Search Results (2,125)

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

A large proportion of global peanut cultivation occurs in arid and semiarid environments, where water scarcity poses a major limitation to productivity. Climate change further intensifies this challenge by causing irregular rainfall patterns. This study aimed to investigate the physiological and yield responses of peanut genotypes under well-watered and water-stressed conditions. Seven genotypes, five drought-tolerant (C76-16, Line-8, PI 502120, AU-NPL-17 and AU16-28) and two drought-sensitive (Valencia-C and AP-3) were evaluated under two irrigation regimes across consecutive years (2024 and 2025). Seven yield-associated traits (number of pods per plant, pod length, pod width, pod yield per plant, seed weight, hundred-seed weight and pod yield per plot) along with three physiological traits (stomatal conductance, photosynthetic efficiency and leaf temperature) were measured at three growth stages. Drought stress caused a significant reduction in almost all traits, including pod yield per plot (42–44%) and hundred-seed weight (24–38%). Stomatal conductance showed the greatest reduction at all stages, especially during flowering (31–80%) and pod filling (45–74%) stages. Correlation analysis revealed that yield-related traits were negatively correlated with stomatal conductance at pod-filling under water-stress conditions. Genotypes such as PI 502120, AU-NPL-17 and C76-16 maintained higher yields with less reduction under water-stressed conditions. This study also confirmed that Line-8 employs a water-saver strategy, whereas PI 502120 uses a water-spender mechanism to cope with water stress. Additionally, findings showed that the flowering and pod-filling stages are more severely affected physiologically by drought stress, which likely contributed to the observed yield reduction. Full article

Nabkhas are a common type of biogenic aeolian landform in arid and semi-arid regions. Their morphological characteristics, surface sediment grain size composition, and spatial distribution patterns can, to some extent, be associated with the interactions between vegetation and the aeolian environment. In this study, nabkhas formed around Caragana tibetica shrubs in the desert steppe of Damao Banner, Inner Mongolia, were selected as the research object. Based on field investigations, UAV image identification, grain size analysis, and spatial point pattern analysis, the characteristics of nabkhas were comparatively analyzed among a control plot without shrubs (CK) and three shrub-covered plots: a low coverage plot (LCP), a medium coverage plot (MCP), and a high coverage plot (HCP). The results showed that (1) some morphological parameters of nabkhas varied among plots with different vegetation cover, but the responses of various indicators were not entirely consistent. The MCP exhibited relatively higher values in indicators such as shrub long axis (L_g), short axis (W_g), and windward slope length (L_y). (2) The surface sediments of nabkhas were mainly composed of silt and fine sand, followed by very fine sand. Compared with the CK, the silt content was generally lower in the shrub-covered plots, whereas the contents of fine sand and very fine sand were higher. The mean grain size (Mz, Φ value) tended to decrease, while the skewness (SK_G) and kurtosis (K_G) tended to increase, and the sorting coefficient (σ_G) showed relatively limited variation. (3) In the LCP, MCP, and HCP, the fractal dimension (D) was significantly positively correlated with the Mz and σ_G (p < 0.05), and significantly negatively correlated with the SK_G and K_G (p < 0.01), suggesting that the D may be associated with variations in sediment grain size structure. (4) Overall, the nabkhas around Caragana tibetica shrubs exhibited a spatial distribution pattern characterized by aggregation at small scales and randomness at large scales, with small-scale clustering being more evident in the MCP and HCP. In general, nabkhas around Caragana tibetica shrubs under different vegetation cover conditions showed observable differences in morphological characteristics, surface sediment grain size composition, and spatial distribution patterns, providing a comparative case reference for the study of nabkhas in desert steppe areas. Full article

High-resolution Köppen–Geiger projections indicate that several cold desert (BWk) regions are likely to transition toward hot desert (BWh) regimes during the twenty-first century, challenging the environmental logic of vernacular architecture. Despite extensive simulation-based research on passive cooling in established BWh contexts, limited attention has been given to climate-type transition zones and to the morphological continuity of traditional housing systems. This study investigates the adaptive capacity of Bukhara’s courtyard houses under projected BWk–BWh reclassification. Employing an analytical generalization approach, the research integrates systematic literature mapping, typological morphological analysis, and a threshold-based compatibility matrix. Findings reveal that climate transition produces a form of morphological discontinuity by weakening diurnal discharge assumptions embedded in high thermal mass systems. However, courtyard typologies retain a resilient passive core when recalibrated through microclimatic amplification strategies. The proposed staged adaptation framework contributes a heritage-sensitive decision model that reconciles climatic performance with spatial integrity, offering transferable guidance for cli-mate-intensifying desert regions. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

