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From the 1990s onwards several Italian rivers have experienced a recent phase characterized by active-channel widening and, generally, by bed-level stability or slight aggradation. However, its triggering factors and its diffusion, along with the relationship between active-channel planform dynamics and vertical adjustments, are still quite debated and only few studies are available. This research deals with the active-channel planform changes occurred along the Scrivia River floodplain reach (NW Italy) over the period 1999–2019 and it aims at investigating in detail the ongoing geomorphological processes under the river management perspective. The study is based on a quantitative multitemporal analysis of aerial photographs and satellite images performed in a GIS environment and supported by field surveys. The outcomes revealed a generalized trend of gentle active-channel widening together with widespread bank instability and several (26% of total banks) intense and localized bank retreats involving both the modern floodplain and the recent terrace. In the investigated 20-year period, the active-channel area has increased by 22.7% (from 613.6 to 753.0 ha), its mean width by 25% (from 151.5 to 189.3 m), whereas no relevant length variations have been noticed. These morphological dynamics have been more or less pronounced both at reach scale and over time. The extreme floods occurred in the investigated period can be considered the most important triggering factor of the active-channel planform changes, most probably together with an increase of the reach-scale unit stream power due to changes in the channel geometry occurred over the 20th century. Full article

In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m³/m³ and very low bias (i.e., <0.01 m³/m³), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m³/m³ (≤0.058 m³/m³). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m³/m³. In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m³/m³ to 0.051 m³/m³ for comparison with ground measurements and SAR estimates, respectively. Full article