Wetlands, as vital components of urban ecological infrastructure, provide essential ecosystem services. However, they face increasing risks of degradation and loss due to their vulnerability, environmental changes, and human activities. Therefore, effective restoration efforts are urgently needed. This study adopts a novel approach by considering the urban–rural gradient and integrates land use data, ecological parameters, and anthropogenic factors in Hefei City. Through morphological spatial pattern analysis, principal component analysis, and affinity propagation, this study identifies and analyzes urban–rural gradients. Using the optimal parameter geographic detector, the drivers of wetland changes from 1990 to 2020 are quantitatively assessed across different urban–rural gradients in Hefei. The findings indicate the following. (1) A persistent reduction in wetland expanse throughout the study duration, diminishing from 1274.56 km² in 1990 to 1119.37 km² in 2020, constituting a decrement of 12.17%. (2) Based on geographic detector outcomes, disparate driving forces underpin wetland dynamics across urban–rural gradients, with urban locales predominantly influenced by organic carbon and the proportion of impervious surface factors. Meanwhile, in agricultural and semi-ecological villages, silt is the primary factor, while ecological villages are primarily modulated by both silt and gross domestic product factors. Additionally, synergistic interactions manifest heightened explanatory power. This study elucidates the mechanistic underpinnings of wetland dynamics along urban–rural gradients, providing pivotal insights for developing targeted wetland restoration and conservation policies pertinent to the urban–rural developmental trajectory in Hefei City. Concurrently, it offers relevant recommendations for the multifaceted stewardship and sustainable development of wetlands in Hefei City in the foreseeable future.

The increasing frequency of extreme weather events presents serious challenges to both regional and global economies. This study focuses on quantifying the economic losses caused by three major types of extreme climate events (heatwaves, droughts, and heavy rain) in Hainan Island from 2001 to 2020. Moreover, a comprehensive dataset of related economic losses was developed. To support the analysis, we constructed an Extreme Climate Economic Loss Model (ECELM). Drought and heavy rain losses were estimated using a loss intensity index based on precipitation and typhoon landfall wind speeds. Heatwave-related losses were assessed through a threshold-based optimization approach. The results show that both heatwaves and heavy rain have exhibited increasing impacts from 2001 to 2020. Although heatwaves were the most frequent extreme event in more than half of the years, heavy rain caused the highest cumulative losses, reaching CNY 75 billion. Spatial analysis indicates that the southeastern coastal areas were the most severely affected. These findings provide valuable quantitative evidence for designing targeted regional climate adaptation strategies.

Ecosystem water use efficiency (WUE) is a key indicator of the coupling between carbon and water cycles. With the increasing frequency of extreme climate events, WUE may also show trends of extremization. Understanding the dominant drivers behind extreme WUE variations is crucial for assessing the impact of climate variability on WUE. We investigate the main drivers and regional sensitivity of extreme WUE variations across seven geographical regions in China. The results reveal that extreme WUE variations are collectively influenced by gross primary productivity (GPP) and evapotranspiration (ET) (43.72%). GPP controls extreme WUE variations in 36.00% of the areas, while ET controls 20.17%. Furthermore, as the climate shifts from arid to humid regions, the area where GPP dominates extreme WUE variations increases, while the area dominated by ET decreases, suggesting a relationship with precipitation. Ridge regression analysis shows that vapor pressure deficit (VPD) is the primary driver of interannual WUE variation in China, with an average relative contribution of 38.64% and an absolute contribution of 0.025 gC·m⁻²·mm⁻¹·a⁻¹. We studied the changes in WUE and its driving mechanisms during extreme disaster events, providing a perspective focused on extreme conditions. In the future, these results may help regulate the carbon–water cycle in different regions, such as by guiding vegetation planting and land use planning based on the spatial characteristics of the dominant factors influencing extreme WUE variations to improve vegetation WUE.

Context Wetlands persistently diminish due to their own vulnerability, along with the combined impact of changes in the ecological surroundings and interference from anthropogenic interventions. Comprehending the driving mechanisms of wetland changes is crucial for wetland conservation. Objectives This research seeks to analyze the dynamic change characteristics of wetlands in Hefei. The objective is to establish a framework for urban-rural gradient analysis, exploring the impact of ecological surroundings and anthropogenic interventions factors, as well as their interactions, on wetland dynamics across different urban-rural gradient regions. Methods In this research, an urban-rural gradient was defined utilizing a clustering indicator derived from the composition and configuration of land use. The optimal parameters-based geographical detector (OPGD) performed a quantitative statistical analysis of the driving forces behind wetland changes under different urban–rural gradients. Results The results indicate that: (1) From 1990 to 2020, wetland area exhibited an ongoing diminishing tendency. (2) The urban–rural gradient can be classified into four categories: urban, agricultural village, semi-ecological village, and ecological village. (3) The driving factors of wetland changes vary across different types of urban–rural gradient regions. The most significant factors influencing wetland changes in each type of gradient region during different periods are the cropland proportion, impervious surface, OC, silt, and GDP. (4) Interactions among driving factors generally exhibit stronger explanatory power. Conclusions The research results reveal the driving mechanisms that underlie wetland dynamics in different urban–rural gradient regions. This information supports the development of more targeted wetland restoration and protection plans, contributing to improved synergy between urban and rural development and ecological conservation efforts.