Search Results (3,698)

Quantifying the relative roles of climate change and human activities in vegetation change is essential for sustainable restoration planning, yet the impacts of extreme climate events and their time-lagged effects are often overlooked, biasing assessments of climatic controls. Here, we developed an integrated pattern–process–attribution framework to evaluate vegetation dynamics across China’s four major climatic zones using a long-term, high-resolution kernel normalized difference vegetation index (kNDVI) dataset for 2000–2024. Theil–Sen trend estimation and the coefficient of variation (CV) were used to characterize long-term changes and interannual stability. Partial correlation analysis was applied to isolate the independent associations between kNDVI and extreme climate indices while controlling for background mean temperature and precipitation, and lagged correlation analysis with 0–3-month lags was used to quantify delayed responses. A regression-based residual attribution was further used to decompose observed kNDVI changes into a climate-driven component and a human-activity-related component (approximated by the residual not explained by temperature and precipitation). Results show widespread greening with pronounced spatial heterogeneity, with the most extensive improvement in the Tropical and Subtropical Humid Region and the Temperate Humid and Semi-humid Region. Vegetation stability exhibits a southeast–northwest contrast, and the highest variability occurs in the Temperate Arid and Semi-arid Region and the western Qinghai–Tibet Plateau. Responses to climate extremes are region-dependent and generally short-lagged (mean 0.35–1.05 months), with drought constraints dominating in arid regions and thermal extremes (TXx) most relevant on the plateau. Nationally, human activities contribute 70.8% of vegetation change, exceeding the climate-driven contribution (29.2%). Full article

Landslides triggered by extreme events, such as heavy rainfall, are often unpredictable and cause significant damage to people and infrastructure. Calculating landslide susceptibility and associated risk in real time is challenging on several fronts, but it would provide valuable assistance in the event of major disasters. In this context, this research project aims to present a cutting-edge system for dynamic landslide susceptibility estimation based on open-source software, open data, and Open Geospatial Consortium (OGC) standards. Using real-time precipitation and geospatial data, the system allows for the calculation of susceptibility following extreme rainfall events, combining Analytic Hierarchy Process (AHP) and Random Forest processing. The proposed framework represents a prototypical, Digital Twin-ready terrain system, where dynamic geospatial data and real-time precipitation data are integrated in a predictive machine learning model and published within a WebGIS-based architecture. The system dynamically updates landslide susceptibility information, supporting local authorities and planners in identifying critical areas and enabling timely intervention in the event of imminent danger. The automated WebGIS processing and visualization environment provides a scalable and extensible foundation for future integration of physically based simulations and bidirectional feedback mechanisms, oriented to Digital Twinning Twinning solutions. Full article

Synchronous emergence is a widespread adaptive strategy in cicadas, yet the proximate cues governing its timing in urban environments remain poorly understood. We examined the emergence phenology of three common urban cicada species (Cryptotympana atrata, Hyalessa maculaticollis, Graptopsaltria nigrofuscata) across two urban parks with contrasting habitat structure (a closed-canopy urban forest park vs. an open urban park) in Seoul, South Korea, over three summers (2015–2017). Despite interannual variation in rainfall amount and timing, peak emergence consistently occurred about two weeks after the monsoon rainfall peak. Poisson generalized estimating equation (GEE) analyses confirmed that antecedent precipitation at a 2–3-week lag significantly increased emergence counts across all three species, while precipitation one week prior had no significant effect. Emergence synchrony varied among species and habitat conditions, but the rainfall–emergence lag relationship was robust across years and sites. These findings demonstrate that precipitation timing is a key driver of peak cicada emergence in urban habitats. As East Asia experiences increasingly variable monsoon rainfall under climate change, understanding precipitation-based phenological cues will be essential for predicting the dynamics of urban insect populations. Full article

Urban wetlands on the Qinghai–Tibetan Plateau are increasingly recognized as potentially important components of city-scale carbon budgets; however, their CO₂ flux dynamics and associated environmental drivers remain insufficiently quantified, particularly under high-altitude urban conditions. In this study, we addressed this knowledge gap by conducting continuous eddy covariance observations at Haihu Wetland Park in Xining City, China. Carbon fluxes were monitored throughout 2023 using the Huangshui Park Station flux tower. We quantified the temporal dynamics of gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem exchange (NEE), and systematically assessed their responses to key environmental drivers across multiple temporal scales. GPP and Re exhibited unimodal seasonal patterns, with substantially higher values during the growing season. NEE showed pronounced diel cycling, with nighttime CO₂ release and daytime uptake, and shifted seasonally between net source and net sink states. At the daily scale (n = 365), Pearson correlations showed that air temperature (T_a), 5 cm soil temperature (T_s5) and volumetric soil water content (SWC) exhibited the strongest associations with the flux components, whereas photosynthetic photon flux density (PPFD) showed moderate associations and precipitation was weak. At the monthly scale (n = 12), Mantel tests further highlighted a dominant thermal control on GPP and Re (T_a and T_s5), whereas precipitation showed additional associations with Re and NEE. Overall, the ecosystem acted as a net CO₂ sink in 2023 (annual NEE = −292.25 g C m⁻² yr⁻¹ under our sign convention), with uptake concentrated in the first eight months of the year. Under the combined effects of multiple environmental factors, plateau urban wetlands functioned as a strong carbon sink, and the results of this study provide a data basis for improving the accuracy of carbon budget estimates for this type of ecosystem. Full article

