The increasing occurrence of widespread drought phenomena is a global environmental emergency, especially for the effects of ongoing climate change on groundwater availability. Dry years and extreme temperatures are common drivers of current climate impacts all over the world, including, for example, the freshwater supply for drinking and agriculture purposes, ecosystems, forestry, health, etc. In this frame, to ensure temporal water availability in water-stressed areas, sustainable groundwater management is an increasing challenge. Most groundwater in the South-East Latium Region, Central Italy, as in the whole Apennine Mountains chain, is stored in karst aquifers. In this area important water resources are present, but even here in the last decades they are affected by groundwater depletion as a consequence of occurring drought events, the upward trend in the global average temperature and the increase in groundwater extraction. Due to the lack of flow rate data of springs in many areas of Italy, spring response modeling could be a useful tool for supporting proper water resource management. Several research studies proposed methods based on relationships between spring discharges and rainfall data. The goal of this paper is to propose a simple linear model, based on rainfall-discharge cross correlations, in order to be able to simulate the effects of variation in rainfall on water resource connected Capodacqua di Spigno Spring minimum discharge. The results obtained using the developed model has been compared to an existing method that uses the Standard Precipitation Index (SPI) for the estimation of the minimum annual spring discharge.

Karst aquifer recharge areas are usually difficult to identify because of the complexity of these aquifers’ characteristics. On the other hand, their identification is very important in the aim of protecting the groundwater resources that these aquifers host. Regarding this topic, this paper presents an approach aimed at identifying karst aquifer recharge areas by the application of oxygen-18 and deuterium isotopes composition of groundwater coupled with hydrological features. Oxygen-18 and deuterium isotope composition of Capodacqua di Spigno Spring, in the South of the Latium Region, has been applied with rainfall and discharge values related to the feeding aquifer of this spring. As δ¹⁸O and δ²H values of groundwater samples are natural tracers of the recharge area’s elevation, we propose a model, based on the distribution of the basin surfaces involved as recharge areas, in relation to elevations. The model estimates, for any discharge value, the percentage of the topographic area involved in the aquifer recharge. The setting up of this simulated distribution is supported by a Weibull cumulative probability function. The results show that the measured discharges increase as larger areas with lower elevations are involved in the recharge process.

Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time.

The identification of recharge areas in karst aquifers allows us to perform sustainable management of these groundwater resources. Stable isotopes (δ¹⁸O and δ²H) have been largely used to provide information about recharge elevation in many mountainous regions. In this paper, an improved version of a recent “isotope-driven model”, for the identification of recharge areas, was applied to Capodacqua di Spigno Spring (south of the Latium region). The model upgrade consists of a preliminary check procedure to estimate the degree of influence of the rainfall’s isotopic variability on the spring water. This additional procedure gives us an indication of the reliability of the model and its applicability conditions. Moreover, the dataset of the spring was updated to analyze the degree of reliability of the isotope-driven model. The purpose of this study was to combine the previously mentioned isotope-driven model with hydrogeological tools. A quantitative study of the basin, based on the estimation of the average monthly infiltration volume, was performed by using the inverse hydrogeological water budget. In this way, the qualitative model for the recharge areas’ estimation was validated by a quantitative hydrogeological tool. Both models show that, for karst mountain basins, the recharge areas decrease as the average recharge elevations increase, including areas at high altitudes.

This paper proposes a simplified GIS-based procedure which aims to preliminary assess areas exposed to debris flows by evaluating erosion and deposition area into channels and along alluvial fans. The methodology is based on the definition of several morphometric parameters on Digital Elevation Models (DEMs); it is independent from landslide volume and its rheological characteristics. By solving a set of parametric equations and excluding, from time to time, the unlikely areas by means of an analysis process that uses on/off-raster masks, potential erosion and deposition areas can be identified. The procedure was applied at six intra-mountain basins upstream of the village of Paupisi, Southern Italy, which was involved in multiple debris flow events in October 2015. The model estimated that a range between 15–21% of the selected basin drainage channels may be identified as erosion areas. Moreover, comparison of the recent debris flow paths with model outputs shows a good performance of the procedure, which was able to predict about 46% of the total actual propagation and invasion areas and about 62% of the distribution deposit areas. The methodology can be considered a useful screening for the identification of debris flow-susceptible channels, suitable for use in preliminary hazard evaluations.

In October 2015, two intense rainfall events hit the central and southern regions of Italy and triggered a combination of different and widespread effects, including floods, landslides, and soil erosion. These outcomes devastated about 68 municipalities of the Benevento province (Campania region), killed two people, and caused millions of euros worth of damage to structures, infrastructures, and agriculture. The town of Benevento was one of the sectors most affected by overflow. Extensive areas characterized by flyschoid outcrops experienced widespread occurrences of soil erosion and landslides, and destructive, high-velocity debris flows (about 50) afflicted areas that had experienced heavy rainfall of higher intensity (total rainfall of 415.6 mm). In this study, the characteristics of these rainfall events and related geomorphological processes were determined by (i) analyzing the available rainfall data to identify the spatial pattern, distribution, and statistical characteristics of the two storms and (ii) mapping the storm effects, such as flooded areas, landslide types, and soil erosion. These effects were then related to the spatial distribution of the storms and the local geological and geomorphologic settings that drove their initiation and development.