Valuation of water purification service of Renukawetland, India: A Ramsar site

Wetlands are very important ecosystems from the ecological, productivity and
 conservation perspective. Economic valuation of ecosystem services provided
 by wetlandsquantifies the various benefits derived from wetlands and puts a
 value on their conservation. One such wetland of importance is the Renuka
 wetland, which is a natural wetland located in the Western Himalayas, in the
 State of Himachal Pradesh of India. The article aims to assess and evaluate
 the wetland for its water purification service. The study is based on
 multisource data and conventional evaluation method. The results show that
 the average depth of the Renuka wetland is 6.01 m and average volume was
 determined as 1072530.176 m3. The economic value of water purification
 service for Renuka wetland is estimated to be INR 31.9 million(0.44 million
 USD) thus identifying the Renuka wetland as a significant healthy ecosystem.
 The water purification value itself advocates its proper management and
 conservation.


INTRODUCTION
Wetlands are one of the highly productive ecosystems of the world, accounting for only about four per cent of the earth's ice-free land surface (Bassi et al. 2014). They provide numerous goods and services to people living in their periphery, as well as people living outside the wetland area (Barbier et al. 1997) including regulating climate and global nitrogen cycle, purifying water, recreational and cultural services, flood regulation and providing habitat for wildlife (Z h a n g et al. 2017, Sharma et al. 2015. Wetlands are very important ecosystems from the ecological and conservation perspective. Although increasing in recognition, the need for their conservation continues to be lost throughout the world (Turner et al. 2000).
Water purification service of wetlands is defined as the capability of wetlands to remove sediments, nutrients, and other contaminants from water, which leads to the widespread utilization of wetlands for wastewater treatments. Wetlands contributes to improvement of water quality by immobilizing various pollutants and nutrients and plays important role in geochemical cycling (Radeva et al. 2019). Economic valuation of ecosystem services provide a mode for measuring the various benefits from wetlands and the costs for conservation (G ro o t et al. 2012). Monetary valuation can interpret the information obtained through qualitative and quantitative indicators into monetary figures. For example, the wastewa-ter purification service provided by healthy wetlands can be valued in monetary terms through the equivalent cost of a wastewater treatment plant that would provide a similar service (R u s s i et al. 2013). With such understanding, degradation can be avoided and reduced (Z l a t i c et al. 2015). Further, these values can help policy-makers and stakeholders to take informed decisions. Thus, valuation can be an approach to assess the importance of wetlands. Water purification service of wetlands varies with different wetland types (Zhang et al. 2017). One such wetland of importance is the Renuka wetland, which is a natural wetlands located in the Western Himalayas, in the State of Himachal Pradesh of India. Owing to the compliance of criteria 3 & 4 of Ramsar convention, it was declared a Ramsar site on 8 th November 2005 because of its unique biodiversity and ecological character. This natural wetland is believed to provide various ecosystem services like habitat for flora and fauna, water purification, nutrient cycling, cultural and recreational value (Gaur 2020). The aim of this study is to evaluate the wetland with respect to existing water quality of the lake.
It focuses on the assessment and estimation of the economic value of water purification service by Renuka wetland based on multisource data and conventional evaluation method. The evaluation results might help decision-makers to comprehend the status of the Renuka wetland and provide a scientific guidance for making strategic decisions for conserving the wetland Therefore; the study will provide a reference point to frame conservation strategies and wetland protection policies for the lake.

Study Site
Renuka Lake, a natural wetland with an extended pond known as Parshuram Tal, is located in the Lesser Himalayas in the Sirmaur district of the State of Himachal Pradesh, India ( Figure  1). It is fed by a small stream arising from lower Himalayan hills. Geographical coordinates of the lake are 31°36'34'' N, 77°27'8'' E and is situated at an elevation of 650 m above mean sea level. The The surface area and perimeter of the Renuka Lake are 17.84 hectare (ha) and 3438.39 m, respectively. According to the rainfall data of India Meteorological Departmentfrom 1979 -2018, the average rainfall varied from 279 mm (minimum) -728 mm (maximum) during July to September (rainy season). The outlet of the wetland is towards Parshuram Tal (pond) from where water outflows into the Giri River in the west. The slopes around wetland are covered with dense sub-tropical forests.

Data
Multi-temporal Landsat image and high-resolution image from Google Earth is used for mapping of Renuka Wetland to determine the area of the lake. The LANDSAT8/OLI TIRS satellite imagerydatawas collected from the United States Geological Survey (USGS) Earth Explorer's image database (https://earthexplorer.usgs.gov/). USGS providesradiometric and geometric corrected-Landsat images. In order to ensure visual quality and accuracy in mapping, only cloud-free image were used. Taking both image quality and availability of images into account, post-monsoon sea-son image of December 2019 with low cloud cover were selected to extract the information of lake water surface area. The details of the Landsat data used are given in Table 1.
The average depth of the Renuka lake is determined by the Bathymetry data generated by Diwate et al. 2020. According to the bathymetry survey results given by D i wate et al. 2020, the depth of Renuka wetland varies from a minimum of 0.03 mto a maximum of 13m. Based on bathymetry contours, the wetland area is classified in to four depth zones as given in Table 2. The average depth of the Renuka wetland was determinedby the weighted average method using Bathymetry data (Table 2) Data pertaining to water quality were obtained from the annual data provided by the Central Pollution Control Board (CPCB), Ministry of Environment, Forest and Climate Change, Government of India under the National Water Quality Monitoring Programme and also from the research article by Ku m a r et al. 2019.CPCB's primary water quality criteria for Class C water body (as designated best use of drinking water after conventional treatment and disinfection)and recommended water quality parameters for different uses by the Bureau of Indian Standards (BIS) (Standard IS 2296:1992 are used in this study (Table 3).

Surface Area and Volume
There are several methods for lake mapping using remote sensing analysis, such as the single band threshold value method, the band ratio method, the water index method and the Normalized Difference Water Index (NDWI) method. We have used the Modified Normalized Difference Water Index (MNDWI) for detecting water body from multispectral Landsat imagery in ArcMap 10.3 platform. Amongst other indices, this method, based on the spectral water index, represents a better approach for delineating wetland. MNDWI is a modified version of the NDWI proposed by the McFeeters 1996.MNDWI was developed by the Xu 2006 that is based on the principle that the water bodies have a stronger absorbability and built-up class has greater radiation in the Shortwave Infrared (SWIR). MNDWI uses green and SWIR bands and defines as: The MNDWI index is most suitable for mapping water bodies.The water bodies generally absorb more SWIR wavesand have greater positive values in MNDWI while, soil, vegetation and built-up classes reflect more SWIR wavesthan green light and have smaller negative values. The MNDWI mostly identifies the presence of water bodiesmore effectively (S u n et al. 2012).To achieve accuracy, best threshold value was setmanually. MNDWI had been widely applied to produce water body maps at different scales in the last few years (Du et al. 2014, Ro kn i et al. 2014. The pre-processing of the multispectral images wascarried outbefore performing the MNDWI. Reflectance values were extracted from the Digital Number (DN) for the SWIR and thermal infrared radiometer (TIR) spectral ranges for Landsat 8 images. Water body exhibits a fine smooth texture in compare to the surrounding rough texture of the hills or land. Due to its high contrast values, wateris one of the distinguishedfeature to delineate on a satellite image. In combination with various image enhancements, visual pattern recognition is also considered in this analysis. The water body tone is light/dark blue or black in the satellite images. The water surface area of wetlandwas calculated using steps given in Figure 2.
After extracting reflectance values from the digital number image,the MNDWI wereperformedon the image to map the wetland. Water features were automatically mapped using the MNDWI with themanual threshold of 0.09 and the obtained raster data were converted into polygon shape file. Due to spectral reflection, some shadow areas were misclassified as water. Misclassified water boundarieswere manually corrected through the visual interpretation of data. The obtainedwetland boundary vector filewas then converted to KML format and loaded into Google Earth Pro for visual examination.
To determine the average volume of the wetland weighted average method was used to find the average depth of the wetland by using data given in Table 2. Subsequently, the average depth was multipliedwith the water surface area of wetland to calculate the average volume of the wetland.

Water Purification service
Water purification service is defined as the improvement role of wetlands on water quality (Zhao et al., 2016). Wetlands are well known for their ability to remove sediments, nutrients, and other contaminants from water, which leads to the widespread utilization of wetlands for wastewater treatments (A l m u kta r et al. 2018). Physicochemical parameters of any water body plays a very significant role in maintaining the various life forms (Ku m a r and P u r i 2012). The water purification service is calculated as the concentration of Biological Oxygen Demand (BOD), Dissolved Oxygen (DO), Nitrate (NO 3-), Sulphate (SO 4 2-) and Total Dissolved Solids (TDS) pollutants removed by the wetlands.
Economists have resorted to use the cost of replacing the service as a valuation approach tovalue those ecosystem services, which are unique to a specific ecosystem and are difficult to value. This approach does not measure benefit derived from the wetland's waste treatment service directly. It instead estimatesit using the cost of providing the ecosystem service that people value (D i c k i e 2003). We have used the replacement cost method (Farber et al. 2002) to obtain the value of water purification service (V w ). S h a b m a n and B at i e , 1978 suggest that this method is reliable for estimating ecological services if the alternative considered during evaluation provides the same services or there are substantial evidence that the service would be demanded by society provided at least-cost alternative or both. The economic value of wetland's water purification service calculated through the removal cost of pollutants using the following equations 2 and 3: (2) (3) Where, W p is the amount of total pollutant removed by a wetland (t), R pi is the pollutant removal capacity of a wetland (mg/L), W v is the amount of stored water by a wetland (L),S i is the standard concentration of different pollutants (mg/L), P i is the concentration of pollutants (mg/L), H represents the average water depth of the wetland (m) and A is the area of the wetland (m 2 ); V w is the total economic value of water purification service of wetland (INR),andP t is the treatment cost of pollutants (INR/t).
The removal cost of water pollutants was adopted through the primary survey of experts and personal communication with few water treatment plant authorities. Treatment cost of water in treatment plants was 20000 INR/ton.

Surface area and Volume
Water surface area and perimeter of the Renuka wetland was determined as 178457.6m 2 and the 3438.39m respectively. Figure 3 shows the LANDSAT False Color Composite (FCC) and MNDWI images of Renuka Wetland. Average depth of the Renuka wetland is 6.01m shown in Table 4.The average volume of the wetland was determined as 1072530.176 m 3 .

Water Purification service
The data of water quality acquired from secondary sources (Table 3) shows that the water quality standards for all the parameters i.e. DO, BOD, NO 3-,SO 4 2 and TDS are within permissible limits. Estimates shows that the Renuka wetland can remove the 1599.58 tonnes of pollutants BOD, DO, NO 3-, SO 4 2and TDS (Table 5). Accordingly, the economic value of water purification service for Renuka wetland is estimated to be INR 31.9 million (0.44 million USD) with an average value of 179.3 INR/m 2 .
It is observedthat the economic value of the water purification service is directly dependson the volume of the wetland. The water quality within permissible limits indicates the proper functioning of water purification process in the wetland. It also indicates that the wetland needs to be conservedbecause it holds great ecological and economical importance. The study suggests that replacement cost method may be used for assessing economic value of the wetland's water purification service. The estimation by suggested method provides substantial evidence regarding the value and importance of natural resource. The results also reflects on the need of conservation of wetlands as it is playing an important role in water purification both ecologicallyas well as economically.

CONCLUSION
Wetlands provides many important ecosystem services to humans, but at the same time wetlands are ecologically sensitive and adaptive systems (Sunkara et al. 2002). Therefore, there isneed to draw attention towards the preparation and operation of sustainable management strategies for wetlands. The study identifies Renuka wetland as significant healthy ecosystem and needs proper management and conservation because of its contribution to water purification services. Therefore, there is need to value such ecosystems irrespective of the non-existence of economic market for ecosystem services. The study suggests that the combination of economic evaluation methods along with remote sensing based approach such as MNDWIcan help in identifying economic value of wetland and other natural ecosystems. Overall, evaluation of the water purification services of wetland ecosystems will help the policymakers forstrategizing thesustainable development programs.