Suspended particulate matter (SPM) in the Baltic Sea -: New empirical data and models
Suspended particulate matter (SPM) in the Baltic Sea -: New empirical data and models
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DOI：
10.1016/j.ecolmodel.2005.03.015
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发表时间：
2005-11-25
影响因子：
3.1
通讯作者：
Eckhéll, J
中科院分区：
文献类型：
--
作者：
Håkanson, L;Eckhéll, J
这项工作提供了波罗的海悬浮颗粒物（SPM）的新数据（来自称为波罗的海的部分）。以前关于波罗的海和其他海洋区域的悬浮颗粒物的报告非常稀少，尽管悬浮颗粒物在水生生态和管理方面具有根本重要性，因为它调节污染物的运输，影响水的透明度和浮游植物的生产以及浮游细菌的生产，因此也影响次级生产（浮游动物和鱼类）。显然，在风暴事件期间收集海上数据的要求非常高，本文介绍了1999年和2001年从5个地点和17个采样点收集的关于温度、盐度、暴露、深度和风（也在风暴期间）的梯度数据。这些数据已被用来解决与SPM的变异性有关的问题，因此也是SPM模型的预测能力。SPM的特征变异系数（CV =标准差/平均值）在各点间和各点内都很高，分别为0.69和0.67。使用统计方法，影响SPM变异性的因素已被量化和排名和回归分层和混合条件下的SPM。分层条件的模型基于以下变量：（1）暴露（风引起的波浪影响的量度），（2）风速低于5 m/s的天数，以及（3）平均盐度。以下因素对于站点内的垂直SPM变化是重要的：（1）水温和（2）距底部的距离。SPM随着温度的升高而增加（与产量增加和分层有关），随着水深、风暴露、盐度和平静天数的增加而减少。对此原因进行了讨论。调节这些地点之间和内部SPM变化的因素也应适用于其他海洋区域。然而，其他领域的研究不能证伪这些经验结果，但它们可以澄清这些模型的边界条件，并提高我们对SPM调节因素的认识。SPM的这一模型主要是作为更全面的模型中的一个子模型使用，其目的是预测关键的生态系统变量，例如，鱼类毒素或生物功能群的生产和生物量。我们还引入并激发了一个新的概念“海岸聚焦”，类似于湖泊中的“沉积物聚焦”。(c)2005 Elsevier B. V.保留所有权利。
This work presents new data on suspended particulate matter (SPM) in the Baltic Sea (from the part called the Baltic proper). Previous reports on SPM from the Baltic Sea and other marine areas are very scarce in spite of the fact that SPM is of fundamental importance in aquatic ecology and management since it regulates the transport of pollutants and influences water clarity and phytoplankton production as well as bacterioplankton production, and hence also secondary production (of zooplankton and fish). It is, evidently, very demanding to collect data at sea during storm events and this paper presents data from 5 sites and 17 sampling occasions in 1999 and 2001 collected along gradients concerning temperature, salinity, exposure, depth and winds (also during storms). The data have been used to address problems related to the variability of SPM, and, hence also the predictive power of models for SPM. The characteristic coefficients of variation (CV = standard deviation/mean value) for SPM among and within these sites are very high,0.69 and 0.67, respectively. Using statistical methods, the factors influencing the SPM-variability have been quantified and ranked and regressions for SPM under stratified and mixed conditions presented. The model for stratified conditions is based on the following variables (1) exposure (a measure of wind-induced wave influences), (2) number of days with wind speeds lower than 5 m/s, and (3) mean salinity. The following factors are important for the vertical SPM-variability within sites (1) water temperature and (2) distance from the bottom. SPM increases with increasing temperatures (related to increased production and stratification) and decreases with increasing water depth, wind exposure, salinity, and with the number of calm days. The reasons for this are discussed. The factors regulating the SPM-variations among and within these sites should also apply to other marine areas. Studies in other areas cannot, however, falsify these empirical results but they could clarify the boundary conditions for these models and improve our knowledge about the factors regulating SPM. This model for SPM is primarily meant to be used as a sub-model within more comprehensive models, which aim to predict key ecosystems variables, e.g., toxins in fish or production and biomass of functional groups of organisms. We have also introduced and motivated a new concept "coastal focusing" analogous to "sediment focusing" in lakes. (c) 2005 Elsevier B.V. All rights reserved.