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Empirical models for describing recent sedimentation rates in lakes distributed across broad spatial scales

机译:描述分布在广泛空间尺度上的湖泊近期沉积速率的经验模型

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Over the last 20 years there has been a surge of interest in paleolimnology and as a result a large accumulation of lake sedimentation records. This emerging archive has allowed us to develop empirical models to describe which variables explain significant variation in sedimentation rates over the past similar to 150 years across large spatial scales. We hypothesized that latitude would be a significant explanatory variable of profundal zone lake sedimentation rates across a temperate to polar gradient. We further hypothesized that along a more longitudinally-constrained dataset (i.e. east coast of North America), latitude would explain a greater proportion of the variance. To test these hypotheses, we collated data from 125 natural, average-sized lakes (with surface area < 500 km(2)) by recording authors' estimates of sedimentation rates (measured as mm/year) or by digitizing recent sediment profiles and calculating sedimentation rates over the past similar to 150 years. We found that, at both scales, latitude was the strongest predictor of lake sedimentation rates (full dataset: r(2) = 0.28, P = 0.001, n = 125; east coast dataset: r(2) = 0.58, P < 0.001, n = 43). By conducting a multiple linear regression analysis, we found that 70% of the variance in sedimentation rates from the east coast transect was explained by latitude and elevation alone. This latter model is of sufficient strength that it is a robust predictive tool. Given that climate and land-use strongly co-vary with latitude and that both of these factors have previously been shown to influence lake sedimentation rates, it appears that latitude is a surrogate measure for climate and land-use changes. We also show support for land-use as an important variable influencing sedimentation rates by demonstrating large increases in recent versus Holocene accumulation rates. These results indicate that it is possible to make generalizations about sedimentation rates across broad spatial scales with even limited geographic data.
机译:在过去的20年中,对古湖泊学的兴趣激增,因此大量的湖泊沉积记录得以积累。这个新兴的档案库使我们能够开发经验模型,以描述哪些变量解释了过去(类似于大空间尺度上的150年)沉积速率的显着变化。我们假设纬度将是温带至极地梯度上深层区湖泊沉积速率的重要解释变量。我们进一步假设,沿着纵向约束更大的数据集(即北美东海岸),纬度将解释更大比例的方差。为了检验这些假设,我们通过记录作者对沉积速率的估计(以毫米/年为单位)或通过数字化近期沉积物剖面并计算,来整理来自125个平均大小的自然湖泊(表面积小于500 km(2))的数据。过去的沉积率接近150年。我们发现,在两个尺度上,纬度是湖泊沉积率的最强预测因子(完整数据集:r(2)= 0.28,P = 0.001,n = 125;东海岸数据集:r(2)= 0.58,P <0.001 ,n = 43)。通过进行多元线性回归分析,我们发现东海岸样带的沉积率变化中有70%仅由纬度和海拔解释。后一种模型具有足够的强度,因此它是可靠的预测工具。鉴于气候和土地利用与纬度密切相关,并且先前已经证明这两个因素都会影响湖泊的沉积速率,因此看来纬度是气候和土地利用变化的替代指标。我们还证明了土地使用是影响沉积速率的重要变量,它表明了近期与全新世相比累积速率大大提高。这些结果表明,即使地理数据有限,也有可能对广泛空间范围内的沉积率进行概括。

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