The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

An estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel‐2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high‐resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 m. We use a multi‐temporal UAV dataset covering an area of 5.25 km². Using a novel convolutional neural network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated.

High-resolution numerical models can be effective in monitoring and predicting natural hazards, especially when dealing with Mediterranean atmospheric and marine intense/severe events characterised by a wide range of interacting scales. The understanding of the key factors associated to these Mediterranean phenomena, and the usefulness of adopting high-resolution numerical models in their simulation, are among the aims of the international initiative HyMeX – HYdrological cycle in Mediterranean EXperiment. At the turn of 2013, two monitoring campaigns (SOPs – Special Observation Periods) were devoted to these issues. For this purpose, a new high-resolution BOlogna Limited Area Model-MOdello LOCale (BOLAM-MOLOCH) suite was implemented in the Institute for Environmental Protection and Research (ISPRA) hydro–meteo–marine forecasting system (SIMM – Sistema Idro-Meteo-Mare) as a possible alternative to the operational meteorological component based on the BOLAM model self-nested over two lower-resolution domains. The present paper provides an assessment of this new configuration of SIMM with respect to the operational one that was also used during the two SOPs. More in details, it investigates the forecast performance of these SIMM configurations during two of the Intense Observation Periods (IOPs) declared in the first SOP campaign. These IOPs were characterised by high precipitations and very intense and exceptional high waters over the northern Adriatic Sea (acqua alta). Concerning the meteorological component, the high-resolution BOLAM-MOLOCH forecasts are compared against the lower-resolution BOLAM forecasts over three areas – mostly corresponding to the Italian HyMeX hydrometeorological sites – using the rainfall observations collected in the HyMeX database. Three-month categorical scores are also calculated for the MOLOCH model. Despite the presence of a slight positive bias of the MOLOCH model, the results show that the precipitation forecast turns out to improve with increasing resolution. In both SIMM configurations, the sea storm surge component is based on the same version of the Shallow water HYdrodynamic Finite Element Model (SHYFEM). Hence, it is evaluated the impact of the meteorological forcing provided by the two adopted BOLAM configurations on the SHYFEM forecasts for six tide-gauge stations. A benchmark for this part of the study is given by the performance of the SHYFEM model forced by the ECMWF IFS forecast fields. For this component, both BOLAM-SHYFEM configurations clearly outperform the benchmark. The results are, however, strongly affected by the predictability of the weather systems associated to the IOPs, thus suggesting the opportunity to develop and test a time-lagged multi-model ensemble for the prediction of high storm surge events.