The Volcán de Colima, one of the most active volcanoes in Mexico, has experienced several volcanic crises over the last century with the emplacement of voluminous block-and-ash flow deposits providing large volumes of loose material along the main ravines. During the rainy season, this material is easily eroded forming lahars. Over 40 events with variable magnitude (105-106 m3) have been detected each year. The largest events that cause damages to infrastructure are usually triggered during the hurricane season (from mid-August to October) when more than 250 mm of rain usually are accumulated over a few days. On 23 October 2015, Hurricane Patricia hit the Volcán de Colima. The hurricane was announced as having reached category 5 thereby representing the largest ever recorded hurricane event in Mexico. It rapidly weakened after landfall but followed a straight trajectory toward the volcano. Up to 400 mm of rain were recorded over 30 hours. The event was recorded at a monitoring station located in the middle reaches of the La Lumbre ravine on the SW flank of the volcano, which was equipped with a rain gauge, a geophone (10 Hz), and a video camera. A multi-pulse lahar started around 8 pm (GMT) and lasted for more than five hours. The seismic signal and the video images were analyzed to identify the timing of the main pulses, the sediment concentrations, and maximum flow peak discharge. Data show that the lahar was characterized by three main pulses, in the range of debris flows with maximum flow-depth of 8 m, interspersed by more dilute tails as hyperconcentrated flow, as also observed from the frequency contents of the seismic signal. A total volume of 2.5 × 106 m3 was estimated based on the strong correlation between the seismic amplitude and the flow discharge. The lahar destroyed one bridge and ~500 m of the interstate road leaving several villages cut off for a few days. Based on the flow magnitude, duration, and the associated damage, this event probably represents the largest one recorded over the last 20 years. The FLO-2D model was used to replicate the observed event to estimate the maximum inundation limits of lahars along the five principal ravines of the volcano, in an attempt to design a hazard map for catastrophic hurricane-induced events.

Gathering systematic information on the effects of extreme weather events (e.g., floods, landslides and debris flows, windthrows) is a fundamental prerequisite to establishing rapid-response strategies and putting into practice management policies. However, the collection of field data requires significant economic and human efforts by local authorities. Furthermore, events occurring in remote areas are rarely detected and mapped accurately as they have a low chance of intersecting human infrastructures. These missed detections lead to incorrect assumptions in relation to both the extreme events' spatial distribution and, especially, the real occurrence probability. This work proposes a framework for obtaining the automatic identification of severe weather events that may have caused important erosion processes or vegetation damage, combined with a rapid preliminary change detection mapping over the identified areas. The proposed approach leverages the free availability of both high-resolution global scale radar rainfall products and Sentinel-2 multi-spectral images to identify the areas to be analyzed and to carry out change detection algorithms, respectively. Radar rainfall data are analyzed and the areas where high-intensity rainfall and/or very important cumulative precipitation has occurred, are used as a mask for restricting the subsequent analysis, which, in turn, is based on a multi-spectral change detection algorithm. The whole procedure feeds a geodatabase (storing identified events, retrieved data and computed changes) for proper data management and subsequent analyses. The testing phase of the proposed methodology has provided encouraging results: applications to selected mountain catchments hit by intense events in northeastern Italy were capable of recognizing flooded areas, debris-flow and shallow landslide activations, and windthrows. The described approach can serve as a preliminary step toward detailed post-event surveys, but also as a preliminary "quick and dirty" mapping framework for local authorities especially when resources for ad hoc field surveys are not available, or in the case of an event that triggers changes in remote areas. Such a systematic potential change identification can help for a more homogeneous and systematic detection and census of the events and their effects. The workflow herein presented is intended as a starting point on top of which more modules can be added (e.g., radar climatology, SAR change detection for near real-time, other severe sources such as lightning, earthquakes or wildfires, machine learning algorithms for image classification, land use and morphological filtering of the results). Future improvements of the described procedure could be finally devised for allowing a continuous operational activity and for maintaining an open-source software implementation.

