Description of Study Area
The Dal Lake is located at an altitude of 1583m above mean sea level, in Srinagar city at 34o5′ N and 74o50′ E coordinates and is also the second largest lake in the union territory of Jammu and Kashmir (Rather and Dar., 2020). Four basins mainly encompass the Dal Lake comprising of Hazratbal basin, Nishat basin, Gagribal basin and Nigeen basin. Although among these, the Nigeen basin is taken as a separate lake as it is also linked and connected to Gilsar Lake through Nallah Amir Khan channel Unni K.S. (2002) and thereby in this study, Nigeen basin is not included. The total catchment area of Dal Lake is 337.17 sq km. which formulates about twenty times more than the area of the lake. A large perennial inflow channel, Telbal nallah, feds the lake normally, it is a stream which comes from the Marsar Lake high up in the mountains draining the largest sub-catchment area of about 145 km2 and puts up about 60–70% of the total inflow to the lake. There are also other smaller streams around the shore line that feeds the lake viz., Boutkal, Merakhsha nallah, Peshpaw nallah, etc., in addition to some contribution and benefaction from the groundwater. At Harzratbal Basin, the Telbal nallah with other small streams enters the lake, and finally from Gagribal basin drains into the river Jhelum. Dal lacks in depth, is a shallow, multi-basin lake with an area of about 25.76 sq. km, out of which open water area is not more than 16.78 sq. km. According to the recent estimations, each year about 327 million cum of water flow into the lake ecosystem out of which 270.34 million cum leave the lake through two outflow channels, and 25.92 cum are drawn and used for drinking purposes and the rest is lost through seepage, evapotranspiration, and suction dredging (Vision Document., 2018).
To understand and keep the track of the variations in water quality of Dal Lake in real-time, the Lakes and Waterways Development Authority, Government of Jammu & Kashmir (LAWDA) has built 11 water quality monitoring stations across the lake to collect and examine water samples on monthly basis. Increased comprehensive and in-detail particulars of these monitoring stations and their locations are laid out in Table. S1, (supplementary data)
Specimen measurement and collection of data:
The water quality data used in this project was procured from head of research laboratory Lakes and Waterways Development Authority (LAWDA) with official consent. The collected water samples are obtained from 11 water quality monitoring locations, on monthly basis, from September 2017 to August 2020 by officials of the research wing of LAWDA. The collection and the procedure used for sample assessment were according to the Standard Methods for the Examination of Water and Wastewater (APHA., 1995). Moreover, using a portable GPS system, geographic coordinates of sampling sites were noted down. A total of 15 parameters were studied which include pH, Electrical Conductivity (µS/cm), water temperature (WT, °C), Turbidity (NTU), dissolved oxygen (DO, mg/L), Chemical oxygen demand (COD, mg/L), Ammoniacal-Nitrogen (NH4-N, mg/L), Nitrate-Nitrogen (NO3-N, mg/L), Total Phosphorus (TP, mg/L), Chlorides (Cl, mg/L), Ortho-Phosphate (OP, mg/L), Iron (mg/L), Calcium (Ca, mg/L), Sulphates (SO4, mg/L) and Magnesium (Mg, mg/L). For each month, the sampling dates at all stations were pick out and organized by research team, and was established on weather conditions, making certain that the water samples collected were specifically done on clear or overcast days in order to cut back on the intercession of rainfall and other precipitations with subsequent data. The samplings were acquitted at the center and along the boundaries in each basin of the lake with 15-20cm depth at each location, below the water surface. EC, pH and WT were determined on-site with multi-parameter instruments. However, a separate DO bottles was used for fixing the DO on site ( modified Winkler’s method) and were kept in travel ice boxes filled with ice packs (0–4°C). Further, for remaining parameters samples were collected in plastic bottles wherein these bottles were pre washed (> 750 mL/sample) and within 6 hours moved the samples straight away to the nearby situated research laboratory for additional examination. Permanent markers were used to label the site description on the bottles for all the samples in order to prevent misjudgment. More details of the chemical methods and instruments used and operated during analysis are recorded in Table.S2, (supplementary data).
Water Quality Index
The Eq. (1) is used to study and analyze the calculations for Water Quality Index, that was filtered and suggested by (Pesce and Wunderlin in 2000) as follows:
where n is used to show the aggregate parameters involved in the research, Ci is used to depict the standardized value of ith parameter, and weight of ith parameter is denoted by Pi. The value of Pi varies from 1 to 4 depending on the effect of parameter on water quality (Table.3) and in previous studies, these values have been substantiated and verified (Kannel et al., 2007; Zhao et al., 2013;Wu et al., 2018). WQI value varies from 0 and 100, where the good water quality conditions are being represented by the high values. Based on the WQI scores there are five levels on which water quality is classified : a) bad (0–25), low (26–50), moderate (51–70), good (71–90) and excellent (91–100) (Dojlido et al., 1994;Jonnalagadda and Mhere., 2001). The WQI value was calculated every month at each monitoring station and was averaged down to obtain a final value. The WQImin model, based on the critical parameters, is formed in order to promote easy and low-cost water quality assessment approach for Dal Lake that is selected by the stepwise multiple linear regression analysis and then calculated using Eq. (1). The parameter weights are fully considered here in the WQImin model as opposed to traditional non weighted models since it performs better which are verified by studies ( Wu et al., 2018;Xizhi et al., 2020). The seasons autumn, winter, spring and summer as defined in this study analogous to periods from September to November, December to February, March to May, and June to August, respectively. Thus, the calculation for the seasonal WQI values for each basin of the lake was also carried out.
Data analysis framework
In the previous studies, in order to examine the water quality trends, the Mann-Kendall trend analysis is broadly applied (walker., 1991;Helsel and Hirsch., 1992;Jaagus., 2005;Misaghi et al., 2005). Computational procedure of the Mann-Kendall analysis was explained in ( Mann., 1945) in detail. In Figure-2, different trends of water quality parameters have been exemplified from the results of Mann-Kendall test, as seen in this study. To test the normal distribution of water quality data, one-sample Kolmogorov-Smirnov test was carried out, and also bartlett's test was done to check the homogeneity of variance prior to the statistical analysis. One-way (ANOVA) was carried out so as to decide if there are significant spatial variation in water quality parameters Table.2 (Varol., 2020). A statistical software R was used to carry out both the M.K test as well as ANOVA, via specific library functions. In this study, the WQImin models for the Dal Lake were set and established using the following steps: (1) In order to obtain the critical water quality variables for WQImin model, WQI value and Ci for every month at each monitoring station from (2017–2018) to (2018–2019) were appraised as “training data”. (2) then WQImin was tested and evaluated for each station in (2019–2020) through coefficient of determination (R2), Root Mean Square Error (RMSE) and the Percentage Error (PE) (Nong., 2020). To encounter homogeneity of variance and normality conditions the data was log transformation (i.e., log(x + 1)) before stepwise multiple linear regression analysis. OriginPro 2019 was used for the graphical abstracts.