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Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations

机译:河岸带植被用水和河段变化对地下水位波动的分形性质

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摘要

Groundwater systems affected by various factors can exhibit complex fractal behaviors, whose reliable characterization however is not straightforward. This study explores the fractal scaling behavior of the groundwater systems affected by plant water use and river stage fluctuations in the riparian zone, using multifractal detrended fluctuation analysis (MFDFA). The multifractal spectrum based on the local Hurst exponent is used to quantify the complexity of fractal nature. Results show that the water level variations at the riparian zone of the Colorado River, USA, exhibit multifractal characteristics mainly caused by the memory of time series of the water level fluctuations. The groundwater level at the monitoring well close to the river characterizes the season-dependent scaling behavior, including persistence from December to February and anti-persistence from March to November. For the site with high-density plants (Tamarisk ramosissima, which requires direct access to groundwater as its source of water), the groundwater level fluctuation becomes persistent in spring and summer, since the plants have the most significant and sustained influence on the groundwater in these seasons, which can result in stronger memory of the water level fluctuation. Results also show that the high-density plants weaken the complexity of the multifractal property of the groundwater system. In addition, the groundwater level variations at the site close to the river exhibit the most complex multifractality due to the influence of the river stage fluctuation.
机译:受各种因素影响的地下水系统可能表现出复杂的分形行为,但是其可靠的表征却并非一帆风顺。这项研究使用多重分形趋势分解分析(MFDFA)探索了受植物用水和河岸带河段波动影响的地下水系统的分形标度行为。基于局部赫斯特指数的多重分形光谱用于量化分形性质的复杂性。结果表明,美国科罗拉多河河岸带的水位变化表现出多重分形特征,这主要是由于水位波动的时间序列记忆所致。靠近河流的监测井处的地下水位表征了随季节变化的缩放行为,包括12月至2月的持续性和3月至11月的反持续性。对于有高密度植物的地方(Tamarisk ramosissima,需要直接获取地下水作为水的来源),由于水生植物对该地区的地下水影响最大,因此春季和夏季的地下水位波动会持续存在。这些季节,可能会导致对水位波动的记忆增强。结果还表明,高密度植物削弱了地下水系统多重分形特性的复杂性。此外,由于河段波动的影响,靠近河流的地点的地下水位变化表现出最复杂的多重分形性。

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