A new model to estimate shallow lake nitrogen removal rate based on satellite derived variables

Abstract

Lake nitrogen (N) removal, mainly resulting from bacterial denitrification that converts nitrate (NO _3 ^− ) to gaseous N (N _2 ), is important for lake water quality and eutrophication control. However, quantifying lake N removal is challenging due to the high background atmosphere N _2 concentration and the heavy burden of field surveys, leading to a decoupling of watershed N management and water quality improvement. Here, we developed and validated an innovative nonlinear model for lake N removal rate estimation by linking the N removal rates with remote sensing-derived variables (chlorophyll- a , chromophoric dissolved organic matter, and lake surface water temperature). The model was validated in shallow eutrophic Lake Taihu in the Yangtze River basin and at the global scale. Based on the new N removal model, we estimated that an annual average of 3.21 × 10 ^4 t N yr ^‒1 was removed in Lake Taihu from 2011 to 2020, accounting for 53%–66% of the total lake N loading. The remaining N loading after denitrification removal in Lake Taihu would be approximately 2.37 mg N l ^‒1 , and 0.79 × 10 ^4 t N y ^‒1 of lake N loading still needs to be removed to meet the target of class IV water quality (1.5 mg N l ^‒1 ). This is the first study linking lake N removal in sediment microcosm incubations with reach-scale remote sensing derived variables, providing timely-much insights into lake N removal. This approach can be easily applied in other lakes with satellite derived data, to better understand lake N budget, drivers of eutrophication control, and watershed N management.

Keywords

• lake N removal
• remote sensing
• chlorophyll-a
• water quality
• watershed N management