In the last several decades, extended dry periods have affected the Mediterranean area with dramatic impacts on water resources. Climate models are predicting further warming, with negative effects on water availability. The authors analyze the hydroclimatic tendencies of a typical Mediterranean basin, the Flumendosa basin located in Sardinia, an island in the center of the Mediterranean Sea, where in the last 30 years a sequence of dry periods has seriously impacted the water management system. Interestingly, in the historic record the annual runoff reductions have been more pronounced than the annual precipitation reductions. This paper performs an analysis that links this runoff decrease to changes in the total annual precipitation and its seasonal structure. The seasonality is a key determinant of the surface runoff process, as it reflects the degree to which rainfall is concentrated during the winter. The observed reductions in winter precipitation are shown here to be well correlated (Pearson correlation coefficient of −0.5) with the North Atlantic Oscillation (NAO) index. Considering the predictability of the winter NAO, there is by extension an opportunity to predict future winter precipitation and runoff tendencies. The recent hydroclimatic trends are shown to impact hydrologic design criteria for water resources planning. The authors demonstrate that there is a dangerous increase of the drought severity viewed from the perspective of water resources planning.

Mediterranean mountainous areas of shallow soil often display a mosaic of tree clumps surrounded by grass. The combined role and dynamics of water extracted from the underlying rock, and the competition between adjacent patches of trees and grass, has not been investigated. We quantified the role rock water plays in the seasonal dynamics of evapotranspiration (ET), over a patchy landscape in the context of current and past seasonal climate changes, and land‐cover change strategies. Soil water budget suggests deep water uptake by roots of trees (0.8–0.9 mm/d), penetrating into the fractured basalt, subsidized grass transpiration in spring through hydraulic redistribution. However, in summer trees used all the rock water absorbed (0.79 mm/d). A 15‐year data set shows that, with increasing seasonal drought‐severity (potential ET/precipitation) to >1.04, the vertical water flux through the bottom of the thin soil layer transitions from drainage to uptake in support of ET. A hypothetical grass‐covered landscape, with no access to deep water, would require 0.68–0.85 mm/d more than is available, forcing shortened growing season and/or reduced leaf area. Long‐term decreasing winter precipitation and increasing spring potential ET suggest drying climate, so far with stable vegetation mosaic but progressively earlier peak of grass leaf area. Intervention policies to increase water yield by reducing tree cover will curtail grass access to rock moisture, while attempting to increase tree‐related products (including carbon sequestration) by increasing forest cover will limit water availability per tree leaf area. Both changes may further reduce ecosystem stability.

Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions.

Mediterranean ecosystems are typically heterogeneous and savanna-like, with trees and grass competing for water use. By measuring sap flow, we estimated high transpiration of wild olive, a common Mediterranean tree, in Sardinia despite dry conditions. This estimate agrees with independent estimates of tree transpiration based on energy balance, highlighting the wild olive’s strong tolerance of dry conditions. The wild olive can develop an adaptation strategy to tolerate dry conditions. In this Sardinian case study, the wild olive grew in shallow soil, and the tree roots expanded into the underlying fractured basalt. The trees survived in dry periods using water infiltrated during wet seasons into fractured rocks and held in soil pockets. We estimated a high upward vertical flux through the bottom soil layer from the underlying substrate, which reached 97% evapotranspiration in August 2011. The water taken up by tree roots from bedrock hollows is usually neglected in ecohydrological modeling.

The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.