Reliable soil moisture data are essential for achieving sustainable water management. In this framework, the performance of devices to estimate the volumetric moisture content by means dielectric properties of soil/water system is of increasing interest. The present work evaluates the performance of the PR2/6 soil moisture profile probe with implications on the understanding of processes involving the unsaturated zone. The calibration at the laboratory scale and the validation in an experimental field in Central Italy highlight that although the shape of the moisture profile is the same, there are essential differences between soil moisture values obtained by the calibrated equation and those obtained by the manufacturer one. These differences are up to 10 percentage points for fine-grained soils containing iron oxides. Inaccurate estimates of soil moisture content do not help with understanding the soil water dynamic, especially after rainy periods. The sum of antecedent soil moisture conditions (the Antecedent Soil moisture Index (ASI)) and rainfall related to different stormflow can be used to define the threshold value above which the runoff significantly increases. Without an accurate calibration process, the ASI index is overestimated, thereby affecting the threshold evaluation. Further studies on other types of materials and in different climatic conditions are needed to implement an effective monitoring network useful to manage the soil water and to support the validation of remote sensing data and hydrological soil models.
The correct estimation of soil moisture data is essential in soil-water management and estimating the hydraulic properties of unsaturated soils. The increased use of Multi-Sensor Capacitance Probes (MCAPs) requires careful calibration. Without accurate calibration, the use of MCAPs leads to incorrect water content estimation, making them of no practical use. This work presents the specific calibration equations for the correct use of the PR2/6 profile probe on sands of different nature. As the iron oxides content of the Tiber River basin sands increases, the calibration lines slope increases, allowing the understanding of the different electromagnetic responses. As for other sands worldwide, sands with high iron oxides content show a relative high specific surface than quartz or calcareous sands, responsible for more adhesive water (e.g., high permittivity values). The water content data are integrated with a hydraulic property estimator allowing the estimation of the hydraulic conductivity of soils. Applying the manufacturer equation of the PR2/6 profile probe instead of the specific equation leads to an overestimation of the hydraulic conductivity values up to two orders of magnitude, making therefore rather incorrect the understanding of the phenomena occurring in the unsaturated zone.
Satellite observations (Copernicus Sentinell-1) can supply antecedent soil moisture data, which helps to predict thresholds triggering runoff and runoff volume. In the paper, we developed a runoff correction factor to the USLE, using rainfall and satellite antecedent soil moisture data, following the approach of the modified USLE models such as the USLE-M and USLE-MM. The runoff and soil loss estimations accuracy are validated by plot-scale measurements (2008–2020 period) provided by SERLAB (Soil Erosion Laboratory) of the University of Perugia. The results show that the event rainfall depth added to the antecedent soil moisture is a fairly suitable predictor of the runoff. Using the simulated runoff in a USLE-MM model, the capability to predict event soil losses is enhanced with an RMSE = 0.57 Mg/ha lower than the RMSE ≈ 3.1 Mg/ha obtained by the USLE model. Using a modified USLE model, albeit with remote estimated runoff data, is still more advantageous at the event scale than the USLE model, which does not consider the runoff. These results are particularly significant for the estimation of runoff and soil losses. Satellite data shows the potential of applying the modified USLE models for large-scale monitoring and quantification of event soil erosion and runoff.
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