Crop simulation models and irrigation decision support systems (IDSS) are essential tools for improving water use efficiency, particularly in Mediterranean and semi-arid regions where water scarcity is a major constraint. However, many platforms are either too complex for widespread adoption or too simplified to capture the combined effects of temperature, water stress, and elevated CO₂ on crop responses. This paper presents the Easy Simulator Crop Model (EaSiCroM), a modular, low-parameterisation system designed to simulate daily crop growth, soil water dynamics, and irrigation requirements. Canopy development follows a beta-function LAI trajectory with Beer–Lambert canopy cover, progressively constrained by temperature (T_lim) and water stress (K_stress, KS_cc). Biomass accumulation combines a water productivity (WP) approach with an optional radiation-use efficiency (RUE) pathway, both scaled by a Michaelis–Menten CO₂ fertilisation sub-model. The soil water balance includes a two-stage bare-soil evaporation formulation and multiple irrigation triggering strategies. EaSiCroM is implemented as a Docker-containerised web application supporting single-crop, multi-plot, and near-real-time irrigation modes, with optional assimilation of user-provided canopy observations from field or remote sensing sources. A proof-of-concept evaluation across four Mediterranean crops (processing tomato, biomass sorghum, sunflower, and durum wheat) yielded RRMSE values between 13.8% and 26.1%, comparable to AquaCrop and CropSyst on the same datasets. Its modular architecture makes it suitable for both research and operational irrigation management in water-scarce environments. Full article

In this study, we aimed to isolate indigenous plant-growth-promoting rhizobacteria (PGPR) with high indole-3-acetic acid (IAA)-producing capacity from alfalfa rhizospheres in arid regions of Northwest China and systematically evaluate their bioacceleration effects on alfalfa growth. Fifteen bacterial strains were isolated from rhizosphere soils collected in Ningxia and Inner Mongolia. Among them, four high-IAA-producing strains were selected and identified as Brevundimonas sp. B3, Pantoea sp. P10, and Microbacterium sp. M1 and M7 based on 16S rDNA sequencing. Pot experiments showed strain-specific growth-promoting effects: P10 significantly increased plant biomass (increasing fresh weight by 10.04% and dry weight by 11.76%, with p < 0.05), while M7 notably enhanced plant height (by 16.48%, with p < 0.05) and branching. Physiological and cytological analyses revealed that the tested strains improved chlorophyll content (30–45% above the control), reduced malondialdehyde (MDA) levels (20–40% below the control), and differentially regulated root-tip cell elongation. Principal component analysis further supported the comprehensive promotive effects of these strains, with P10 exhibiting the highest overall performance (PC1–PC4 cumulative variance: 83.1%). Within the limitations of controlled pot experiments, these findings highlight the potential of native PGPR strains, particularly P10 and M7, as promising candidates for developing region-specific microbial inoculants with which to enhance alfalfa productivity in arid and semi-arid environments. Full article

Under intensifying climate change and increasingly frequent extreme heat events, improving outdoor thermal environments has become critical for sustainable human settlements. While prior studies have mainly focused on urban contexts, systematic investigations of rural microclimates—particularly regarding the regulatory mechanisms of landscape configurations—remain limited. This study examines a mountain-adjacent village in the Loess Tableland region of China, integrating field measurements with ENVI-met simulations to analyze thermal characteristics of rural road spaces and the effects of vegetation and paving materials on human thermal comfort. The results show that village boundary areas experience the largest fluctuations in air temperature and relative humidity during midday and evening, indicating higher thermal sensitivity. Model validation demonstrates satisfactory accuracy, with RMSE values of 0.39–3.62 °C for air temperature, 1.32–3.22% for relative humidity, and 1.35–2.24 m/s for wind speed, and MAPE ranging from 0.80% to 9.05%. Furthermore, Basalt Brick and Populus alba show the best cooling performance, but when considering multiple factors such as temperature, humidity, and wind speed, Ligustrum lucidum has the most significant effects in improving thermal comfort and increasing humidity. Analysis based on Physiological Equivalent Temperature (PET) further indicates that vegetation configurations play a more substantial role in thermal comfort regulation than paving materials, and that different landscape elements exhibit synergistic and trade-off relationships in terms of cooling, humidification, and ventilation. This study provides quantitative reference for vegetation configuration and material selection in rural roads within the Loess Tableland region and similar semi-arid areas, enriches the research scope of rural microclimate studies, and offers scientific support for climate-adaptive rural planning and optimization of rural living environments. Full article