Although Afghanistan’s contribution to global and regional greenhouse gas (GHG) emissions is minimal, it remains among the countries most vulnerable to the impacts of climate change. Rising temperatures and decreasing precipitation have significantly disrupted the country’s natural resources, including water supplies, agriculture, forests, rangelands, and ecosystems, threatening its agrarian economy and socio-economic stability. Simultaneously, Afghanistan has substantial untapped renewable energy potential, especially in hydropower, solar, wind, and biomass. This study analyzes historical (1970–2014) and projected (2015–2099) climate trends across Afghanistan by examining mean annual temperature and precipitation using the Mann–Kendall test and Sen’s Slope estimator. Results indicate a significant warming trend, with a 1.58 °C rise in temperature and a 36 mm decrease in annual precipitation over the past five decades. Future projections based on Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) suggest continued temperature increases, while precipitation trends vary geographically and over time, showing increases, decreases, or little change. The study also evaluates Afghanistan’s GHG emissions, which are negligible on regional and global scales. Despite its abundant renewable energy resources, the country still depends heavily on electricity imports from neighboring nations, leaving much of its domestic potential untapped. Harnessing these renewable resources can provide a practical path toward energy independence, zero-emission energy generation, and sustainable long-term development. This research emphasizes the urgent need for Afghanistan to strategically develop its renewable energy sector to boost climate resilience, enhance energy security, and promote sustainable economic growth. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

The Yellow River Basin is among the regions in China most severely affected by soil erosion. Elucidating the evolution trend of its ecological environment quality and identifying the key driving factors can provide a theoretical basis for the management and protection of the ecological environment in the Yellow River Basin. In this study, an improved remote sensing ecological index (ARSEI) was constructed by incorporating the soil erosion factor (A) into the original remote sensing ecological index (RSEI). Subsequently, the Theil–Sen slope estimator, Mann–Kendall trend test, coefficient of variation, Hurst index and Geodetector were employed to analyze the spatiotemporal evolution trend and driving factors of the ecological environment quality in the basin from 2002 to 2022. The results were as follows: (1) During the study period, the mean ARSEI of the basin increased from 0.518 to 0.568, representing an increase of 9.65%, with a spatial pattern of “poor in the north and excellent in the south.” (2) 62.12% of the basin exhibited improved ecological quality, 75.74% of the area showed medium or lower fluctuation levels, and 35.12% of the region is projected to be at risk of degradation in the future. (3) Annual precipitation was identified as the dominant factor influencing the spatial variation in ARSEI (q = 0.428), followed by land use type (q = 0.299). All interactions between factors exhibited either nonlinear enhancement or bi-factor enhancement. Specifically, the interaction between annual precipitation and land use type was the strongest, with a maximum q-value of 0.693. This study provides a novel approach for assessing the ecological environment quality in regions severely affected by soil erosion. Full article

Accurate runoff estimation in ungauged basins is critical for water resource management but often relies on static parameters like the runoff threshold (P₀), derived from the Soil Conservation Service Curve Number method, which fail to capture spatiotemporal soil moisture variability. This study proposes an automated methodology utilising Google Earth Engine to dynamically adjust P₀ by integrating daily soil moisture data from SMAP L4, land cover from MODIS, and precipitation from GSMaP. Unlike traditional approaches that use antecedent precipitation as a proxy, this method classifies moisture conditions using historical percentiles to update the threshold daily. The methodology was validated in two sub-basins within the Guadiana River basin (Spain). The results highlight a stark contrast between methods: while static regulatory values remained invariant (36 and 48 mm), the proposed dynamic model revealed significant fluctuations, with P₀ values ranging from over 50 mm in dry periods down to less than 14 mm during saturation. Conversely, the proposed dynamic method effectively captures real-time soil saturation, exhibiting adaptability with reductions in P₀ of up to 72% immediately following rainfall events. This satellite-based approach provides a scalable, physically consistent alternative for assessing runoff potential in data-scarce regions, significantly enhancing the reliability of hydrological modelling compared to conventional regulatory standards. Full article

