Search Results (7,226)

Agricultural drought represents a critical threat to water-dependent economies in arid Andean regions; however, forecasting tools tailored to data-scarce, high-altitude basins remain limited. This study developed and evaluated a multi-model spatiotemporal framework for agricultural drought forecasting in Candarave, in the northern Atacama Desert; we forecast the 3-month Standardized Precipitation–Evapotranspiration Index (SPEI-3) one month ahead (lead time t + 1) for an agricultural, data-scarce Andean basin. Seven modeling approaches were compared: three machine learning baselines (XGBoost, Random Forest, and Elastic Net), two statistical time-series models (ARIMA and ARIMAX), and two deep learning architectures (CNN-LSTM and ConvRNN). A driver analysis based on Elastic-Net coefficients identified spatiotemporal persistence (SPEI_neighbor, SPEI_lag1), precipitation, maximum temperature, and the Coastal El Niño Index (ICEN) as the dominant drought predictors. ARIMAX achieved the best overall performance (RMSE = 0.377; R² = 0.909; NSE = 0.909; KGE = 0.889), demonstrating that incorporating exogenous climatic drivers substantially enhances forecasting skill. Among machine learning baselines, Elastic Net outperformed tree-based models (KGE = 0.924). Deep learning models revealed the weakest performance, with very low R² values, attributed to insufficient training data, overparameterization, and the predominantly linear and persistence-driven nature of drought dynamics in Candarave. Under an operationally realistic configuration (all predictors lagged to forecast time), the approach provides a useful decision-support tool for one-month-ahead agricultural drought early warning, rather than a turnkey operational system. Full article

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO₂_only simulations showed similar increases, underscoring the dominant role of CO₂ fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin. Full article

The Temperature-Vegetation-Precipitation Drought Index (TVPDI) performs poorly in complex terrain due to Normalized Difference Vegetation Index (NDVI) saturation and land surface temperature (LST) retrieval inaccuracies. To address this, we adopted an improved TVPDI (ITVPDI) by incorporating Leaf Area Index (LAI) and the land surface–air temperature difference (LST−T). By using multi-source data from 2000 to 2022 in Xinjiang, China, we validated ITVPDI and analyzed drought dynamics. Results show: (1) ITVPDI correlates better with solar-induced chlorophyll fluorescence (SIF) (r = 0.17) and the moisture index (MI) (r = 0.22) than the traditional TVPDI, demonstrating superior performance in densely vegetated and topographically complex areas. (2) Drought frequency ranked as follows: severe (31.55%) > moderate (29.04%) > extreme (23.44%) > mild (15.94%). Mild and moderate droughts occurred in Northern Xinjiang and the Tianshan Mountains, while severe and extreme droughts clustered around the Tarim Basin and Eastern Xinjiang desert margins. As drought intensity increases, its center of gravity shifts “from north to south” and “from mountains to basins.” (3) ITVPDI showed a slight upward trend over the 23-year period, with autumn experiencing the most severe drought (mean ITVPDI = 0.293). (4) A mean Hurst index of 0.468 indicates weak anti-persistence, suggesting the current wetting trend may reverse, and increasing future drought risk. The ITVPDI proves to be a robust tool for drought monitoring in arid and semi-arid regions with complex terrain. This study provides crucial scientific support for regional water resource allocation, precision irrigation, and collaborative drought resistance and disaster mitigation in Northwest China. Full article

Wetlands and surface-water features regulate the thermal environment of cities through evaporative cooling, yet in arid metropolitan regions these hydrological buffers are scarce and rarely quantified against urban heat. Here, we link satellite-derived surface-water wetness to land surface temperature (LST) and urban heat island (UHI) intensity in Riyadh, Saudi Arabia, using an explainable Geospatial Artificial Intelligence (GeoAI) framework. We assembled 2000 cloud-masked Landsat 8/9 sample points for July 2014 and 2024 in Google Earth Engine and derived the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and two surface-water indices, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Water Index (NDWI), together with LST, UHI, terrain and population. Surface-water wetness was the strongest cool-side correlate of thermal stress: MNDWI related negatively to LST (r = −0.48) and to UHI intensity (r = −0.53), stronger than either vegetation or built-up density (both p < 0.001). Each 0.1 increase in MNDWI corresponded to a 2.2 °C reduction in LST. Five machine-learning algorithms predicted LST with test R² of 0.71–0.76 and UHI with R² of 0.68–0.72, and SHapley Additive exPlanations (SHAPs) identified MNDWI as the single most important thermal driver, ahead of elevation and vegetation. Point-level LST rose by 1.99 °C between 2014 and 2024 (p < 0.001), while open surface water was absent from all 2000 samples, indicating a hydrological deficit in the city’s thermal regulation. These findings suggest that protecting and expanding blue–green features along corridors such as Wadi Hanifah offers a measurable cooling lever for arid-city climate adaptation. Full article

