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Propagation of uncertainty in ecological models of reservoirs: From physical to population dynamic predictions

机译:水库生态模型中不确定性的传播:从物理到种群的动态预测

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摘要

Ecological models are widely accepted in the scientific community as tools to describe, interpret and predict ecosystem functioning. However, to be used in environmental management, model uncertainties, their magnitude and sources need to be carefully assessed. A one-dimensional coupled physical-ecological model is applied to a deep Mediterranean reservoir (Lake Béznar) to determine whether or not the uncertainty existing in physical predictions affects ecological predictions, and, then to quantify this uncertainty. The sources of uncertainty include light penetration in the water column, inflow mixing and geometry, and boundary conditions at free surface. Uncertainty in the model results was evaluated following the procedures outlined in Beven (2001), based on Montecarlo simulations. At least during summer time, the largest sources of uncertainty in the physical predictions are associated to the input variables used to construct the surface (heat and momentum) boundary conditions. Uncertainties in the physical model propagate to the ecological results. Average chlorophyll-a concentration predicted by the ecological module in the water column, their standard deviations, and the timings of the successional changes in the algal community all vary depending on the magnitude of the error accepted in the physical predictions. Our results illustrate that the analysis and quantification of model uncertainty are fundamental to properly express model results and, consequently, to optimize monitoring programmes and guide management decisions.
机译:生态模型在科学界被广泛接受为描述,解释和预测生态系统功能的工具。但是,要用于环境管理,必须仔细评估模型的不确定性,不确定性的大小和来源。将一维耦合的物理生态模型应用于地中海深水库(贝兹纳湖),以确定物理预测中存在的不确定性是否会影响生态预测,然后对这种不确定性进行量化。不确定性的来源包括水柱中的光穿透,流入混合和几何形状以及自由表面的边界条件。根据蒙特卡洛模拟,按照Beven(2001)中概述的步骤评估模型结果的不确定性。至少在夏季,物理预测中最大的不确定性来源与用于构造表面(热和动量)边界条件的输入变量相关。物理模型的不确定性会传播到生态结果。水柱中生态模块预测的平均叶绿素a浓度,它们的标准偏差以及藻类群落相继变化的时间都取决于物理预测所接受的误差的大小。我们的结果表明,模型不确定性的分析和量化对于正确表达模型结果以及优化监控程序和指导管理决策至关重要。

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