García Molinos Jorge, Viana Mafalda, Brennan Michael, Donohue Ian
School of Natural Sciences, Department of Zoology, Trinity College Dublin, Dublin, Ireland; Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan.
School of Natural Sciences, Department of Zoology, Trinity College Dublin, Dublin, Ireland.
PLoS One. 2015 Mar 10;10(3):e0119253. doi: 10.1371/journal.pone.0119253. eCollection 2015.
Lakes are disproportionately important ecosystems for humanity, containing 77% of the liquid surface freshwater on Earth and comprising key contributors to global biodiversity. With an ever-growing human demand for water and increasing climate uncertainty, there is pressing need for improved understanding of the underlying patterns of natural variability of water resources and consideration of their implications for water resource management and conservation. Here we use Bayesian harmonic regression models to characterise water level dynamics and study the influence of cyclic components in confounding estimation of long-term directional trends in water levels in natural Irish lakes. We found that the lakes were characterised by a common and well-defined annual seasonality and several inter-annual and inter-decadal cycles with strong transient behaviour over time. Importantly, failing to account for the longer-term cyclic components produced a significant overall underestimation of the trend effect. Our findings demonstrate the importance of contextualising lake water resource management to the specific physical setting of lakes.
湖泊对人类而言是极为重要的生态系统，地球上77%的液态地表水存于湖泊中，湖泊也是全球生物多样性的关键贡献者。随着人类对水的需求不断增长以及气候不确定性增加，迫切需要更好地理解水资源自然变化的潜在模式，并考虑其对水资源管理和保护的影响。在此，我们使用贝叶斯调和回归模型来描述水位动态，并研究在混淆爱尔兰天然湖泊水位长期趋势估计中循环成分的影响。我们发现，这些湖泊具有共同且明确的年度季节性以及几个年际和年代际周期，且随着时间推移具有强烈的瞬态行为。重要的是，未考虑长期循环成分会导致对趋势效应的显著总体低估。我们的研究结果表明，将湖泊水资源管理置于湖泊特定物理环境背景下的重要性。
