Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine
Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in wa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2193 |
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| author | Deepakrishna Somasundaram Fangfang Zhang Sisira Ediriweera Shenglei Wang Ziyao Yin Junsheng Li Bing Zhang |
| author_facet | Deepakrishna Somasundaram Fangfang Zhang Sisira Ediriweera Shenglei Wang Ziyao Yin Junsheng Li Bing Zhang |
| author_sort | Deepakrishna Somasundaram |
| collection | DOAJ |
| description | Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (Z<sub>SD</sub>) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. Z<sub>SD</sub> varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean Z<sub>SD</sub>. Notable intra-annual variations were also indicating the highest and lowest Z<sub>SD</sub> during the north-east monsoon and south-west monsoon, respectively. The highest Z<sub>SD</sub> was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on Z<sub>SD</sub> changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the Z<sub>SD</sub> of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies. |
| first_indexed | 2024-03-10T10:42:40Z |
| format | Article |
| id | doaj.art-24cea6ecd5d745db9c9cb565b1cad0da |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:42:40Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-24cea6ecd5d745db9c9cb565b1cad0da2023-11-21T22:47:55ZengMDPI AGRemote Sensing2072-42922021-06-011311219310.3390/rs13112193Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth EngineDeepakrishna Somasundaram0Fangfang Zhang1Sisira Ediriweera2Shenglei Wang3Ziyao Yin4Junsheng Li5Bing Zhang6Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaDepartment of Science and Technology, Faculty of Applied Sciences, Uva Wellassa University, Badulla 90000, Sri LankaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAddressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (Z<sub>SD</sub>) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. Z<sub>SD</sub> varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean Z<sub>SD</sub>. Notable intra-annual variations were also indicating the highest and lowest Z<sub>SD</sub> during the north-east monsoon and south-west monsoon, respectively. The highest Z<sub>SD</sub> was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on Z<sub>SD</sub> changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the Z<sub>SD</sub> of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.https://www.mdpi.com/2072-4292/13/11/2193Google Earth EngineLandsat 8quasi-analytical derivationSecchi disk depthSri Lankawater transparency |
| spellingShingle | Deepakrishna Somasundaram Fangfang Zhang Sisira Ediriweera Shenglei Wang Ziyao Yin Junsheng Li Bing Zhang Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine Remote Sensing Google Earth Engine Landsat 8 quasi-analytical derivation Secchi disk depth Sri Lanka water transparency |
| title | Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine |
| title_full | Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine |
| title_fullStr | Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine |
| title_full_unstemmed | Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine |
| title_short | Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine |
| title_sort | patterns trends and drivers of water transparency in sri lanka using landsat 8 observations and google earth engine |
| topic | Google Earth Engine Landsat 8 quasi-analytical derivation Secchi disk depth Sri Lanka water transparency |
| url | https://www.mdpi.com/2072-4292/13/11/2193 |
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