Hydrological drought forecasting in semi-arid basins is challenging due to the combined influence of meteorological forcing, large-scale atmospheric teleconnections, and basin memory processes, which are rarely jointly analysed within a leakage-free predictive framework. This study addresses this gap by evaluating gradient-boosted trees and neural forecasting models for one-month-ahead prediction of the Standardized Runoff Index (SRI) in two sub-basins of the Wadi Sahaouat Basin, Algeria. The models include gradient-boosted regression trees (GBRT), A-N-BEATS, A-N-HiTS, and TiDE, representing distinct forecasting architectures. Predictors consist of the Standardised Precipitation Index (SPI), seven teleconnection indices (NAO, AO, EAWR, SCAND, MEI, SOI, WeMO), and their one- to three-month lags. Two scenarios are tested: Scenario 1 uses SPI and teleconnection lags only, while Scenario 2 additionally includes lagged SRI values (SRI_lag1–3) to represent hydrological memory. A train-only Variance Inflation Factor (VIF > 10) procedure is applied to remove multicollinearity without data leakage. In Basin 1, SRI lags were excluded due to strong collinearity with SPI lags (r = 0.984), resulting in identical inputs for both scenarios. In Basin 2, SRI lags were retained to assess their predictive contribution. GBRT achieved the best overall performance across both basins and scenarios, with mean RMSE, NSE, and KGE values of 0.0682, 0.9907, and 0.8945, respectively. TiDE ranked second overall, with a mean RMSE of 0.1166, followed by A-N-HiTS in third place with a mean RMSE of 0.1203 and A-N-BEATS with the weakest overall performance, with a mean RMSE of 0.2159. These results indicate that gradient-boosted trees remain highly competitive with neural models for small monthly hydrological datasets and that the value of hydrological memory is basin-dependent and varies according to its independence from concurrent meteorological forcing. Full article

Diluted solar saltworks effluent, applied via fertigation, can contribute to circular economy strategies by recycling nutrients and reducing the environmental impact associated with the disposal of hypersaline effluents. However, its adoption in drip irrigation systems is still limited, as are its effects on emitter performance. This study investigated whether electronic pulses could mitigate emitter clogging by applying dilution of saltworks bittern. Three systems (freshwater + saltworks bittern + electronic pulses; freshwater without electronic pulses; and freshwater + saltworks bittern without electronic pulses) were evaluated in Mossoró, Brazil, using three emitter designs over 0–320 h. Water physicochemical properties and hydraulic performance were monitored, and deposits were characterized by SEM—EDS and FTIR. Electronic pulses did not change bulk water chemistry but were associated with lower total suspended solids. Clogging risk was mainly related to alkaline pH, high electrical conductivity, and elevated Ca²⁺ and Mg²⁺ concentrations, which kept effluent dilutions within a high-risk range. Untreated effluent reduced irrigation uniformity, whereas treated effluent performed similarly to supply water. Electronic pulses reduced deposit complexity and the severity of critical events but did not eliminate clogging, and responses dependent on the emitter labyrinth’s geometry. Full article

Sidr (Ziziphus spina-christi L.) is an underutilized fruit native to arid and semi-arid regions that possesses considerable nutritional and phytochemical value. However, its potential for controlled lactic fermentation and development into value-added fermented products has received limited scientific attention. This study investigated the effects of five brine formulations on the controlled fermentation of Sidr fruit pickles and monitored changes in physicochemical properties, bioactive compounds, microbial dynamics, texture, color, and sensory attributes during 90 days of storage at ambient temperature. The treatments consisted of 10% NaCl (control), NaCl supplemented with sodium sorbate, NaCl with sucrose and vinegar, NaCl with sucrose and Lactobacillus plantarum starter culture, and NaCl with sucrose, vinegar, and garlic. Brine formulation significantly influenced fermentation kinetics, microbial succession, and product quality throughout storage. The inoculated treatment containing L. plantarum exhibited the most rapid acidification, reaching a pH of 4.02 and titratable acidity of 0.24%, while maintaining the highest lactic acid bacteria population (>9 log CFU g⁻¹) and enhanced microbiological stability. This treatment also showed superior retention of ascorbic acid, total phenolic compounds, antioxidant activity, and texture compared with the non-inoculated treatments. Pearson correlation analysis and principal component analysis (PCA) further demonstrated strong associations between starter-culture fermentation, bioactive compound preservation, and overall product quality. Sensory evaluation indicated that all treatments remained acceptable throughout storage; however, the inoculated samples consistently received the highest scores for taste, texture, and overall acceptability. Overall, the results indicate that controlled lactic fermentation using L. plantarum represents an effective approach for enhancing the quality, stability, and bioactive retention of fermented Sidr fruit products, supporting the valorization of this underexploited fruit resource for sustainable food applications. Full article

Ozone (O₃), as a key secondary pollutant, exhibits pronounced spatiotemporal heterogeneity, posing significant challenges to coordinated regional air pollution control. However, systematic understanding of high-resolution O₃ spatial inversion and its driving mechanisms in arid urban agglomerations remains limited. In this study, the Tianshan North Slope Urban Agglomeration (TNSUA) was selected as the study area, and a multi-model comparative framework was established to comprehensively evaluate the O₃ inversion performance of 16 machine learning and deep learning models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extremely Randomized Trees (ET), and Gradient Boosting Decision Tree (GBDT). Based on the optimal model performance, high-precision daily O₃ spatial reconstruction for the year 2023 was achieved across the study region. The contributions of individual driving factors and their nonlinear response relationships were quantitatively interpreted using Shapley Additive Explanations (SHAP). Furthermore, a backward trajectory model combined with the Weighted Potential Source Contribution Function (WPSCF) and Weighted Concentration Weighted Trajectory (WCWT) methods was employed to identify potential source regions and transport pathways of O₃. The results indicate that: (1) The XGBoost model exhibited the best performance (R² = 0.93, RPD > 3). The reconstructed results reveal that high O₃ concentrations in 2023 were primarily distributed in southern Urumqi, southern Changji, and southern Tacheng, with southern Urumqi identified as the most prominent hotspot. (2) The spatial variability of O₃ was predominantly driven by downward shortwave radiation (DSR) and air temperature (TEM), both of which showed significant nonlinear responses and threshold effects on O₃ formation. (3) Source apportionment analysis indicates that westerly transport serves as a major exogenous contribution pathway, with potential source regions mainly located in the surrounding areas of the northern Tianshan slope as well as Central Asia, particularly eastern Kazakhstan and northern Kyrgyzstan. This study systematically elucidates the formation mechanisms of O₃ pollution in arid urban agglomerations from three aspects—high-precision inversion, driving mechanism analysis, and cross-regional transport identification—thereby providing a scientific basis for precise air pollution control strategies. Full article

Sweet sorghum is a promising crop for bioethanol production in semi-arid regions, due to its tolerance to drought and salinity, where conventional energy crops face limitations. This study aimed to evaluate the agronomic performance of sweet sorghum irrigated with treated domestic wastewater (TDW) and its application as a substrate for bioethanol production under high-gravity (HG) and very-high-gravity (VHG) fermentation conditions. Field experiments were conducted in the Brazilian semi-arid using a 5 × 5 full factorial design consisting of five irrigation depths (40–160% crop evapotranspiration, ETc) combined with five potassium fertilization doses (0–80 kg·ha⁻¹), totaling 25 treatments. Agronomic performance, biomass production, and total reducing sugar accumulation were evaluated in both plant cane and ratoon crops. Sweet sorghum juice was subsequently combined with sugarcane molasses and fermented using Saccharomyces cerevisiae in batch and fed-batch processes. Irrigation with TDW associated with moderate potassium fertilization enhanced plant development, biomass yield, and sugar accumulation, particularly at irrigation depths between 100% and 130% of ETc, reaching up to 1908 kg·ha⁻¹ of TRS. Bioethanol production achieved fermentation efficiencies of 91.83% and 84.80% and productivities of 4.63 and 4.21 g·L⁻¹·h⁻¹ under HG and VHG conditions, respectively. These findings indicate that sweet sorghum irrigated with TDW is a promising feedstock for bioethanol production under high-gravity fermentation conditions while supporting the use of alternative water resources in semi-arid environments. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

The interaction between salt solutions and expansive soils is critical for engineering in chemically aggressive environments. However, the effect of iodide salts on pore water distribution in expansive soils remains poorly understood. This study investigated the transverse relaxation time (T₂) characteristics of pore water in expansive soils under varying KI concentrations (0–20%), moisture content (8.7–26.0%), and dry density (1.26–1.79 g/cm³) using nuclear magnetic resonance (NMR). All T₂ curves exhibited a single peak. Increasing moisture content from 8.7% to 26.0% resulted in increases of approximately 63% in T₂ at peak and 408–439% in peak area. Increasing KI concentration decreased both T₂ at peak by up to 33.3% and peak area by up to 44.0% within the tested range, attributed to diffuse double-layer compression and signal loss. Increasing moisture content broadened the T₂ distribution and linearly increased T₂ at peak and peak area, indicating water gradually occupied larger pore spaces as moisture content rose. T₂ at peak was independent of dry density, while the peak area showed a linear relationship with dry density, consistent with mass balance. The observed systematic linear relationships among T₂ at peak, peak area, and the three experimental variables suggest that NMR is a promising tool for the quantitative assessment of salt solution effects on pore water in expansive soils. These findings provide a theoretical basis for evaluating salt-affected expansive soils in coastal and arid regions. Full article

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze the spatial pattern and nonlinear driving mechanism of water yield depth for sustainable water resource management. Based on the InVEST model, water yield depth during 2001–2024 was simulated, and trend analysis was conducted. Annual XGBoost models with SHAP were used to explain nonlinear driver effects. Results showed a significant east-high and west-low pattern, with significantly increasing and decreasing areas accounting for 12.35% and 4.5%, respectively. Precipitation was the dominant driver, with higher ∣SHAP∣ values in wet years than in dry years. Zonal SHAP showed Pre led in all zones (48.8%, 63.5%, 37.7%), with secondary drivers shifting from forest/topography in the East to temperature in the West. SHAP values increased rapidly after precipitation exceeded thresholds of 200–300 mm in dry years and 400–500 mm in wet years. Under high precipitation, precipitation–non-forest interactions increased rapidly, whereas forest interactions changed little or became negative, showing a scissor-like divergence pattern. XGBoost reproduced the InVEST-simulated water yield depth well (R² = 0.91 ± 0.03). This workflow provides a reproducible pathway for water resource assessment in arid and semi-arid regions. Full article

Lithological mapping with multispectral remote sensing remains challenging when diagnostic spectral information is limited and reliable labeled samples are scarce. This problem is particularly relevant when convolutional neural networks (CNNs) are applied to lithological classification, because limited spectral dimensionality and scarce training samples may hinder the learning of discriminative spatial–spectral features. In this study, we developed a limited-sample lithological mapping framework for the Shibaocheng area of Subei County, Gansu Province, China, using band-integrated ASTER and Sentinel-2A multispectral imagery. ASTER shortwave infrared (SWIR) bands were co-registered and resampled to Sentinel-2A imagery, and then integrated with Sentinel-2A visible and near-infrared (VNIR) and red-edge bands to construct a complementary multispectral dataset. A compact spectrally enhanced multi-scale CNN was designed, incorporating a residual spectral feature enhancement module for inter-band representation learning and a parallel multi-scale hybrid convolution module for capturing spatial–spectral features. Eight lithological units were classified under limited-label conditions using 8158 training samples and 3497 spatially independent validation samples. Experimental results show that the band-integrated ASTER–Sentinel-2A dataset improved classification performance compared with single-sensor inputs. Using the proposed model, the band-integrated dataset achieved an overall accuracy (OA) of 94.12%, average accuracy (AA) of 94.04%, and Kappa coefficient of 0.932, compared with OA values of 93.14% and 92.40% obtained using ASTER and Sentinel-2A alone, respectively. The positive effect of band-level integration was also observed for spectral angle mapper (SAM), support vector machine (SVM), and 3D-CNN, whose OA values increased to 54.33%, 86.12%, and 92.29%, respectively. The proposed CNN achieved the highest OA among the evaluated methods, outperforming SAM, SVM, and the conventional 3D-CNN. In addition, t-SNE visualization indicated that incorporating spatial texture features produced more compact and better-separated lithological clusters than using spectral features alone. Ablation experiments further demonstrated that the proposed spectral feature enhancement and multi-scale hybrid convolution modules each contributed to improving lithological classification performance. These results demonstrate that integrating freely available multispectral data with a lightweight spectral–spatial CNN provides a practical and cost-effective solution for lithological mapping in bedrock-exposed arid to semi-arid regions, especially where hyperspectral imagery and dense field samples are unavailable. Full article

Aiming at severe soil secondary salinization, poor water retention and insufficient salt tolerance of conventional acrylic-based modifiers in arid and semi-arid regions of China, a poly(ethylene glycol) maleate citrate (PEGMC) crosslinking monomer was synthesized through esterification, and a dual covalent–hydrogen crosslinked P(PEGMC/AA) hydrogel was fabricated via free radical copolymerization with acrylic acid (AA). The hydrogel was characterized by NMR, FTIR, SEM, TGA and elemental mapping, while its binding mechanism with saline–alkali ions was elucidated through DFT calculations and molecular dynamics simulations. Its amelioration performance was evaluated through swelling, soil water retention, desalination and pot germination experiments. The hydrogel exhibited outstanding water absorbency, salt resistance and dry–wet cycling stability, with swelling ratios of 712 g/g in deionized water and 285 g/g in 0.9% NaCl solution, and remained 200 g/g after four dry–wet cycles. It enhanced soil water retention remarkably (over 93% after 72 h). At 0.30% dosage, soil salt content declined from 7.1 g/kg to 1.3 g/kg with desalination efficiency exceeding 80%, owing to porous physical adsorption and chemical chelation toward Na⁺, Ca²⁺ and Mg²⁺, with a binding energy of −136.936 kJ/mol. Pot tests revealed that crop germination rate rose from 19% (blank) to 75% under severe saline–alkali stress. Meanwhile, the hydrogel inhibited nutrient leaching and favored soil-water conservation. This work first incorporated PEGMC monomer into agricultural hydrogels to construct a stable dual crosslinked network, clarifying its synergistic mechanisms for salt fixation and water retention macroscopically and microscopically. It provides a promising functional material and theoretical basis for green, efficient in situ amelioration of dryland saline–alkali soil. Full article

The Chihuahuan Desert Biome (CDB), the largest semi-arid region in North America, has undergone repeated climatic fluctuations during the Interglacial–Glacial Oscillation (IGO) of the last eight million years. We investigated biogeographic and evolutionary patterns of endemic cacti within the present-day Interglacial and the Last Glacial by examining 119 strict endemics, including 75 suitable for Species Distribution Modeling (SDM) and 44 microareal strict endemics, together representing 36.17% of the 329 species in the biome. Cacti probably originated in South America after substantial separation from Africa, with pollen fossils documenting their presence in Mexico by 51.6 Ma. Climatic reconstructions for each phase were developed using regional numerical and co-kriging methods following Sánchez-Santillán and García, complemented by paleoclimatic evidence from Scotese, Roy-Priyadarsi, Van Devender, and Betancourt. MAXENT SDMs and PANBIOTRACKS’ track-node analyses were applied to 3719 specimens representing 2015 localities to explore the colonization patterns and broad evolutionary trends. Combined suitability layers and panbiogeographic analyses revealed a predominant southeastern-to-northwestern colonization pattern, largely following the western flank of the Sierra Madre Oriental and intermontane valleys. The northern sectors were less diverse, more arid, and apparently colonized more recently, whereas the southern sectors concentrated much of the endemic richness and connectivity. The concordance among climatic suitability patterns, tracks, nodes, and the available phylogenetic evidence supports a major role of climatic oscillations in shaping the spatial and evolutionary history of endemic cacti throughout the CDB. Full article