The Loess Plateau, a region in China that is highly sensitive to climate change, serves as a focal point for investigating the spatiotemporal evolution of relative humidity (RH). Understanding the interplay between RH and geographical and meteorological factors is essential for comprehending the climate change dynamics within the plateau. This study leverages daily RH observations from 90 meteorological stations in the Loess Plateau and its environs to analyze the temporal and spatial variations in RH from 1960 to 2021, both before and after the implementation of ecological initiatives such as the conversion project of farmland to forest and grass. Using trend analysis, sensitivity analysis, and spatial interpolation, we unveil the following findings: (1) The average annual RH in the Loess Plateau exhibited a notable decrease of -0.376%·(10a)^-1 (P<0.05) from 1955 to 2021, undergoing a decadal variation characterized by a “weakening-strengthening-weakening” process. While autumn experienced a slight increase, all other seasons displayed a declining trend, with the most pronounced decrease observed in spring [-0.945%·(10a)^-1] and the least in winter [-0.194%·(10a)^-1]. (2) Spatially, the winter average RH in the Loess Plateau peaked in the south, gradually diminishing from east to west in the central and northern regions. Conversely, other time series demonstrated a gradual decline from south to north. The spatial patterns of the annual and seasonal average RH variation trends in the Loess Plateau were different. (3) Postimplementation of the ecological project, the average RH throughout the year, as well as in spring, summer, and winter, exhibited varying degrees of decrease. The trends in annual, summer, and winter RH shifted from an increasing to a decreasing trajectory. Noteworthy differences emerged in the spatial distribution characteristics of annual and seasonal average RH, coupled with their respective trend changes. The prevailing trend change combination type for all temporal RH patterns was consistently low. (4) The primary seasonal factor influencing long-term changes in annual RH in the Loess Plateau is spring, with spatial dominance primarily by a single dominant type in spring and a combination of dominant types in spring and summer. (5) The annual and seasonal average RH in the Loess Plateau demonstrated a significant negative correlation with latitude (P<0.01) and a positive correlation with precipitation (P<0.01). The geographical factors exerted the most significant influence on summer average RH. Annual, spring, and summer average RH were most sensitive to average temperature, whereas autumn and winter were most responsive to wind speed.

This study examines various flood disaster types in Xinjiang, China, encompassing extreme temperature fluctuations, snowstorms, and warm, humid conditions attributed to global climate change. This study focuses on the underexplored issue of snowmelt floods, addressing their escalating frequency, severity, and associated disaster risk in Xinjiang. Using VOSviewer software, this study analyzes the research keywords and collaboration networks among different authors investigating snowmelt floods in Xinjiang. In addition, it scrutinizes the research priorities of scholars at various stages. A comparative analysis of the characteristics and research status of diverse snowmelt runoff models is presented. The study advocates for future research in Xinjiang to delve into the physical mechanisms and snowmelt processes integral to snowmelt runoff models, aiming to enhance prediction and warning accuracy. An assessment of the technologies employed for predicting and warning snowmelt floods in Xinjiang is conducted, highlighting pertinent issues such as wind-blown snow, frozen ground surface snow, and rain-on-snow. Furthermore, the study recommends key technologies to augment the overall capability of flood simulation, prediction, warning, and response to abrupt floods. It also explores strategies for optimizing the use of flood resources. In conclusion, this study furnishes recommendations for refining the prediction and warning technology pertinent to snowmelt floods in Xinjiang.

Although precipitation serves as a common statistical indicator, its utility is limited by the availability of representative station data, which is affected by geographical, economic, technical, and other effects. Such limitations can introduce inaccuracies in the assessment of the effectiveness of artificial precipitation enhancement. To address these challenges, this study focused on daily meteorological data from the Bayanbulak meteorological station and monthly runoff data from the Dashankou hydrological station in Xinjiang, China, from May to September in 1973—2018. This study involved building a runoff simulation equation using the Budyko model. To objectively and quantitatively analyze the impact of artificial precipitation enhancement in the Bayanbulak mountain area during warm seasons, sequence tests, unpaired rank sum tests, and t-tests were used. The results show the following: (1) The use of the Budyko model for building simulated runoff not only synchronized with the changing trends and growth rates of precipitation but also demonstrated a highly significant correlation (R²=0.9971, P<0.001). This shows that the simulated runoff not only captured the overall precipitation trends but also quantified the impact of precipitation on runoff. (2) Utilizing an unpaired rank sum test and t-test, it was found that both precipitation and runoff significantly increased (P<0.02) after artificial precipitation enhancement when considering measured runoff, simulated runoff, and precipitation as statistical variables. (3) The best statistical indicator for assessing the impact of artificial precipitation enhancement was precipitation, accounting for only 11.59% of the added value to statistically test the effect. Compared with the measured runoff, the test efficiency value of the simulated runoff decreased by 3.72%, indicating that the test efficiency was improved. (4) With a selected significance level of a 90% confidence interval, the absolute increase in precipitation was 5.38 mm, representing a relative increase rate of 12.05%. The absolute increase in the simulated runoff was 4.53 m³·s^-1, indicating a relative increase rate of 14.70%. The absolute increase in the measured runoff was 28.48 m³·s^-1, corresponding to a relative increase rate of 18.48%. This indicates the important impact of artificial precipitation enhancement during the warm seasons in the operation period (1994—2018) compared with the historical period (1973—1993) in the Bayanbulak mountain area.