Search Results (7,798)

Soil salinisation is a critical problem in northern China’s arid and semi-arid irrigated regions, posing a substantial impediment to the sustainable advancement of agriculture in these areas. This research utilises the Donghaixin Irrigation District, located on the southern bank of the Yellow River in Inner Mongolia, as a case study. This study examines the spatial distribution and determinants of soil salinisation through macro-environmental variables and micro-ion composition, integrating regression models and groundwater ion characteristics to elucidate the patterns and causes of soil salinisation systematically. The findings demonstrate that soil salinisation in the study region displays notable spatial clustering, with surface water-irrigated regions exhibiting greater salinisation levels than groundwater-irrigated areas. More than 80% of the land exhibits moderate salinity, predominantly characterised by the ions Cl⁻, HCO₃⁻, and SO₄²⁻. The hierarchy of ion concentration variation with escalating soil salinity is as follows: Na⁺ > K⁺ > SO₄²⁻ > Cl⁻ > Mg²⁺ > HCO₃⁻ + CO₃²⁻ > Ca²⁺. The susceptibility of ions to soil salinisation is ordered as follows: Ca²⁺ > Na⁺ > HCO₃⁻ + CO₃²⁻ > Mg²⁺ > K⁺ > Cl⁻ > SO₄²⁻. In contrast to the ordinary least squares (OLS) model, the geographic weighted regression (GWR) model more effectively elucidates the geographical variability of salinity, evidenced by an adjusted R² of 0.68, particularly in high-salinity regions, where it more precisely captures the trend of observed values. Ecological driving elements such as organic matter (OM), pH, groundwater depth (GD), total dissolved solids (TDS), digital elevation model (DEM), normalised difference vegetation index (NDVI), soil moisture (SM), and potential evapotranspiration (PET) govern the distribution of salinisation. In contrast, anthropogenic activities affect the extent of salinisation variation. Piper’s trilinear diagram demonstrates that Na cations mainly characterise groundwater and soil water chemistry. In areas irrigated by surface water, the concentration of SO₄²⁻ is substantially elevated and significantly affected by agricultural practises; conversely, in groundwater-irrigated regions, Cl⁻ and HCO₃⁻ are more concentrated, primarily driven by evaporation and ion exchange mechanisms. Full article

Hippophae rhamnoides subsp. turkestanica is mainly distributed in the mountains, valleys, and desert edges of Central Asia. It plays a vital role in maintaining ecological stability in arid and semiarid areas. In this study, the MaxEnt model was used to simulate the habitat suitability of H. rhamnoides subsp. turkestanica, and the key environmental factors affecting its distribution were identified. Additionally, we explored habitat sensitivity to climate change, and provided essential information for the conservation and management of this important subspecies in arid and semiarid regions. Under four different climate scenarios (SSP126, SSP245, SSP370, and SSP585) in 2040, 2060, 2080, and 2100, the prediction of habitat suitability and changes in species distribution centroids in the future were simulated. The results revealed that suitable habitats for H. rhamnoides subsp. turkestanica are primarily located in Tajikistan, Kyrgyzstan, China, Pakistan, and Afghanistan. Altitude (Alt), isothermality (bio3), and slope (Slo) emerged as the main environmental factors. Projections suggest a significant expansion in the total area of suitable habitat under future climate scenarios. By 2100, the suitable habitat areas under the SSP126, SSP245, SSP370, and SSP585 scenarios will reach 10,526,800 km², 12,930,200 km², 15,449,900 km² and 14,504,800 km², respectively. In addition, a slight northwestward shift was observed in the distribution centroid. These findings provide important insights for conservation efforts aimed at protecting H. rhamnoides subsp. turkestanica and supporting its biodiversity. By understanding the factors affecting habitat suitability and predicting changes in climate scenarios, this study provides valuable guidance for developing long-term conservation strategies. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

Nutrient deficiencies, low organic matter content, and a limited soil–water saturation percentage in calcareous soils hinder plant growth and crop production. To address these challenges, sustainable and green-based farming practices have been introduced. This study investigates the synergistic effects of biochar and nitrogen levels as sustainable solutions for improving soil fertility and supporting wheat growth in calcareous soils. A pot experiment assessed the effects of biochar (5-, 10-, and 15-tons ha⁻¹) and nitrogen levels (60, 90, and 120 kg ha⁻¹) on soil physicochemical properties, nutrient availability, and wheat growth. The randomized complete block design included three replicates and a control. The results highlight that the highest biochar rate (15 tons ha⁻¹) combined with the highest nitrogen level (120 kg ha⁻¹) significantly (p ≤ 0.05) improved soil physicochemical properties and nutrient status. Notably, soil pH increased by 2.8%, electrical conductivity by 29.8%, and soil organic matter by 185%, while bulk density decreased by 22.3%. Soil total nitrogen surged by 163.7%, soil–water saturation percentage by 27.2%, plant-available phosphorus by 66.8%, and plant-available potassium by 96.8%. Wheat growth parameters also showed marked improvement, with plant height up 29.7%, spike length by 20.7%, grains per spike by 41.5%, thousand-grain weight by 24.7%, grain yield by 81.3%, and biological yield by 26.5%. There was a strong positive correlation between enhanced soil properties and improved wheat growth, except for soil bulk density, which showed a negative correlation. This underscores the role of biochar in boosting soil fertility and crop productivity. A principal component analysis further validated these findings, suggesting that integrating biochar with appropriate nitrogen fertilization offers a sustainable strategy to enhance soil health, manage nutrient availability, and strengthen crop yields in calcareous soil. Biochar application combined with elevated nitrogen levels significantly enhances soil fertility and wheat productivity in semi-arid regions, offering a sustainable solution for improving calcareous soils. Future studies should explore the long-term impacts and scalability of this approach. Full article

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions. Full article

Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate change, has underscored the critical role of dams as essential water reservoirs. These dams serve multiple purposes, including flood management, hydropower generation, irrigation, and drinking water supply. Accurate estimation of reservoir flow rates is vital for effective water resource management, particularly in the context of climate variability. The prediction of monthly runoff time series is a key component of water resources planning and development projects. In this study, we employ Machine Learning (ML) techniques—specifically, Random Forest (RF), Support Vector Regression (SVR), and XGBoost—to predict monthly river flows in the Bouregreg basin, using data collected from the Sidi Mohamed Ben Abdellah (SMBA) Dam between 2010 and 2020. The primary objective of this paper is to comparatively evaluate the applicability of these three ML models for flow forecasting in the Bouregreg River. The models’ performance was assessed using three key criteria: the correlation coefficient (R²), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that the SVR model outperformed the RF and XGBoost models, achieving high accuracy in flow prediction. These findings are highly encouraging and highlight the potential of machine learning approaches for hydrological forecasting in semi-arid regions. Notably, the models used in this study are less data-intensive compared to traditional methods, addressing a significant challenge in hydrological modeling. This research opens new avenues for the application of ML techniques in water resource management and suggests that these methods could be generalized to other basins in Morocco, promoting efficient, effective, and integrated water resource management strategies. Full article

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions. Full article

Droughts are projected to become prevalent throughout the 21st century, endangering agricultural productivity and global food security. To address these challenges, novel strategies to enhance water management and augment plant resilience are imperative. Bacterial encapsulation has emerged as a promising approach, offering benefits such as enhanced bacterial survival, soil compatibility, and sustainable plant growth. This study evaluated the osmotolerance of bacteria from arid environments and determined their plant growth-promoting ability in drought conditions. The encapsulation of these bacteria in bio-compatible capsules led to a substantial enhancement in the performance of maize plants under drought stress. Maize plants treated with encapsulated bacteria demonstrated a 35% increase in root biomass and a 28% enhancement in shoot growth compared to untreated controls. Furthermore, significant physiological and biochemical adaptations were observed, including a 45% increase in photosynthetic pigment concentration and higher osmolyte levels, which contributed to improved drought stress tolerance. The findings of this study demonstrate the potential of encapsulated bacteria to enhance maize resilience to drought, thereby supporting robust growth under water-limited conditions. This approach presents a sustainable strategy to improve drought tolerance, and it may reduce irrigation dependency and maintain crop yields in the face of increasing climate uncertainty. Full article

