Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space–time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil–crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results.

In recent decades, water scarcity has become a frequent and widespread phenomenon. Intensification of water scarcity will have economic impact on the main water-using sectors. The highest pressure on the water resources is exerted by agriculture. Irrigation is the largest consumer of the agriculture sector and the efficient use of water is of utmost importance. The aim of this study is to explore the capability of an innovative platform that combines Earth Observation data, weather forecasts and numerical simulations to plan more precisely water allocation in space and time in the irrigated agriculture. This platform, created in the framework of MOSES, provides in Romania short-term irrigation forecasts adapted to the hydrological behavior of alluvial soils specific to the Lower Danube Floodplain. The short-term irrigation forecasts have been tested with applied water volumes and in situ water resources measurements in order to assess the water allocation in the irrigated agriculture. Although irrigation forecast was run operationally only one crop year (2017–2018), in the framework of MOSES Project, the comparison revealed that the irrigation scheduling in this area is more influenced by the infrastructure characteristics and less by the spatial distribution of crop water needs and availability of water resources. Our results show that short-term irrigation forecasts accompanied by real time monitoring of water resources could be successfully used in the irrigation scheduling activity for improving water allocation in space and time in the irrigated agriculture.

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications.

Many urban areas face an increasing flood risk, which includes the risk of flash floods. Increasing extreme precipitation events will likely lead to greater human and economic losses unless reliable and efficient early warning systems (EWS) along with other adaptation actions are put in place in urban areas. The challenge is in the integration and analysis in time and space of the environmental, meteorological, and territorial data from multiple sources needed to build up EWS able to provide efficient contribution to increase the resilience of vulnerable and exposed urban communities to flooding. Efficient EWS contribute to the preparedness phase of the disaster cycle but could also be relevant in the planning of the emergency phase. The RainBO Life project addressed this matter, focusing on the improvement of knowledge, methods, and tools for the monitoring and forecast of extreme precipitation events and the assessment of the associated flood risk for small and medium watercourses in urban areas. To put this into practice, RainBO developed a webGIS platform, which contributes to the “planning” of the management of river flood events through the use of detailed data and flood risk/vulnerability maps, and the “event management” with real-time monitoring/forecast of the events through the collection of observed data from real sensors, estimated/forecasted data from hydrologic models as well as qualitative data collected through a crowdsourcing app.

Facing global warming’s consequences is a major issue in the present times. Regarding the climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise; thus, the hazard related to rainfall-induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist, and many of them are forced to make simplifications in order to reproduce landslide occurrences over space and time. Process-based models can help in quantifying the consequences of heavy rainfall in terms of slope instability at a territory scale. In this study, a narrative review of physically based deterministic distributed models (PBDDMs) is presented. Models were selected based on the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), and the inclusion of new approaches in modeling slope stability through the ISS. The models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and providing researchers and practitioners with basic knowledge of what scholars have proposed so far. The results indicate that including vegetation’s effects on slope stability has raised in importance over time but that there is still a need to find an efficient way to include them. In recent years, the literature production seems to be more focused on probabilistic approaches.

This paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.