Typhoons, as highly influential meteorological disasters, cause extensive damage to both property and lives. The accurate and efficient prediction of typhoon disaster losses (TDL) is crucial for pre-disaster risk management, emergency response strategies and post-disaster loss assessment. While existing studies typically reveal linear relationships between TDL and its influencing factors, the focus need to be shifted toward developing non-linear models that can capture the complexities of these relationships. To this end, this study leverages historical TDL data at the county level from 2001 to 2023 in Zhejiang Province, China. Combining RF, XGBoost, LightGBM, MLR models and SHAP values, based on meteorological, environmental and economic data, this study develops interpretable TDL prediction models. The results demonstrate that the XGBoost model performs best with a R2 of 0.7060. The most important influencing factors are accumulated precipitation, the nighttime light index, maximum daily extreme wind speed (max EWS) and maximum daily maximum wind speed (max MWS). Meteorological factors, especially wind speed and atmospheric pressure, were found to be the most significant contributors to TDL prediction.

The urban agglomeration in middle reaches of the Yangtze River is a key area for the implementation of the Central Rise Strategy in China. Over the past decade， land use changes have intensified the vulnerability and instability of the regional ecosystem. Therefore， accurately assessing the landscape ecological risks in this area is of crucial significance for ensuring its sustainable development in the future. Based on the land use data of 2010， 2015， and 2020， the landscape ecological risk assessment model and statistical methods were used to quantitatively analyze the spatio-temporal evolution characteristics of landscape ecological risks from 2010 to 2020. Moreover， the PLUS model was utilized to simulate and predict the distribution patterns of the production-living-ecological spaces and the corresponding spatial differentiation characteristics of landscape ecological risks in 2035 under three scenarios： natural development， production priority， and ecological protection. From 2010 to 2020， the area of ecological space in urban agglomeration in the middle reaches of the Yangtze River decreased by approximately 1 876.665 5 square kilometers， while the areas of production space and living space expanded by 2 117.629 1 square kilometers and 1 855.684 8 square kilometers， respectively. During the period from 2010 to 2020， the overall landscape ecological risk showed an upward trend， with the area of high-risk zones increasing from 3 625 square kilometers to 4 203 square kilometers. Spatially， they were mainly concentrated in the Wuhan City and its surrounding areas including Huangshi and Xiaogang； the Changsha-Zhuzhou-Xiangtan urban agglomeration； as well as the riverfront cities of Nanchang， Yichang， and Yueyang. In the multi-scenario simulation of production-living-ecological spaces in 2035， the areas of production-living-ecological spaces remained basically unchanged under the natural development scenario and production priority scenario， while the area of ecological space increased under the ecological protection scenario， mainly derived from the agricultural production space. Among the three scenarios， the ecological protection scenario had the best ecological security pattern， with the area of high-risk zones accounting for only 1.06%. The production priority scenario had the most imbalanced risk structure， with the combined proportion of high-risk and very high-risk zones reaching 10.27%， mainly concentrated along the Yangtze， Xiang， and Gan Rivers. The research results reveal the differentiated impacts of different land development strategies on landscape ecology， providing a scientific basis for the future land use planning and ecological protection of urban agglomeration in the middle reaches of the Yangtze River.

Extreme humid heatwaves (HS) have emerged as one of the most threatening compound events under climate change. Yet their dynamic evolution and mechanism, especially the relation with antecedent precipitation, remain unclear. Here, we provide a global assessment of HS dynamics using reanalysis data from 1979 to 2024. Results reveal significant intensification trends in HS frequency, duration, and intensity. The occurrences of HS are mainly driven by humidity anomaly in 73.41% of global land areas. The relations between antecedent precipitation and HS are strengthening. HS tends to occur more rapidly after the cessation of long-duration heavy rainfall, mainly due to rapid soil moisture evaporation enhancing near-surface humidity; in contrast, HS following weak or short-duration precipitation exhibit a slower transition and stronger dependence on large-scale high-pressure systems. The distinct patterns demonstrate differentiated regulatory mechanisms of HS across different antecedent precipitation types and necessitating context-specific adaptation strategies tailored to these divergent precipitation-HS relationships.

