Linking human-biophysical interactions with the trophic status of Dal Lake, Kashmir Himalaya, India

Accepted Manuscript Title: Linking Human-Biophysical Interactions with the Trophic Status of Dal Lake, Kashmir Himalaya, India Author: Irfan Rashid Shakil Ahmad Romshoo Muzamil Amin Shabir A Khanday Prakash Chauhan PII: DOI: Reference:

S0075-9511(16)30215-8 http://dx.doi.org/doi:10.1016/j.limno.2016.11.008 LIMNO 25551

To appear in: Received date: Revised date: Accepted date:

30-1-2016 27-10-2016 4-11-2016

Please cite this article as: Rashid, Irfan, Romshoo, Shakil Ahmad, Amin, Muzamil, Khanday, Shabir A, Chauhan, Prakash, Linking Human-Biophysical Interactions with the Trophic Status of Dal Lake, Kashmir Himalaya, India.Limnologica http://dx.doi.org/10.1016/j.limno.2016.11.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

This manuscript is submitted for publication in Limnologica Linking Human-Biophysical Interactions with the Trophic Status of Dal Lake, Kashmir Himalaya, India Irfan Rashida, Shakil Ahmad Romshooa*, Muzamil Amina, Shabir A Khandaya,b and Prakash Chauhanc a

Department of Earth Sciences, University of Kashmir, Srinagar, INDIA Department of Environmental Sciences, University of Kashmir, Srinagar, INDIA c Space Applications Centre, Indian Space Research Organization (ISRO), Ahmadabad, India b

Corresponding Author Email: [email protected]

Highlights 1. Satellite, field, archived and lab data was integrated in GIS to generate baselines 2. Lake status was assessed on the basis of land cover, water quality and demography 3. Knowledge about various drivers affecting the lake will inform lake conservation

Abstract: The study analyses the long-term biophysical and demographic changes in Dal lake, located in the heart of Srinagar city, Kashmir India, using a repository of historical, remote sensing, socio-economic and water quality data supported by the extensive field observations. The lake faces multiple pressures from the unplanned urbanization, high population growth, nutrient load from intensive agriculture and tourism. The data showed that the lake has shrunk from 31 km2 in 1859 to 24 km2 in 2013. Significant changes were observed in the land use and land cover (LULC) within the lake (1859-2013) and in the vicinity of the lake (19622013). Analysis of the demographic data indicates that the human population within the lake has shown more than double the national growth rate. Additionally, 7 important water quality parameters from 82 well distributed sites across the lake were analysed and compared with the past data to determine the historical changes in the water quality from 1971-2014. The

changes in the LULC and demography have adversely affected the pollution status of this pristine lake. Ortho-phosphate phosphorous concentration has increased from 16.75µgL-1 in 1977 to 45.78µgL-1 in 2014 and that of the nitrate-nitrogen from 365µgL-1 to 557µgL-1, indicating nutrient enrichment of the lake over the years. Built-up area within the lake has increased 40 times since 1859, which, together with the changes in the population and settlements, have led to the high discharge of untreated nutrient-rich sewage into the lake. Similarly the expansion of floating gardens within the lake and agriculture lands in the catchment have contributed to the increased nutrient load into the lake due to the increasing use of fertilizers. The information about the existing land cover, demography and water quality was integrated and analyzed in GIS environment to identify the trophic status of the lake. The analysis indicated that 32% of the lake falls under sever degradation, 48% under medium degradation while as 20% of the lake waters are relatively clean. It is believed that the results provide improved knowledge and insights about the lake health and causal factors of its degradation necessary for effectively restoring its ecological and hydrological functionality.

Keywords: Dal lake; Ecosystem Dynamics; Remote Sensing; Water quality; Urbanization; Long-Term Ecological Research

1.

Introduction: Lakes are sensitive ecosystems that contribute to regional hydrology (Kebede et al.,

2006; Thiery et al., 2015) and pristine biodiversity (Jeppesen et al., 2000; Zutshi and Gopal, 2000) and play an important role in sustaining the socio-economy of the dependent populations (Carpenter and Cottingham, 1997; Eshenroder, 1987). Past 50 years of limnological research reveals that the anthropogenic activities, within the lakes and their catchments, are deteriorating the health of these pristine ecosystems at an alarming pace by altering the bio-physical setup (Du et al., 2011; Jones and Orr, 1994; León-Muñoz et al., 2013; Samecka-Cymerman and Kempers, 2001) and lake biogeochemistry (Kundangar and Abubakr, 2004; Kiage et al., 2007; Søndergaard and Jeppesen, 2007). The land system changes impair the water quality, degrade biodiversity and irreversibly alter biogeochemical cycles of a lake ecosystem (Carpenter et al., 1999; Gao et al., 2015; Huang et al., 2014; Romshoo, 2003; Scavia et al., 2014; Schindler, 2006; Smith et al., 1999; Vadeboncoeur et al., 2003). Most of the lakes in Kashmir Himalaya, India have been subjected to anthropogenic pressures which include unplanned urbanization and deforestation of catchments, extensive use of fertilizers and pesticides for agriculture and horticulture respectively and tourism infrastructure development (Hassan et al., 2015; Masoodi et al., 2013; Pandit, 1988; Rashid et al. 2013a; Romshoo and Muslim, 2011; Zutshi et al., 1980). Although eutrophication in Kashmir lakes is a recent phenomenon (Khan and Ansari, 2005; Vass, 1980; Zargar et al., 2012a), yet in the short span of time, it has adversely impacted the functionality of lake ecosystems in the region. Additionally, the depleting cryosphere in the Kashmir Himalayan region, under the changing climate, has significantly reduced the stream flows that sufficiently contribute to the lake inflows in the Kashmir valley (Romshoo et al., 2015). Many lake studies have focused on assessing the state of health of Kashmir lakes, yet the knowledge about the factors responsible for their deterioration remains inadequate. Studies conducted so far are unifocal taking into consideration just a single aspect such as water quality assessment (Bhat et al., 2013; Khan, 2010; Sarwar, 1999; Wani et al., 1996), and aquatic vegetation (Shah et al., 2014; Zargar et al., 2012b). A few studies, based on the

use of remotely sensed data, have determined the spatio-temporal changes of a single or a couple of factors within the lakes and their catchments in the Indian Himalaya and linked it to the lake ecosystems (Chopra et al., 2001; Mushtaq and Pandey, 2014; Romshoo et al., 2011; Sharma et al., 2015). In order to holistically comprehend and quantify the influence of causal factors on lake degradation, it is important to integrate all the available data from archives, remotely sensed platforms, in-situ measurements and field surveys in a geospatial modelling environment (Hereher, 2015; Liu et al., 2007; Nielsen et al., 2012; Wasige et al., 2013; Yan et al., 2015). The study is an effort in this direction and aims to integrate long-term information pertaining to the observed changes in the land system within the lake (1859-2013) and its immediate catchment (1962-2013), water quality (1981-2011) and socio-economy (19772013) in a GIS modelling environment. Integration of the long-term multi-source spatial and non-spatial information would help to better comprehend the influence of various factors on the Lake ecosystem and the approach could be replicated for other Himalayan lakes that are showing widespread deterioration due to the multiple pressures.

2.

Materials and Methods:

2.1.

The Dal Lake

Dal lake, situated in Kashmir Himalaya, is a multi-basin drainage lake (Figure-1) covering an area of 24 km2 with the open water spread of about 10.5 km2 and a water holding capacity of 15.45x106 m3 (Solim and Wanganeo, 2007). The lake lies between 34°5'-34°9' N to 74°49'74°53'E at a mean altitude of 1585 m asl. The lake has tremendous ecological, socioeconomic and cultural importance in the region and its deteriorating health is a cause of concern for the people of Kashmir. Being a major tourist attraction in Kashmir, the lake is socio-economically important as the livelihood of a large section of the population of Srinagar city is dependent on the services and products provided by the lake. The lake is an important source of vegetables, fisheries, recreation and drinking water to the people of Srinagar city. The catchment area, spread over 337 km2, is geologically composed of Panjal

Traps, Aglomeratic slates, Alluvium and Karewa deposits (Wadia, 1971; Varadan, 1977; Data, 1983; Bhat, 1989). As per Bagnolus and Meher-Homji (1959), climate of the area is sub-Mediterranean with four seasons. The total annual precipitation and average temperature recorded at the nearest meteorological station is 870 mm and 11oC respectively. 2.2.

Datasets

In order to accomplish the research, data from multiple sources was used to assess the longterm changes in the Dal lake and its immediate vicinity. The data included satellite images, survey maps, demographic data, water quality data and GPS measurements. For the land system change detection, satellite data of the same period/season has been used to eliminate the errors in the land cover information due to the changing season or time gaps in the acquisition of the data. The details of the multi-source datasets used in this study are provided in Table 1. 2.3.

Methodology

The methods employed to generate the long term information about the changes in the land system, demography and water quality within and in the vicinity of the lake made use of the data from various sources including remote sensing, laboratory analysis, census, fieldwork, and archived maps are discussed hereunder: 2.3.1. Long-Term land system changes within the lake Quantifying the long-term changes of environmental variables is important for getting deeper insights into the environmental changes occurring within an ecosystem (Lindenmayer et al., 2012; Magnuson, 1990). At the same time, it becomes essential to link the changes within an ecosystem with the causal factors like socio-economy (Redman et al., 2004). In the present study, land system changes within the Dal lake were assessed over a period of 155 years from 1859-2013 using the available data. For generating the reference data, an old survey map dating back to 1859 (Stein, 1859) was used. The map was digitized in GIS for delineating the lake boundaries and the land cover information as it existed in 1859. In addition to the survey maps, multi-date satellite data of the period from 1972-2013 was used to assess the land system changes within the lake ecosystem. Data analysis involved the processing of satellite

