Search Results (522)

Regional management of the urban thermal environment is essential for sustainable development. However, both the surface urban heat island (SUHI) spatiotemporal patterns and driving mechanisms across humid–arid regions remain uncertain. Therefore, 329 cities from various humid–arid regions were selected to investigate the interannual, seasonal, and diurnal distribution characteristics of SUHIs across regions. By constructing six-dimensional influencing factors and using CatBoost-SHAP and SEM methods, the contributions and action pathways of these factors to SUHIs were analyzed across humid–arid regions. The influence mechanisms, differences in feature importance, and similarities and discrepancies in action pathways were thoroughly examined. The findings are as follows: 1. During the day, higher SUHII values occur in humid and semihumid regions, exceeding those in arid and semiarid regions by 1.521 and 0.921, respectively. At night, arid and semiarid regions exhibit UHI effects (SUHII > 0). The SUHI distribution across humid–arid regions demonstrates seasonal variations. 2. ΔSA and ΔNDVI are stable dominant influencing factors across all regions. The contribution rank varies along the humid–arid region: Pollution factors are more important in arid and semiarid regions, whereas surface features and 2D/3D dominate in humid and semihumid regions at night. 3. SUHI regulation by influencing factors across humid–arid regions follows both similar paths and regional variations. This study reveals the SUHI distribution across humid–arid regions and provides reference data for regional thermal environment management. Full article

Relative humidity (RH) is an important meteorological factor that affects both the climate system and human activities. However, the existing observational station data are insufficient to meet the requirements of regional scale research. Machine learning methods offer new avenues for high precision RH estimation, but the performance of different algorithms in complex geographical environments still needs to be thoroughly evaluated. Based on Chinese observational station data from 2011 to 2020, this study systematically evaluated the performance of three methods for estimating RH: the generalized linear mixed model (GLMM), random forest (RF) and the XGBoost algorithm. The results of ten-fold cross validation indicate that the two machine learning methods are significantly superior to the traditional GLMM. Among them, RF performed the best (the determinant coefficient (R²) = 0.73, root mean square error (RMSE) = 8.85%), followed by XGBoost (R² = 0.72, RMSE = 9.07%), while the GLMM performed relatively poorly (R² = 0.58, RMSE = 11.08%). The model performance shows significant spatial heterogeneity. All models exhibit high correlation but relatively large errors in the northern regions, while demonstrating low errors yet low correlation in the southern regions. Meanwhile, the model performance also shows significant seasonal variations, with the highest accuracy observed in the summer (June to September). Among all features, dew point temperature ($T_d$) aridity index (AI) and day of year (DOY) are the main contributing factors for RH estimation. This study confirms that the RF model provides the highest accuracy in RH estimation. Full article

Afforestation is recognized as a highly effective strategy for enhancing ecosystem carbon sequestration. However, the changes and drivers of soil organic carbon (SOC) following afforestation are still debated due to climate differences. Clarifying these responses is critical for improving the effectiveness of afforestation-based carbon sequestration strategies. In this study, we analyzed nine 20-year-old afforestation sites (coniferous and broad-leaved) along a Chinese climatic gradient to quantify SOC and its fractional changes following farmland-to-forest conversion, and to identify the dominant factors controlling SOC sequestration across climatic gradients and forest types. The results showed that afforestation enhanced SOC (5.1%–210.5%, p < 0.05) in humid and semi-humid regions, but showed no significant effect in semi-arid regions, and it even reduced SOC in arid regions (−19%–−53.8%). Across all climatic zones, mineral-associated organic carbon was the dominant contributor to SOC accumulation throughout the entire soil profile (0–60 cm). Climatic-scale analyses based on the aridity index determined that root and litter C/N ratios were the primary drivers of SOC sequestration in coniferous forests, whereas in broad-leaved forests, they were more strongly controlled by soil physicochemical properties, particularly total nitrogen, bulk density, and soil water content. This study identified that SOC responses to afforestation are strongly mediated by climate and forest type, which is helpful for managers to take targeted measures to increase soil carbon sequestration in forest management. Full article

Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. In this study, we first improved the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model by replacing the relative humidity-based soil moisture constraint with the land surface water index (LSWI), aiming to enhance model performance in water-limited environments. Second, we developed a Crop Unmixing and Weight Fusion Model for ET (CUWFM) to generate daily ET products at a 30 m spatial resolution by integrating high-resolution but infrequent PT-JPL-ET data with coarse-resolution but frequent PML-V2-ET data. The CUWFM employs a hybrid approach combining sub-pixel crop fraction decomposition with similarity-weighted regression, allowing for more accurate ET estimation over heterogeneous agricultural landscapes. The proposed methods were evaluated in the Changji region of Xinjiang, China, using field-measured ET data from two-flux-tower sites. The results show that the improved PT-JPL model increased ET estimation accuracy compared with the original version, with higher R² and Nash–Sutcliffe efficiency (NSE), and lower root mean square error (RMSE). The CUWFM outperformed benchmark spatiotemporal fusion methods, including STARFM, ESTARFM, and Fit-FC, in both pixel- and field-scale assessments, achieving the highest overall performance scores based on the All-round Performance Assessment (APA) framework. This study demonstrates the potential of integrating vegetation indices and crop-specific spatial decomposition into ET modeling, providing a feasible pathway for producing high spatiotemporal resolution ET datasets to support precision agriculture in arid and semi-arid regions. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

Under concurrent global warming and multi-scale climate anomalies, regional precipitation has become more uneven and less stable, and extreme events occur more frequently, amplifying water scarcity and ecological risk. Focusing on mainland China, we analyze nearly 70 years of monthly station precipitation records together with eight climate drivers—the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Multivariate ENSO Index (MEI), Arctic Oscillation (AO), surface air pressure (AP), wind speed (WS), relative humidity (RH), and surface solar radiation (SR)—and precipitation outputs from eight CMIP6 models. Using wavelet analysis and partial redundancy analysis, we systematically evaluate the qualitative relationships between climate drivers and precipitation and quantify the contribution of each driver. The results show that seasonal precipitation decreases stepwise from the southeast toward the northwest, and that stability is markedly lower in the northern arid and semi-arid regions than in the humid south, with widespread declines near the boundary between the second and third topographic steps of China. During the cold season, and in the northern arid and semi-arid zones and along the margins of the Tibetan Plateau, precipitation varies mainly with interdecadal swings of North Atlantic sea surface temperature and with the strength of polar and midlatitude circulation, and it is further amplified by variability in near-surface winds; the combined contribution reaches about 32% across the Northeast Plain, the Junggar Basin, and areas north of the Loess Plateau. During the warm season, and in the eastern and southern monsoon regions, precipitation is modulated primarily by tropical Pacific sea surface temperature and convection anomalies and by related changes in the position and strength of the subtropical high, moisture transport pathways, and relative humidity; the combined contribution is about 22% south of the Yangtze River and in adjacent areas. Our findings reveal the spatiotemporal variability of precipitation in China and its responses to multiple climate drivers and their relative contributions, providing a quantitative basis for water allocation and disaster risk management under climate change. Full article

The unique environmental settings and increasing human activity in Northeast China have intensified gully erosion, threatening food security and sustainable development. However, systematic studies of environmental thresholds driving gully erosion remain scarce. This study analyzed erosion gullies across four typical regions of Northeast China using Google Earth imagery (2011 to 2021) and field survey data (2021) to investigate the (1) conditions under which gullies most frequently form and develop and (2) conditions conducive to gully stabilization. Results showed that, in semi-humid areas, gullies mainly developed on cultivated land with a gradient of 6–15°, though catchment area thresholds varied. In contrast, in the semi-arid mountain and hilly area, developing gullies grew fastest in forested areas with low vegetation coverage. Overall, while there were differences across the four regions, gullies were most likely to form on cultivated land, while stabilized gullies were concentrated in forested areas. These findings indicate that the conversion of cultivated land to forested land slows the development of erosional gullies. In addition, rainfall promotes the formation of new gullies and inhibits the growth of eroded gullies by reducing the effective drainage area. The results provide a theoretical basis for the prevention and control of gully erosion. Full article

