Natural and anthropogenic causes jointly lead to land degradation and eventually to desertification, which occurs in arid, semiarid, and dry subhumid areas. Furthermore, extended drought periods may cause soil exposure and erosion, land degradation and, finally, desertification. Several climatic, geological, hydrological, physiographic, biological, as well as human factors contribute to desertification. This paper presents a methodological procedure for the quantitative classification of desertification severity over a watershed with degraded groundwater resources. It starts with drought assessment using Standardized Precipitation Index (SPI), based on gridded satellite-based precipitation data (taken from the CHIRPS database), then erosion potential is assessed through modeling. The groundwater levels are estimated with the use of a simulation model and the groundwater quality components of desertification, based on scattered data, are interpolated with the use of geostatistical tools. Finally, the combination of the desertification severity components leads to the final mapping of desertification severity classification.

The growing number and effectiveness of Earth observation satellite systems, along with the increasing reliability of remote sensing methodologies and techniques, present a wide range of new capabilities in monitoring and assessing droughts. A number of drought indices have been developed based on NOAA-AVHRR data exploiting the remote sensing potential at different temporal scales. In this paper, the remotely sensed Reconnaissance Drought Index (RDI) is employed for the quantification of drought. RDI enables the assessment of hydro-meteorological drought, since it uses hydrometeorological parameters, such as precipitation and potential evapotranspiration. The study area is Thessaly, central Greece, which is a drought-prone agricultural region characterized by vulnerable agriculture. Several drought features are analyzed and assessed by using monthly RDI images over the period 1981–2001: severity, areal extent, duration, periodicity, onset and end time. The results show an increase in the areal extent during each drought episode and that droughts are classified into two classes, namely small areal extent drought and large areal extent drought, respectively, lasting 12 or 13 months coinciding closely with the hydrological year. The onset of large droughts coincides with the beginning of the hydrological year, whereas the onset of small droughts is in spring. During each drought episode, the maximum occurs usually in the summer and they all last until the end of the hydrological year. This finding could justify an empirical prognostic potential of drought assessment.

Future changes in drought characteristics in Greece were investigated using dynamically downscaled high-resolution simulations of 5 km. The Weather Research and Forecasting model simulations were driven by EC-EARTH output for historical and future periods, under Representative Concentration Pathways 4.5 and 8.5. For the drought analysis, the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI) were calculated. This work contributed to achieve an improved characterization of the expected high-resolution changes of drought in Greece. Overall, the results indicate that Greece will face severe drought conditions in the upcoming years, particularly under RCP8.5, up to 8/5 y of severity change signal. The results of 6-month timescale indices suggest that more severe and prolonged drought events are expected with an increase of 4 months/5 y, particularly in areas of central and eastern part of the country in near future, and areas of the western parts in far future. The indices obtained in a 12-month timescale for the period 2075–2099 and under RCP8.5 have shown an increase in the mean duration of drought events along the entire country. Drought conditions will be more severe in lowland areas of agricultural interest (e.g., Thessaly and Crete).

Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.

Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R² where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies.

Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in Spain, “Sidi Bouzid” in Tunisia, and “Bekaa” valley in Lebanon. To achieve this, time series analysis with advanced geoinformatic techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water-limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated with WLGE zones to identify non-crop-specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters are combined with NCSAZ to create the suitability zones. The results are promising as compared with the current crop production systems of the three areas under investigation. Due to climate change, the results indicate that these arid or semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the employment and use of remote sensing data and methods could be a significant tool for quickly creating detailed, and up to date agroclimatic zones.