Youssef Youssef

Introduction: Saudi Arabia meets most of its water demand from largely non-renewable aquifers, yet the magnitude and spatial organization of storage depletion and the expression of interannual hydroclimate in the storage record have rarely been examined within one baseline-benchmarked framework. Methods: We integrate 22 valid years (2002–2024) of GRACE/GRACE-FO mascon gravimetry with GLDAS-2.1 surface storage; CHIRPS precipitation; and MODIS vegetation, temperature, evapotranspiration, and land-cover observations on a 0.5° grid and decompose each cell’s groundwater-storage (GWS) anomaly into a Theil–Sen trend and a detrended residual. Results: Kingdom-mean GWS declined at –0.91 cm yr ^-1 (95% CI –0.93 to –0.88; –19.8 cm in total, ≈4.3 × 10 ² km ³ , range 2.5−4.3 × 10 ² km ³ across surface-storage treatments), with a median detrended residual variance fraction of 4.3%. Under leave-one-year-out validation, five machine-learning models do not exceed a climatological baseline for the residual (best R ² = –0.02; 0.05 with fold-wise detrending), whose magnitude (≈1.5 cm RMSE) is comparable to GRACE annual-mean uncertainty. A rate-based Pettitt test detects no Kingdom-wide change point in the depletion rate (p = 0.96). Regional interrupted-time-series and difference-in-differences tests regarding the 2016–2018 agricultural reforms show a post-2018 deceleration over the north-western wheat belt (0.42 cm yr ^-1 , 95% CI 0.09−0.73) that is distinguishable from zero, whereas its contrast against the other agricultural regions (0.31 cm yr ^-1 , 95% CI –0.07 to 0.70) is not; the pattern is consistent with, but does not demonstrate, a redistribution of depletion. A cross-sectional model reproduces the spatial pattern of depletion rates under leave-spatial-block-out validation (R ² = 0.60 with cell coordinates; 0.24, 95% CI –0.01 to 0.39, with surface covariates alone); the evapotranspiration trend carries the largest permutation importance, followed by the precipitation climatology and daytime land-surface temperature, with the irrigated-area covariates below them. Discussion: The results provide an uncertainty-qualified evidence base for groundwater governance under Vision 2030 and a transferable protocol for hyper-arid, abstraction-dominated regions.

Gulf cities contain extensive undeveloped land inside their urban boundaries, yet its extent is poorly documented. This study examined the urban–vacant land paradox in the Dammam Metropolitan Area, Saudi Arabia, using Google Earth Engine. Annual Landsat composites (2000–2024) were classified with a spectral index rule capturing a built-and-bright-bare (BBB) signal and potential vacant land, and a Random Forest with 14 predictors was applied to 2024 Sentinel-2 imagery. The BBB extent rose from 89,893 ha in 2000 to 94,043 ha in 2015 and fell to 87,875 ha in 2024. The decline was significant but matched the effect of a 0.01 shift in the NDBI threshold and coincided with the withdrawal of Landsat 7, whose composites yield higher NDBI values than those of Landsat 8; it is not interpreted as land-cover change. The negative BBB–vacant correlation (r = −0.864) arose from compositional closure. The 2024 classification mapped 46,766 ha of urban and 42,474 ha of vacant/bare land (overall accuracy 75.05%, κ = 0.620, against WorldCover-derived labels; area-weighted accuracy 72.27%). Accuracy was weakly sensitive to hyperparameters, and disagreement with the reference clustered along class boundaries. Undeveloped land rivals the urban footprint in extent, a finding relevant to infill-oriented planning in arid cities.

