Schiavo Massimiliano, Colombani Nicolò, Mastrocicco Micòl
Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Via dell'Università 16 - 35020 Legnaro (PD), Italy; Department of Civil and Environmental Engineering (DICA), Politecnico di Milano, Piazza L. Da Vinci 32, 20133, Milano, Italy.
Department of Materials, Environmental Sciences and Urban Planning (SIMAU), Marche. Polytechnic University, Via Brecce Bianche 12, 60131, Ancona, Italy.
Water Res. 2023 May 15;235:119885. doi: 10.1016/j.watres.2023.119885. Epub 2023 Mar 20.
The issue of freshwater salinization in coastal areas has grown in importance with the increase of the demand of groundwater supply and the more frequent droughts. However, the spatial patterns of salinity contamination are not easy to be understood, as well as their numerical modeling is subject to various kinds of uncertainty. This paper offers a robust, flexible, and reliable geostatistical methodology to provide a stochastic assessment of salinity distribution in alluvial coastal areas. The methodology is applied to a coastal aquifer in Campania (Italy), where 83 monitoring wells provided depth-averaged salinity data. A Monte Carlo (MC) framework was implemented to simulate depth-averaged groundwater salinity fields. Both MC stochastic fields and the mean across MC simulations enabled to the delineation of which areas are subject to high salinity. Then, a probabilistic approach was developed setting up salinity thresholds for agricultural use to delineate the areas with unsuitable groundwater for irrigation purposes. Furthermore, steady spatial patterns of saline wedge lengths were unveiled through uncertainty estimates of seawater ingression at the Volturno River mouth. The results were compared versus a calibrated numerical model with remarkable model fit (R=0.96) and versus an analytical solution, obtaining similar wedge lengths. The results pointed out that the high groundwater salinities found inland (more than 2 km from the coastline) could be ascribed to trapped paleo-seawater rather than to actual seawater intrusion. In fact, the inland high salinities were in correspondence of thick peaty layers, which can store trapped saline waters because of their high porosity and low permeability. Furthermore, these results are consistent with the recognition of depositional environments and the position of ancient lagoon alluvial sediments, located in the same areas where are the highest (simulated) salinity fields. This robust probabilistic approach could be applied to similar alluvial coastal areas to understand spatial patterns of present salinization, to disentangle actual from paleo-seawater intrusion, and more in general to delineate zones with unsuitable salinity for irrigation purposes.
随着地下水供应需求的增加以及干旱愈发频繁，沿海地区淡水盐碱化问题的重要性日益凸显。然而，盐分污染的空间格局不易理解，其数值模拟也面临各种不确定性。本文提供了一种稳健、灵活且可靠的地质统计方法，用于对冲积型沿海地区的盐分分布进行随机评估。该方法应用于意大利坎帕尼亚的一个沿海含水层，那里有83口监测井提供了深度平均盐度数据。实施了蒙特卡罗（MC）框架来模拟深度平均地下水盐度场。MC随机场以及MC模拟的平均值都能够描绘出哪些区域盐度较高。然后，开发了一种概率方法，设定农业用水的盐度阈值，以划定不适用于灌溉目的的地下水区域。此外，通过对沃尔图诺河河口海水入侵的不确定性估计，揭示了盐楔长度的稳定空间格局。将结果与校准后的数值模型进行比较，模型拟合效果显著（R = 0.96），并与解析解进行比较，得到了相似的楔长度。结果表明，在内陆（距离海岸线超过2公里）发现的高地下水盐度可能归因于被困的古海水，而非实际的海水入侵。事实上，内陆高盐度区域对应着厚厚的泥炭层，由于其高孔隙率和低渗透率，能够储存被困的咸水。此外，这些结果与沉积环境的识别以及古代泻湖冲积沉积物的位置一致，这些沉积物位于（模拟）盐度最高的相同区域。这种稳健的概率方法可应用于类似的冲积型沿海地区，以了解当前盐碱化的空间格局，区分实际海水入侵与古海水入侵，更广泛地划定不适用于灌溉的盐度区域。