images using standard algorithms (Lillesand and Kiefer, 1987). Using the standard geometric correction algorithm (Jensen, 1996), satellite data was corrected for geometrical distortions using map-to-image and image-to-image geo-referencing algorithms. The tie-points were taken all across the satellite image to ensure better geometric correction of the data, achieving a root mean square (RMS) error of less than 1.00. All the satellite and survey data were coregistered with respect to each other in order to ensure the precise delineation of the lake boundary. Image enhancement techniques like histogram equalization (Yang and Lo, 2000) and contrast stretch (Gillespie et al., 1986) were applied to the satellite data to highlight the land features. On-screen visual interpretation (manual digitization) of the satellite data was employed to map the land cover types (Romshoo et al., 2011) at 1:40000 scale. On-screen digitization, with cognitive inputs from the image analyst, was chosen instead of digital classification algorithms as it is advantageous for delineating land cover in mountainous areas (Rashid et al., 2016a; Rashid et al., 2016b; Rashid and Abdullah, 2016). Only four major land use and land cover (LULC) types were mapped using the multi-source data; Aquatic vegetation, Floating Gardens, Built-up and Open Waters. In absence of any ground truth data, we used high-resolution Google Earth images for checking and validating 2010 satellite data.

We quantified the uncertainties associated with the use of different resolution data for mapping physical features (Murtaza and Romshoo, 2016; Bhambri et al., 2012) advocated by Williams et al. (1997) and Hall et al. (2003) using the following formula while assuming image registration error as zero:

U  a2  b2

...(1)

where ‘a’ and ‘b’ are the spatial resolutions of two images and σ is the error in the image registration. The uncertainty with respect to area ( Uarea ) was estimated using the following formula:

U area  2UV

...(2)

where U is boundary change uncertainty and V is the spatial resolution of the image.

Further, we kept the mapping scale for LULC delineation constant so as to minimize the mapping errors and uncertainty associated with the use of different resolution satellite data. Shadows and clouds which are posing a challenge for digital image processing of satellite data in mountainous terrain make visual image interpretation, with cogitative inputs from an analyst, more preferable for delineation of different LULC types and surface features (Rashid et al., 2013b). A Land Cover Index (LCI) based on the current land cover setting of the lake was developed by assigning weights, ranging from 1-4, to different LULC types owing to the propensity of a specific land cover type to pollution or lake degradation. Smallest weight of 1 was assigned to Open Water category followed by Aquatic Vegetation (2), Floating Gardens (3) and Builtup (4) areas within the lake. The weightage scheme is logical as the hamlets within the lake hosting about 5461 households and 595 houseboats are discharging substantial amounts of untreated household wastes directly into the lake. Similarly, the likelihood of any nutrient source from the open waters is per se very improbable. The use of fertilizers and pesticides for growing vegetables on floating gardens makes them to contribute more nutrients to the lake than the aquatic vegetation. 2.3.2. Land system changes in the immediate lake catchment Land system changes in the immediate catchment of the Dal lake were analyzed for 52 years from 1962-2013. Survey of India (SoI) topographic map of 1962 was used as a reference for mapping the baseline LULC data. In addition, multi-spectral time series of satellite data was used to determine the LULC changes in the vicinity of Dal lake from 1972-2013. The methods employed to generate the LULC information in the immediate catchment are same as those detailed in the section 2.3.1 above. However, compared to the 4 LULC categories mapped within the lake, 10 LULC categories were mapped in the vicinity of the lake which include; Agriculture, Aquatic Vegetation, Built-up, Forests, Open Waters, Orchards, Parks/Gardens, and Plantations.

Both overall accuracy as well as class-wise accuracy assessment of delineated land cover within and in the vicinity of the lake was carried for the 2013 data using the following formula:

  n N 100

...(1)

where 'ρ' is classification accuracy; 'n' is the number of points correctly classified on the image when validated on ground and 'N' is the number of points checked in the field. 2.3.3. Demographic changes Information about demographic changes is vital for linking the ecosystem degradation with anthropogenic activities (Mouri et al., 2011; Verschuren et al., 2002). In this context, population and household data at hamlet level within the lake for 93 hamlets and 17 city wards in the vicinity of the lake, procured from the Census of India, was analyzed at decadal time scale from 1981-2013. Based on the areal extents, the data for 93 hamlets within the lake was aggregated (Batty and Xie, 1994; Reibel, 2007) into 10 point locations in a GIS environment to bring it in conformity with the data available at ward level. The demographic data, both within the lake and in the vicinity, was represented by 43 point locations comprising of recreational, commercial and built-up areas besides ward-level and hamlet data. A Demographic Index (DI) was developed taking into account the total population and number of households within Dal lake and in the vicinity. The data from 43 locations was interpolated using natural neighbour (NN) algorithm (Sibson, 1981; Ledoux and Gold, 2005) to generate a seamless surface distribution of the two demographic variables in and around the lake. NN interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas in order to interpolate a value. To determine value of an attribute at location x, values of the points which are natural neighbours of x and their relative weights are used. The weight of each neighbour is equal to the natural neighbour coordinate of x with respect to the neighbour. If we assume that each data point in a set has a scalar attribute ai, then the natural neighbour interpolation is described in Equation 3:

n

f ( x)   wi ( x)ai i 1

...(3)

where f(x) is the interpolated function value at the location x. Both the layers, total population and number of households, were reclassified into 4 classes using equal intervals. The layers were then merged in GIS environment and the average of population and household indices were reclassified into four categories; as per the Equation 4:

DI 

1 n  HI , PI 2 i 1

...(4)

where HI is household index and PI is population index 2.3.4. Water quality assessment 82 lake water sampling sites, well distributed across the lake, were selected for assessing the water quality of the Dal lake. Water samples were collected during the summer months of July and August (2014) which coincide with the peak agriculture, horticulture and tourism in the area. The water samples were analysed for 7 parameters, viz., pH, Total Alkalinity, Total Hardness, Chloride, Nitrate-Nitrogen, Phosphate-Phosphorous and Total Dissolved Solids using standard methods (APHA, 2005). pH and TDS were measured real-time in the lake using pH and TDS meters respectively while as the rest of the parameters were analyzed in the lab within 48 hours of the sampling. All the water samples were refrigerated at 4oC (APHA, 2005). Total alkalinity, Total hardness and Chloride ions were determined using titration methods. Nitrate-Nitrogen was determined spectrophotometrically using Salicylate method (CSIR, 1974) while as the ortho-Phosphate Phosphorous was estimated using Stannous Chloride method (APHA, 2005). The locations of all the 82 sampling sites were recorded using a high precision GPS with +2m error. All the 7 water quality parameters were interpolated in GIS for generating their spatial distribution using the NN technique. Only 75 sites were included for performing different interpolation techniques of the water quality parameters. The rest 7 sites were used for validation and it was found that NN provides the best-fit closely resembling the observed distribution.

A Water Quality Index (WQI) was developed based on the concentrations of nitrate nitrogen, ortho-phosphate phosphorous and TDS because these three parameters are considered important determinants of anthropogenic-driven land system changes (Carpenter et al., 1998; Foley et al., 2005; Pang et al., 2013) and are good indicators of the trophic state of a water body (Finlay et al., 2013; Rashid and Romshoo, 2013; Schindler, 2012). The pixel-wise average concentration of these three parameters gives WQI for the lake (Equation 5). The resultant WQI values were then reclassified into four classes (1-4: 1 representing least polluted and 4 severely polluted) with equal class intervals for the final WQI of the lake as:

WQI 

1 n  NN , OPP, TDS 3 i 1

...(5)

where NN, OPP and TDS represent weights for nitrate nitrogen, ortho-phosphate phosphorous and total dissolved solids respectively. 2.3.5. Determination of Trophic Status of the Lake The trophic status of Dal lake were determined by linking the human and biophysical interactions to the ecological health of the lake using multi-criteria analysis (MCA). For assessing the trophic status, a degradation status index (DS) incorporating information about physical settings (LCI), population data (DI) and hydrochemistry (WQI) was analysed in GIS environment (Janssen, 2001; Carver, 1991; Zhang and Huang, 2011; Bagdanavičiūtė and Valiūnas, 2013) as these are thought to be the main factors responsible for triggering the eutrophication of the of Dal lake (Najar and Khan, 2012; Vass et al., 2015). All the three indices were assigned equal weights assuming that every index has an equal influence in determining the trophic status of the Lake. The three indices were merged in GIS environment to generate the polluted areas within the Lake by taking the pixel-wise arithmetic mean of the three indices as shown in the Equation 6 below:

DS 

n

1 3

 LCI i 1

i

 DIi  WQIi

...(6)

where DS is the degradation or trophic status of the lake for pixel i. Using equal intervals of the summed-up weights, the output from the Eq. 5 was reclassified into four categories, i.e., Low, Medium, High and Severe degraded lake area. 3.

Results:

3.1.1. The Lake Size Changes An assessment of the multi-date and multi-source spatial data revealed that the lake has significantly reduced in size with the area having shrunk by 7.8km2 from 31.85 km2 in 1859 to ~24 km2 in 1979; however, the lake area has remained more or less constant at ~24 km2 during the past 34 years. It was in the late 1970s that the fore-shore road was built along the northern part of the lake, as a boundary, to purportedly stop the encroachment of its banks from that side. The construction of the road reduced the size of the lake with its major axis reducing by 2.24 km from 9.46 km in 1859 to 7.22 km in 1979. However, there is no change in the length or area of the lake since 1979. The error associated with the mapping of the lake boundary using the coarse resolution Landsat MSS data w.r.t. Landsat TM / Landsat ETM+ / Landsat OLI is 67.08 m. While as the uncertainty error was only 64.44 m with respect to IRS LISS III data. The uncertainty associated with the lake area estimates from Landsat MSS data with respect to Landsat TM / Landsat ETM+ / Landsat OLI was ±0.008 km2 and ±0.004 km2 respectively. The uncertainty associated with the area computation from MSS data with respect to IRS LISS III data was ±0.004 km2. However, the uncertainty values were found negligible when computing the boundary or lake area using 30m Landsat data (Landsat TM, Landsat ETM+ and Landsat OLI) with respect to IRS LISS III data because of their almost same spatial resolution. 3.1.2. Land System Changes within and in the Vicinity of the Lake The analysis of the data from 1859-2013 revealed that the LULC within the lake has undergone drastic changes during the last 155 years (Table-2). Marshy lands and plantations in the northern part of the lake have vanished, due to the ceding of the lake areas for road

construction, leading to the significant loss of the lake area during 1970s. Dynamic floating aquatic vegetation on the water surface of the lake has tripled in spatial extent from 2.91 km2 in 1859 to 8.64 km2 in 2013, an indication of the nutrient enrichment of the lake over the years from the catchment and settlements within and in the vicinity of the lake. The aquatic vegetation in the lake reached its peak in 2010 (10.40 km2) with the consequent reduction of the open water expanse. Due to the large-scale dredging undertaken since 2012 by the lake managers, the spatial extent of the aquatic vegetation has reduced to 8.64 km2 which is also reflected by the 2013 emergent aquatic vegetation extent observed during the month of October (Figure-2). However, a longer time series of the aquatic vegetation distribution at an improved temporal resolution would be required to determine the efficacy of the recent interventions on the control of aquatic weeds in the lake. As per the analysis, Built-up area within the lake has increased more than 40 times during the past 155 years from 0.05 km2 in 1859 to 2.02 km2 in 2013. The massive and injudicious urbanization of the lake interiors, with no provision for the scientific disposal of the household wastes, has adversely affected the quality of the lake waters. Correspondingly, open water extent of the lake has shrunk by 50% from 20.63 km2 in 1859 to 10.5 km2 in 2013. However, the loss of open water has been more gradual since 1962 (Table 2). Similar extents of the open water have been reported by Amin et al. (2014) for the Dal lake over 20 years from 1981-2011. Due to the flooded lake in 1903 and 1992, the data of these two years is not consistent with the long-term observation trends of the open waters (Table 2). We also analyzed the land system changes from 1962-2013 in the immediate catchment of Dal lake (Figure-3), spread over an area of ~110 km2. Ten LULC types were delineated using on-screen image interpretation technique at 1:40,000 scale (Table-3). During the 52-year observation period, the land under agriculture has shrunk by 69%. The aquatic vegetation and parks/gardens showed slight changes. The built-up areas in the vicinity of the lake have increased by three folds. Badar et al. (2013a) also reported significant increase in the built-up of Dal lake catchment from 1992-2005. The increase in the built-up areas in the vicinity has significantly increased the nutrient load due to the large influx of the untreated household

wastes directly into the lake, particularly from the southern and eastern sides of the lake. Though, the lake vicinity has lost 60% the forest cover during the last 52 years. Orchard and Plantation areas have increased significantly during the period. A total of 183 locations, both within the lake as well as its vicinity, were used for accuracy assessment of 2013 delineated land cover data. The overall accuracy of the delineated LULC types was 95.05%. In addition, the class-wise accuracies of different land cover types are provided in Table-3. 3.1.3. Demographic Changes Within the Lake Analysis of the demographic data from 1981-2011 reveals significant increase in the population and the number of households within the Dal lake. The total population within the lake increased by ~157% from 12928 in 1981 to 33288 in 2011(Figure-4) which is more than double the national population growth rate (Showqi et al., 2014). A corresponding increase of ~198% in the households within the lake, from 1896 to 5652, was observed during the period. These massive demographic changes are corroborated by the expansion of built-up areas, delineated from satellite data, within the lake from 0.80km2 in 1979 to 2.03km2 in 2010 (Table 2). Although some of the hutments within the lake have been demolished, as can been seen from the reduction in built-up areas within the lake from 2010 to 2013 by 0.1km2 (Table 2), by the lake managers, yet the untreated sewage and domestic wastes from the human settlements within the lake are directly discharged into the lake waters adversely affecting the water quality and aquatic biodiversity of the lake (Iqbal et al., 2006; Raina and Vass, 1993). 3.1.4. Lake Water Quality Dal lake has been traditionally divided into four basins - Bod Dal, Gagribal, Hazratbal and Nigeen (Ishaq and Kaul, 1988; Trisal and Kaul, 1983) as indicated in the Fig-1. The physicochemical sampling of the lake was planned in a way to cover all the four lake basins. The summary, location and spatial distribution of the physico-chemical parameters are given in (Table-4 and Figure-5). The lake water is predominantly alkaline with an average pH close to 8. High pH values were found in Hazratbal basin and Nigeen basin. TDS was found highest in the Hazratbal basin (~290mgL-1) probably because of it being the feeding basin to entire

Dal lake (Figure-5). The Telbal tributary, with high load of sediments from the mountainous catchment, discharges into the lake via Hazratbal basin (Badar et al., 2013b; Badar and Romshoo, 2007). Chloride ions which are a direct indication of the anthropogenic pressure are high in the Hazratbal and Nigeen basins. Being the deepest basin of the lake (Trisal and Kaul, 1983), the high values of chloride in the Nigeen is attributed to the relatively higher retention time of water in the basin. The waters of the lake are hard with the average hardness value of ~157mgL-1. The total alkalinity values range from 60-230 mgL-1 with an average of 116.16mgL-1 indicating good buffering capacity of the lake with moderate to high alkalinity (Wurts and Durborow, 1992). The high buffering capacity is also supported by the fact that the waters of Dal lake are predominantly basic (Ticku and Zutshi, 1993). Nitrate nitrogen and ortho-phosphate phosphorous are a direct manifestation of anthropogenic activity (Turner et al., 2003; Valiela and Bowen, 2002). Nitrate nitrogen values of the lake range from 2001300µgL-1 with high nitrate nitrogen concentration in the lake interiors, parts of Nigeen and Gagribal basins owing to their close proximity to settlements. On contrary the open waters in the Nishat and Hazratbal basins showed low concentration of nitrate nitrogen (Figure-5). Similar values have been reported by Mushtaq et al. (2013). The concentration of orthophosphate phosphorous in the lake ranged from 10-100µgL-1 with an average of 45µgL-1. Ortho-phosphate phosphorous and nitrate nitrogen showed similar variability because of the common anthropogenic sources of both these pollutants. Relatively higher values of TDS were observed in the lake interiors and Hazratbal basin. The water quality of the lake from 1977-2014 was also analysed from the literature in terms of total alkalinity, nitrate nitrogen and ortho-phosphate phosphorous (Trisal, 1977; Trisal, 1987). A comparison of these three parameters, shown in (Figure-6), reveals that the water quality of the lake has significantly deteriorated over the past ~4 decades. The total alkalinity has increased 1.5 times thus increasing the pH of the lake waters. Both the nitrate nitrogen and ortho-phosphate phosphorous concentrations have increased manifold since 1977. From Figure-6, it is clear that the concentration of nitrate nitrogen has increased 15 times while as

the ortho-phosphate phosphorous concentration has increased 27 times during the past 37 years. 3.1.5. Trophic Status of the Lake Depending upon the DS values, the lake areas were categorized into Low, Medium, High and Severe degradation types (Figure-7, Table-5). It is quite evident from the analysis that ~32% of lake area falls in High to Severe degradation zones located towards northern, central and south-eastern portions of the lake. Lake degradation in the northern areas of the lake could be attributed predominantly to the ingress of untreated sewage from the agriculture and settlements in the catchment. However, high degradation observed in the central and southeastern part is primarily due to the direct influx of sewage from the settlements lying within the lake and the application of fertilizers and pesticides for the production of vegetables on floating gardens (locally called raads) within the lake. Around 48% of the lake area comes under medium degradation zone and only 20% of the lake is without any degradation as per the integrated DS analysis of the data. These are mainly open waters in the north-east and south-eastern part of the lake. The results of the MCA indicate that the anthropogenic activities within and in the vicinity of the lake are responsible for the deterioration of the lake health as most of the ecologically degraded areas (Figure-7) are in the close proximity to the human settlements and anthropogenically driven land cover types (Qadri and Yousuf, 2007). 4.

Discussion:

Being an urban lake, the water quality of Dal is severely impacted by anthropogenic activities like urbanization, agricultural-intensive practices both within the lake as well as its catchment and untreated sewage from the catchment. This is substantiated by the deteriorated physicochemical quality of its waters over past few decades. A drastic increase in the concentration of nitrates, phosphates and sediments often changes the trophic status of lake ecosystems (Chislock et al., 2013; Dunne et al., 2013; Lewis et al., 2011). The past four decades of water quality assessments indicate deterioration of lake waters which have turned Dal lake from mesotrophic (Khan, 1986) to eutrophic (Solim and Wanganeo, 2008). The land system changes in the catchment have led to high influx of nutrient and sediment into the lake

ecosystem (Badar et al., 2013b). Similar findings have been reported in other lake studies worldwide (Cloutier et al., 2015; Jain et al., 1999; Lehto and Hill, 2013; Romshoo and Rashid, 2014). Although the area under agriculture has significantly decreased but the reckless use of a large quantity of fertilizers has degraded the water quality in freshwater ecosystems of Kashmir Himalaya (Rashid et al., 2013a). The problem is further aggravated due to the application of pesticides and other chemicals in orchards which finally find their way into the lake waters either through runoff or ground water (Somura et al., 2012; Zhang et al., 2015). Analysis of the Water Quality Index (WQI) data, based on the concentrations of nitrate nitrogen, ortho-phosphate phosphorous and TDS reveals the magnitude of the lake degradation. Based on the WQI values, the lake was categorized into four WQ deterioration zones; Low, Medium, High and Severe It is observed that the lake is highly polluted towards the south-west and northern part being in close proximity to the settlements. However, some lake areas in the Hazratbal, Nishat and Gargribal basin do not show any significant deterioration in the water quality. It is pertinent to mention that these areas are far away from the direct anthropogenic interference. Although, there are 4 Sewage Treatment Plants (STP) around the lake but none of them is located to the south-eastern portion of the lake where the waters are severely polluted. Although the present study has used one time water quality (Autumn) but keeping in view the seasonal variability of water quality parameters (Mushtaq et al., 2013), it would be prudent to assess water quality dynamics across a year or a longer period in the lake ecosystem. The demographic index (DI), developed from the population and household data in the Dal lake and its immediate vicinity, reveals a clear human footprint on pacing up the deterioration of this socio-economically important lake. Densely populated areas, mostly in the northern and central part of the lake, fall in the high degradation zones. Many studies have reported similar impact of human settlements on the quality of freshwaters globally (Liu et al., 2015; Purushothaman et al., 2012; Strohschön et al., 2013; Woodward et al., 2014). The zonation of the ecological and trophic condition of the lake should help the lake managers in designing a

better framework for ecological restoration of the lake health. Currently, there are only 4 Sewage Treatment Plants (STP) in the Dal lake with only 3 of them being operational. Even the operational STPs located in different parts of the lake are not efficient in treating the sewage influx from the lake surroundings. Although, the three fluidized aerobic bioreactor based STPs reduce BOD / COD by 68% and microbial load by 54%, their efficiency removal is much below the expected range of 85-90% (Jan et al., 2013). It is pertinent to mention that all the STPs are located mostly towards north of the lake and therefore a substantial amount of wastewater and storm runoff is discharged untreated into the lake from the other sides. Integrated analysis of the data revealed that the central portion of the lake, with human settlements and floating gardens, falls in a high pollution zone, primarily because of the discharge of untreated household wastes and nutrient-rich agriculture runoff into the lake. It, therefore, becomes imperative for the lake planners to set-up additional and adequate STPs all along the periphery of the lake and a few within the settlements in the lake interiors so as to restore the quality of waters in the lake. The MCA indicted that ~80% of the lake area falls under medium to severe pollution threat. Keeping in view the pollution status of the lake areas, we propose 6 additional STPs, both in the lake interiors and peripheral areas (Figure7). Out of the six proposed STPs, 3 should be installed in the lake interiors and the remaining 3 along the northern part of the lake. The 6 proposed STPs are suggested to overcome the current deteriorating ecological condition of the lake as the existing 4 STPs are not sufficient and efficient enough to satisfactorily treat the wastewater influx into the lake. It is very important to devise a robust strategy for minimizing the impacts of the environmental unfriendly anthropogenic activities going on unabated in the lake interiors on the health of lake. As is evident from the pollution status of the lake around human settlements, the increasing human-biophysical interactions observed in the lake interiors have adversely affected the overall health of the lake ecosystem. It is hoped that the results of the present study shall provide improved knowledge and insights about the lake health and causal factors of lake degradation for effectively restoring its ecological and hydrological functionality. Such information will be very important to lake

managers who otherwise adopt a unisectoral approach for mitigating the lake eutrophication (Van Rooy et al., 1998). 5.

Conclusions: From the analysis of the multi-source and multi-date data, it is observed that the lake

has lost 24.49% of its area during the last 155 years. The unregulated changes in the LULC within and in the vicinity of the lake showed significant changes in the built up area. This is corroborated by the census data which shows higher than national population growth rate. These changes in the land system and demography have adversely impacted the water quality of the lake as evident from the very high concentration of nitrate nitrogen and orthophosphate phosphorous. The high nutrient enrichment together with the observed LULC changes in and around the lake have led to the proliferation of floating-aquatic vegetation including some alien invasive species. The growth and expansion of the aquatic vegetation has reduced the open water spread of the lake to 10.5km2, a reduction of 50% compared to the water spread in 1859. The proliferation of floating aquatic vegetation has severely impeded the transmission of sunlight into the lake subsurface, impairing photosynthesis and allied biogeochemical processes in the water column and thus accelerating the lake eutrophication. Based on the pollution status of the lake, it is proposed to install at least 6 additional STPs to prevent the synergistic impact of land use and anthropogenic activities on water quality of the lake, as the 3 existing operational STPs are inadequate. Keeping in view the failure of the previous efforts by the lake managers to relocate the lake dwellers, whose livelihood is linked to services and products provided by the lake, the long-term efforts need to focus on densifying one or two human settlements within the lake by moving there people from less dense hutments, installing the wastewater treatment plants there, restricting the agriculture and olericulture activities within and in the vicinity of the lake and removing the post 1970s

floating garden landmasses from the lake interiors by dredging it on scientific basis. These steps, if taken in the right earnest, have potential to substantially improve the health of this socio-culturally, economically and ecologically important lake ecosystem, inter alia, increasing the open water spread of the lake.

Acknowledgement: This research work has been accomplished under a research grant provided by the Space Application Centre (SAC), Indian Space Research Organization (ISRO), Ahmadabad, India for the project titled “Bio-optical characterization of Optically Complex Dal Waters”. The authors express their gratitude to the funding agency for the financial assistance. The authors express gratitude to the anonymous reviewers for their valuable comments and suggestions on the earlier version of the manuscript that has greatly improved its content and structure.

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List of figures Figure 1: Location of study area

Figure 2: Land use land cover within the Dal lake from 1859-2013

Figure 3: Land cover in the immediate catchment of the lake from 1962-2013

Figure 4: Socioeconomic index for Dal lake based on total population and households

Figure 5: Present day water quality of Dal lake

Figure 6: Comparison of existing water quality with that of Trisal (1977, 1987)

Figure 7: Lake Degradation status based multi-source information from land cover, water quality and demographic data

Table-1: Data sets used in the present study Data Set SATELLITE DATA AND MAPS: Landsat Multi-Spectral Scanner (MSS) Landsat Multi-Spectral Scanner (MSS) Landsat Multi-Spectral Scanner (MSS) Landsat Thematic Mapper (TM) Landsat Enhanced Thematic Mapper Plus (ETM+) Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS III)

Acquisition Date 17 November 1972 6 September 1979 24 October 1980 17 October 1992 3 October 2001

19 October 2005

Google Earth Imagery

2010-2012

Landsat Operational Land Imager (OLI)

25 October 2013

Survey of India (SoI) Toposheets Survey Illustration map (M A Stein1859 , Anonymous 1903 ) HAMLET-WISE DEMOGRAPHIC DATA Census Department of India, Srinagar FIELD DATA: Ground Truth using GPS Water quality Samples

1962

Path/row 160/36 160/36 160/36

Spatial Resolution/ Scale 68/83m resampled to 60 m 68/83m resampled to 60 m 68/83m resampled to 60 m

149/36

30 m

149/36

30 m

92/46

23.5 Better than 5m for Dal lake and its immediate catchment

149/36 -

30 m 1:50000

1859, 1903

-

1:63000

1981, 2001, 2011

-

Total Population Total Households

2013 2013

-

Point data Point data

Table-2: Land system changes within Dal lake from 1859-2013 Class Name Aquatic vegetation Builtup Floating gardens Marshy land Plantation Water

1859 2.91 0.05 0.78 1.49 6.02 20.59 31.84

1903 1.35 0.06 0.82 1.44 3.99 23.98 31.64

1962 3.85 0.84 5.66

1972 8.23 0.68 1.1288

1979 9.42 0.80 1.39

1992 7.75 1.83 1.36

2001 8.75 2.10 2.52

2010 10.40 2.03 2.70

2013 8.64 2.02 2.89

3.63 13.84 27.82

3.16 13.19 26.40

12.41 24.02

13.10 24.04

10.68 24.04

8.91 24.04

10.50 24.04

Table-3: Land System changes in the immediate catchment of the lake from 1962-2013 Class Name Agriculture Aquatic Vegetation Builtup Fallow land Floating gardens Forest Cover Parks/Gardens Horticulture Plantation Water Total Area

Area (km2) 1962 1980 40.50 26.80 5.13 6.92 12.48 13.24 3.40 4.09 9.47 8.06 2.08 1.46 0.58 2.54 17.37 20.37 4.05 13.02 15.32 13.88 110.38

1990 21.42 5.21 20.23 2.69 6.34 1.52 3.34 20.84 15.01 13.75

2001 21.30 5.02 23.74 2.16 3.96 0.90 2.14 21.47 16.81 12.88

2005 13.82 4.79 31.97 1.38 3.15 0.97 1.23 23.52 17.69 11.86

2013 12.68 4.94 39.39 1.28 2.69 0.84 0.97 20.64 15.70 11.25

Accuracy assessment of 2013 land cover data N n ρ 26 24 92.31 12 11 91.67 28 28 100.00 6 6 100 17 16 94.12 8 8 100 16 15 93.75 34 32 94.12 31 29 93.55 5 5 100 183 174 95.08

Table-4: Current status of water quality of Dal Lake Min

Max

Mean

Std Dev

pH

6.8

9.4

7.98

0.78

Hardness (mgL-1)

40

300

156.67

47.53

Chloride (mgL-1)

15

30

21.57

5.25

TDS (mgL-1)

187

290

222.94

24.15

Alkalinity (mgL-1)

60

230

116.14

32.11

OPP (µgL-1)

10

100

45

29

NN (µgL-1)

200

1300

557

250

Table-5: MCA-based degradation status of Dal Lake

Low

Area(km2) 4.87

Medium

11.52

High

6.26

Severe

1.39

Total Lake area

24.04

Degradation Status

43