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Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis

机译:使用时间序列分析评估气候和人为干预对地下水及其相关生态系统的影响

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摘要

Groundwaters and their dependent ecosystems are affected both by the meteorological conditions as well as from human interventions, mainly in the form of groundwater abstractions for irrigation needs. This work aims at investigating the quantitative effects of meteorological conditions and man intervention on groundwater resources and their dependent ecosystems. Various seasonal Auto-Regressive Integrated Moving Average (ARIMA) models with external predictor variables were used in order to model the influence of meteorological conditions and man intervention on the groundwater level time series. Initially, a seasonal ARIMA model that simulates the abstraction time series using as external predictor variable temperature (T) was prepared. Thereafter, seasonal ARIMA models were developed in order to simulate groundwater level time series in 8 monitoring locations, using the appropriate predictor variables determined for each individual case. The spatial component was introduced through the use of Geographical Information Systems (GIS). Application of the proposed methodology took place in the Neon Sidirochorion alluvial aquifer (Northern Greece), for which a 7-year long time series (i.e., 2003-2010) of piezometric and groundwater abstraction data exists. According to the developed ARIMA models, three distinct groups of groundwater level time series exist; the first one proves to be dependent only on the meteorological parameters, the second group demonstrates a mixed dependence both on meteorological conditions and on human intervention, whereas the third group shows a clear influence from man intervention. Moreover, there is evidence that groundwater abstraction has affected an important protected ecosystem.
机译:地下水及其依赖的生态系统既受到气象条件的影响,也受到人类干预的影响,主要是为灌溉需要抽取地下水。这项工作旨在调查气象条件和人为干预对地下水资源及其相关生态系统的定量影响。为了模拟气象条件和人为干预对地下水位时间序列的影响,使用了带有外部预测变量的各种季节自回归综合移动平均(ARIMA)模型。最初,准备了一个季节性ARIMA模型,该模型使用外部变量温度(T)作为抽象时间序列来模拟抽象时间序列。此后,开发了季节性ARIMA模型,以使用针对每种情况确定的适当预测变量来模拟8个监测地点的地下水位时间序列。通过使用地理信息系统(GIS)引入了空间成分。拟议方法的应用发生在希腊北部的Nesi Sidirochorion冲积含水层中,存在着长达7年的时间序列(即2003-2010年)的测压和地下水提取数据。根据已开发的ARIMA模型,存在三个不同的地下水位时间序列组。第一组证明仅依赖于气象参数,第二组证明既依赖于气象条件又依赖于人为干预,而第三组则表明受到人为干预的明显影响。此外,有证据表明,地下水的抽取已经影响了重要的受保护生态系统。

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