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.

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.

The role of effective irrigation management for optimal food production is well recognized and can be partially solved through the improvement of water use efficiency (WUE). To control the quantity of the water applied to actual crops, net irrigation water requirements (NIWR) are needed. The computation of NIWR is based on the estimation of crop water requirements (CWR) and soil water balance, where crop evapotranspiration (ET_c) is the main component. Earth observation (EO) using remote sensing (RS) has already become an important tool for the quantification and the detection of spatial and temporal distributions and variability in several environmental variables at different scales. Remotely sensed models are currently considered to be suitable for crop water use estimation in the field and at regional scales.

Drought is expected to intensify under climate change, posing significant risks to Mediterranean agroecosystems. This study provides long-term projections of drought and wetness conditions for three representative Mediterranean regions—Eastern Mancha (Spain), Sidi Bouzid Governorate (Tunisia), and the Beqaa Valley (Lebanon)—to support climate-resilient planning. Future monthly precipitation (2020–2050) was dynamically downscaled using the Weather Research and Forecasting (WRF) model under the RCP4.5 scenario, and the Standardized Precipitation Index (SPI12) was subsequently applied to quantify drought severity at annual and monthly scales. By integrating dynamically downscaled WRF projections with pixel-based SPI analysis across three spatially distinct Mediterranean regions, the study provides a novel, spatially explicit and comparative framework for assessing future drought and wetness extremes in support of climate-resilient planning. The results reveal spatial variability and moderate temporal fluctuations across the three regions, reflected in differing timings and intensities of their driest and wettest hydrological years. Spain is projected to experience its driest hydrological year in 2046–2047, Tunisia in 2030–2031, and Lebanon in 2047–2048. The wettest years are projected to occur in 2045–2046 for Spain and Tunisia, and in 2028–2029 for Lebanon. Although extreme drought events are not widely anticipated, localised severe dry periods emerge in many parts of the study areas. while in Lebanon, these conditions also extend into the winter and spring. These findings underscore the need for spatially targeted adaptation rather than uniform regional measures. Identifying both driest and wettest projected years enhances preparedness, informs water-resource optimisation, and supports agricultural land-use planning, especially in areas with favourable future climatic conditions. Integrating drought projections into multi-hazard planning (i.e., drought and floods) frameworks can further strengthen territorial resilience in regions facing increasing climate-related extremes.

The required amount of water for crop production differs from region to region, mainly due to the decreasing “effectiveness” of rainfall in maintaining plant growth due to increasing evaporation. Effective rainfall is related to the available moisture in the plant’s root zone, allowing the plant to germinate, emerge, and maintain its growth. There is, thus, a need to design agrometeorological and rain gauge networks for monitoring suitable crops, leading to sustainable agriculture. However, the minimum density needed for a rain-gauge network depends on the variability in space and time of the intended precipitation fields, and a variety of factors, including topographical features, accessibility, and ease of maintenance, influence the choice of rain-gauge locations. The number of rain gauges, rather than their location, is what optimizes a rain gauge network for various time scales, such as daily or monthly data. To map mean daily or mean monthly precipitation over watersheds or medium-sized agroclimatic zones and monitor crops for sustainable agriculture, this study focuses on the number of grid cells leading to the design of a rain gauge network, specifically a regular rain gauge grid scenario, considering the areal extent of land suitable for agriculture based on the agroclimatic zoning methodology. The universal kriging approach, an interpolation method based on geostatistical analysis, is used to calculate the required number of grid cells for the network’s density. Geostatistical analysis focuses on estimating and selecting the semi-variogram (SV) to determine the range or the decorrelation distance between rain gauges for precipitation estimation, superimposed on a grid mesh that encompasses the area’s agricultural land, such as Thessaly in Greece, as delineated by the agroclimatic zoning methodology. The daily and monthly mean climatological precipitation database is used, based on the CHIRPS-v3 dataset. The results indicate that the best fit for both daily and monthly mean precipitation is provided by the Spherical SV model, and the corresponding maximum range, which also represents the decorrelation distance, is calculated at 28.162 km for both mean daily precipitation and mean monthly precipitation, respectively. In summary, the size of the selected grid cells, based on the computed range of the selected SV, is 28 × 28 km² for the region of Thessaly, where one rain gauge is expected to be installed in each grid, leading to a total number of 18 grid cells.