The Nile River has played a central role in Egypt’s historical and cultural development, shaping ancient civilizations and settlement patterns. However, its course has changed dynamically over millennia, leaving behind buried channels and geomorphological features that are critical for reconstructing past hydrological landscapes. This study utilized Sentinel-2 satellite imagery within Google Earth Engine to develop a remote sensing method for analyzing spectral and temporal variations in vegetation as indicators of paleofluvial landforms and past river activity. The approach, applied to create ten seasonal representations, enhanced the detection of moisture-driven vegetation patterns. Here, the Moisture-Gradient Enhanced Vegetation Index (MGEVI) was developed to identify stable vegetated landforms and differentiate persistent moisture conditions from seasonal variations. Through this method, former river channels, river islands, and channel belts were identified, revealing patterns of past river activities. The results suggest a late anabranching phase of the Nile, characterized by the gradual stabilization of fluvial features in response to evolving hydrological conditions. A comparison between fluvial features identified through remote sensing and those mapped from TanDEM-X radar elevation data and historical maps revealed strong agreement, affirming the reliability of the remote sensing approach developed by this study. Evidence from sediment core analyses, stratigraphic correlation, and high-precision RTK field surveys further corroborated the existence of ancient, buried channels and islands within the study area. The study highlights the utility of multi-temporal satellite imagery analysis for reconstructing hydrological evolution and assessing past settlement suitability. Specifically, an inferred paleochannel near the Dendera Temple Complex suggests a possible hydrological connection between a former course of the Nile River and this archaeological site. These findings underscore the potential of remote sensing for large-scale geoarchaeological studies, offering scalable methodologies for identifying ancient river networks and supporting cultural heritage conservation in arid regions. Full article

This study presents the first watershed-scale assessment of the impact of thermal spring discharges on the hydrochemistry and water quality of the Soummam basin (northeastern Algeria). Fourteen stations were monitored during three campaigns (October 2024, December 2024 and March 2025), combining physicochemical analyses, hydrochemical diagrams, and water quality indices (WQI and IWQI). The results reveal a clear spatial gradient in water composition, from low-mineral Ca-HCO₃/Ca-SO₄ facies in upstream areas to highly mineralized Na-Cl facies associated with thermal springs (Sidi Yahia and Sillal). Electrical conductivity reaches up to 27,359 µS/cm, reflecting intense mineralization driven by evaporite dissolution and deep water–rock interaction. This thermomineral signature propagates downstream through mixing and ion exchange processes, leading to progressive salinity enrichment. Water quality indices highlight significant degradation in thermally influenced zones, with approximately 50% of samples unsuitable for drinking (WQI > 300) and more than 60% classified as highly restricted for irrigation (IWQI < 40). Cluster analysis further confirms the distinction between severely impacted, moderately affected, and relatively preserved waters. Overall, the findings demonstrate that thermal discharges represent a major and persistent driver of salinization, emphasizing the need to incorporate geothermal influences into water resource management strategies in semi-arid environments. Full article

Tree and shrub planting is a widely used strategy to restore degraded semi-arid grasslands. Although nutrient resorption is a key adaptation to nutrient-limited environments, its dynamics at decadal scales remain poorly understood. In this study, we measured species-averaged nitrogen resorption efficiency (NRE) at both community and functional group levels, together with soil nutrients, across 20- and 40-year shrub-planted sites and a 40-year tree-planted site in Inner Mongolia, China. At the community level, green and senesced leaf nitrogen (N) concentrations, NRE, and aboveground biomass did not differ significantly among sites. However, clear differences emerged at the functional group level: Poaceae exhibited higher NRE than forbs and lower senesced leaf N than both forbs and Fabaceae. As restoration progressed, Poaceae replaced forbs as the dominant group, coinciding with increased soil nutrient availability. Notably, NRE in Poaceae declined with increasing soil nutrients, suggesting a shift toward greater reliance on direct soil nutrient uptake. This shift, combined with the production of low-nitrogen litter by dominant Poaceae species, may ultimately slow soil nutrient accumulation. Our findings highlight the importance of functional group dynamics in regulating long-term nutrient resorption and cycling and suggest that managing Poaceae dominance could enhance long-term soil nutrient enrichment and biodiversity in restored semi-arid grasslands. Full article

