Wildfires lead to socio-economic and environmental impacts. These impacts include hydrological instability, which can cause severe damage, especially where infrastructures are present. Post-rehabilitation measures can be useful in reducing or preventing erosion or hydrogeological risks. Decision-makers are called on to prioritize post-fire intervention areas and allocate public funds for this purpose. This work focuses on the assessment of erosion and hydrological risk potential in forested slope areas affected by wildfire using a Multi-Criteria Decision Analysis (MCDA) approach integrated with a GIS environment on a regional scale. Expert perception was considered using the pairwise comparison method as part of the Analytical Hierarchy Process (AHP). This allows expert stakeholders to rank relevant criteria, providing a quantitative metric (weight) for qualitative data. Two MCDA methods are used and compared: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA). Fire frequency, slope (gradient and length), and proximity to infrastructures were found to be the most important factors by the stakeholders. The WLC method provides evidence classified into high and moderate suitability class areas characterized by high values for fire frequency or slope gradient. Conversely, the OWA method, ranging from low to high risks, makes it possible to adapt the method and obtain a range of suitability maps. Novelties of the MCDA-GIS combined methodology adopted in this work are its application on a regional scale and the combination of vulnerability and driving-force factors (namely presence of grey infrastructures, fire frequency). The MCDA-GIS methodology can be suitable for public administrations in that it allows for mapping a regional area more quickly and thus facilitates sector planning.

This study aimed to emphasize the significance of spatial variability in soil strength parameters on the behavior of nailed walls, highlighting the necessity of probabilistic design approaches. The investigation involved a 7.2-m nailed wall reinforced with five nails, simulated using the local average subdivision random field theory combined with the limit equilibrium method and the FEM, known as the random limit equilibrium method (RLEM) and the random finite-element method (RFEM) approaches. Initially, the wall stability was evaluated by RLEM using 10,000 Latin hypercube sampling realizations. The wall was globally stable among all samples for a correlation length equal to its height (7.2 m). The wall behavior, associated displacements, moments, wall shear forces, nail axial forces, and ground settlements were examined using RFEM. The RFEM analysis reveals that different random fields influence the maximum displacement (H-max), maximum moment (M-max), and maximum shear force (Vmax) experienced by the wall. The cumulative distribution function plots were generated for the wall critical parameters, including H-max, M-max, and V-max. Leveraging the simple weighted averaging and ordered weighted averaging techniques, different combinations of H-max, M-max, and Vmax were assessed with varying weight assumptions. This allowed us to identify critical random field realizations and estimate the level of risk using a newly introduced parameter, the decision index. Finally, the effect of different correlation lengths (isotropic and anisotropic) for two different coefficients of variation of soil strength parameters on the distribution of H-max, M-max, and Vmax was studied. The findings highlight the importance of considering the spatial variability of soil properties to achieve a reliable design of nailed walls.

Alpine vegetation is considered to be particularly sensitive to climate changes. Here we document changes in species richness, distribution and composition over the past 50 years by resurveying vegetation in Rondane, a well-studied alpine area in central Norway. We estimated changes in species occurrences, species richness and species' realized optima to study relationships between vegetational and environmental change. We used a weighted average approach with elevation and indicator values for light, temperature, pH, moisture, nutrients and tolerance to snow-cover duration. Permutation tests, allowing for unequal sampling in the original survey and the resurvey, indicated whether vegetation changes were statistically significant. We found no significant change in the average number of species per plot since 1950. Of 21 species analysed for changes in frequency and realized optimum, ten showed statistically significant changes in frequency (six decreased, four increased), and six exhibited statistically significant changes in their optimum along the soil-pH gradient. Statistically significant optimum changes were found along the nutrient and light gradients (three species) and the elevation and snow-cover gradients (two species). No statistically significant changes were found along the temperature or moisture gradients. In comparison with other studies, our results suggest that recent climate changes have had a relatively low impact on alpine vegetation in the Rondane mountains. This is indicated by our species optimum analysis, which revealed few changes along gradients that can be directly linked to the climate (temperature and soil moisture) whereas most detected changes appear to be responses to factors related to soil pH. The relative constancy of species' optima and hence species composition may be explained most parsimoniously by the species pool in the Rondane area, which consists largely of common and widespread species with wide ecological amplitudes and hence broad tolerances to environmental change.