The climate effect and environmental pollution caused by dust discharged into the atmosphere have attracted much attention. However, the driving factors of dust emissions have not been studied thoroughly. Here, spatiotemporal variations in dust emissions and the relationship between dust emissions and large-scale atmospheric circulation in East Asia from 2000 to 2021 were investigated using Modern-Era Retrospective Analysis for Research and Applications version 2, Cloud-Aerosol Lidar Pathfinder Satellite Observations, ERA5 reanalysis data, and climate indices. Results showed that the Taklimakan Desert in the Tarim Basin, the Gurbantonggut Desert in the Junggar Basin, the Turpan Basin, and the Gobi Desert in western Inner Mongolia and southern Mongolia are the main sources of dust emissions in East Asia. The period of strong dust emissions is from March to May, and emissions to the atmosphere were mainly distributed at 0–4 km in the troposphere. In the eastern and southwestern Tarim Basin, northern Junggar Basin, and parts of the Gobi Desert in southern Mongolia, dust emissions have significantly increased over the past 22 years, whereas in the southwestern Tibetan Plateau, southwestern Inner Mongolia, and a small part of the northern Mongolian Gobi Desert there was a significant decreasing trend. The winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with East Asian dust emissions the following spring. The various phases of the AO/NAO coupling have clear different effects on East Asian dust emissions in the spring. When the AO/NAO coupling was negative (positive), the East Asian trough and Siberian High were strengthened (weakened), the frequency of cold air activity increased (weakened), 800 hPa wind speed strengthened (weakened), and East Asian emissions increased (decreased). In AO−/NAO+ years, the Asian polar vortex was stronger to the south and the East Asian trough was stronger to the west. The Lake Baikal trough was in the deepening phase, which caused more polar cold air to move into East Asia, aggravating the intensity of dust activity. In the AO+/NAO− years, the Siberian High and East Asian trough weakened, which was unfavorable to the southward movement of cold air from Siberia. Therefore, the frequency of windy weather in East Asia decreased, partly weakening dust emissions. However, a positive geopotential anomaly in northeast China and a negative geopotential anomaly in South Asia triggered an anomalous enhancement in easterly wind in the tropospheric area over northwest China. Strengthening of the Balkhash trough provides favorable conditions for gale weather in northwest China. The frequency of gale weather increased, and dust emissions were enhanced in northwest China.

In alpine areas in Northwest China, such as the Tianshan Mountains, the lack of climate data (because of scarce meteorological stations) makes it difficult to assess the impact of climate change on runoff. The main contribution of this study was to develop an integrated method to assess the impact of climate change on runoff in data-scarce high mountains. Based on reanalysis products, this study firstly downscaled climate data using machine learning algorithms, then developed a Batch Gradient Descent Linear Regression to calculate the contributions of temperature and precipitation to runoff. Applying this method to six mountainous basins originating from the Tianshan Mountains, we found that climate changes in high mountains are more significant than in lowlands. In high mountains, the runoff changes are mainly affected by temperature, whereas in lowlands, precipitation contributes more than temperature to runoff. The contributions of precipitation and temperature to runoff changes were 20% and 80%, respectively, in the Kumarik River. The insights gained in this study can guide other studies on climate and hydrology in high mountain basins.

Wetland ecosystems in the Qinghai–Tibet Plateau are pivotal for global ecology and regional sustainability. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai–Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest (RF) method for annual long-term land cover classification. Standardized precipitation evapotranspiration indices (SPEI3, SPEI6, SPEI9, and SPEI12) on different time scales were used to assess meteorological drought conditions. We employed a Pearson correlation analysis to examine the relationship between wetland changes and various SPEI scales. The BFASAT method was used to evaluate the impact of SPEI12 trends on the wetlands, while a cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our conclusion is as follows: The wetlands, including lake, glacier, and marsh wetlands, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990–1997, 1998–2007, and 2008–2020), featuring extensive conversions between wetlands and other types, notably the conversion from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. There were significant negative correlations between the SPEI at various scales and the total wetland area and types. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, and WP) exhibited varying degrees of resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai–Tibet Plateau.

The rise in seasonal lifestyle tourism, characterized by winter-escape health and wellness stays and long-term leisure residence, has intensified peak–off-peak imbalances and pressures on the allocation of tourism service supply in tropical island destinations. However, existing research lacks a systematic comparison of seasonal fluctuations and long-term evolution for this subgroup at the city/county level. Therefore, this study aims to characterize the seasonal pattern, long-term trend features, and typological differentiation of seasonal lifestyle tourism at the county level, and to compare differences across types. Using monthly data on seasonal lifestyle tourism for 18 cities/counties in Hainan from 2021 to 2024, we apply TRAMO/SEATS decomposition to identify seasonal structures and measure seasonal amplitude and employ the Hodrick–Prescott (HP) filter to extract trend components and determine their directions of change. We further construct five development types by integrating trend categories and changes in seasonal amplitude and test between-type differences using one-way analysis of variance (ANOVA). Results show that Hainan exhibits a stable “winter–spring peak and summer–autumn trough” pattern (peaks concentrated in January–March and December, with the off-season typically spanning May–October), with strong seasonality and pronounced spatial heterogeneity. The four-year mean seasonal range at the county level is 215.01, with high values clustered in southern Hainan; Haikou remains relatively low, while Wenchang shows an upward trend. Long-term trends are clearly differentiated: 13 counties show sustained growth, 2 show decline, and 3 display a U-shaped recovery (decline followed by rebound). Growth rates also vary substantially, with Qionghai increasing at roughly 27 times the rate of Qiongzhong. Integrating seasonal and trend characteristics yields five types, of which the Robust Development type accounts for the largest share (50%). Between-type differences are mainly reflected in tourism service supply capacity: the number of star-rated hotels (p = 0.033, η² = 0.530) and overnight visitors (p = 0.004, η² = 0.676) differ significantly across types, whereas differences in natural-environment conditions are not significant. This study provides a scientific basis for zoning management and optimizing low-season strategies in Hainan.

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