The lack of observed streamflow datasets for calibration of rainfall-runoff models imposes substantial problems for their applicability, especially in poorly gauged or ungauged river basins. Developing satellite technologies and increasing computational powers over the past decades, have provided an environment for researchers to simulate several water balance components globally using these datasets and assimilation techniques. Due to importance of accurate hydrologic modeling, this study aims to investigate the applicability of global water resources reanalysis (GWRR) datasets including surface soil moisture (SSM), evapotranspiration (ET), and surface runoff (SR) components for calibration of the macro-scale hydrological model (VIC-3L) over the SefidRood basin (SRB) in Iran at different calibration scenarios. Results show that in the case of using SSM datasets, the model's performance in the simulation of streamflow hydrograph, with the NSE value higher than 0.65, is better than using other datasets. Among different datasets, the SSM based on LISFLOOD and HBV are the best ones for calibration of VIC-3L model over SRB. In contrast, using ET datasets aren't so reliable for hydrological calibration in the study area. Furthermore, in the cases of using SSM and surface runoff datasets, the model tends to overestimation of low-flows, while, ET datasets are more reliable for simulation of such these flows. Also, findings displayed that the combination of ET and SSM datasets for hydrological calibration performed better than using only one dataset. In conclusion, this research gives useful and applied insights in the applicability of GWRR data sources for hydrological modeling and water resources studies, especially in data limited regions.

Despite a detailed knowledge of the spatial-temporal dynamics of irrigation being necessary to optimize the agricultural production without exacerbating the pressure exercised on the water resource, such information is still often lacking worldwide. In this study, a double-scale analysis on the detectability of the irrigation occurrence over an area in central Italy through remote sensing soil moisture is proposed; the period of interest is a 3-year time span from 2017 to 2019. The detectability of district- or sub-district-scale irrigation signals through remotely sensed soil moisture data is investigated at two different spatial resolutions: 1 km and plot scale. Three soil moisture products sampled at 1 km resolution are evaluated: a DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled version of SMAP (Soil Moisture Active Passive) and two Sentinel-1-derived products, namely the 1 km version delivered by Copernicus and a plot-scale-born version developed by THEIA and aggregated at 1 km. The THEIA Sentinel-1 product aggregated at 100 m is used in the plot-scale analysis. Over the study area, the irrigation extent is determined by the fragmentation of the agricultural fields and the complex topography, making the adoption of plot-scale data necessary. Satisfactory results are obtained by comparing maps of irrigated areas at 100 m spatial resolution produced through the k-means clustering algorithm with ground-truth data, since the method fails only once out of seven in properly reproducing the irrigated or non-irrigated conditions occurred over four pilot agricultural fields.

Irrigation is the most impacting and uncertain human intervention on the water resource. The possibility of retrieving information on irrigation practices through remote sensing technology opens unprecedented perspectives on the monitoring of anthropized basins. This study is aimed at assessing the impact of different approaches to model the contribution of the evapotranspiration in retrieving the amounts of water applied for irrigation through a soil-moisture-based (SM-based) inversion algorithm; such a contribution is conclusive especially over semi-arid regions. Three modeling approaches relying on both calculated and remotely sensed actual and potential evapotranspiration (ET and PET) data sets were implemented to represent the evapotranspiration rate within the SM-based inversion method, which allows backward estimation of irrigation through the soil water balance inversion. By combining the different evapotranspiration data sources and modeling approaches, seven experiments aimed at inverting DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled SMAP (Soil Moisture Active Passive) soil moisture at 1 km to estimate irrigation over four heavily irrigated agricultural districts located in Spain (Aragon and Catalonia) were compared. The results highlighted that the application of the FAO56 guidelines parametrization relying on the use of optical data as proposed by the authors in a previous study remains the most reliable configuration. In fact, the implementation of a simplified approach not considering the transpiration component of the specific crop led to irrigation underestimates. Finally, it is interesting to note that the application of the method with remotely sensed ET from MODIS (MODerate-resolution Imaging Spectroradiometer) produced reliable district-aggregated irrigation estimates, thus opening the perspective of an algorithm configuration forced with remote sensing data only.

