The Qaidam Basin is an alpine arid inland basin characterized by water resource shortages and fragile ecological environments. Studying the evapotranspiration and water consumption characteristics of the Qaidam Basin is important for regional water-resource management and environmental protection. Based on eddy covariance flux data for four underlying surfaces (Golmud cropland, shrubland, Nomuhong cropland, and alpine meadow) in the Qaidam Basin in 2020, the evapotranspiration variation characteristics for different underlying surfaces at different time scales were analyzed, the influence of different typical meteorological factors on actual evapotranspiration was explored, and water consumption characteristics of the different underlying surfaces were analyzed. The results showed that the evapotranspiration of each underlying surface was mainly concentrated in the growing season. The Golmud cropland and alpine meadow had the highest evapotranspiration in July, at 62.50 mm and 88.92 mm, respectively, while the shrubland and Nomuhong cropland had the highest evapotranspiration in August, at 40.47 mm and 100.02 mm, respectively. The average daily evapotranspiration of Golmud cropland, shrubland, Nomuhong cropland, and alpine meadow was 1.12 mm, 0.78 mm, 1.98 mm, and 1.79 mm, respectively. The half-hour evapotranspiration reached a maximum value from 14:00–15:00. The evapotranspiration of different underlying surfaces was strongly correlated with air temperature, followed by relative humidity, and weakly correlated with wind speed. Daily evapotranspiration was strongly correlated with the soil temperature of Golmud cropland, shrubland, and alpine meadow, and the soil volume water content of shrubland and alpine meadows. The water consumption variation characteristics indicated that each underlying surface was dominated by water consumption, accounting for 96.73%, 96.26%, 96.26%, 74.30% in Golmud cropland, shrubland, Nomuhong cropland, and alpine meadow, respectively. Among them, precipitation was the main factor affecting the water consumption of the different underlying surfaces. The purpose of this study was to explore the actual evapotranspiration characteristics of different underlying surfaces, the results of which can be used as a reference for studies of the water cycle in Qaidam Basin.

Many rivers are tightly coupled and intersected with aeolian sand dunes, whose geomorphological evolution involves not only fluvial processes but also aeolian processes that pose a new challenge to fluvial geomorphological studies. However, due to few field studies, our overall understanding of the desert channel geomorphic process is limited. In this paper, we present an outstanding example of desert river channel evolution regulated by aeolian–fluvial interactions in the Ulan Buh Desert of the Yellow River, based on a long time series data set (1966–2019) of channel cross-sections. The results indicate that the lateral addition of aeolian sand, the water–sediment relationship and human interference have a significant role at different periods of channel evolution. Before 1986, higher discharge, lower sediment content and greater intensity of aeolian activity caused aeolian–fluvial interactions and a relative scouring and silting balance in the channel, with little human activity. From 1986 to 2000, an increase in large reservoir operation, vegetation coverage and floodplain farming, coupled with water–sediment relationship variation, caused rapid deposition and shrinkage of the river channel. From 2000 to 2014, the channel kept a slight scouring state. With Haibowan reservoir operation beginning in 2014, the talweg experienced rapid scouring and undercut rebound. However, an expanding and stable floodplain accelerated sedimentation on the floodplain and weakened river lateral erosion, indicating that the channel has shown a shrinkage trend. Meanwhile, wavelet analysis results indicate that human interferences and aeolian activities have no significant role in the periodical characteristics of the channel’s longitudinal erosion and deposition. Therefore, on the whole, increasing human interferences and decreasing wind dynamics have driven this desert wandering channel to be stable, and to gradually form a new balance between erosion and sedimentation.

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin.