Wenxiang Wu

Hainan Province is the only tropical province in China and is an important tropical rice-producing region, playing a critical role in regional food security. Rising temperatures associated with climate change may adversely affect rice growth and yield in tropical and subtropical regions, increasing production risks. This study integrated meteorological data from six national agro-meteorological stations in Hainan Province with long-term observations of rice phenology and yield to assess future climate impacts using a multi-climate model and multi-crop model ensemble framework. The CERES-Rice, ORYZA2000, and WOFOST models were separately calibrated and validated using historical observations. Subsequently, CMIP6 projections from five GCMs under SSP1-2.6, SSP3-7.0, and SSP5-8.5 were used to simulate rice phenology and yield during the 2050s, 2070s, and 2090s. A multi-model ensemble mean was further used to integrate inter-model differences across scenarios and future periods. All three crop models showed satisfactory performance in simulating rice phenology and yield. Future warming generally advanced flowering and maturity dates of early rice, whereas late rice exhibited greater spatial and inter-model variability. Yield losses became more pronounced under SSP3-7.0 and SSP5-8.5, with late rice showing greater reductions than early rice. Sowing date adjustment partially offset climate-induced yield losses, although the optimal strategy differed between rice seasons and climate scenarios. Overall, the multi-model framework provided a robust basis for climate risk assessment and adaptation planning for tropical double-rice production in Hainan Province.

Tropical–subtropical monsoon Asia faces pronounced hydroclimatic change under future warming, yet the regional patterns and emission scenario dependence of drought risk remain unclear. Using a 14-member CMIP6 multi-model ensemble from the NEX-GDDP-CMIP6 dataset, we assessed drought evolution under SSP1-2.6 and SSP5-8.5 (2015–2100). Combining the 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) with event-based run theory and Mann–Kendall trend analysis, we evaluated four complementary drought dimensions: annual mean SPEI, frequency, intensity, and duration. Regional drought area was used as a spatial diagnostic of extent. A persistent west-to-east moisture gradient characterizes present-day conditions: chronic deficits prevail in southwestern China, southern Myanmar, and Thailand, whereas southeastern China and the equatorial belt maintain relative surpluses. This contrast intensifies through the century, with negative SPEI anomalies deepening by 0.2–0.4 units and chronic drought zones expanding into previously marginal areas. Under SSP1-2.6, no metric shows statistically significant trends. By contrast, SSP5-8.5 produces robust drying, with 8.0% of the area showing significant SPEI declines and up to 30.9% exhibiting significant intensification. Scenario-driven increments affect 89–99.7% of the area across all indicators, confirming that higher emissions systematically elevate drought frequency, persistence, and severity. Scenario uncertainty surpasses model structural uncertainty first for intensity (2067), then area (2078), duration (2079), and frequency (2090), reflecting earlier model convergence on the forced thermodynamic shift than on threshold-dependent frequency counts. Stringent mitigation consistent with the Paris Agreement can largely stabilize regional moisture regimes, whereas continued high emissions will drive pervasive drought intensification across one of the world’s most populous and biodiverse forest regions.

Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981–2018 (baseline period) and 2041–2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO₂) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO₂ fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO₂ fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice.

Jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Anthropogenic activities are accelerating biodiversity loss and ecosystem degradation worldwide. Expanding conservation has become a global conservation strategy, but designing them amid competing land-use pressures, particularly from renewable energy development—a critical decarbonization strategy—remains challenging. Here, we develop a multi-objective planning framework to reconcile ecological conservation with renewable energy development in China. We mapped spatial distributions of biodiversity, ecosystem services, and vulnerable carbon stocks alongside development potentials for onshore wind power, solar power, and hydropower. Using these layers, we designed three land-use scenarios—Conservation-First, Energy-First, and Multi-Zones—to optimize spatial allocations of conservation and energy zones under 30% and 50% land conservation targets. We find significant ecological gaps in the existing conservation system: only 27.4% and 43.91% of current protected areas overlap with the top 30% and 50% priority areas for ecological conservation, respectively. If energy development proceeds without ecological considerations, over 2 million km² of land, 3602 Gt of vulnerable carbon, and over 80 km² of endangered species habitat face risk. In contrast, the Multi-Zones scenario avoids ~400 Gt of carbon exposure, safeguards 10% more critical habitats, and preserves over 85% of national renewable energy potential. Key trade-off regions are identified in southern Tibet and northern Inner Mongolia. Our findings highlight the necessity of adaptive conservation strategies and conflict-sensitive energy planning to balance ecological and developmental goals. This study provides practical guidance for integrated land-use planning that supports China’s dual carbon commitments and global biodiversity targets, while contributing to the concurrent achievement of SDG 15 and SDG 7.

