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使用水生生态系统模型预测浅水湖泊的生态系统状态变化:以丹麦 hinge 湖为例。

Predicting ecosystem state changes in shallow lakes using an aquatic ecosystem model: Lake Hinge, Denmark, an example.

机构信息

Department of Bioscience, Aarhus University, 8600, Silkeborg, Denmark.

Sino-Danish Center for Education and Research, University of Chinese Academy of Sciences, Beijing, 100049, China.

出版信息

Ecol Appl. 2020 Oct;30(7):e02160. doi: 10.1002/eap.2160. Epub 2020 Jun 11.

Abstract

In recent years, considerable efforts have been made to restore turbid, phytoplankton-dominated shallow lakes to a clear-water state with high coverage of submerged macrophytes. Various dynamic lake models with simplified physical representations of vertical gradients, such as PCLake, have been used to predict external nutrient load thresholds for such nonlinear regime shifts. However, recent observational studies have questioned the concept of regime shifts by emphasizing that gradual changes are more common than sudden shifts. We investigated if regime shifts would be more gradual if the models account for depth-dependent heterogeneity of the system by including the possibility of vertical gradients in the water column and sediment layers for the entire depth. Hence, bifurcation analysis was undertaken using the 1D hydrodynamic model GOTM, accounting for vertical gradients, coupled to the aquatic ecosystem model PCLake, which is implemented in the framework for aquatic biogeochemical modeling (FABM). First, the model was calibrated and validated against a comprehensive data set covering two consecutive 7-yr periods from Lake Hinge, a shallow, eutrophic Danish lake. The autocalibration program Auto-Calibration Python (ACPy) was applied to achieve a more comprehensive adjustment of model parameters. The model simulations showed excellent agreement with observed data for water temperature, total nitrogen, and nitrate and good agreement for ammonium, total phosphorus, phosphate, and chlorophyll a concentrations. Zooplankton and macrophyte coverage were adequately simulated for the purpose of this study, and in general the GOTM-FABM-PCLake model simulations performed well compared with other model studies. In contrast to previous model studies ignoring depth heterogeneity, our bifurcation analysis revealed that the spatial extent and depth limitation of macrophytes as well as phytoplankton chlorophyll-a responded more gradually over time to a reduction in the external phosphorus load, albeit some hysteresis effects still appeared. In a management perspective, our study emphasizes the need to include depth heterogeneity in the model structure to more correctly determine at which external nutrient load a given lake changes ecosystem state to a clear-water condition.

摘要

近年来,人们做出了相当大的努力,试图通过恢复富营养化、以浮游植物为主的浑浊浅水湖泊,使其成为一个以沉水植物为主的清水状态。各种简化了垂直梯度物理表示的动态湖泊模型,如 PCLake,已被用于预测这种非线性状态转变的外部营养负荷阈值。然而,最近的观测研究对状态转变的概念提出了质疑,强调渐变比突然转变更为常见。我们通过研究如果模型考虑到系统的深度依赖性异质性,是否会使状态转变更加渐进,即包括水柱和沉积物层中垂直梯度的可能性,来研究整个深度的变化。因此,我们使用 1D 水动力模型 GOTM 进行了分岔分析,该模型考虑了垂直梯度,并与在水生生物地球化学建模框架 (FABM) 中实现的水生生态系统模型 PCLake 耦合。首先,该模型使用涵盖丹麦浅而富营养化的 Hinge 湖两个连续 7 年周期的综合数据集进行了校准和验证。应用自动校准程序 Auto-Calibration Python (ACPy) 实现了对模型参数的更全面调整。模型模拟与水温和总氮以及硝酸盐的观测数据非常吻合,与铵、总磷、磷酸盐和叶绿素 a 的浓度也吻合良好。浮游动物和大型植物的覆盖率是为了本研究的目的进行模拟的,并且总体而言,与其他模型研究相比,GOTM-FABM-PCLake 模型模拟表现良好。与之前忽略深度异质性的模型研究相反,我们的分岔分析表明,大型植物和浮游植物叶绿素 a 的空间范围和深度限制对外部磷负荷减少的响应时间更加渐进,尽管仍然存在一些滞后效应。从管理角度来看,我们的研究强调需要在模型结构中包括深度异质性,以更准确地确定在何种外部营养负荷下,给定的湖泊会将生态系统状态转变为清水状态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1666/7583379/2d80ee9087ae/EAP-30-e02160-g001.jpg

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