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Spatial variability estimation and risk assessment of the aquifer level at sparsely gauged basins using geostatistical methodologies

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Abstract

The spatial variability evaluation of the water table level of an aquifer provides useful information in water resources management plans. Three different approaches are applied to estimate the spatial variability of the water table in the study basin. All of them are based on the Kriging methodology. The first is the classical Ordinary Kriging approach, while the second involves information from a secondary variable (surface elevation) and the application of Residual Kriging. The third calculates the probability to lie below a certain groundwater level limit that could cause significant problems in groundwater resources availability. The latter is achieved by means of Indicator Kriging. A recently developed non-linear normalization method is used to transform both data and residuals closer to normal distribution for improved prediction results. In addition, the recently developed Spartan variogram model is applied to determine the spatial dependence of the measurements. The latter proves to be the optimal model, compared to a series of models tested, which provides in combination with the Kriging methodologies the most accurate cross validation estimations. The variogram form is explained with respect to the radius of influence of the pumping wells representing the spatial impact of the pumping activity. Groundwater level and probability maps are developed providing the ability to assess the spatial variability of the groundwater level in the basin and the risk that certain locations have in terms of a safe groundwater level limit that has been set for the sustainability of the groundwater resources of the basin.

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Correspondence to Emmanouil A. Varouchakis.

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Communicated by: H. A. Babaie

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Varouchakis, E.A., Kolosionis, K. & Karatzas, G.P. Spatial variability estimation and risk assessment of the aquifer level at sparsely gauged basins using geostatistical methodologies. Earth Sci Inform 9, 437–448 (2016). https://doi.org/10.1007/s12145-016-0265-3

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