This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe-in Spain and in Italy-with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.

This data paper describes the multinational Database of Flood Fatalities from the Euro-Mediterranean region FFEM-DB that hosts data of 2,875 flood fatalities from 12 territories (nine of which represent entire countries) in Europe and the broader Mediterranean region from 1980 to 2020. The FFEM-DB database provides data on fatalities' profiles, location, and contributing circumstances, allowing researchers and flood risk managers to explore demographic, behavioral, and situational factors, as well as environmental features of flood-related mortality. The standardized data collection and classification methodology enable comparison between regions beyond administrative boundaries. The FFEM-DB is expandable, regularly updated, publicly available, and with anonymized data. The key advantages of the FFEM-DB compared to existing datasets containing flood fatalities are its high level of detail, data accuracy, record completeness, and the large sample size from an extended area.

Groundwater is the main water supply for agricultural and industrial needs in many coastal plains worldwide. Groundwater depletion often triggers land subsidence, which threatens manmade infrastructure and activities and aggravates other geohazards. We applied a multi-temporal interferometric synthetic aperture radar technique to Sentinel-1 datasets to detect ground motion in the Gioia Tauro plain (Calabria, Southern Italy) from 2018 to 2021. The InSAR data were analysed through the integrated use of groundwater head, stratigraphical and geomorphological data, and land use information to distinguish the potential subsidence divers. The results show that subsiding areas, with a mean rate of about 10 mm/yr, are in the middle of the plain, and their location is influenced by the spatial distribution of compressible sediments included in the shallow aquifer. Furthermore, the subsidence arrangement is spatially accordant with the main groundwater depression area, which can be ascribed to the ongoing and increasing water pumping for predominantly agricultural usage. We also observed that subsidence (up to 10 mm/yr) affects the western dock of the Gioia Tauro harbour, in front of which, in very shallow water, are two submarine canyon heads already affected by slides in the past.

Gully rehabilitation is often applied as part of catchment management strategies aimed at reducing downstream sediment yields. However, the influences of gully control measures on the runoff and sediment transport processes in agroforestry systems have been seldom studied. In this paper, a thorough analysis of these processes was carried out in a valley-bottom gully located in a dehesa from SW Spain. The gully was monitored before and after implementation of different runoff and sediment control measures that included: gabion check-dams, brushwood check-dams and livestock exclusion through fencing. The aims of this work are: (1) to analyze the effect of the gully control measures on the hydrological dynamics and sediment load, and (2) to evaluate their effect on sediment connectivity at the catchment and channel scales. Changes in topography and connectivity were estimated using sequential Digital Elevation Models (DEMs) with a resolution of 0.02 m generated by Structure - from - Motion photogrammetry from aerial images acquired by an Unmanned Aerial Vehicle. Discharge and suspended sediment were monitored at the outlet. Results indicate that flood discharge was not influenced by the control measures, but suspended sediment concentration was reduced by 65%. The integration of the difference of connectivity index with topographic change maps highlighted the impact of the gully control measures on changes in sediment connectivity. A strong relationship between geomorphic dynamics and the spatial pattern of hydrological and sediment pathways were observed in the gully. The connectivity index (IC) increased in eroded areas, while deposition sites showed a decrease in the IC. Connectivity also decreased in the bank headcuts located within an area isolated from livestock. The implementation of runoff and sediment control measures in the channel was successful in stabilizing the expansion of the channel network and had beneficial effects in the short-term but further monitoring would be necessary to understand long-term effects.

Sulle Alpi europee, negli ultimi decenni, la temperatura dell'aria mostra un riscaldamento a un tasso medio di 0,3 °C/10 anni, un valore superiore al tasso di riscaldamento globale che è di 0,2 °C/10 anni. L'ambiente periglaciale alpino è particolarmente importante per diversi aspetti (es. energetico, ecologico e turistico). Tuttavia mancano studi specifici e aggiornati relativi alle tendenze della temperatura in questo ambiente. Per colmare questa lacuna sono state analizzate le tendenze delle temperature per il periodo 1990-2019. L'ambiente periglaciale delle Alpi ha mostrato un tasso di riscaldamento di 0,4 °C/10 anni, 0,6 °C/10 anni e 0,8 °C/10 anni rispettivamente per la temperatura media, massima e minima. Questi tassi di riscaldamento sono superiori a quelli osservati per l'intera area alpina. Nel 2050 si prevede l'estinzione di molti ghiacciai delle Alpi al di sotto dei 3000 m di altitudine e tutte le aree precedentemente occupate dai ghiacciai diventeranno periglaciali.

A better detection of landslide occurrence is critical for disaster prevention and mitigation. Over the past four decades, great achievements have been made, ranging from inventories to mapping, susceptibility analysis to triggering threshold identification. Here, we proposed a model to establish global distributed rainfall thresholds, by linking triggering rainfall with geo-environmental causes related to landslide events. The model was based on multiple linear regression method, to define rainfall thresholds as a function of diverse geo-environmental variables, fitted and validated by a combined and relatively accurate landslide dataset. Results show primarily feasible performances for training and testing datasets, with low mean absolute error (0.22 log(mm)) and a high coefficient of determination (0.67) totally. We further prepared global distributed threshold maps for sub- and multi-daily rainfall durations. They share similar spatial distributions in line with previous research. The normalized rainfall index, defined as the ratio of precipitation amount over distributed rainfall thresholds, can be an index of possible landslide occurrence, that is, regions with a normalized index over 1.0 correspond to high probability. We argue that distributed rainfall threshold models are an improvement of empirical threshold models and susceptibility assessments by considering the interaction between triggering rainfall and geo-environmental causes, and promising for better hazard assessment.

This Policy Brief succinctly presents the Ecological Observing System of the Adriatic Sea (ECOAdS), aimed at integrating the ecological and oceanographic dimensions within the conservation strategy of the Natura 2000 network, and to propose a way to go for its future development and maintenance. After a definition of marine ecological observatories, we describe the current structure of ECOAdS, its key components and potential relevance in relation to the main European strategies for biodiversity and marine observation for the next decade. Finally, we suggest some actions that could be undertaken for the future development of ECOAdS, targeting possible perspectives in different regional, macro-regional, national and European strategic contexts. This Policy Brief is one of the outcomes of the Interreg Italy-Croatia Project ECOSS (ECological Observing System in the Adriatic Sea: oceanographic observations for biodiversity; https://www.italy-croatia.eu/web/ecoss), which had the main purpose to design and carry out the first steps for the establishment of ECOAdS.

In a context of cryosphere degradation caused by climate warming, rock temperature is one of the main driving factors of rockfalls that occur on high-elevation mountain slopes. In order to improve the knowledge of this critical relationship, it is necessary to increase measurement capability of rock temperature and its variability in different lithological and slope/aspect conditions, and also to increase local scale studies, increasing the quality and the comparability of the data. This paper shows an example of metrological characterization of sensors used for rock temperature measurement in mountain regions, by means of the measurement uncertainty. Under such approach, data and results from temperature measurements carried out in the Bessanese high-elevation experimental site (Western European Alps) are illustrated. The procedures for the calibration and field characterization of sensors allow to measure temperature in different locations, depths and lithotypes, within 0.10 °C of overall uncertainty. This work has highlighted that metrological traceability is fundamental to asses data quality and establish comparability among different measurements; that there are strong differences between air temperature and near-surface rock temperature; and that there are significant differences of rock temperature acquired in different aspect conditions. Finally, solar radiation, slope/aspect conditions and lithotype, seem to be the main driving factors of rock temperature.

Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and build one susceptibility map for each type. The analysis - carried out by using a Bayeian version of a Generalized Additive Model with a multiple intercept for each Italian region - revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the datases being assembled from twenty sub-inventories, each prepared by different administrations of the Italian regions. As a result, this spatial inhonomegenity may lead to a biased national-scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis suggested by the multiple intercepts results. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine. We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine sectors. The whole procedure is implemented in R-INLA. This allowed to examine fixed (linear) and random (nonlinear) effects from an interpretative standpoint and produced a full prediction equipped with an estimated uncertainty. We propose this overall modeling pipeline for any landslide datasets where a significant mapping bias may influence the susceptibility pattern over space.