Wildfires can rapidly alter soil physicochemical and biological properties, but short-term paired field evidence from North African forests remains limited. We assessed changes in 14 soil indicators at ten forest sites in northeastern Algeria and interpreted these field observations within a regional GIS analysis of Mediterranean wildfire exposure. The same locations were sampled in March 2026, before the seasonal fire period, and revisited in August 2026. The measured variables comprised soil pH, electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN), available phosphorus (P), C:N ratio, microbial biomass carbon (MBC), basal respiration, dehydrogenase activity (DHA), MBC:SOC ratio, gravimetric soil moisture, bulk density, water-stable aggregates, and surface litter biomass. Within-site changes were evaluated using exact paired Wilcoxon tests with Holm correction and paired-bootstrap confidence intervals. Across the ten sites, pH increased by 0.60 units, EC by 112.9%, and available P by 79.6%. In contrast, SOC, TN, MBC, basal respiration, soil moisture, water-stable aggregates, and litter biomass decreased by 26.7%, 36.8%, 49.4%, 44.2%, 40.5%, 37.1%, and 71.0%, respectively. Bulk density and C:N increased by 11.2% and 11.3%, while DHA increased by 29.8%. All 14 paired comparisons remained significant after Holm adjustment (adjusted p = 0.027). PCA accounted for 96.2% of the total variance and separated the March and August profiles. At the regional scale, the complementary EFFIS analysis identified 800,540 ha of mapped wildfire exposure through 6 August 2026, with substantial concentration in Spain, Algeria, France, Italy, and Portugal. Collectively, the paired observations indicate a post-fire-period shift characterized by an ash-associated increase in soluble ions and available phosphorus alongside losses of organic matter, microbial biomass, moisture, aggregate stability, and surface litter. Because contemporaneous unburned control plots and independent plot-level burn-severity measurements were unavailable, the observed changes cannot be attributed exclusively to wildfire and may also reflect seasonal environmental variation.

Fine particulate matter (PM_2.5) over the Arabian Peninsula is poorly monitored at the surface. This study emulates the relative change in summer (June–August) PM_2.5 between 2020 and 2025 in the NASA GEOS-CF composition system with interpretable ensemble machine learning, using thirteen predictors from Sentinel-5P TROPOMI, ERA5-Land, CHIRPS, and MODIS Aqua on the native 0.25° grid (3933 cells), and tests how far the resulting skill transfers in space. Under 10 × 10 spatial blocking, tree-based ensembles reached cross-validated R² = 0.82–0.84, against 0.55 for linear regression. An identically tuned Random Forest using only latitude and longitude performed better (R² = 0.91) and remained better under larger blocks, exclusion buffers, and four of five held-out sectors; combined with the coordinates, the predictors added only modest skill. The predictor model lost all skill once training cells within 300 km were excluded, failed on an unseen eastern sector containing the single contiguous cluster of changes above +40% (r = 0.11), and did not transfer to other summers, and interpolation of the reference filled simulated gaps more accurately than every emulator. The leading predictor, the total-column O₃ change, is a latitudinal gradient (r = 0.94). Split-conformal intervals under a separated spatial design covered 82–98% of test cells at the 90% nominal level but 60% in the eastern sector. The spatially blocked skill of such emulators is therefore interpolative and should be reported together with transfer diagnostics.

Lake Nasser represents Egypt’s principal strategic freshwater reservoir; however, its long-term phytoplankton dynamics at the basin scale remain insufficiently characterized. This study presents a detailed spatiotemporal investigation of chlorophyll-a (Chl-a) variability across the reservoir during the 2002–2020 period using exclusively MODIS-Aqua Level-3 satellite observations. As no concurrent in situ measurements were available for validation, the reported values are treated as satellite-derived Chl-a estimates rather than direct observations of phytoplankton biomass. A non-parametric statistical approach, incorporating the Mann–Kendall test, Theil–Sen slope estimator, coefficient of variation (CV), and relative anomaly analysis, was used to characterize pixel-level climatology and temporal trends. The findings demonstrate that the reservoir exhibits pronounced spatial heterogeneity and a well-defined bimodal seasonal pattern associated with the annual Blue Nile flood pulse during August–October and winter convective mixing processes between November and February. Although annual-scale analysis did not indicate a statistically significant monotonic trend, seasonal investigations revealed strongly contrasting temporal responses throughout the year. At the pixel level, significant increases dominated ten of the twelve calendar months, from September through June, with median slopes among significant pixels of +0.36 to +1.47 mg m⁻³ yr⁻¹. July and August reversed this pattern, combining significant declines along the northern and central main channel with the steepest increases in the record at the extreme southern inflow. The directional contrast was unaffected by control of the false discovery rate within each calendar month (q = 0.05), which retained 66% of the significant pixels. Summer conditions were additionally marked by substantial temporal variability (CV > 60%) and pronounced positive anomalies, reaching +274.51% during August. Notably, these anomalies were spatially concentrated within the central and southern sectors of the reservoir, rather than within the conventionally productive northern region. This spatial redistribution, together with the broad enrichment tendency indicated by the pixel-level trends, points to an intensification of the summer maximum within the southern inflow zone and to a possible emergence of localized eutrophication risk, a hypothesis that requires confirmation through concurrent nutrient, transparency, and dissolved-oxygen observations. Overall, the study provides valuable geospatial insights for the environmental management of Lake Nasser, emphasizing the importance of implementing targeted early-warning systems for harmful algal bloom monitoring. The results also demonstrate the critical role of satellite-based Earth observation technologies in supporting adaptive water resource management under evolving climatic and hydrological conditions.

