A theory for the relationship between lake surface area and maximum depth

A theory for the relationship between lake surface area and maximum depth
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湖泊表面积与最大深度关系的理论

DOI:
10.1002/lol2.10269
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发表时间:
2022
影响因子:
7.8
通讯作者:
Cael B
Cael B
中科院分区:
环境科学与生态学2区
文献类型:
--
作者:
Cael B

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最大深度对许多湖泊过程和生物群至关重要,但解释其变化的尝试几乎没有取得预测能力。本文根据分形布朗运动理论的最新进展,描述了最大深度的概率分布。理论分布是右尾的,充分捕捉了美国东北部8164个湖泊(最大深度0.1-135米)数据集中最大深度的变化。最大深度随表面积的增加而增加,但有很大的随机变化——95%的预测区间跨越了任何特定表面积的湖泊的一个数量级以上。我们的研究结果解释了观测到的湖泊最大深度的变化,捕捉了地形特征和湖泊水深之间的联系,并提供了一种方法来增加最大深度依赖过程,我们通过增加从北部湖泊到大气的甲烷扩散通量来说明这一点。
Maximum depth is crucial for many lake processes and biota, but attempts to explain its variation have achieved little predictive power. In this paper, we describe the probability distribution of maximum depths based on recent developments in the theory of fractal Brownian motions. The theoretical distribution is right‐tailed and adequately captures variations in maximum depth in a dataset of 8164 lakes (maximum depths 0.1–135 m) from the northeastern United States. Maximum depth increases with surface area, but with substantial random variation—the 95% prediction interval spans more than an order of magnitude for lakes with any specific surface area. Our results explain the observed variability in lake maximum depths, capture the link between topographic characteristics and lake bathymetry, and provide a means to upscale maximum depth‐dependent processes, which we illustrate by upscaling the diffusive flux of methane from northern lakes to the atmosphere.
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