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.

Sinkholes pose a significant hazard in Mexico City (CDMX), causing substantial economic damage. While the link between sinkhole formation and groundwater extraction has been studied, specific mechanisms vary by site. Our overall aim is to characterize the phenomenon of sinkholes in CDMX. To achieve this, we create a database with 13 influencing factors, including population density, well density, distance to faults, fractures, roads, streams, elevation, slope, clay thickness, lithology, subsidence rate, geotechnical zones, and soil texture. Sinkhole locations were obtained from CDMX's Risk Atlas (2017-2019). We shaped a susceptibility map based on statistical regression methods derived from applying linear regression models. For the susceptibility map, results showed that 40% of variables are significantly correlated with sinkhole density. Despite the regression model explained 24% of sinkhole density variability, it helped choosing variables for the susceptibility map that correlate better (89.7%). Hence, we identified that the northeast CDMX was the most susceptible zone. Therefore, the compound assessment of environmental factors is useful for the evaluation of susceptibility maps to identify prone factors for the generation of sinkholes. This framework provides relevant information for better use of the territory throughout the development of public policies.

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 degrees C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin.