Xiaohua Dong

Persistent compound heat–drought extremes (PCHDEs) have become more frequent over the Middle and Lower Yangtze River (MLYR) since the 1960s, yet their relationship with Tibetan Plateau (TP) whole‐column heating remains unclear. We use the vertically integrated apparent heat source over the TP (TP) to examine six MLYR PCHDEs during 1961–2022. All events coincided with positive TP anomalies, but the associated circulation configurations differed between weaker and stronger heating conditions. Under weaker heating, with a mean TP increase of 9.7%, the events were mainly associated with eastward expansion of the South Asian high (SAH) or structural anomalies of the western Pacific subtropical high (WPSH), whereas the East Asian subtropical jet (EASJ) played a limited role. Under stronger heating, with a mean increase of 24.5%, enhanced downstream Rossby‐wave propagation and coordinated adjustments of the SAH, WPSH, and EASJ were associated with stronger regional subsidence, reduced moisture transport, and persistent heat and drought conditions over the MLYR. A particularly favorable configuration occurred when TP was anomalously strong, and the ascending branch of the Rossby wave pattern was located west of the MLYR. Under this configuration, the MLYR was situated beneath persistent anticyclonic subsidence. Across the six events, regional maximum temperature increased by about 3.2°C and precipitation decreased by about 46.8% relative to corresponding periods in other years. These results reveal that the large‐scale circulation configurations accompanying MLYR PCHDEs vary with TP atmospheric heating intensity.

Accurate and reliable latent heat (LH) and sensible heat (SH) flux data are crucial for determining energy exchange on the Tibetan Plateau (TP) and for studying land–atmosphere interactions and their impacts on the climate. Improving the accuracy of heat flux datasets over the TP and comprehensively assessing their reliability and uncertainty, especially in data-sparse regions, remain insufficiently explored. This study utilized the Optuna–categorical boosting (CatBoost) model to fuse daily TP heat flux datasets from 2000 to 2022, incorporating five reanalysis datasets (CRA, ERA5, MERRA2, NCEP, and JRA55) and environmental variables. Shapley Additive exPlanation (SHAP) analysis and the generalized three-cornered-hat (TCH) method were applied for result interpretation and spatiotemporal uncertainty quantification, respectively. The results showed that the fusion method can significantly increase the accuracy of the data, i.e., the Pearson CC can be increased from 0.74 to 0.85 for the LH and from 0.52 to 0.70 for the SH. Moreover, it can significantly decrease the uncertainty of the fused LH data and decrease the uncertainty of the fused SH data in general (the average relative uncertainty (RU) was 14.84% for LH and 27.88% for SH). Dewpoint and LST are found to be the most important environmental factors influencing the LH and SH simulations (with average contribution rates of 12.9% and 15.6%, respectively). When the dewpoint exceeds 266.56 K and the LST exceeds 13.79 °C, their influence on LH and SH shifts from negative to positive. Wetlands and glaciers are the land cover types with the highest uncertainty in LH and SH data, respectively. The Qaidam Basin shows the highest mean uncertainties among all the climatic zones. The machine learning-based fusion method developed by this paper not only effectively enhances the quality of fused data but also provides valuable insights for future site expansion and further data quality improvement through its uncertainty analysis findings.

Extreme climate events can disturb vegetation dynamics and alter ecosystem stability. This study investigated the evolution of extreme climatic conditions and vegetation greenness across the Yangtze River Basin during 1982–2022 by integrating CN05.1 daily meteorological records with the PKU-GIMMS NDVI V1.2 dataset. Using the index framework of the ETCCDI, fifteen extreme weather-related climate indices were calculated. Trend magnitudes were quantified using Sen’s slope estimation, while the Mann–Kendall trend test was used to determine the direction and significance of long-term changes. Both standard correlation and lagged cumulative correlation were used to assess NDVI–climate relationships; the latter tested ten predefined windows formed from climate conditions between the current month and three months before each NDVI observation, with the largest absolute correlation defining the optimal window. The basin experienced a distinct warming signal in temperature extremes during 1982–2022, characterized by reduced occurrences of TN10p and TX10p, together with more frequent TN90p and TX90p events. Precipitation extremes showed an overall intensification, with R95p and R99p increasing by 9.2 mm per decade and 5.6 mm per decade. NDVI also increased throughout the analysis period, with a basin-wide rate of 0.0038 per decade and the fastest greening in the middle reaches at 0.0069 per decade. Vegetation NDVI was generally positively associated with warm-related temperature indices and negatively associated with cold-related indices. The lagged cumulative analysis identified the strongest NDVI–climate associations within windows combining current and antecedent climate conditions, with marked differences among subregions and vegetation types. Because the analyses are correlation-based and did not quantify non-climatic drivers such as land-use change or ecological restoration, the results should be interpreted as statistical associations rather than causal attribution. Overall, these findings characterize how vegetation greenness covaries with extreme climate indices across the Yangtze River Basin.

The Tibetan Plateau (TP) atmospheric heat source crucially modulates East Asian summer monsoon precipitation, yet its synergy with upstream oceanic signals remains elusive. Using observations (1971–2020) and CMIP6 simulations, we investigate mechanisms coupling the summer TP heating and precipitation over the Middle and Lower Yangtze River (MLYR). SVD analysis reveals a robust positive coupling between them. Mechanistically, TP heating triggers a quasi-stationary Rossby wave train, inducing a “saddle-like” circulation that drives intense MLYR moisture convergence (contributing >90% to precipitation changes). Crucially, we re-examine the upstream oceanic precursor to propose a “dual-pathway superposition” framework. Contrary to the assumed linear causal chain, four-quadrant analysis reveals the spring Indian Ocean Basin Warming (IOBW) and summer TP heating are largely independent drivers (R = 0.24). While IOBW thermodynamically excites an Anomalous Anticyclone supplying abundant MLYR moisture, it lacks robust control over TP heating, which is dominated by internal atmospheric dynamics. However, our findings reveal a critical non-linear synergy: extreme MLYR rainfall strictly requires the coincidental phase overlap of these independent pathways (strong dynamic lifting coupled with oceanic moisture). CMIP6 simulations corroborate this independence, further emphasizing that extreme MLYR rainfall results from phase superposition rather than a single causal chain.

Assessing the runoff response to land use and climate change in karst basins is essential for sustainable water resources management and for advancing the understanding of basin-scale hydrometeorological processes. This study applied the SWAT model integrated with CA-Markov–based land use projections and CMIP6 climate data under the SSP245 (medium emissions) and SSP585 (high emissions) scenarios to conduct multi-scenario simulations, evaluating the impacts of these changes and projecting future runoff in the Wujiang River source region. The results indicate that (1) the SWAT model performed satisfactorily in simulating hydrological processes in this karst basin, with R² and NSE values during calibration and validation reaching at least 0.75 and 0.7, respectively—furthermore, the PBIAS absolute values were below 10% during both calibration and validation; (2) runoff variations under four land use scenarios from 2000 to 2015 showed limited deviation from the baseline; (3) more pronounced runoff alterations were observed under extreme land use scenarios when compared to grassland-dominated conditions; (4) future climate scenarios SSP245 (medium emissions) and SSP585 (high emissions) consistently project a decreasing trend in runoff; and (5) combined scenario analyses reveal that climate change acts as the dominant factor driving runoff reduction in karst basins. These findings improve the mechanistic understanding of karst hydrological processes under global change, and the methodology established here holds potential for extension to other karst regions, thereby supporting strategic water resources planning.

Vegetation–heat flux feedbacks have a great influence on ecosystems, but the interaction between them is still unclear. This is particularly critical in ecologically fragile areas, where plant growth is especially sensitive to land–atmosphere interactions that help plants withstand environmental pressures. To the causal relationship between vegetation and heat flux under different topographies on the Tibetan Plateau, we improved the Granger causality model to handle nonstationary scenarios, enabling us to uncover previously unknown interaction patterns between unstable vegetation change and heat fluxes. Further sensitivity analysis was performed to assess the strength of causal influences. The results showed that the sensible heat (SH) and latent heat (LH) fluxes were increasing at rates of 0.28 W·m⁻²·decade⁻¹ and 0.105 W·m⁻²·decade⁻¹, respectively. The interaction between them on vegetation change depends on terrains, at low elevations below 3000 m and high elevations of 5000–6000 m, SH and LH jointly regulate vegetation growth of shady and gentle to moderate slopes, predominantly involving dense grasslands, but the influence of SH is stronger. While at middle elevations of 3000–5000 m and on steep slopes, LH and vegetation of all types interact to form an intensive local energy cycle. Conversely, vegetation change also influences heat flux. Below 6000 m (excluding the 2000–3000 m), vegetation only regulates LH, and this influence appears largely independent of terrain, contributing to energy redistribution and water cycle maintenance in these regions. These interactions suggest that vegetation plays a central role in shaping energy distribution on the plateau, maintaining the water cycle, and regulating climate in alpine regions by regulating heat flux.

The climate model provides simulation results in studying the climate change and its consequences. However, its application in a specific relatively small area (compared to global scale) is somehow confined for its lackness in high resolution and poverty in accuracy. Therefore, downscaling and bias correction are necessary to be undertaken to improve the output data from the climate model. Because a single EOF model is difficult to identify the time series change trend, this paper uses EOF and ensemble empirical mode decomposition (EEMD) coupling to accurately identify the statistical characteristics of time series to extract the temporal and spatial variation characteristics of meteorological data. In this study, the monthly mean temperature observation data set of ERA5 from 1970 to 2014 was used. First of all, six climate models and Multi-Model Ensemble (MME) average models of CMIP6 were evaluated and optimized by Taylor diagram, Taylor index, interannual variability assessment index and rank scoring method, and the best data set were chosen for the later treatment. Then, the Delta bias correction method and Normal distribution matching method were used to correct the chosen data. Finally, the temporal and spatial variation characteristics of temperature in the Tibet Plateau from 2015 to 2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios were analyzed. The results show that: (1) Among the six CMIP6 models and MME average models selected in this paper, the EC-Earth3 model has the best performance in simulating temperature. (2) Comparing the results of the EC-Earth3 model after the Delta bias correction with the observation results, the regional averages of the deterministic coefficient (R2) and the Nash efficiency coefficient (NSE) are 0.992 and 0.983, respectively. After the Normal distribution matching method is used to correct, the regional averages of the deterministic coefficient (R2) and the Nash efficiency coefficient (NSE) are 0.990 and 0.978, respectively. Therefore, the Delta bias correction has a better correction effect on the monthly temperature of the model. (3) By coupling the EOF-EEMD method, it is found that the first typical field shows consistent changes in the whole region under the three scenarios, and there are common temperature change sensitive areas and non-sensitive areas under the SSP1-2.6 and SSP2-4.5 scenarios, namely, the northern Tibetan Plateau and the Pamir Plateau. The temperature of the second typical field shows a distribution that gradually decreases (SSP1-2.6, SSP2-4.5) or increases (SSP5-8.5) from the upper reaches of the Zhaqu River to the surrounding areas. Under the SSP1-2.6 scenario, the plateau as a whole is cooling down in the east and warming up in the west. Under the SSP2-4.5 and SSP5-8.5 scenarios, the plateau first warms up in the east and cools down in the west, and then cools down in the east and warms up in the west. This study can provide a reference bias correction method for a more accurate application of climate model data in the Tibet Plateau, and provide key basic information supporting in-depth assessment of the impact of temperature changes on water resources, ecosystems and environment in the Tibet Plateau.

In this paper, the Artificial Neural Network (ANN) was utilized to predict the peak discharge of dam failures, which was based on the combined Genetic Algorithm (GA) and Back Propagation (BP) neural network. The dataset comprises 40 samples from self-conducted experiments and available literature. To compare the efficiency of the suggested approach, three evaluation metrics, including the coefficient of determination (R²), the root mean square error (RMSE) and the mean absolute error (MAE), were analyzed for both the BP neural network and the GA-BP neural network. The findings suggest that (1) The prediction accuracy of the GA-BP was better than that of the BP; and (2) Compared to BP, GA-BP demonstrated a 9.07% average improvement in R², a 57.36% average reduction in MAE, and a 57.53% average reduction in RMSE. In addition, the results of GA-BP and semi-empirical formulas were compared and the effect of three parameters on the peak discharge was analyzed. The results showed that the GA-BP model could effectively predict the peak discharge of dam failures.