Accurate characterization of precipitation in complex terrain is essential for hydrological modeling and climate studies. This study uses daily observations from 156 rain gauges in Sichuan Province (2015–2020) to evaluate two high-resolution satellite products (GSMaP-GNRT and IMERG-Early) and to develop a Transformer-based fusion framework at the gauge scale. All three datasets reproduce the regional seasonal cycle with more rainfall in summer and less in winter. At the daily scale, the fused product attains correlation comparable to GSMaP, while GSMaP and the fusion slightly overestimate precipitation (Bias = 6.24% and 5.21%), and IMERG shows stronger underestimation (Bias = −11.46%). At the monthly scale, the fused dataset achieves the best overall performance in terms of correlation, bias and RMSE. Spatially, the fusion reduces bias and RMSE and yields more homogeneous patterns over Sichuan’s complex terrain. Detection metrics indicate that the fused product increases the probability of detection and slightly improves the critical success index, while the false alarm ratio remains relatively high and comparable to the original products. This implies a gain in event sensitivity and spatial consistency rather than substantially reduced false alarms. Overall, the Transformer-based fusion provides a useful compromise between GSMaP and IMERG, adding value particularly for bias reduction, monthly statistics and event detection. The fused dataset offers a promising input for precipitation monitoring, hydrological simulation and disaster-risk analysis in Sichuan and similar mountainous regions. Full article

Phenotypic and genetic divergence along environmental gradients often reflects local adaptation in broadly distributed species. The Fabaceae family is one of the largest and most ecologically important angiosperm groups; it has a centre of diversity in South America and shows high versatility in arid and disturbed environments. Here, we selected Vachelliacaven, a native tree with ecological breadth and taxonomic complexity, to investigate whether phenotypic trait variation among populations reflects adaptive divergence. We examined neutral genetic differentiation in six varieties among populations from Argentina, quantified the phenotypic differentiation of quantitative traits by an ANOVA, and performed P_ST—F_ST comparisons. We also assessed correlations between phenotypic variation, environmental variables, genotypic variation, and geographic distances. F_ST estimates revealed significant genetic divergence (0.329), in line with isolation by distance and environmental heterogeneity. P_ST—F_ST comparisons showed that all traits were under diversifying selection, supporting the hypothesis of adaptive phenotypic variation. We further detected that fruit width and length were significantly correlated with specific environmental variables like precipitation and temperature. These findings confirm that phenotypic divergence in V. caven is shaped by both geographic and environmental factors. This study offers a preliminary insight into the local adaptation of the examined traits, highlighting how morphological and genetic differentiation has enabled V. caven to thrive in diverse environments and contributing information as to how to face climate change scenarios. Full article

This study analyzed the hydroclimatic functioning of the Lake Fitri basin (Chad) by combining rainfall records, in situ hydrological observations, water balance analysis, and spatial remote sensing data. Results show a strong Sahelian climatic control, with rainfall concentrated in a short-wet season (July–September) and potential evapotranspiration largely exceeding precipitation. Batha River flows are highly seasonal, generating short flood pulses that drive lake level fluctuations and aquifer recharge. Water balance estimates indicate that recharge is limited and episodic (approximately 70–120 mm in 2020), representing only 14–24% of annual rainfall, occurring almost exclusively during extreme rainfall events. Compared with Lake Chad, Lake Fitri is more directly sensitive to local rainfall variability, reflecting its dependence on a single tributary. Overall, the findings underline the fragility of this hydrosystem and the need for reinforced monitoring and integrated management to ensure sustainable water resources under increasing climatic variability. This work constitutes the initial reference for the hydroclimatic characterization of Lake Fitri, thanks to a methodology combining in situ and satellite data. Full article

The Three-River Connectivity Zone in the lower Tarim River Basin (TRCZ) is a typical area that has experienced decades of river cut-off, followed by artificial ecological water transfers and vegetation restoration. However, the long-term patterns of ecological water requirements and their response mechanisms to ecosystem services in this region remain unclear. This study aims to quantify the spatiotemporal dynamics and driving factors of ecological water requirements in the TRCZ from 1990 to 2020. We integrated multi-temporal remote sensing land cover data with the FAO Penman–Monteith equation to estimate vegetation evapotranspiration (as a proxy for ecological water requirement) and coupled the InVEST model with Random Forest modeling to identify key climatic and hydrological drivers. Unlike previous studies that focused primarily on precipitation inputs, our approach explicitly considers the ecosystem’s water yield function alongside water demand, offering new insights into the constraints on ecosystem services. Key findings reveal: (1) During the period of 2005–2010, the land cover types underwent significant changes, characterized by a marked expansion of sparse forest (14–21%) and a pronounced decline in forest land, which fundamentally reconfigured the ecosystem’s water demand structure. (2) Accordingly, the multi-year average ecological water requirement quota in the study area is 2.95 × 10⁷ m³, and the total ecological water requirement exhibited a fluctuating decline at a rate of −1.39 × 10⁵ m³/yr, yet sparse forest persisted as the dominant water-consuming component. (3) The Random Forest model (R² = 0.942) identified water yield (importance: 0.527) and precipitation (0.255) as the primary drivers, establishing the ecosystem’s water yield function rather than precipitation input alone as the critical constraint. (4) A widespread increase in the unit area ecological water requirement across vegetation types signaled escalating pressures from climate change. This research provides a quantitative framework and a transferable methodology for adaptive water resource management and ecological restoration in arid regions, emphasizing the balance between ecosystem water demand and supply functions. Full article

Estimating future air quality under the warming climate is an urgent task for all populated regions. Often, climate models are evaluated with respect to air temperature and precipitation, but without a focus on other air quality-related meteorological variables. This study evaluated a regional ensemble system over the southeast Australian region driven by five selected CMIP6 global climate models (downscaled by two regional models, making the ensemble size ten) in terms of a range of surface variables relevant for air quality from seasonal to diurnal timescales. Results showed that the two regional climate models, although only differing in their planetary boundary layer (PBL) parameterizations, performed quite differently. In general, the regional model with the MYNN2 PBL scheme (named R3) performed better than the other. While most meteorological variables, including surface wind speed, were verified well, wind direction showed large biases and variability among models. When downscaled (~4 km resolution) atmospheric variables were applied to drive the Community Multiscale Air Quality (CMAQ) model, the ensemble members, particularly the two versions of the regional model, resulted in different chemical species concentrations. A model ranking scheme was developed based on various spatiotemporal timescales and identified slightly superior performance by the regional model R3. The findings provide a valuable reference for selecting optimized model members for future air quality projections. Full article

Tick-borne diseases account for a substantial proportion of the global incidence of infectious diseases, and their recent expansion has been increasingly associated with climate change. Nevertheless, previous studies have produced heterogeneous and often inconclusive results, largely due to differences in spatial scale, variable selection, and limited integration of climatic, ecological, and host-related drivers. Here, we assess the modeled impact of climate trends on the global distribution patterns of ticks parasitizing humans and livestock, rather than changes in tick abundance or pathogen transmission. This study is not an evaluation of human or animal contact rates with ticks. Using the largest curated compilation of georeferenced tick records available to date (213,513 records from 138 Ixodidae species), we adopt a global, climate-centered perspective based on the Holdridge life zones framework. The study characterized current climatic niches of tick genera and projected changes in suitability under future climate scenarios for 2040, 2060, 2080, and 2100. Our results reveal a strong association between tick occurrence patterns and large-scale gradients of temperature and atmospheric water balance, while precipitation plays a comparatively minor role. Projections indicate increasing climatic suitability for human-biting ticks at higher northern latitudes, concurrent with declining suitability across parts of central and southern Africa. By integrating modeled suitability with human population projections and livestock distributions, we estimated future changes in exposure risk. Although local processes such as tick abundance and pathogen prevalence are beyond the scope of this study, our findings provide a coherent global synthesis of how climate change may reshape tick distributions and associated risks. Full article

Limited research has examined how increasing extreme precipitation affects active transportation across the United States. This study assesses the longitudinal relationship between extreme precipitation and walking and cycling to work in the context of rising extreme weather and flooding. We conducted a county-level longitudinal analysis using data from the National Environmental Public Health Tracking Network (2005–2018). Five-year estimates of walking and cycling to work among adults aged 16 years and older were obtained from the American Community Survey, and annual population-weighted averages of days with extreme precipitation (≥2 inches) were derived from the North American Land Data Assimilation System. Mixed-effects models with restricted maximum likelihood estimation assessed associations with active transportation, accounting for county-level clustering and adjusting for year, region, poverty rate, water cover, metropolitan status, and park access. Across 3142 U.S. counties, extreme precipitation days increased over time, while walking and cycling to work declined. Each additional extreme precipitation day was associated with a 12.3% decrease in walking and a 3.7% decrease in cycling at baseline, with stronger negative associations over time. Effects were most pronounced in non-metropolitan and Midwestern counties. Findings underscore the importance of climate-resilient transportation planning for sustaining low-carbon, equitable mobility and advancing sustainable development. Full article