15.263 11.78

5 Aquatic Vegetation 3.538 2.73

6 Plantation 34.087 26.32 7 Scrubs 12.274 9.48 8 Shadows 12.578 9.71 9 Boulders 7.166 5.53 10 Forest 22.778 17.59

11 Moist Bare Rock 0.774 0.59

12 Built-up 2.464 1.90 13 Agriculture 8.863 6.84 TOTAL 129.468 100

Watershed Map A total of 14 watersheds (Figure 6; Table 3) were represented by the watershed map of the study area.

The map forms an essential input to the model. Pollutant loadings in PLOAD are estimated by watershed and/or watershed area. Many water quality problems are best solved at the watershed level rather than at the level of an individual water body or discharger. It is important that a watershed approach be used to address water quality concerns because land-use decisions made in one area of a watershed will inevitably affect those living in another part of the watershed. Watershed data was then used in the model to get the pollution load for each watershed.

Figure 6: Watershed map of Dal lake catchment. Export Coefficient Table Export coefficient table was generated from the export coefficient values (in lbs/ac/yr) collected from the literature. The values were obtained for total dissolved solids (TDS), total suspended solids (TSS), biochemical oxygen demand (BOD), total phosphorus (TP), total nitrogen (TN) and ammonia (NH3), pertaining to different land use/ land cover categories. The values after validation were given the shape of an export coefficient table (Table 4), which was used in the estimation of the pollutant loads. Model Simulations After running the PLOAD model, graphic and tabular output results were obtained for two simulation categories viz. annual pollutant loading by watershed area [Figure 7(a-f); Table 5] and total annual pollutant loading by watershed [Figure 8(a-f); Table 6]. PLOAD model, thus, simulated the pollutant loads coming from the urban watershed into the Dal lake.

Table 3. Watersheds in the Dal lake catchment.

ID Perimeter (m) Area (km2) Dominant land use 1 19321.694 15.05 Boulder, moist bare rock 2 9300.177 5.01 Meadow, moist bare rock 3 11291.921 7.43 Forest, meadow 4 14754.858 11.23 Forest 5 7348.905 3.26 Plantation, forest 6 29676.351 36.81 Barren, agriculture 7 58245.645 70.56 Barren, built-up, plantation, agriculture 8 34558.923 41.13 Forest, barren 9 22248.563 24.30 Meadow, moist bare rock

10 8305.129 3.14 Scrub 11 8734.282 3.37 Scrub 12 41659.792 40.05 Meadow, forest 13 31346.233 22.66 Scrub, forest 14 48918.202 37.04 Built-up, aquatic vegetation

Table 4. Export Coefficient table. LUCODE CLASS BOD TSS TDS NH3

Total Nitrogen

Total Phosphorus

(lbs/ac/yr) 1 Plantation 59 124 424 4.7 31 6.4 2 Meadow 21 143 493 3.6 25 5.1 3 Scrub 23 581 151 3.7 19 5.0 4 Barren 29 492 137 2.8 18 5.3 5 Forest 33 162 521 3.9 22 5.6 6 Shadow - - - - - - 7 Boulder 43 632 197 4.4 33 9.7 8 Agriculture 52 136 587 4.4 34 9.5 9 Cloud - - - - - - 10 Water - - - - - - 11 Built-up 132 505 201 6.1 30 12.6

12 Aquatic Vegetation 157 144 462 5.5 31 8.6

13 Stream 106 673 219 5.0 34 10.1

Table 5. Annual pollutant loading by watershed area. Watershed ID BOD TSS TDS NH3 TN TP (lbs/ac/yr) 1 39.54 323.80 252.95 3.37 21.07 6.39 2 35.32 255.71 296.86 3.32 21.16 5.46 3 24.89 85.88 257.29 2.37 14.81 3.31 4 29.44 112.75 332.49 2.93 17.65 4.11 5 37.87 146.06 475.75 3.93 23.32 5.56 6 36.17 202.49 278.77 3.19 20.54 4.86 7 43.87 186.95 370.98 3.89 24.91 5.82 8 36.90 206.73 344.90 3.55 21.65 5.29 9 25.77 277.12 220.38 2.98 17.49 4.62 10 32.11 348.16 315.22 3.83 21.65 5.31 11 37.53 320.87 292.82 3.90 23.16 5.40 12 28.76 209.24 259.92 2.96 18.06 4.34 13 36.36 249.24 348.86 3.74 22.20 5.35 14 65.25 171.67 367.42 4.15 26.44 6.90

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(a) (b) (c) (d)

(e) (f) Figure 7: Maps showing annual pollutant loading of BOD, TSS, TDS, NH3, TN and TP respectively by watershed area.

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(a) (b)

(c) (d)

(e) (f) Figure 8: Maps showing annual pollutant loading of BOD, TSS, TDS, NH3, TN and TP respectively by watershed.

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Table 6. Annual pollutant loading by watershed.

BOD TSS TDS NH3 TN TP Watershed ID (lbs/year)

1 149853.96 1226989.99 958503.14 12791.93 79861.29 24244.65 2 43763.11 316811.76 367805.36 4118.33 26224.36 6772.03 3 45769.34 157885.81 472989.04 4375.02 27234.62 6094.73 4 81750.44 313033.08 923097.54 8135.51 49009.60 11416.79 5 30598.79 117986.85 384310.85 3176.82 18841.79 4493.01 6 329052.82 1842158.41 2536028.47 29089.99 186875.89 44278.88 7 766676.00 3266902.93 6482909.83 68018.00 435384.67 101822.96 8 378500.43 2120489.50 3537732.81 36453.80 222110.55 54335.86 9 154819.05 1664633.68 1323771.28 17923.80 105096.70 27751.87 10 25026.23 271295.55 245622.53 2989.38 16870.35 4144.56 11 31299.92 267548.47 244157.49 3257.04 19314.08 4510.18 12 288657.70 2099839.81 2608347.57 29711.56 181260.60 43603.91 13 205522.65 1408589.33 1971549.72 21167.35 125475.17 30252.81 14 597314.72 1571417.83 3363200.55 38065.33 242028.21 63187.95

DISCUSSION During the present investigation, a total of 14 watersheds of the Dal Lake catchment were assessed in terms of pollution loadings coming from different land use/land covers. Amongst these, watersheds number 7 and 14 witnessed the highest annual loads of almost all the pollutants. The reason for this may be attributed to the fact that these watersheds are under direct biotic interference. Also the maximum number of land use/land cover classes is present in these watersheds. Land use classes such as built up (urban), agriculture and aquatic vegetation contribute to the maximum of these pollutants such as total phosphorous, ammonia, nitrates and nitrates which ultimately find their way into the Dal lake through agricultural runoff, domestic sewage, etc.

Excessive nutrients such as nitrates and phosphates, commonly originate in domestic sewage, runoff from agricultural fields, waste material from animal feed lots, packing plants etc. These nutrients are responsible for water pollution primarily because they stimulate the growth of microorganisms which often increase the biochemical oxygen demand (BOD). Highest BOD values were recoded for watershed number 7 and 14 with total annual loads of 766676.00 lb/yr and 597314.72 lb/yr respectively. BOD is an indicator and not a pollutant. Higher BOD values indicate strong sewage and hence it is used to measure the degree of water pollution and waste strength.

The highest load for total dissolved solids and total suspended solids came from watershed number 7 with a total load of 6482909.83 lb/yr and 3266902.93 lb/yr respectively. Total solids of water are dependent upon the concentration of suspended and dissolved solids. Increase in the dissolved solids

indicates increase in the concentration of the included elements and therefore eutrophication. Brehmer (1965) observed the latter is caused as a result of decrease in compensation depth by increasing the concentration of suspended solids. Hem (1970), Seddon (1972) and Goel et al. (1980) related deterioration of water quality to increase of total solids, and used total solids content of water as a criterion for confirming the eutrophic nature of water.