Terrain classification based on slope units (SU). Colors match the figures below and correspond to ten classes (clusters), obtained by k-means clustering with slope unit aggregation level. Clustering was applied using mean and standard deviation of morphometric variables calculated within each slope unit polygon: elevation is the dominant contribution. The inset shows the location of the study area (blue polygon), mostly in Nepal. Maps in EPSG:4326. © Journal of Maps, 2022

Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies, and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km2, and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale, and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases.

The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration rainfall extremes and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2-RED), we present a systematic study of the relationship between geomorphological forms and the average of rainfall extremes (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. After analyzing the results, we repeated the analysis on geomorphological subdivisions of Italy. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of rainfall extremes. The longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall resulted to be significantly related to the index rainfall, and to play different roles for different durations (1- to 24-hours). However, when comparing the results of the best multivariate regression models with univariate regressions for morphological subdivisions, we found that "local" rainfall-topography relationships within the geomorphological subdivisions outperformed the country-wide multiple regressions and offered a reasonable representation of the effect of morphology on rainfall extremes.

In mountain environments, the coupling of hillslopes processes with the channel network during extreme events is of great importance for rivers dynamics, as debris flows and landslides are among the most important sources of sediments. The Stolla Creek (40 km drainage area, South Tyrol, Italy) is a confined and partly confined mountain channel that was affected by an extreme flood in August 2017, followed by a smaller event in August 2020. The geomorphic effects of the two floods were investigated both in the main channel and over the entire basin with the aim to assess the impacts of the lateral sediment connectivity to the channel response and to the event-scale sediment export. An integrated approach was applied, including radar rainfall estimation, hydrologic-hydraulic analysis, analysis of morphological changes and sediment delivery to the stream network. Hillslope and channel processes were mapped and characterized by using geomorphological analysis of multitemporal orthophotos and Digital Terrain Models. Debris-flow connectivity to the main channel was derived by combining field evidence and GIS-based analysis. The 2017 flood was caused by rainfall with a short duration (6 h) and a rainfall intensity exceeding 45 mm/h. More than 600 debris flows were triggered in the Stolla basin, and the main channel experienced widening (width ratio between 1.3 and 4.9) through bank erosion and overbank deposition. Widening was accompanied by aggradation in the river corridor up to 1.2 m or incision down to -2.2 m. The 2020 flood was characterized by lower rainfall intensity (max 17 mm/h) and a longer duration (48 h), and debris flows were not triggered. The moderate magnitude of the 2020 flood peak did not lead to channel widening, but marked bed incision (up to -1.4 m) occurred in the reaches where aggradation took place during the 2017 event. In both flood events, limited volumes of sediments were exported from the catchment outlet. Overall, our results highlight how structural connectivity at the basin scale determines the potential sediment cascades linking hillslopes to channels but time-varying functional connectivity - driven by hydrological drivers as rainfall intensities and durations - eventually control the actual sediment transport effectiveness both on hillslopes and along the channel.

Naples has always had a very special relationship with its underground layers: under the 0 level, there is the negative face of the aboveground city; so far, not entirely rediscovered, monitored, or valorized. Those spaces offer the key to a deeper knowledge on the urban history of the city because, even though this geological area is not characterized by natural caves at all, all Neapolitan artificial cavities are a testimony to the life of the inhabitants and the skills they adopted to maximize the potential of the profitable natural location of the city. Nowadays monitoring underground networks is fundamental to guarantee the security of the present aboveground city, but, at the same time, underground space can also be a resource for future networking, in the respect of archeological layers. Underground space can also be adopted in the definition of cultural routes and can stimulate the creation of virtual tours as well. With such a multifaceted character, a multidisciplinary approach to the underground layers of Naples is required. This chapter is the result of a multidisciplinary approach to the study of Naples' underground.