Water scarcity, climate variability, and increasing water demands are placing growing pressure on water supply and distribution systems, particularly in water-scarce environments. Optimization-based approaches have become central to improving system design, planning, and operation. This study presents a structured review of optimization techniques applied to water distribution systems under conditions of scarcity, intermittency, or aridity, and introduces a context-aware classification framework incorporating system scale, population, and scarcity severity. PRISMA (“Preferred Reporting Items for Systematic Reviews and Meta-Analyses”) principles are adopted. Relevant studies are identified through Scopus and Google Scholar, screened using criteria focused on system type, optimization relevance, and explicit consideration of scarcity, intermittency, or aridity, and classified by optimization stage, methodological approach, geographical context, and main findings. The review is dominated by benchmark network studies under water scarcity, while real-world applications in arid regions, such as Sub-Saharan Africa and parts of the Middle East, remain underrepresented. Deterministic least-cost designs are inadequate under water scarcity, whereas multi-objective approaches deliver more reliable systems. The review shows a mismatch between the optimization focus of the benchmark studies, which is mainly in the design phase, and the real-world applications, which mainly focus on optimization of the operations of the existing systems. Full article

Soil salinization in arid oases constrains soil functioning and crop production, making spatially explicit monitoring important for land management. Multispectral optical remote sensing enables large-area salinity assessment, but in oasis environments such as the Keriya Oasis, its performance can be limited by spectral confusion between salt crusts and bright bare soils, sparse vegetation cover, and strong surface heterogeneity. Synthetic aperture radar (SAR), by contrast, provides all-weather imaging capability and sensitivity to surface scattering and dielectric-related conditions, but its salinity interpretation is often affected by surface complexity and environmental coupling. To address these, a spectral index–polarimetric scattering integration framework that combines RADARSAT-2 and Landsat-8 OLI features within a simple two-dimensional (2D) feature space was developed. Two groups of models were constructed from variables selected through a data-driven screening process: (1) polarimetric feature space models based on combinations such as VanZyl volume scattering with Pauli odd-bounce or Touzi alpha scattering; and (2) multi-source feature space models that integrate the optimal polarimetric component with key spectral indicators such as SI4 and MSAVI. Among all tested models, VanZyl_vol-SI4 achieved the best performance (fitting: R² = 0.749, RMSE = 5.798 dS m⁻¹, MAE = 4.086 dS m⁻¹; validation: R² = 0.716, RMSE = 5.566 dS m⁻¹, MAE = 4.528 dS m⁻¹). The results indicate that integrating PolSAR scattering information with optical indices can improve salinity mapping relative to single-source feature spaces in the Keriya Oasis. The proposed 2D framework provides a concise way to compare different feature combinations and supports regional identification of salt-affected soils. Full article

Water reuse is increasingly recognized as a key strategy for sustainable water management in semi-arid regions, where drinking water treatment plants generate significant volumes of filter backwash water with limited reuse. This study evaluates the effectiveness of assisted clarification for enhancing backwash water recovery under full-scale operational conditions in a semi-arid Moroccan treatment facility. Settling experiments were conducted with and without coagulant addition, and clarification performance was monitored through physicochemical parameters and statistical analysis. Coagulant addition markedly accelerated sedimentation kinetics, enabling clarified water recovery exceeding 90% within 20 min compared with substantially lower recovery in the control condition. A two-way ANOVA confirmed a significant treatment effect and a strong interaction between coagulant addition and settling time. These findings demonstrate that coagulant-assisted clarification can substantially improve backwash water recovery under real operating conditions. The results highlight the potential of this approach as a practical and scalable option for promoting circular water reuse in water-stressed environments while maintaining operational simplicity. Full article