The pressing need to address climate change and advance global sustainable development has heightened the emphasis on ecosystem services, especially carbon sequestration. This research assesses the supply and demand dynamics of carbon sequestration services on Hainan Island, China, highlighting its significant contributions to global biodiversity conservation and carbon balance. The analysis considers the spatial distribution and interrelation of these services in light of recent land use and ecological policy changes. The methodology incorporates land use and land cover data, the Normalized Difference Vegetation Index (NDVI), meteorological data, and soil data. A gravity model is employed to elucidate the supply–demand relationship for carbon sequestration services, examining the flow across different regions and identifying spatial connections and their intensities. The results indicate a notable increase in carbon sequestration supply in Hainan from 2000 to 2020, particularly in the central mountainous areas. Conversely, the demand for these services has risen, especially in the northern plains’ urban areas and southern coastal towns. The gravity model reveals a strong spatial interdependence between the central mountainous supply zones and the high-demand urban locales. This study underscores the disparities in carbon sequestration supply and demand on Hainan, emphasizing the need for the strategic management of these elements. It provides critical data for ecological compensation policies and offers insights into the roles of regional ecosystems in climate change mitigation. The research highlights the necessity of incorporating ecosystem services into land-use planning and decision-making to foster sustainable development and strengthen climate resilience.

Latitudinal patterns of tree β-diversity reveal important insights into the biogeographical processes that influence forest ecosystems. Although previous studies have extensively documented β-diversity within relatively small spatial extents, the potential drivers of β-diversity along latitudinal gradients are still not well understood at larger spatial extents. In this study, we determined whether tree β-diversity is correlated with latitude in forests of southeastern China, and if so, what ecological processes contribute to these patterns of tree β-diversity. We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents. We delineated regional communities comprising multiple nearby national forest inventory (NFI) plots around random focal plots. The number of NFI plots in a regional community served as a surrogate for spatial extent. We also used a null model to simulate randomly assembled communities and quantify the deviation (β-deviation) between observed and expected β-diversity. We found that β-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents. In addition, latitudinal patterns of β-deviation were explained by the degree of species spatial aggregation. We also identified environmental factors that drive β-deviation in these forests, including precipitation, seasonality, and temperature variation. At larger spatial extents, these environmental variables explained up to 84% of the β-deviation. Our results reinforce that ecological processes are scale-dependent and collectively contribute to the β-gradient in subtropical forests. We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk.

Flash droughts pose large threats to crop yields and ecosystem services due to their sudden onset and rapid intensification, arousing wide public concern in a warming climate. Their long-term characteristics of change, underlying mechanisms, and especially potential impacts on agriculture, forests, and populations at a global scale, however, remain largely unknown. We used in situ observations, two observation-based global reanalysis data sets, and 22 Earth system models to determine that flash droughts are shifting toward more frequent, accelerated-onset, and longer duration. These changes increased the exposure of agricultural areas, forested areas, and populations to flash droughts by 20.3%, 17.1%, and 30.0%, respectively, during 2001–2020 compared to 1981–2000, with a disproportionate increase in integrated risks across the Amazon Basin and eastern and southern Asia. The increase in concurrent hot and dry climatic conditions driven by warming has been mostly responsible for enabling and intensifying flash droughts over large regions. State-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models, however, failed to identify the acceleration of the onset time of flash droughts and widely underestimated the occurrence of flash droughts that are driven only by precipitation deficits or by heat waves, probably because they misrepresent the dependence between precipitation and temperature and underestimate the sensitivity of soil moisture to temperature and precipitation at short timescales (e.g., 5 days). These syntheses comprehensively advance our understanding of the characteristics and impacts of flash droughts but also highlight that the CMIP6 models need to be validated to represent the correct covariability between climatic variables at short timescales to provide more reliable projections of flash droughts.