Study region 11 representative flux sites spanning China's major climate gradients (24˚– 43˚N, 91˚–128˚E), from Tibetan Plateau's alpine grasslands to Xishuangbanna's tropical rainforests. Study focus Accurately quantifying dewfall remains a central ecohydrological challenge. Previous data-driven estimations predominantly relied on coarse monthly data, which fundamentally obscures the sub-diurnal physical thresholds of condensation. To address this gap, we developed interpretable machine learning models—Random Forest, Support Vector Regression, and Artificial Neural Networks—using continuous multi-site data to simulate dew formation at daily and monthly scales. We then applied Explainable AI (SHAP, lag, and partial correlations) to systematically decouple the underlying environmental drivers. New hydrological insights for the region Daily-scale modeling is essential for capturing threshold-driven condensation. It successfully avoids the severe overfitting inherent in traditional monthly aggregations, which falsely learn seasonal trends rather than physical mechanics. Furthermore, dew acts as a ubiquitous water subsidy, contributing 0.98–6.72% of annual precipitation. This buffering capacity is disproportionately critical for mitigating short-term drought stress in water-limited and alpine environments. Finally, Explainable AI reveals that RE (ecosystem respiration) /NEE (net ecosystem carbon exchange) a relatively high contribution within the model are not direct causal drivers of condensation, but act as integrated proxies for nocturnal microclimatic conditions.

Drought-flood abrupt alternation (DFAA) events, characterized by compound extremes and rapid transitions, pose significant challenges for accurate identification. Although existing research has reviewed DFAA event identification, it pays insufficient attention to emerging concepts related to DFAA and characterization variables and lacks a comprehensive summary of methodological advancements under climate change. To fill these gaps, this study systematically reviews 55 publications by proposing a unified definition framework, synthesizing identification and characterization methods, evaluating recent methodological advances, and outlining future directions for improving DFAA identification. This review shows that (1) 58% of studies utilize traditional drought-flood indices and indicators, whereas 42% propose DFAA-specific indices; (2) traditional methods often disregard key DFAA characteristics, treating droughts and floods as separate events rather than as a unified process; (3) advanced methods incorporate key features such as alternation points, transition time, and transition speed, yet challenges remain in accurately capturing abrupt transitions; (4) future research should integrate multi-source datasets and apply dynamic time windows to improve DFAA identification, while aligning advances with policy to strengthen early warning and risk management.

To quantitatively analyze the coupling relationships between railway safety risk factors, identify key factors contributing to railway accidents, and develop scientific strategies for accident prevention, this study introduces a complex network-based N-K model to investigate the coupling relationships of railway safety risk factors. First, we identified 18 railway safety risk factors by analyzing case data from railway accidents. The occurrence probabilities and coupling values of these risk factors were then calculated using the N-K model. Subsequently, based on the constructed railway safety risk complex network, reachability and centrality analyses were performed to determine the key factors of railway safety risk. Results indicate that the occurrence of railway accidents is directly proportional to the risk coupling value; the greater the number of coupling factors, the higher the risk value. The coupling of personnel factors and equipment factors is particularly prone to leading to railway accidents. Conversely, effective management of the coupling between personnel and equipment factors can significantly reduce the likelihood of accidents. Inadequate maintenance and unsafe human behavior were identified as critical factors contributing to railway accidents and should be prioritized in prevention efforts.

Due to climate warming, extreme precipitation events have intensified in frequency and intensity. This trend has raised significant concerns about its impact on natural reserves in eastern China’s monsoon region. A risk assessment is, therefore, needed to evaluate the vulnerability of these protected areas. Based on observed and simulated daily precipitation data, this study analyzed the spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and assessed the exposure risk of the protected areas to rainstorm events both in the historical and future periods. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China’s Eastern Monsoon Region display a north–south gradient of precipitation exposure risk that intensifies from historical (1995–2014) to near future (2031–2050) to far future (2081–2100) under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time.

Under the backdrop of global climate change, the increasing intensity and frequency of anomaly climate events have led to a rise in compound extreme events. China's large population exacerbates the pressure of agricultural production, and compound drought and extreme rainfall events (CDER) can cause considerable damage to soil structure, thereby disrupting normal agricultural activities. Previous studies have revealed the impacts of the individual event, but the spatiotemporal characteristics of CDER and their effects on agricultural production remain obscure. This study focuses on compound disaster events in China's nine major agricultural regions, where drought and extreme rainfall events occur within 5 d. The results show that compound disasters are mainly concentrated in the northwest, southwest, and northern regions. The impact area of compound disasters is largest in summer, and the frequency and intensity of drought-rainfall events are higher than those of rainfall-drought events. Further analysis at the crop growth stage scale reveals the exposure of the three major cereal crops (rice, wheat, and maize) during their growth stage. The study reveals that maize generally has the highest and most variable disaster risk, rice has the lowest risk with minimal fluctuations, and wheat has moderate risk with large variations. The risk evolution in each agricultural region follows a universal pattern of “first rising and then declining”, with the peak occurring around 2010. This study elucidates the spatiotemporal distribution patterns of this novel compound disaster and provides constructive insights for disaster prevention and mitigation through more refined risk assessments.

Extreme drought has profound effects on global vegetation, shaping carbon and water cycles and drawing significant research attention. Physiological responses and structural adaptations are two main aspects when vegetation dealing with drought. Traditional remote sensing methods, relying on indicators like Leaf Area Index (LAI), Solar-Induced Fluorescence (SIF), and Near Infrared reflectance of vegetation (NIRv), face challenges in disentangling mixed signals and capturing fine-scale physiological changes. To address this issue, we proposed a multi-spectral remote sensing approach to construct models that disentangle remote sensing signals only representing vegetation's physiological response to drought. To achieve that, two separate random forest models were constructed using vegetation structural variables and hydro-meteorological variables to predict total and structural components of functional anomalies, quantified using SIF, Evapotranspiration (ET), and Vegetation Optical Depth (VOD) ratio. Subsequently, model residuals were calculated from the two models and used to disentangle the physiological component in observed remote sensing signals. The results in Amazon rainforest revealed that the physiological component explained the majority of functional anomalies during drought, with the physiological contributions of photosynthesis, transpiration, and water regulation functions accounting for 74.1%, 64.2%, and 71.8% of the anomalies in wet regions, and 67.7%, 62.6%, and 66.2% in dry regions, respectively. Attribution analysis indicated that regional hydro-meteorological conditions and vegetation types contributed to shaping the spatial patterns of vegetation phys iological responses to drought, explaining 75.28% and 82.17% of the spatial variability in the physiological components during drought development and recovery phases. Structural equation modeling further elucidating causal pathways linking key environmental drivers to these physiological responses. The uncertainty of model predictions was quantified using the leave-one-out approach, yielding interquartile ranges of 0.72, 0.41, and 0.82 for the physiological component proportions of the three functional variables. This research disentangles physiological and structural responses with finer spatial and temporal resolution, providing a clearer view of vegetation dynamic changes and adaptation mechanisms. These findings emphasize the value of multi-spectral remote sensing in understanding vegetation functions under extreme drought conditions, offering a more detailed and accurate representation of vegetation dynamics.

Background aridity shapes ecosystem traits and governs the pattern of grassland gross primary productivity (GPP) loss under dry-heat extremes. However, in response to the intensification of such events, how the spatial heterogeneity of background aridity affects grassland GPP responses remains unclear. Furthermore, the trade-off between GPP loss rate (Glr) and loss intensity (Gli)-where higher Glr is generally associated with lower Gli and vice versa-serves as a key indicator of ecosystem stability. The breakdown of this trade-off (i.e., trade-off decoupling), marked by concurrent directional changes in both Glr and Gli, indicates that grassland productivity is in a highly unstable state. To systematically elucidate the regulatory role of background aridity and reveal the actual state of the Glr-Gli trade-off under dry-heat extremes, this study utilized multi-source daily remote sensing data from mainland China’s grasslands during 1985-2018 to quantify the Glr and Gli, identify the dominant drivers of GPP, and assess the Glr-Gli trade-off. The results indicated that compared to drought events (DEs) and heatwave events (HEs), the Glr and Gli induced by compound drought-heatwave events (CDHEs) increased with increasing background aridity, specifically manifesting as lower than DEs and HEs in humid and semi-humid zones, but higher than DEs and HEs in semi-arid and arid zones. Increased background aridity significantly amplified Glr under dry-heat extremes. Compared to humid and semi-humid zones, Glr in semi-arid and arid zones increased by 54.67%, 80.50%, and 288.11% under DEs, HEs, and CDHEs stress, respectively. Soil moisture (SM) was the dominant factor regulating GPP under dry-heat extremes, and its regulatory capacity strengthened with increasing background aridity. Compared to DEs and HEs, the regulatory capacity of SM on GPP was strongest under CDHEs stress. Dry-heat extremes led to a significant breakdown of the Glr-Gli trade-off, and increased background aridity exacerbated this trade-off decoupling. Specifically, compared to humid and semi-humid zones, the spatial extent without a Glr-Gli trade-off in semi-arid and arid zones increased by 12.18%, 22.99%, and 14.10% under DEs, HEs, and CDHEs stress, respectively. Except in arid zones, CDHEs caused more severe Glr-Gli trade-off decoupling compared to DEs and HEs. Across mainland China, a transition occurred from low Glr and Gli in the southwest to high Glr and Gli in the northeast, highlighting the vulnerability of grassland GPP in the semi-arid zones of North China and the transition zones between semi-arid and semi-humid/humid zones. These findings provide critical implications for risk management under intensifying dry-heat extremes in China.

Irrigation has excellent potential for altering surface characteristics and the local climate. Although studies using site observations or remote sensing data have demonstrated an irrigation cooling effect (ICE) on the air temperature (Tem) and land surface temperature (LST), it is difficult to eliminate other stress factors due to different backgrounds. We characterized the irrigation effect as the differences (Δ) of LST and DCT (DCT = LST − Tem) between irrigated and adjacent non-irrigated areas. An improved method was proposed to detect it over the North China Plain (NCP) based on satellite observations. We also investigated the effects of irrigation on Tem, precipitation, NDVI, and ET, and explored the relationships between them. The results show that irrigation induced a decrease in the daytime/nighttime LST and DCT (−0.13/−0.09 and −0.14/−0.07 °C yr−1), Tem (−0.023 °C in spring), and precipitation (−1.461 mm yr−1), and an increase in NDVI (0.03 in spring) and ET (0.289 mm yr−1) across the NCP. The effect on nighttime LST and NDVI increased by 0.04 °C 10 yr−1 and 0.003 10 yr−1, and that on ET weakened by 0.23 mm 10 yr−1 during 2000–2015. The ICE on the LST had evident spatiotemporal heterogeneity, which was greater in the daytime, in the spring, and in the northern area of the NCP (dry–hot conditions). The daytime ICE in the NCP and northern NCP was 0.37 and 0.50 °C during spring, respectively, with the strongest ICE of 0.60 °C in Henan; however, the ICE was less evident (<0.1 °C) in the southern NCP throughout the year. The ΔNDVI, ΔET, and ΔTem were the main factors driving ICE, explaining approximatively 22%, 45%, and 25% of the daytime ICE, respectively. For every unit of these measures that was increased, the daytime ICE increased by about 7.3, 4.6, and 1.5 °C, respectively. This study highlights the broad irrigation effect on LST, ET, NDVI, and the climate, and provides important information for predicting climate change in the future. The improved method is more suitable for regions with uneven terrain and a varying climate.

Compound drought and heatwave events (CDHEs) are more devastating than single drought or heatwave events and have gained widespread attention. However, previous studies have not investigated the impacts of the precipitation attenuation effect (PAE) (i.e., the effect of previous precipitation on the dryness and wetness of the current system is attenuated) and event merging (EM) (i.e., merging two CDHEs with short intervals into a single event). Moreover, few studies have assessed short-term CDHEs within monthly scales and their variation characteristics under different background temperatures. Here we propose a novel framework for assessing CDHEs on a daily scale and considering the PAE and EM.We applied this framework to mainland China and investigated the spatiotemporal variation of the CDHE indicators (spatial extent (CDHEspa), frequency (CDHEfre), duration (CHHEdur), and severity (CDHEsev)) from 1968 to 2019. The results suggested that ignoring the PAE and EM led to significant changes in the spatial distribution and magnitude of the CDHE indicators. Daily-scale assessments allowed for monitoring the detailed evolution of CDHEs and facilitated the timely development of mitigation measures. Mainland China experienced frequent CDHEs from 1968 to 2019 (except for the southwestern part of Northwest China (NWC) and the western part of Southwest China (SWC)), whereas, hotspot areas of CDHEdurand CDHEsev had a patchy distribution in different geographical subregions. The CDHE indicators were higher in the warmer 1994-2019 period than in the colder 1968-1993 period, but the rate of increase of the indicators was lower or there was a downward trend. Overall, CDHEs in mainland China have been in a state of remarkable continuous strengthening over the past half a century. This study provides a new quantitative analysis approach for CDHEs.

Recent research has revealed that the dynamics of autumn phenology play a decisive role in the inter-annual changes in the carbon cycle. However, to date, the shifts in autumn phenology (EGS) and the elements that govern it have not garnered unanimous acknowledgment. This paper focuses on the Yellow River Basin (YRB) ecosystem and systematically analyzes the dynamic characteristics of EGS and its multiple controls across the entire region and biomes from 1982 to 2015 based on the long-term GIMMS NDVI3g dataset. The results demonstrated that a trend toward a significant delay in EGS (p < 0.05) was detected and this delay was consistently observed across all biomes. By using the geographical detector model, the association between EGS and several main driving factors was quantified. The spring phenology (SGS) had the largest explanatory power among the interannual variations of EGS across the YRB, followed by preseason temperature. For different vegetation types, SGS and preseason precipitation were the dominant driving factors for the EGS in woody plants and grasslands, respectively, whereas the explanatory power for each driving factor on cultivated land was very weak. Furthermore, the EGS was controlled by drought at different timescales and the dominant timescales were concentrated in 1–3 accumulated months. Grasslands were more significantly influenced by drought than woody plants at the biome level. These findings validate the significance of SGS on the EGS in the YRB as well as highlight that both drought and SGS should be considered in autumn fall phenology models for improving the prediction accuracy under future climate change scenarios.

Urban and rural areas play an important role in the greenness change in China, despite exhibiting divergent landscape ecologies. Although recent studies have revealed an overall greening pattern in China, the relative contribution of urban and rural vegetation to nationwide greening trend and their driving mechanisms behind these changes remain poorly understood. Here, we first utilized a high-resolution land use/cover dataset (GlobeLand30) to establish a framework for distinguishing between urban and rural areas. We then assessed and compared the greenness changes in both urban and rural areas using multiple vegetation indices from 2000 to 2020. By employing Random Forest model and generalized linear model regression, we further investigated drivers behind the changes in urban and rural vegetation trends. Our results demonstrated a significant greening trend in China, and the greenness increased 13.71% from 2000 to 2020. Vegetation changes in both urban (+4.96%, 0.0011 yr ^−1 ) and rural areas (+14.25%, 0.0026 yr ^−1 ) have contributed positively to China’s greening trend, with their contribution being 11.3% and 88.7%, respectively. Urban core areas exhibited the largest trend magnitudes (0.0043 ± 0.0035 yr ^−1 ) among all the urban–rural subregions. Increased tree cover was identified as the primary driver of greening trends in both urban and rural areas, explaining 36% and 29% of the greening, respectively. However, the pathways of tree cover increase differed between urban and rural areas, with urban areas focusing on green space construction and rural areas implementing afforestation programs. In contrast, climate change and the CO _2 fertilization effect had a greater contribution to the greening trend in rural areas than in urban areas. Our study demonstrates the positive role played by both urban and rural areas in China’s greening trends and elucidates the underlying mechanisms driving these changes, highlighting the need for differentiated strategies in urban and rural areas for future vegetation restoration.

