Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data

Zihong Qin, Baozhen Ruan, Jian Yang, Zushuai Wei, Weiwei Song and Qiang Sun ()
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Zihong Qin: South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China
Baozhen Ruan: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Jian Yang: South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China
Zushuai Wei: South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China
Weiwei Song: South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China
Qiang Sun: South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China

Sustainability, 2022, vol. 14, issue 24, 1-15

Abstract: Chlorophyll-a plays an essential biochemical role in the eutrophication process, and is widely considered an important water quality indicator for assessing human activity’s effects on aquatic ecosystems. Herein, 20 years of moderate resolution imaging spectroradiometer (MODIS) data were applied to investigate the spatiotemporal patterns and trends of chlorophyll-a concentration (Chla) in the eutrophic Lake Taihu, based on a new empirical model. The validated results suggested that our developed model presented appreciable performance in estimating Chla, with a root mean square error (MAPE) of 12.95 μg/L and mean absolute percentage error (RMSE) of 29.98%. Long-term MODIS observations suggested that the Chla of Lake Taihu experienced an overall increasing trend and significant spatiotemporal heterogeneity during 2002–2021. A driving factor analysis indicated that precipitation and air temperature had a significant impact on the monthly dynamics of Chla, while chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were important driving factors and together explained more than 81% of the long-term dynamics of Chla. This study provides a 20 year recorded dataset of Chla for inland waters, offering new insights for future precise eutrophication control and efficient water resource management.

Keywords: chlorophyll-a; spatiotemporal dynamics; long-term trends; driving factors; remote sensing; Lake Taihu (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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