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In recent decades, the increasing number and severity of wildfires have become a major concern in many regions of the world. Besides having direct consequences for forests, crops, and settlements, wildfires can have significant effects on the catchment's hydrology, altering their response to rainfall and promoting enhanced runoff and erosion. Increased runoff in burnt areas can result in catastrophic damage and loss of life from destructive debris flows and floods. Although observed in many regions of the world, post-wildfire debris flows/floods seem to prevail in the western USA and southern Europe. In Italy, post-wildfire debris flows/floods have been observed in a number of sectors of the Apennine range, and a total of 113 events occurred between 2001 and 2021 in the Campania region. A recent event occurred on August 24, 2020, in the Fosso delle Pescine catchment, on the southwestern slope of Mt. Omo in the L'Aquila municipality, hit by a severe wildfire at the beginning of the month. The consequences of the event on people and properties indicate the need for an evaluation of debris flow/flood hazard in both pre- and post-wildfire conditions. The analysis of these events may often be complex, requiring time and many input data. In this context, a simplified method of analysis which accounts for event initiation and propagation through a shallow water equation-based approach would represent a new and innovative solution for fast and preliminary debris flow/flood analyses. In this research, a reduced complexity analysis based on probability analysis of rainfall and 2D hydrodynamic modeling was carried out by estimating (i) wildfire extent and severity, (ii) sediment characteristics and availability, (iii) potential runoff induced by rainfall, and (iv) inundation by debris flow/flood. A debris flow/flood hazard model was developed on the basis of multiple return periods (10, 50, 100, and 300 years) for debris flow inundation scenarios derived by non-Newtonian 2D hydrodynamic modeling of debris flow hydrograph and qualitatively validated on the basis of the extent of alluvial fan deposits. Potential drawbacks of the model are related to the possible overestimation of surface runoff, related to the consideration of the whole catchment contributing area, and the limited volumetric sediment budget available at the headwater, which might not sustain a high return period event generation. A debris flow/flood event might originate from rainfall concentrated at the headwater of the catchment. [ABSTRACT FROM AUTHOR]

In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes (parroquias) located in the southern sector of the study area (i.e. Turi, Santa Ana, Tarqui, Nulti, Baños and Paccha). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi, San Joaquin, Tarqui, Sidcay, Baños, Ricaurte, Paccha and Chiquintad) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The methodological approach used, which accounts for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing population cities in low- to mid-income countries, where data usually needed for a comprehensive landslide risk analysis are non-existing or only partially available. [ABSTRACT FROM AUTHOR]

Statistical landslide susceptibility mapping is a topic in complete and constant evolution, especially since the introduction of machine learning (ML) methods. A new methodological approach is here presented, based on the ensemble of artificial neural network, generalized boosting model and maximum entropy ML algorithms. Such approach has been tested in the Monterosso al Mare area, Cinque Terre National Park (Northern Italy), severely hit by landslides in October 2011, following an extraordinary precipitation event, which caused extensive damage at this World Heritage site. Thirteen predisposing factors were selected and assessed according to the main characteristics of the territory and through variance inflation factor, whilst a database made of 260 landslides was adopted. Four different Ensemble techniques were applied, after the averaging of 300 stand-alone methods, each one providing validation scores such as ROC (receiver operating characteristics)/AUC (area under curve) and true skill statistics (TSS). A further model performance evaluation was achieved by assessing the uncertainty through the computation of the coefficient of variation (CV). Ensemble modelling thus showed improved reliability, testified by the higher scores, by the low values of CV and finally by a general consistency between the four Ensemble models adopted. Therefore, the improved reliability of Ensemble modelling confirms the efficacy and suitability of the proposed approach for decision-makers in land management at local and regional scales. [ABSTRACT FROM AUTHOR]