针对现有研究方法中未考虑空气污染跨域影响的问题,在压力-状态-响应(pressure-state-response,PSR)框架基础上构建了大气环境的影响指标体系,利用DEA(data en-velopment anaylysis)模型度量各评价单元输入变量排放压力P和治理措施R对大气环境状况S的效率贡献,根据效率的异常状况及指标的空间相关性甄别空气污染的跨域影响,进而分析各单元大气环境治理的真实效率.以2014—2017年31个省级行政区的数据进行实例分析,结果表明:排放压力P大和响应措施R不足是目前导致效率低下的主要原因,南部沿海S较高,华北平原及其周边S较低.PSR系统关系方面,跨域影响类型所占频次较高,跨域影响显著.

Irrigation substantially alters land surface temperature (LST) in different regions of the world. Studies have recently focused on quantifying irrigation-induced LST change based on remote sensing technology due to its high spatiotemporal resolution. However, the biophysical mechanisms of irrigation on LST remains poorly understood. Here we first investigated the impact of irrigation on LST during 2003-2012 over the North China Plain (NCP), which is one of the most intensively irrigated areas around the word. We then attributed the mechanisms underlying LST change between adjacent irrigated and non-irrigated croplands based on two surface energy balance-based methods: the Decomposed Temperature Metric (DTM) method and the intrinsic biophysical mechanism (IBM) method. The results indicate that at annual scale, irrigation produce an overall cooling effect over the NCP, with the mean observed LST change of -0.098 K, calculated LST change of -0.096 K for DTM method and -0.165 K for IBM method, respectively. Furthermore, the agreement between the annual observed and calculated LST difference indicate that DTM is a more robust method than IBM in quantifying irrigation-induced LST change over the NCP. The attribution method DTM reveals that components of albedo and emissivity has an average cooling effect of -0.012 K and -0.005 K, respectively, while incoming radiation lead to a weak warming effect of +0.01 K. The enhanced turbulent fluxes of latent heat flux dominate the cooling effect (-0.174 K on average), further offsets the sensible heat flux warming effect (+0.085 K). Another attribution method IBM demonstrates that the annual cooling effect of irrigation is mostly induced by changes in aerodynamic resistance (-0.175 K), whereas the biophysical contributions of albedo (-0.0005 K) and Bowen ratio (+0.001 K) have a negligible impact on LST. This study provides a useful reference for assessing local climate impact of irrigation when implementing environmental protection projects.

在气候变化背景下,海绵城市改造热岛效应减缓效果成为人们最为关注的焦点问题之一,准确获取海绵城市改造前、后温度数据是精准评价海绵城市改造对城市热岛减缓效果的重要前提,气象观测站点往往距离海绵改造区具有较远的空间距离,其数据的不适用性极大限制了评价的开展.以武汉市青山、四新示范区为研究区,基于遥感监测、红外热成像技术,采用设立对比区法、以空间换时间法分别从宏观和微观上估算海绵区改造前后的温度变化,评估海绵城市建设对城市热岛效应减缓的效果.结果 表明:在宏观上,海绵城市改造对工业区(青山示范区)热岛效应减缓有一定效果,对原始开发程度较低地区(四新示范区)未体现出明显效果;在微观上,不同的改造方式、布设方式,甚至同一材料不同颜色等均对海绵改造热岛减缓效应有影响.设立对比区法、以空间换时间法能够为评价海绵城市改造对城市热岛减缓效果估算提供新的思路,具有很强的实用价值.