In semi-arid regions, wetlands often face water scarcity, salinity, and alkalinity stresses. Agricultural drainage water has been used to restore degraded wetlands, but it alters water quality and plant growth and resource distribution. Nitrogen (N) and phosphorus (P) stoichiometry reflect plant resource strategies. In China’s Songnen Plain, Bolboschoenus planiculmis, a key plant in soda–alkali wetlands and food for the rare white crane (Grus leucogeranus), is impacted by agricultural water input. However, the N and P stoichiometry in B. planiculmis and the influencing water variables remain unclear. This study analyzed N and P contents in B. planiculmis leaves, stems, tubers, and roots, and water variables. Results showed that leaf N content was highest, while tuber P content exceeded that of other organs. Leaf N:P ratio was highest, and tuber’s was the lowest. N and P contents in plants were positively correlated, except between roots and stems. Redundancy analysis (RDA) revealed water temperature (T), oxidation-reduction potential (ORP), N contents, and water depth (WD) as key factors influencing N and P stoichiometry. Structural equation models (SEMs) indicated water T negatively affected plant N, while water nitrate nitrogen positively affected it. Water P content directly influenced leaf and stem P, and ammonium nitrogen affected aboveground P accumulation. Water T and WD directly impacted N:P ratios. These findings show that while agricultural drainage water alleviated aridification and salinization in degraded soda–alkali wetlands, exogenous N and P inputs significantly affected vegetation’s N and P utilization strategies. Full article

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m⁻²·mm⁻¹·a⁻¹. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE. Full article

Improving microclimate conditions is a pivotal aspect of urban design, particularly in hot, arid climates, where it directly influences outdoor comfort, mitigates the urban heat island (UHI) effect, and reduces the indoor cooling energy demand. The objective of this study is to quantitatively assess the impacts of neighborhoods’ urban size when combined with compact streets’ geometry regarding the outdoor thermal comfort generated in a typical vernacular settlement of the Saharan region of Algeria. The Ksar of Al-Atteuf in the city of Ghardaïa is taken as a case study. The related interior thermal conditions of buildings assumed to be potentially affected by the urban morphology are also examined. To study the effectiveness of the two urban morphology parameters (i.e., urban size and compactness) on outdoor and indoor thermal conditions, a mixed methods approach was adopted, integrating in situ climatic measurements and dynamic simulations. Indoor temperatures were examined in a traditional house located in the core of the Ksar. Year-round operative temperature (OT) simulations were achieved using the Ladybug tool within Grasshopper, and they were complemented by the Universal Thermal Climate Index (UTCI) values calculated during peak hot and cold weeks. Furthermore, a parametric analysis was conducted, focusing on the thermal performance of the compact urban fabric by varying progressively the neighborhood sizes from 20 m, 40 m, and 60 m. The results indicate stable indoor thermal conditions across the monitored residential building, which suggests that the architectural envelope is closely affected by its immediate surroundings. On the other hand, the UTCI analysis revealed significant differences in outdoor thermal comfort since the larger urban area provides better mitigation of heat stress in summer and cold stress in winter, the improved outdoor thermal conditions generated at the neighborhood level, being proportional to the size of the urban area. The findings underscore the value of compact urban fabrics in creating climate-responsive built environments and provide further insights into sustainable urban planning and energy-efficient design practices in hot, arid regions. Full article

Land use land cover (LULC) changes due to rapid urbanization pose critical challenges to sustainable development, particularly in arid and semi-arid regions like Saudi Arabia, where cities such as Abha are experiencing unprecedented expansion. Urban sprawl is accelerating environmental degradation, affecting key natural resources such as vegetation, water bodies, and barren land. This study introduces an advanced machine learning (ML) and deep learning (DL)-based framework for high-accuracy LULC classification, urban sprawl quantification, and ecosystem service assessment, providing a more precise and scalable approach compared to traditional remote sensing techniques. A hybrid methodology combining ML models—Random Forest, Artificial Neural Networks, Gradient Boosting Machine, and LightGBM—with a 1D Convolutional Neural Network (CNN) was fine-tuned using grid search optimization to enhance classification accuracy. The integration of deep learning improves feature extraction and classification consistency, achieving an AUC of 0.93 for Dense Vegetation and 0.82 for Cropland, outperforming conventional classification methods. The study also applies the Markov transition model to project land cover changes, offering a probabilistic understanding of urban expansion trends and ecosystem dynamics, providing a significant improvement over static LULC assessments by quantifying transition probabilities and predicting future land cover transformations. The results reveal that urban areas in Abha expanded by 120.74 km² between 2014 and 2023, with barren land decreasing by 557.09 km² and cropland increasing by 205.14 km². The peak ecosystem service value (ESV) loss was recorded at USD 125,662.7 between 2017 and 2020, but subsequent land management efforts improved ESV to USD 96,769.5 by 2023. The resilience and recovery of natural land cover types, particularly barren land (44,163 km² recovered by 2023), indicate the potential for targeted restoration strategies. This study advances urban sustainability research by integrating state-of-the-art deep learning models with Markov-based land change predictions, enhancing the accuracy and predictive capability of LULC assessments. The findings highlight the need for proactive land management policies to mitigate the adverse effects of urban sprawl and promote sustainable ecosystem service recovery. The methodological advancements presented in this study provide a scalable and adaptable framework for future urbanization impact assessments, particularly in rapidly developing regions. Full article

