bioclim: An R package for bioclimatic classifications via adaptive water balance

bioclim is a software package in R language for bioclimatic classification based on the Type of Bioclimatic Regime approach, which combines climatic and soil properties to classify a region according to its suitability for plant vegetative activity. We present the software’s operating modes, capabilities and limitations, including real-world usage examples. Using monthly temperature, precipitation, and field capacity as inputs, bioclim follows a straightforward workflow using three functions to compute: i) a comprehensive water balance describing water resource dynamics throughout the year; ii) a bioclimatic balance to estimate plant vegetative activity; and iii) a collection of bioclimatic intensities quantifying vegetative activity changes. The program uses the results of these functions to classify bioclimatic type at zonal, regional and local scales. The three functions’ outputs can be calculated independently, strengthening the software’s cross-disciplinary application potential, such as climatology and hydrology. bioclim uses numeric and raster formats as input data and contains highly flexible options for a wide range of purposes, from scientific research to end users’ representations. The water and bioclimatic balance results can be presented in numerical, graphical, or cartographic formsThe present research was conducted within the framework of project SI3-PJI-2021-00398 funded by the Universidad Autonoma de Madrid and the Comunidad de Madri

Spatio-temporal Variability of daily precipitation concentration in Spain based on a high-resolution gridded data set

An analysis of the spatial and temporal variability of daily precipitation concentration (CI) in Spain was made based on a high-resolution (5×5km) daily gridded precipitation data set for the 1950-2012 period. For each grid point in the Iberian Peninsula (IP) and Balearic and Canary Islands, the average annual CI was computed, as well as its coefficient of variation and the 5th and 95th percentiles. Annual values were also computed, and the time series of the index were used to assess temporal trends over the whole period.The spatial distribution of the CI showed a strong relationship with the orographic barriers near the coastlines.The Canary Islands showed the highest values of CI, along with the eastern Mediterranean facade of the IP.The highest inter-annual variations oft he CI occurred in the southern IP and in the southern Canary Islands.The trends of CI were, overall, positive and significant, which indicates an increase of daily precipitation concentration over the study period and an increasing environmental risks scenario where erosivity, torrentiality, and floods may become more frequent