No-linear dynamics of lake ecosystem in responding to changes of nutrient regimes and climate factors : Case study on Dianchi and Erhai lakes, China

Chen, Q. and Wang, S. and Ni, Z. and Guo, Y. and Liu, X. and Wang, G. and Li, H. (2021) No-linear dynamics of lake ecosystem in responding to changes of nutrient regimes and climate factors : Case study on Dianchi and Erhai lakes, China. Science of the Total Environment, 781: 146761. ISSN 0048-9697

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Abstract

As different algae growths responding to a set of nutrients can occur under different conditions, the nutrient load management based on the relationship between chlorophyll a (Chla) and total phosphorus (TP) alone may not always effective for lake algae bloom control. It is not clear whether the lagged response of algae to reduced nutrients related to the utilization efficiency of algae to phosphorus (UEAP), and how UEAP could response to climate and water quality factors. Here we analyzed over 20 years monitoring data in two lakes with similar geology but different nutrient levels by using statistical and modeling methods. The aim was to reveal the impact of UEAP on lake algae dynamics and the driving factors of UEAP changes. The results showed that UEAP is one of the key factors affecting algae dynamics, the incorporation UEAP and its driving factors achieved greater modeling reliability. UEAP, Nitrogen phosphorus ratio (NPr) was the key driving factor in Dianchi Lake, while total nitrogen (TN) and air temperature (AT) were the key driving factors in Erhai Lake. The changes of nutrients and climate drove UEAP into the paralysis or sensitive phase depending on lake specific factors and conditions. This correlated to algae density dynamics, in particular to those characteristic of algae growth thresholds. The future trend of climate change will continue to promote the increase of UEAP in both lakes, but severer in Erhai Lake. The key finding here is of the value of a proxy index (UEAP) for phosphorus utilization was associated with the lagged response of algae to nutrient reduction. We demonstrated the related modeling procedures with two-function variable (UEAP) of both prediction and response can predict the trend of algae growth and determine the states of the lake ecosystem. Hence, the approaches are of great value for lake management policy making.