Understanding significant precipitation in Madeira island using high-resolution numerical simulations of real cases

In order to advance the knowledge about precipitation development over Madeira island, four rainfall patterns are investigated based on high-resolution numerical simulations performed with the MESO-NH model. The main environmental conditions during these precipitation periods are examined, and important factors leading to significant accumulated precipitation in Madeira are shown. We found that the combination of orographic effect and atmospheric conditions is essential for the establishment of each situation. Under a moist and conditionally unstable atmosphere, convection over the island is triggered, and its location was determined mainly by variations of the ambient flow, which was also associated with different moist Froude numbers. Interestingly, our results showed some similarities with situations discussed in idealized studies. However, the real variations of the atmospheric configuration confirm the complexity of significant precipitation development in mountainous regions. In addition, precipitating systems initially formed over the ocean were simulated reaching the island. The four periods were characterised by different time durations, and the local terrain interacting with the mesoscale circulation was decisive in producing a large part of the precipitation, which concentrated in distinct regions of the island induced by the airflow dynamic.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.