Forests provide many environmental services, especially those related to the water cycle. In semiarid areas where water is a limiting factor for ecosystem functioning, forested areas can have a strong impact on ground water recharge. In these areas, proper knowledge of forests’ water balance is necessary to promote management practices that may ensure ecosystem properties and environmental services like water or carbon fixation. In this article, we review several ecohydrology topics within the framework of Mediterranean water-limited environments in two representative ecosystems: Kermes oak (Quercus ilex L.) and Aleppo pine (Pinus halepensis Mill.) forests. Both are the commonest species in countries that surround the Western Mediterranean Basin. We analysed the Blue and Green water components, i.e., green water is the water demand of forests, represented by evapotranspiration and interception; while blue water is the part of the balance involving runoff and deep percolation, which can be regarded as water directly usable by society. In general, different studies conducted in Mediterranean areas have pointed out that the water balances of Q. ilex and P. halepensis forests have low values for the Blue to Green water (B/G) ratios. Adaptive forest management like forest thinning can compensate for these ratios. Thinning has demonstrated to reduce losses by interception, but at same time, it can also increase individual tree transpiration and evaporation rates. However, these practices lead to higher B/G ratios when considering the whole stand. In future global change scenarios, in which drought conditions are expected to intensify, management practices can improve the water balance in these ecosystems by minimizing the risk of plant mortality and species replacement due to intense competence by water resources.

Acer monspessulanum (Montpellier Maple) is an important deciduous tree species native to the Mediterranean region. It is largely distributed in the southern part of western Europe; however, it is geographically less present in north Africa and western Asia. The effects of the most significant environmental variables for its habitat suitability, and climate change, are unclear in terms of the future changes to its distribution. The objective of the present study was to model the current and future geographical potential distribution of the Montpellier Maple in the Mediterranean basin and West Asia using maximum entropy modeling software (MaxEnt). The value of the Area Under the Curve (AUC) of MaxEnt was used to analyze the model’s performance. More than 5800 well-distributed presence points, elevation, slope, aspect, topographic wetness index (TWI), natural vegetation characteristics from MODIS products, and 19 bioclimatic variables were used to conduct the study. Regarding the projections of the species distribution under climate change, 17 global climatic models were used under two RCP scenarios (4.5 and 8.5) for the 2040–2060 and the 2060–2080 time periods. The results show that temperature seasonality (40% contribution to the model), elevation (33.5%), mean annual temperature (6.9%), mean annual precipitation (6.2%), and max temperature of the warmest month (4.5%) were identified as the primary factors that accounted for the current distribution of the Montpellier Maple. Under the climate change scenarios, MaxEnt predicts a large decrease in the species suitability area, with a shift towards the southwestern regions of the species distribution, especially to the mountainous zones of the Moroccan Atlas. Our results show that climate largely limits the distribution of the Montpellier Maple in the Mediterranean basin, as its change in the future is expected to significantly reduce the suitable area by more than 99% from the historical climate conditions, to reach only 16,166.9 and 9874.7 km² under the moderate RCP4.5 and extreme RCP8.5 scenarios, respectively, by the end of the 21st century. Our study can provide a good view of the future changes in the distribution of Montpellier Maple for its protection and sustainable management.

The Mediterranean Basin is one of the regions most affected by climate change, which poses significant challenges to agricultural efficiency and food security. While rising temperatures and decreasing precipitation levels already impose great risks, the effects of compound extreme events (CEEs) can be significantly more severe and amplify the risk. It is therefore of high importance to assess these risks under climate change on a regional level to implement efficient adaption strategies. This study focuses on false-spring events (FSEs), which impose a high risk of crop losses during the beginning of the vegetation growing period, as well as heat–drought compound events (HDCEs) in summer, for a high-impact future scenario (Representative Concentration Pathway (RCP) 8.5). The results for 2070–2099 are compared to 1970–1999. In addition, deviations of the near-surface atmospheric state under FSEs and HDCEs are investigated to improve the predictability of these events. We apply a multivariate, trend-conserving bias correction method (MBCn) accounting for temporal coherency between the inspected variables derived from the European branch of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). This method proves to be a suitable choice for the assessment of percentile-threshold-based CEEs. The results show a potential increase in frequency of FSEs for large portions of the study domain, especially impacting later stages of the warming period, caused by disproportionate changes in the behavior of warm phases and frost events. Frost events causing FSEs predominantly occur under high-pressure conditions and northerly to easterly wind flow. HDCEs are projected to significantly increase in frequency, intensity, and duration, mostly driven by dry, continental air masses. This intensification is several times higher than that of the univariate components. This study improves our understanding of the unfolding of climate change in the Mediterranean and shows the need for further, locally refined investigations and adaptation strategies.

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.