Protective forests are vital to ecological security in arid desert regions, but their spatial distribution is often inefficient. This study aims to optimize the spatial distribution of protective forests in Alaer City using a combination of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Future Land Use Simulation (FLUS) model. The optimization focuses on three objectives: economic benefits, ecological benefits, and food security. A neural network model is applied to analyze forest distribution suitability based on spatial factors. The results show that the optimized distribution significantly enhances GDP, carbon sequestration, water yield, and food production, while reducing soil erosion. The forest area is mainly concentrated along rivers, agricultural fields, and desert edges, with increased coverage at the Taklamakan Desert’s periphery improving wind and sand resistance. The FLUS model is validated with high accuracy (90.73%). This study provides a theoretical foundation for the sustainable development of protective forests, balancing ecological and economic goals in Alaer City. Full article

Evapotranspiration (ET) and water availability (WA) are critical components of the global water cycle. Although the effects of ecological restoration on ET and WA have been widely investigated, quantifying the impacts of multiple environmental factors on plant water consumption and regional water balance in dryland areas remains challenging. In this study, we investigated the spatial and temporal trends of ET and WA and isolated the contributions of vegetation restoration and climate change to variations in ET and WA in the Beijing–Tianjin Sand Source Region (BTSSR) in Northern China from 2001 to 2021, using the remote sensing-based Priestley–Taylor-Jet Propulsion Laboratory (PT-JPL) model and scenario simulation experiments. The results indicate that the estimated ET was consistent with field observations and state-of-the-art ET products. The annual ET in the BTSSR increased significantly by 1.28 mm yr⁻¹ from 2001 to 2021, primarily driven by vegetation restoration (0.78 mm yr⁻¹) and increased radiation (0.73 mm yr⁻¹). In contrast, the drier climate led to a decrease of 0.56 mm yr⁻¹ in ET. In semiarid areas, vegetation and radiation were the dominant factors driving the variability of ET, while in arid areas, relative humidity played a more critical role. Furthermore, reduced precipitation and increased plant water consumption resulted in a decline in WA by −0.91 mm yr⁻¹ during 2001–2021. Climate factors, rather than vegetation greening, determined the WA variations in the BTSSR, accounting for 77.6% of the total area. These findings can provide valuable insights for achieving sustainable ecological restoration and ensuring the sustainability of regional water resources in dryland China under climate change. This study also highlights the importance of simultaneously considering climate change and vegetation restoration in assessing their negative impacts on regional water availability. Full article

Drought and salinity are major factors that hinder crop cultivation and significantly impair agricultural productivity, particularly in (semi)arid regions. These two abiotic constraints cause deterioration in soil structure and reduced fertility and hamper plant growth by limiting access to mineral elements and water, thereby threatening global food security. What’s more, the excessive, long-term use of chemical fertilizers to boost crop productivity can disrupt the balance of agricultural ecosystems, particularly soil health. Faced with these challenges, the sustainable exploitation of natural resources, in particular rhizospheric microorganisms, is an environmentally friendly solution. Arbuscular mycorrhizal fungi play an important role as biofertilizers due to their symbiotic relationship with the roots of nearly 80% of plants. They promote not only the growth of host plants but also their resistance to abiotic stresses. Among these fungi, the Glomus genus stands out for its predominance in plants’ rhizosphere thanks to its richness in high-performance species and ecological adaptability. This review highlights the importance of species within this genus in soils, particularly in terrestrial ecosystems subject to (semi-)arid climates. Molecular mechanisms underlying plant tolerance to drought and salt stress in symbiosis with species of the Glomus genus are also explored. Full article