Multi-decadal trajectories of large arid-zone reservoirs are seldom described by integrated satellite observation. This study characterises the optical and thermal variability of Lake Nasser, Egypt, over 2005–2025. Eight monthly MODIS indicators—NDVI, EVI, NDWI, NDTI, a near-infrared reflectance index, white-sky albedo, and day- and night-time land surface temperature (LST)—together with the derived diurnal temperature range (DTR), were compiled in Google Earth Engine over 186 cells of a 5 km grid and analysed by non-parametric trend, change-point and correlation procedures under false-discovery-rate control, correction for serial correlation, and effective-sample-size significance testing. Because a fixed polygon cannot separate environmental change from shoreline migration, every trend was recomputed on four domains of decreasing shoreline exposure. Three signals survive on all four: night-time LST rises (+0.51 to +0.76 °C decade⁻¹), DTR contracts (−0.83 to −1.95 °C decade⁻¹) and albedo declines (−0.0034 to −0.0193 decade⁻¹). The NDVI, EVI, near-infrared and day-time LST declines that the fixed polygon reports are not reproduced under the control, whereas NDWI declines (−0.048 to −0.077 decade⁻¹) only once it is applied. Change-point tests date a shift in level to 2016–2017, three years before the first filling of the Grand Ethiopian Renaissance Dam.

Earthquakes represent one of the most destructive natural hazards, particularly in developing regions along the convergence zone of the Arabian and Eurasian tectonic plates, where they result in severe human casualties, extensive infrastructure damage, and major economic losses. This study applies an integrated methodology that combines geospatial data with a Geographic Information Systems (GIS)-based Analytic Hierarchy Process (AHP) to produce the first regional-scale, screening-level Seismic Hazard Zonation (SHZ) map for the Kurdistan Region of Iraq (KRI), situated at this tectonic boundary. Eight governing factors, including distance to active faults, tectonic lineaments, lithological characteristics, soil properties, earthquake magnitude, seismic frequency, and focal depth, were analyzed and weighted according to their relative influence on seismic hazard potential. Validation using the Frequency Ratio (FR) approach demonstrated FR values below 1 in low-susceptibility areas, approximately 1 in moderate-hazard zones, above 1 in the high hazard class (FR = 1.66), and markedly elevated in the very high hazard class (FR = 9.27), indicating a positive spatial association between the modeled hazard distribution and recorded seismic events; the latter value is partly inflated by the small areal extent of the very high class and the spatial clustering of events within it. The analysis categorized KRI into five hazard levels: very low, low, moderate, high, and very high. Approximately 2,284 km ² (4.9%) of the region falls within the very high hazard class, highlighting the urgent need for site-specific mitigation measures and disaster preparedness strategies. Overall, this screening-level relative zonation provides a first-order spatial prioritization of seismic hazard in the KRI, supporting regional planning and guiding policymakers toward areas where detailed site-specific investigations, probabilistic seismic hazard analysis, and geotechnical characterization should be prioritized.