The annual loads of ammonia were highest in the watershed numbers 7, 14 and 8 with values of 68018, 38065.33 and 36453.80 lb/yr respectively. The higher values of ammonia may be attributed to the presence of organic pollution. Baumeister (1972) reported that ammonia is a good indicator of sewage pollution. Srivastava and Sahai (1976) reported very high free ammonia concentration in polluted area of the Chilwa Lake.

Similarly, the annual loads of total nitrogen and total phosphorous were also highest for the two watersheds 7 and 14, which are subject to human interference and exploitation, the values being 435384.67 and 242028.21 lb/yr for nitrogen and 101822.96 and 63187.95 lb/yr for phosphorus, respectively. Hutchinson (1957) reports that higher levels of phosphorous are results of sewage contamination. Thomas (1969) pointed out that addition of phosphorous brings about eutrophication mechanism by increasing the bacterial content, increase in the oxygen demand and increase in growth of algae. Hammer (1971) reveals that as contrast to most of other major ions the contribution of nitrogen and phosphorous to water is directly or indirectly attributable to the activities of man. Edwards and Goodman (1972) while outlining the main pollution problems of air, water and soil support the view of phosphorous and nitrogen being

implicated in accelerating eutrophication process. Frecker and Davis (1975) discussed the man made eutrophication in a Newfoundland (Canada) Harbour and found that phosphorous and nitrogen were the major culprits in accelerating the process. Similar findings have been reported by Prasad and Qyum (1976). CONCLUSION Dal lake pollution provides us with a classic example of how little we appreciate the beauty of nature. There has never been a time when man has not modified his environment, but the changes that are taking place now are major and rapid as compared to the past. Different watersheds have a maximum loading of different pollutants which is dependent upon different land use/ land covers .The highest loads were witnessed for those watershed numbers 7 and 14 in the catchment in which built up (urban), agriculture, aquatic vegetation and stream (Telbal Nallah) were the dominant land use / land covers. The different pollutants coming from these watersheds are contributing towards the steady degradation of this beautiful lake. As a result, the Dal is dying an unsung death. It is concluded that the urban watershed in Dal lake catchment assumes top most priority and thus needs to be effectively managed on pollutant basis.

Despite some problems, this research has demonstrated the usefulness of remote sensing, GIS and simulation modeling for understanding the pollution loads and mechanisms in a lake catchment area. Due to the shortage of time and resources, the data on the best management practices and point source location data which are used as inputs to the PLOAD model could not be generated / collected. The research can be taken forward by incorporating these data inputs required by the model to calculate pollution loads having higher degree of precision and accuracy. RECOMMENDATIONS In order to protect the Dal lake from severe environmental degradation and for its effective management and monitoring, the following recommendations are made:

• An effective watershed prioritization needs to be worked out.

• Land use in the catchment should be regulated so as to prove best for the lake.

• An effective and comprehensive rehabilitation and management system should be evolved by irrigation engineers and planners for regular monitoring of Lake.

• To avoid environmental degradation and to provide assured domestic water and irrigation supply as well as to meet other needs, desilting and deweeding of the lake should be carried out on regular basis.

• To ensure sustainable development of the lake and environs as well as its recreational value, the growth of uncontrolled natural vegetation (aquatic weeds) should be stopped.

• The water reservoir capacity of the lake has been considerably reduced. Therefore, large scale dredging should be carried out to retain its original position.

• Water quality monitoring should be done with greater frequency and results should be made public.

• Temporal, environmental and other related geomorphological changes taking place in the lake environs should be monitored with the help of high resolution satellite data in conjunction with ground based information. Satellite based image maps should be used as a basic input parameter for environmental mapping and monitoring of the lake and environs.

ACKNOWLEDGEMENTS This article forms a part of the P.G.Diploma project work of the first author carried out at the P.G.Department of Geology and Geophysics, University of Kashmir. The author is highly thankful to the Head, P.G. Department of Environmental Science, University of Kashmir for providing laboratory facilities. REFERENCES A.P.H.A. 1998. Standard Methods for the Examination of

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