摘要：Soil erosion continues to be a threat to soil quality, impacting crop production and ecosystem services delivery. The quantitative assessment of soil erosion, both by water and by wind, is mostly carried out by modeling the phenomenon via remote sensing approaches. Several empirical and process-based physical models are used for erosion estimation worldwide, including USLE (or RUSLE), MMF, WEPP, PESERA, SWAT, etc. Furthermore, the amount of sediment produced by erosion phenomena is obtained by direct measurements carried out in experimental sites. Data collection for this purpose is very complex and expensive; in fact, we have few cases of measures distributed at the basin scale to monitor this phenomenon. In this work, we propose a methodology based on an expeditious way to monitor the volume of hilly lakes with GPS, sonar sensor and aquatic drone. The volume is obtained by means of an automatic GIS procedure based on the measurements of lake depth and surface area. Hilly lakes can be considered as sediment containers. Time-lapse measurements make it possible to estimate the silting rate of the lake. The volume of 12 hilly lakes in Tuscany was measured in 2010 and 2018, and the results in terms of silting rate were compared with the estimates of soil loss obtained by RUSLE and MMF. The analyses show that all the lakes measured are subject to silting phenomena. The sediment estimated by the measurements corresponds well to the amount of soil loss estimated with the models used. The relationships found are significant and promising for a distributed application of the methodology, which allows rapid estimation of erosion phenomena. Substantial differences in the proposed comparison (mainly found in two cases) can be justified by particular conditions found on site, which are difficult to predict from the models. The proposed approach allows for a monitoring of basin-scale erosion, which can be extended to larger domains which have hilly lakes, such as, for example, the Tuscany region, where there are more than 10,000 lakes.
