Integrating Time Domain Reflectometry and Soil Sample Analysis to Monitor Soil Salinization in the Po River Delta

Seawater intrusion (SWI), driven by climate change and anthropogenic pressure, is a critical issue in coastal regions, with soil salinization as one of its most severe consequences. In low-lying agricultural areas, such as the Po River Delta in Northeast Italy, SWI-induced salinization degrades soil quality by altering structure, reducing porosity, and suppressing microbial activity, ultimately threatening agricultural productivity. The combination of prolonged summer droughts, high evapotranspiration rates, and reduced river flow intensifies SWI, facilitating salt accumulation in soils. Understanding the mechanisms and impacts of soil salinization is essential to understand SWI-related challenges. Accumulated salts in soils not only induce stress in vegetation, impairing metabolic functions and crop cycles, but also accelerate soil degradation, with long-term risks such as micro-desertification. Addressing these challenges requires precise soil measurements to monitor salinity levels and fluctuations over time, especially in the critical summer months. This study aims to integrate multi-temporal on-site observations of soil moisture, electrical conductivity (EC), and temperature with the collection and analysis of EC and ion content from water extracts, conducted throughout the summer of 2023 (July to September). In selected study areas within the Po Delta territory, point measurements of soil temperature, moisture, and electrical conductivity, obtained biweekly using a Time Domain Reflectometry (TDR) probe, were spatially interpolated. These interpolated data were then compared with spatially interpolated values of electrical conductivity derived from water extracts of soil samples collected at the same locations on the same biweekly schedule. In addition, leaching tests were performed to detect major ions in the leachates. This approach allowed for a detailed assessment of the link between soil salinity and other soil properties across the study areas. This information, combined with precipitation data, facilitates the detection of salinization patterns, enabling the identification of the most affected zones.