Our ability to estimate surface deformation rates in the Central Mediterranean has considerably enhanced in the last decade thanks to the growth of continuous Global Navigation Satellite System (GNSS) networks. Focusing on the Apennine/Alpine seismogenic belt, this area offers the opportunity to test the use of geodetic strain rates for constraining active tectonic processes and for seismic hazard assessments. Given the importance of geodetic strain rate models in modern hazard estimation approaches, however, one has to consider that different approaches can provide significantly different strain rate maps. Despite the increasing availability of GNSS velocity data, in fact, strain rate models can significantly differ, because of the spatial heterogeneity of GNSS stations locations and inherent strategies in computing strain rates. Using a dense GNSS velocity dataset, this study examines three methods for estimating horizontal strain rates, described in the recent literature and selected to represent approaches of increasing mathematical complexity. Advantages, drawbacks and optimal settings of each method are discussed. The main result is an ensemble of strain rate models that enable the evaluation of epistemic uncertainties in seismicity rates models constrained by geodetic velocities.

We present the BVAL method, designed to forecast potentially damaging earthquakes (Mw ≥ 5.0) in Italy based on temporal variations of the b-value of the Gutenberg–Richter frequency–magnitude distribution. The b-value is used as an indicator of stress within the Earth's crust, with lower b-values associated with higher stress levels and an increased likelihood of significant seismic events. This method issues alarms when the b-value falls below a critical threshold. It is optimized using the HOmogenized instRUmental Seismic catalogue data from 1990 to 2004 and validated pseudo-prospectively using data from 2005 to 2022. Our analysis uses the recently developed b-positive (b+) method to compute the b-value from magnitude differences, providing resilience against data incompleteness. We compare the performance of the BVAL method with two established models: the Epidemic Type Aftershock Sequence (ETAS) model, which forecasts earthquake rates based on the epidemic principle that each shock triggers subsequent shocks, and the FORE model, which relies on the occurrence of strong foreshocks. Additionally, we evaluate two ensemble models that combine BVAL and FORE through additive (EADD) and multiplicative (EMUL) strategies to balance false alarms and missed events. The EADD model declares an alarm when either BVAL or FORE signals it, while the EMUL model triggers alarms only when both methods agree. We assess the predictive efficiency of these models using the area skill score, derived from Molchan diagrams, which plot the miss rate against the fraction of space-time occupied by alarms. Our results demonstrate that BVAL is less effective than FORE and ETAS at high space-time fractions, but it outperforms ETAS at low fractions (⁠ τ < 2–4 per cent), indicating its potential utility in scenarios where minimizing false alarms is critical. This comprehensive comparison highlights the strengths and limitations of each method, suggesting that integrating multiple forecasting strategies can enhance the reliability of earthquake preparedness and response efforts in Italy.

Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification forurgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present ProbShakemap, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine. ShakeMap modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the OpenQuake-engine libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. ProbShakemap provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between ProbShakemap and ShakeMap output.

The research scope of the papers published in this Special Issue mainly focuses on high- precision and high-reliability positioning, navigation, and timing (PNT) with Global Navigation Satellite System (GNSS) or multi-source sensors, resilient PNT with GNSSs or multi-source sensors in challenging environments, integrated PNT with GNSSs and multi-sensor systems, applications of PNT with GNSSs or multi-source sensors, etc.

The Apennines are a tectonically active belt that has experienced significant earthquakes (Mw6). The largest events primarily occurred along the chain axis, where a complex system of normal faults accommodates 2–3 mm/yr of SW-NE oriented extension, as precisely measured by a dense Global Navigation Satellite System network. Geodetic strain rates are now frequently used in earthquake hazard models; however, the impact of using such estimates, computed through different methods, for seismic hazard assessments may be difficult to evaluate. This study explores the relationship between geodetic strain rates and seismicity rates in the Apennines using three distinct horizontal strain rate maps and an instrumental seismicity catalog. We find that the principal directions of geodetic strain rate are kinematically consistent with those of strain release. We estimate a spatially heterogeneous seismogenic thickness using the distribution of earthquake depths, and we isolate likely independent seismicity using three different declustering methods. We observe a correlation between independent seismicity rates and the magnitude of strain rate, which can be represented by either a linear or, more accurately, by a power-law relationship. The variability in the strain-seismicity relationship depends on the combination of independent seismic catalogs and strain rate maps. This relationship is primarily influenced by the declustering technique more than the choice of the strain rate map and, in particular, by the number of aftershocks excluded during declustering. Seismicity models derived from these combinations were used to estimate and compare the seismic moment release rate with the tectonic moment rate estimated from strain rate maps and seismogenic thickness. Findings indicate that the tectonic moment rate exceeds the seismic moment release rate. We highlight uncertainties and potential causes, one of which could be a possible aseismic release of part of the moment rate.

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km2 is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km2 for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon.