This paper studied the spatial and temporal variability of the statistical structures of precipitation across Xinjiang, China, by analysing the time series of daily precipitation from 50 weather stations during the period from 1961 to 2008. Three indices precipitation concentration index (CI), precipitation concentration degree (PCD) and precipitation concentration period (PCP) were used to detect precipitation concentrations and the associated spatial patterns. The results show that higher precipitation CI values were mainly observed in Southern Xinjiang, whereas lower precipitation CI values were mostly detected in Northern Xinjiang. The precipitation CI values are noticeably larger in places where both annual total precipitation and number of rainy days are lower. The Mann–Kendall trend test demonstrates that the most parts of Xinjiang are characterized by no significant trends of precipitation CI at the 0.05 significance level. The periodicity characteristic of precipitation CI time series in Xinjiang could be detected by wavelet power spectrum analysis, and significant periods of that in most of Xinjiang were concentrated on 2–5 years band. The results of PCP reveal that rainfall in Xinjiang mostly occurs in summer, and the rainy season arrives earlier in Eastern Xinjiang than Western Xinjiang, whereas the results of PCD indicate that the rainfall in Northern Xinjiang was more dispersed within a year than that in Southern Xinjiang. Copyright © 2010 Royal Meteorological Society

Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon–water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002–2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 ± 0.35 gC kg⁻¹ H₂O), and that of alpine vegetation was the lowest (0.2 ± 0.15 gC kg⁻¹ H₂O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002–2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.

Based on the empirical data of traditional villages, this paper analyzes the correlation of influencing factors of the spatial distribution of traditional villages by using the method of the geographic detector, and discusses the spatial differentiation characteristics of traditional villages in Gansu Province by using the method of geoscience statistics. The following results were obtained: (1) In the spatial differentiation, the spatial agglomeration is obvious and different, which shows the distribution along the upper reaches of the Yellow River to the southeast, and the distribution of the prefecture is related to the landform type. In the vertical spatial distribution, the altitude data span is large, the highest in the Gannan city of Mula village in MaQu County, and the lowest in the Longnan city of Zhengjiashe village in Bingkou Town. With increasing altitude, the number of traditional villages within the 1000–1999 m level first rose to a maximum and then gradually decreased. (2) The spatial differentiation of the traditional villages has a normal distribution with the elevation, and the spatial differentiation is low, showing a distinct gourd-like structure; the eastern and southern regions are more concentrated, while the northern and western regions are less concentrated. (3) The spatial differentiation of traditional villages shows the characteristics of a regional difference, which is mainly distributed in the mountainous area of South Gansu, the Loess Plateau of central Gansu, and the plateau of South Gansu. The main factors of the spatial distribution difference of traditional villages are average slope, elevation, geological hazard risk, distance from the city, per capita GDP, and climate index.

Significant uncertainties persist in current snow depth (SD) datasets due to variations in sensor characteristics and retrieval algorithms. This study systematically evaluated five SD datasets derived from ICESat-2, passive microwave products (AMSR2 and GlobSnow), and reanalysis products (ERA5 and MERRA2) across 13 representative snow-covered regions in the Northern Hemisphere. A novel dynamic upscaling approach was developed by integrating simple averaging (SA) with regression kriging (RK) for ICESat-2 SD data. Spatial matching of multi-source datasets was then conducted using 3,342 SD observation sites to comprehensively evaluate the uncertainties among the five datasets. The results indicate that the retrieval errors of each dataset are positively correlated with the mean SD across different regions. During snow accumulation and melt periods, ICESat-2 demonstrates significant advantages in non-forested areas with SDs ranging from 5-45 cm. Both passive microwave and reanalysis SD products demonstrate reliable performance during stable snow periods. However, products often miss snow during melt seasons despite ground confirmation. The causes of this phenomenon differ between the two datasets: passive microwave retrievals are primarily dominated by the physical properties of liquid water, whereas reanalysis products face limitations due to model structure and insufficient input data. In conclusion, the integration of station and ICESat-2 SD in non-forested regions may provide new possibilities for validating products.