Climate change is a fundamental challenge for humanity as it deeply and pervasively affects the way we live on the planet. All human activities are affected by climate variability, which is due to natural factors (changes of natural cycles of atmospheric and oceanic mechanisms) and anthropic activities (greenhouse gas production). Climate change has an extremely heterogeneous character in terms of space, temporal variability, and distribution. This peculiarity implies the need to identify key local factors for the geographical area of interest along with knowledge of remote forces and an effective multidisciplinary approach to tackle its negatives impacts.

In developing countries, information on the area and spatial distribution of paddy rice fields is an essential requirement for ensuring food security and facilitating targeted actions of both technical assistance and restoration of degraded production areas. In this study, Sentinel 1 (S1) and Sentinel 2 (S2) imagery was used to map lowland rice crop areas in the Sédhiou region (Senegal) for the 2017, 2018, and 2019 growing seasons using the Random Forest (RF) algorithm. Ground sample datasets were annually collected (416, 455, and 400 samples) for training and testing yearly RF classification. A procedure was preliminarily applied to process S2 scenes and yield a normalized difference vegetation index (NDVI) time series less affected by clouds. A total of 93 predictors were calculated from S2 NDVI time series and S1 vertical transmit–horizontal receive (VH) and vertical transmit–vertical receive (VV) backscatters. Guided regularized random forest (GRRF) was used to deal with the arising multicollinearity and identify the most important predictors. The RF classifier was then applied to the selected predictors. The algorithm predicted the five land cover types present in the test areas, with a maximum accuracy of 87% and kappa coefficient of 0.8 in 2019. The broad land cover maps identified around 12,500 (2017), 13,800 (2018), and 12,800 (2019) ha of lowland rice crops. The study highlighted a partial difficulty of the classifier to distinguish rice from natural herbaceous vegetation (NHV) due to similar temporal patterns and high intra-class variability. Moreover, the results of this investigation indicated that S2-derived predictors provided more valuable information compared to VV and VH backscatter-derived predictors, but a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs. An example is finally provided that illustrates how the maps obtained can be combined with ground observations through a ratio estimator in order to yield a statistically sound prediction of rice area all over the study region.

A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

This paper discusses some methodological aspects of the historical analysis of drought, particularly the trend assessment. The Standardized Evapotranspiration Index (SPEI) is widely used as a measure of drought condition. Since different SPEI thresholds allow classifying the risk into moderate, severe, and extreme, the drought occurrence becomes a counting process. In this framework, would a statistical trend test based on a Non-Homogeneous Poisson Process (NHPP) give a similar result of the nonparametric Mann–Kendall (M-K) test? In this paper, we demonstrate that the NHPP approach is able to characterize the information given by the classical M-K approach in term of drought risk classes. Furthermore, we show how it can be used to reinforce the framework of drought trend analysis in combination with a standard non-parametric approach. At a global scale, we find that: (1) areas under increasing risk of drought identified by the NHPP approach are considerably larger in comparison to those identified by M-K; and (2) the results of the two tests are different during crucial periods such as hydrological droughts in winter and spring.

Key message: We found a significant increase in the latewood density of European beech, and a decrease in the latewood and mean wood density of silver fir and Norway spruce in European mountain forests over the period 1901–2016. In the past century, drought did not directly influence the wood density trend of the three studied species. However, for both fir and spruce, drought indirectly affected the mean wood density via changes in the latewood to earlywood ratio, i.e., in the case of extreme drought, trees with high values of latewood to earlywood ratio experienced a slight attenuation in the declining trend of their mean wood density. Context: Century-long wood density measurements can provide novel information on tree response to climate change and the carbon sequestration potential of forest ecosystems. Still, the knowledge about long-term changes in wood density of European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.), and Norway spruce (Picea abies (L.) H.Karst.) in European mountain forests needs to be further explored. Aims: We assessed long-term changes in tree-ring mean wood density, earlywood density, and latewood density in trees of the three species between 1901 and 2016. We investigated the influence of endogenous factors (i.e., tree-ring width, current tree diameter, and latewood to earlywood ratio) and drought events on wood density. Methods: In total, 150 tree cores were sampled from mountain forests in Bulgaria, Bosnia and Herzegovina, Slovenia, Switzerland, and Germany. The mean, early, and latewood density of these samples were measured with the LIGNOSTATION™ system. To address our research aims, we applied a linear mixed-effect modelling approach using the data from 101 correctly cross-dated cores that spanned the entire period of analysis. Results: In the absence of drought, the latewood density of European beech increased by 7.1%, the late and mean wood density of silver fir decreased by 16.8% and 11.0%, respectively, and the late and mean wood density of Norway spruce decreased by 16.1% and 7.2%, respectively, between 1901–2016. In the past century, drought influenced the trends of wood density through an effect mediated by changes in the latewood to earlywood ratio. Specifically, in cases of extreme drought, silver fir and Norway spruce trees with a latewood to earlywood ratio value 50% higher than the median experience a slight attenuation in the declining trend of their mean wood density, making the negative impact of drought marginally less severe. Conclusions: Our findings have significant implications for the accuracy of carbon stock assessments, national greenhouse gas inventories, and the utilization of wood from the three species. Given the fact that changes in wood density follow species-specific patterns and the expectation of more frequent drought events in Europe, in the future, it is essential to build further tree-ring density time series for other species and sites to improve our understanding of how climate change alters wood density and carbon sequestration of forest ecosystems.