STUDY & MODELLING OF WATER QUALITY IN INLAND WATER BODIES AND ITS INTERACTIONS WITH LAND USE.

Abstract

Water is an essential resource for sustaining human life. The ever-increasing demand for clean water from the growing human population and other anthropogenic needs have pushed several regions into highly water- stressed areas. Diminishing freshwater resources and high amounts of water contamination are increasing the threat of severe water scarcity even further. Since the total available freshwater resources cannot be modified, the options are to maintain and/or improve water quality in water bodies and increase the reusability of the water. The former can be achieved by continuous monitoring of the water quality in the water bodies and identifying and regulating the source of contaminations that pollute these water bodies. So, water quality in rivers and large inland water bodies are being measured occasionally through in-situ approaches. While these do provide valuable inputs, they are not sufficient to understand the underlying causes for such changes in water quality, including the seasonal and other drivers of contamination. Hence, there is a need to monitor water bodies, especially the lakes and reservoirs, regularly. Constant inflows and long storage time of water with limited or no proper outflow make these water bodies much more susceptible to the adverse effects of contaminants than rivers. This study takes a three-pronged approach to study and understand the water quality in inland water bodies – first, by developing and evaluating a methodology for monitoring nutrient contamination and its changes in inland water bodies using remote sensing satellite data; second, to hypothesize and study the probable source of the contamination as land use and its practices; and three, to understand, analyze and model the interactions between the land use changes in the contributing watershed and the water quality to help improve the regional- scale decision making capabilities. The case study area is Nagarjuna Sagar (NS) reservoir in the Krishna River basin, one of the largest inland water bodies in India. NS is a multipurpose dam and inland water body (reservoir) with a spatial spread of 285 Km2 and a catchment area of 215,000 Km 2 . In addition to Irrigation and Power generation, it is also a primary source of drinking water to Hyderabad, a large metropolis in India with nearly 10million population. All these make it essential to study and maintain the water quality of the NS water body. While the literature suggests that the remote sensing approach can help monitor the nutrient content in the water body, most studies and analyses have been done for oceans, seas, and enormous water bodies. Their application to inland water bodies is still a challenge due to the spatial and spectral limitations of the remote sensing sensors. Hence, the study explored the effectiveness of the remote sensing data in studying the inland water body nutrient contamination. Here, the presence and spatial spread of Chl-a, a remote sensing detectable agent, present in the photosynthetic organisms such as algae, phytoplankton, and cyanobacteria (AP) was used as a proxy for monitoring nutrient contamination in water bodies. The unique spectral signature of Chl-a was used to detect and identify its spatial spread across the water body. For this process, a decision-tree-based classification technique was developed that exploits the spectral response peaks within the region of 705nm to 860nm for each pixel to detect and classify them into No to Low contamination, Moderate and High Chl-a levels. The spatial spread of moderate and high Chl-a areas was used to understand the severity of the contamination in the water body. Further, through meta-analysis and comparative study, it was found that MODIS and Sentinel satellite data are best suited for the detection of Chl-a content in the water body using the spectral signature-based method. While various works have well-studied MODIS data, Sentinel data is relatively recent and needs to be tested for its sensitivity towards Chl-a across various concentrations of Chl-a in water bodies situated in different environments. Since the NS water quality data is not available, it is prudent to use the water quality data from earlier published studies to evaluate the method. As part of this, Lake Taihu (TL, with an area of 2250 Km 2 ) and Lake Bebe (LB, with an area of 24 Km 2 ) with different concentrations of Chl-a were used to test the sensitivity of the method. The Manasarovar Lake (ML) (area of 411 Km 2 ) was used as a zero baseline case as it is free of any Chl-a contamination to check if the method was working without any false positives. The results from TL showed that more than 50% of the water body indicates the presence of Chl-a content which matched with the data given in the literature. Similarly, the method could not detect any Chl-a content in the ML, which agrees with the literature but showed some limitations in the case of LB. On applying the technique to study the contamination in the case study region of NS, the results indicated an increase in contamination from 21 Sq Km in 2005 to 205 Sq Km in 2018, showing nearly a ten times rise in contamination spread. In NS, large areas are covered by moderate Chl-a content with some patches of high concentrations. If necessary steps are not taken, then there is a risk that NS can get highly polluted like TL, a similar water body. viIt is important to understand what is causing the rise in the contamination levels in the water body. So, this study assessed the potential causes and hypothesized that the land use and land use practices present in the contributing watershed could be majorly responsible for the nutrient and contaminant inflow into the inland water bodies. The land use in the entire contributing watershed of all four different water bodies in different land use settings was analyzed, and the results showed that water bodies TL and NS, whose upstream with a larger share of urban and agricultural land use areas, resulted in higher contamination levels. In the case of NS, it is largely surrounded by agricultural land use, and it could be the main reason for the increasing nutrient contamination in NS. To understand this better, a temporal comparative study of land use change and land use intensification practices like fertilizer application for the period 2005 to 2015 in the NS watershed was mapped and compared against the maximum Chl-a spatial spread observed in the water body measured using the MODIS data. The results show that over this period, the agricultural land use in the watershed increased by 40% or more than 1000 Sq Km in the area, and the fertilizer consumption increased by 50% in the same period. Correspondingly, the Chl-a spatial spread in the water body increased five times, from 21 Sq Km in 2005 to 106 Sq Km in 2015. This shows that there is indeed a strong relationship between the intensification of anthropogenic land use and its practices on inland water body nutrient contamination. In addition, the Sentinel data based analysis of Chl-a spatial spread area from 2016 to 2018 revealed that the contamination spread has further increased to 205 Km 2 . Based on the land use and water body interactions established in the earlier part, the study adopted the SWAT hydrological model to quantify and assess the impact on the contamination levels for changing land use scenarios. The SWAT model was set up for the entire Krishna River basin. The river flow (in Cumecs) and in-stream nitrate concentrations (in mg/L) were calibrated and validated with an R 2 value of 0.71 and 0.70 respectively for the entire basin for 2005 to 2018 using the data from the Wadenapally gauge station. Since NS contributing watershed is a part of the river basin, the flow and in-stream nitrate for this watershed are considered calibrated for further analysis. SWAT model was then used to obtain the Total Nitrogen (TN) output and Chl-a content (Kg) from the NS watershed into the reservoir from 2005 to 2015 under current existing land use. Further, to quantify the extent of the impact that land use and its practices can have on contamination levels, different land-use scenarios were developed and given input to the model. The four land use scenarios are - (1) Business As Usual scenario with and without fertilizers (BAUF and BAUNF), (2) Natural Forest Scenario (NFS), and (3) Anthropogenic Land use Scenario (ALS). While BAUF and BAUNF show the variations in nutrient output for current land use practices under varying hydrological conditions, NFS and ALS provide the lower and upper bound of the contaminants that could enter the water body. The simulations showed that the maximum yield of TN and Chl-a under the BAUF scenario was around 800 and 107 tonnes, respectively. While for BAUNF scenario, the maximum production of contaminants decreased by three times and 0.25 times that of the BAUF scenario, respectively, indicating that the application of fertilizers has a significant role on the water quality of the inland water body as it influences the production of the contaminants from the watershed. Thus, establishing the hypothesis proposed in this study. While the potential scenario analysis of NFS delivered the lowest TN and Chl-a content, the ALS scenario displayed a meteoric increase in the production of contaminants compared to BAUF. The maximum Chl-a output from the ALS scenario increased by around 6.5 times of the maximum Chl-a production simulated for BAUF conditions and TN increased by about two times. In addition to this, the study compared the annual totals of TN, Chl-a, and Nitrate content being produced from the contributing watershed with the highest Chl-a spatial spread observed from the remote sensing data for the 2016 to 2018 period. This clearly shows that as the contamination input from the watershed increases, the Chl-a spatial spread in the water body also increases proportionally. This indicates that the model predictions and remote sensing based observations have a good agreement and are correlated, but establishing a direct relationship will need further research. But, these results and analysis do support the possibility of developing a water quality monitoring system for inland water bodies that derives inputs from both the model and remote sensing observations. Thus, this study demonstrated using remote sensing data to monitor the inland water body contamination continuously using Chl-a spatial spread as a quality indicator obtained from the MODIS and Sentinel satellite images. It was also able to establish that the land use and the land use practices in the contributing watershed are mainly responsible for the changing levels of nutrient contamination in these land-locked water bodies, viiespecially due to the excessive nutrient yield from the contributing watershed caused due to increased agricultural land use and excessive fertilizer application. Using a hydrological model SWAT and correlating its results with the remote sensing derived observations provides an opportunity to develop water quality monitoring systems to keep a watch on the deteriorating conditions of the water bodies and provide valuable inputs to the decision-makers to understand the regional land-water interactions. Future work in this direction can look at integrating these multiple approaches over a geospatial platform.