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Groundwater salinity in a floodplain forest impacted by saltwater intrusion

机译:咸水入侵对洪泛区森林中地下水盐分的影响

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Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, C_(eff) = 0.85; 0.52 ≤C_(eff) ≤ 0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (C_(eff) = 0.58; 0.38 ≤ C_(eff) ≤ 0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain.
机译:沿海湿地在淡水和咸水的交汇处占据着脆弱的位置。气候变化和流域水文学的变化可能导致盐水入侵历史上的淡水系统,从而导致植物死亡和淡水栖息地的丧失。了解受潮汐影响的洪泛区森林的水文功能对于推进生态系统保护和恢复目标至关重要,然而,在地表水,地下水和大气通量之间相互作用的情况下,要发现水文输入与洪泛区水文学之间的直接关系变得很困难,因为土壤中的水和地下水与具有非均质植被和植被的土壤饱和度不同。地形。因此,需要用于识别共同趋势和因果关系的替代方法。动态因子分析(DFA)是一种时间序列维数减少技术,将观察到的数据的时间变化建模为常见趋势的线性组合,这些趋势代表无法解释的常见变异性和解释变量。 DFA被用于模拟Loxahatchee河(美国佛罗里达)森林泛滥的湿地中的浅层地下水盐度,那里流域水文学的改变导致水文周期和盐度制度的变化以及不希望有的营养变化。长期,高分辨率的地下水盐度数据集揭示了季节和年度时间以及潮汐周期和暴风雨事件的动态。 DFA识别了盐度时间序列之间的共享趋势,并很好地模拟了观测序列的完整动态因子模型(总效率系数,C_(eff)= 0.85; 0.52≤C_(eff)≤0.99)。仅基于DFA中确定的解释变量的简化多线性模型具有相当好的结果(C_(eff)= 0.58; 0.38≤C_(eff)≤0.75),可用于评估恢复和管理方案对浅层的影响Loxahatchee河洪泛区的地下水盐度。

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