Machine learning (ML) has emerged as a practical approach to forecasting crop yields in climate-vulnerable, rainfed agricultural systems where production uncertainty is strongly influenced by monsoon variability. In India’s semi-arid and sub-humid regions, reliable yield forecasts are critical for agribusiness planning and managing climate risks. This study presents a standardized evaluation of three widely used ML forecasting models—Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR)—for rainfed cereal yields in eight Indian administrative divisions from 2000 to 2025. The study applied a unified methodological framework that included data cleaning, z-score normalization, domain-informed feature selection, strict chronological train–test splitting, and five-fold cross-validation. The dataset integrates agroclimatic and soil variables, including temperature, precipitation, relative humidity, wind speed, and soil pH, comprising approximately 1250 division-year observations. Model performance was assessed on an independent, temporally held-out test set using root mean square error (RMSE), mean absolute error (MAE), and R². The results show that RF provides the most robust predictive performance under realistic forecasting conditions. It achieved the lowest RMSE (0.268 t/ha) and the highest R² (0.271), outperforming LR and SVR. Although the explained variance is modest, it reflects strict temporal validation and the inherent uncertainty of rainfed systems. Feature importance analysis highlights temperature and precipitation as dominant yield drivers. Overall, this study establishes a conservative and reproducible baseline for operational machine learning (ML)-based yield forecasting in precision agribusiness. Full article

Cattle tick fever (CTF), caused by Anaplasma marginale, Babesia bovis and Babesia bigemina, remains a sanitary and economic challenge for cattle farming in Brazil. Thus, this study evaluated the prevalence, regional distribution, co-infection patterns, and risk factors associated with CTF causative agents in cattle the semi-arid region of Paraíba, the semi-arid region of Ceará, and the Tropical Humid region of Paraíba, Northeast Brazil. Blood samples were collected from 336 cattle, from 60 farms, and analyzed by means of conventional PCR and nested-PCR, while epidemiological data were obtained through questionnaires applied to producers. The overall infection prevalence by at least one pathogen was 82.7% (278/336), with higher rates in the tropical humid region of Paraíba at 94.8% (109/115), followed by the semi-arid region of Ceará, with 88.1% (89/101) and the semi-arid region of Paraíba with 66.6% (80/120). Co-infections were frequent, especially the association between A. marginale and B. bigemina, detected in 23.2% (78/336) of the animals, while triple infections occurred in 15.8% (53/336) and were most frequent in the semi-arid region of Ceará at 21.8% (22/101). The semi-arid region of Paraíba had the fewest entirely positive properties (7/20) and the highest number of entirely negative properties (2/20). The tropical humid region of Paraíba had the highest number of entirely positive properties (17/21), with no properties entirely free of CTF agents. Multivariate analysis identified the presence of horn fly (OR = 7.23; CI 3.05–18.86; 95% CI), needle reuse (OR = 5.8; CI: 2.62–13.90; 95% CI), animal purchase and introduction without quarantine (OR = 5.4; CI: 2.17–14.93; 95% CI), and pasture sharing (OR = 3.21; CI: 1.08–11.25; 95% CI) as risk factors, while beef herds showed lower susceptibility (OR = 0.28; CI: 0.15–0.52; 95% CI). These findings demonstrate that infections by CTF causative agents are endemic but exhibit region-specific epidemiological patterns, reflecting the combined effects of climate and management practices, and localized transmission foci that may be intensified by commercial cattle movement. Full article

The Lagunera Region, located in northern Mexico, is home to the country’s most important dairy basin, situated in a semi-arid environment. In this region, forage corn (Zea mays L.) is the main input in dairy cattle feed. In this context, optimizing water use and nitrogen nutrition is a priority to ensure the sustainability of this activity. The main objective of this study was to evaluate the productivity and water use efficiency of forage corn under different humidity, nitrogen, and substrate type levels. A randomized block design with sub-subdivided plots was used. The larger plot contained two usable moisture levels (80 and 50%); the subplots were assigned according to three nitrogen levels: 13.6 (N1), 6.8 (N2), and control 0.35 (N3) NO₃ mmol·L⁻¹; the sub-subplots were assigned based on two substrates: sand and a mixture (MI) of sand, perlite, and peat moss. The results showed significant triple interactions (p < 0.05) in the root volume traits, where nitrogen played a determining role, as well as double interactions (Nutrition*Substrate) for all vegetative and radicle production variables and water use efficiency. Principal components analysis explained 91.4% of the total observed variation, where basal diameter had the vector with the highest load value. Cluster analysis identified that the main discriminant factor was nutrition. It is concluded that usable moisture levels up to 50% with 6.8 mmol·L⁻¹ of NO₃ show acceptable levels of vegetative production and root volume in forage corn. These results suggest the possibility of reducing water and nitrogen fertilizer consumption without compromising yield, with significant economic and environmental benefits for agriculture in arid and semi-arid regions. Full article

In arid open-pit mines, rainfall-triggered slope instability presents significant risks, but quantitative thresholds are poorly defined due to limited integration of transient seepage and stability in low-permeability soils. This study fills this gap by using GeoStudio’s SEEP/W and SLOPE/W modules to simulate rainfall effects on a moderately steep-slope (51° average) limestone mine slope in Ningxia’s Kazimiao Mining Area (annual precipitation: 181.1 mm). The novelty lies in identifying a 12 h saturation window under intense rainfall (≥100 mm h⁻¹), during which pore water pressure stabilizes as soil reaches saturation, creating an “infiltration buffering effect” driven by arid soil properties (hydraulic conductivity: 2.12 × 10⁻⁴ cm s⁻¹). Results show that the factor of safety (FOS) drops sharply within 12 h (e.g., from 1.614 naturally to 1.010 at 200 mm h⁻¹) and then stabilizes, with FOS remaining >1.05 (basically stable) under rainfall intensities ≤ 50 mm h⁻¹, but drops into the less-stable range (1.00–1.05) at 100–200 mm h⁻¹, reaching marginal stability (FOS ≈ 0.98–1.02) after 24 h of extreme events, according to GB/T 32864-2016. Slope protection measures increase FOS (e.g., 2.518 naturally). These findings quantify higher instability thresholds in arid compared to humid regions, supporting regional guidelines and informing early-warning systems amid climate-related extremes. This framework enhances sustainable slope management for mines worldwide in arid–semi-arid zones. Full article

Malaria transmission in sub-Saharan Africa is dominated by the An. gambiae complex and An. funestus group, whose distribution varies across ecological settings. Secondary species occur at lower densities, but their role in transmission may differ from one locality to another depending on local conditions. Assessing Anopheles biodiversity using ecological indices is therefore essential to characterise their diversity and relative abundance. This study investigated the biodiversity and spatial distribution of Anopheles species across the three climatic zones of Burkina Faso to guide effective vector control strategies. Indoor resting mosquitoes were collected from 67 health districts across the 13 regions of Burkina Faso between September and December 2022 using pyrethroid spray catches. A total of 30,521 Anopheles mosquitoes were identified, with An. gambiae s.l. dominating (94.4%). The Sudano-Sahelian zone recorded the highest abundance, followed by the Soudanian and Sahelian zones. Biodiversity decreased from humid southern to arid northern areas, with the Soudanian zone showing the highest diversity. Molecular analysis of 2026 An. gambiae s.l. specimens revealed marked heterogeneity: An. coluzzii predominated in Sahelian (74.9%) and Sudano-Sahelian (71.2%) zones, while An. gambiae s.s. was most frequent in the Soudanian zone (53.8%). These results highlight spatial and ecological differences in Anopheles composition across Burkina Faso and emphasize the need for locally adapted malaria vector control strategies. Full article

Elucidating shrub ecohydrological adaptation is critical for optimizing vegetation-restoration strategies in arid regions and maintaining regional ecological stability. This study examined typical desert shrubs at the northern edge of the Mu Us Sand Land. During the growth peak season (July–September), we measured understory-soil δ¹⁸O, soil water content (SWC), leaf δ¹³C_p, stem δ¹⁸O, and gas-exchange rates, and evaluated shrub drought resistance and water-use efficiency using Mantel tests and principal component analysis (PCA). Based on the VPDB standard, the δ13Cp values of leaves ranked as follows: Caragana microphylla (−27.21‰) > Salix psammophila (−27.80‰) > Artemisia ordosica (−28.48‰). The results indicate that leaf δ¹³C_p and water δ¹⁸O are effective indicators of shrub water-use efficiency, reflecting Cᵢ/Cₐ dynamics and water-transport pathways, respectively. The three shrubs exhibit distinct water-use strategies: Caragana microphylla follows a conservative strategy that relies on deep-water sources and tight stomatal regulation; Salix psammophila shows an opportunistic strategy, responding to precipitation pulses and drawing from multiple soil layers; Artemisia ordosica displays a vulnerable, shallow-water-dependent strategy with high drought susceptibility. SWC was the primary driver of higher Long Water Use Efficiency (WUE), whereas Mean Air Temperature (MMAT) and Mean Relative Humidity (MMRH) exerted short-term regulation by modulating the vapor-pressure deficit (VPD). We conclude that desert-shrub water-use strategies form a complementary functional portfolio at the community scale. Vegetation restoration should prioritize high-WUE conservative species, complement them with opportunistic species, and use vulnerable species cautiously to optimize community water-use efficiency and ecosystem stability. Full article

The Tarim Basin is currently the largest petroliferous basin in China, with hydrocarbons primarily hosted in Ordovician marine carbonate paleokarst fracture–vug reservoirs—a typical example being the Tahe Oilfield located in the northern structural uplift of the basin. The principle of “the present is the key to the past” serves as a core method for studying paleokarst fracture–vug reservoirs in the Tahe Oilfield. The deep and ultra-deep carbonate fracture–vug reservoirs in the Tahe Oilfield formed under humid tropical to subtropical paleoclimates during the Paleozoic Era, belonging to a humid tropical–subtropical paleoepikarst dynamic system. Modern karst types in China are diverse, providing abundant modern karst analogs for paleokarst research in the Tarim Basin. Carbonate regions in Eastern China can be divided into two major zones from north to south: the arid to semiarid north karst and the humid tropical–subtropical south karst. Karst in Northern China is characterized by large karst spring systems, with fissure–conduit networks as the primary aquifers; in contrast, karst in Southern China features underground river networks dominated by conduits and caves. From the perspective of karst hydrodynamic conditions, the paleokarst environment of deep fracture–vug reservoirs in the Tarim Basin exhibits high similarity to the modern karst environment in Southern China. The development patterns of karst underground rivers and caves in Southern China can be applied to comparative studies of carbonate fracture–vug reservoir structures in the Tarim Basin. Research on modern and paleokarst systems complements and advances each other, jointly promoting the development of karstology from different perspectives. Full article

With its millennia-long agricultural history, Olive (Olea europaea L.) is one of the most strategic crops of the Mediterranean basin and a key component of the Turkish economy. This study assessed the effects of climate change on the potential distribution of olive in Türkiye using machine learning-based species distribution models (SDMs). Analyses were conducted using the 1970–2000 reference period and future projections for 2041–2060 and 2081–2100 under the SSP2-–4.5 and SSP5–8.5 scenarios, incorporating bioclimatic variables as well as topographic factors such as elevation, slope, and aspect. The model showed strong predictive performance (AUC = 0.93; TSS = 0.77) and identified elevation, winter precipitation (Bio19), and mean temperature of driest quarter (Bio9) as the primary variables influencing the distribution of olive trees. Model results predict a significant shift in suitable areas for olive cultivation, both northward—from the traditional Aegean and Mediterranean coastal belt toward the Marmara and Black Sea regions—and upward in elevation into higher-altitude inland areas. High-suitability areas, which accounted for 4.4% of Türkiye’s land area during the reference period, are projected to decline to 0.2% by the end of the century under the SSP5–8.5 scenario. UNEP Aridity Index analyses indicate increasing aridity pressure on olive habitats. While 87.2% of suitable habitats were classified as sub-humid in the reference period, projections for 2081–2100 under SSP5–8.5 suggest that 40.1% of these areas will shift to dry sub-humid and 26.4% to semi-arid conditions. Full article