Small-scale portable rainfall simulators are an essential research tool for investigating the process dynamics of soil erosion and surface hydrology. There is no standardisation of rainfall simulation and such rainfall simulators differ in design, rainfall intensities, rain spectra and research questions, which impede drawing a meaningful comparison between results. Nevertheless, these data become progressively important for soil erosion assessment and therefore, the basis for decision-makers in application-oriented erosion protection.

Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P < 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

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Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests.

Forest fires represent a major driver of change at the ecosystem and landscape levels in the Mediterranean region. Environmental features and vegetation are key factors to estimate the ecological vulnerability to fire; defined as the degree to which an ecosystem is susceptible to, and unable to cope with, adverse effects of fire (provided a fire occurs). Given the predicted climatic changes for the region, it is urgent to validate spatially explicit tools for assessing this vulnerability in order to support the design of new fire prevention and restoration strategies. This work presents an innovative GIS-based modelling approach to evaluate the ecological vulnerability to fire of an ecosystem, considering its main components (soil and vegetation) and different time scales. The evaluation was structured in three stages: short-term (focussed on soil degradation risk), medium-term (focussed on changes in vegetation), and coupling of the short- and medium-term vulnerabilities. The model was implemented in two regions: Aragón (inland North-eastern Spain) and Valencia (eastern Spain). Maps of the ecological vulnerability to fire were produced at a regional scale. We partially validated the model in a study site combining two complementary approaches that focused on testing the adequacy of model's predictions in three ecosystems, all very common in fire-prone landscapes of eastern Spain: two shrublands and a pine forest. Both approaches were based on the comparison of model's predictions with values of NDVI (Normalized Difference Vegetation Index), which is considered a good proxy for green biomass. Both methods showed that the model's performance is satisfactory when applied to the three selected vegetation types.