Cryosphere services (CSs) refer to various benefits that humans directly or indirectly obtain from the cryosphere, which makes significant contributions to human well-being (HWB). To facilitate such research, we first present a classification system for conceptualizing, monitoring and assessing CSs based on the current process-based understanding of their nature and sustainability. Specifically, the CSs are grouped into five major categories (provisioning, regulating, cultural, bearing and supporting services) and 18 sub-categories. Then we provide a detailed overview on formation, current status and anticipated future changes of the identified types of the services, and their impact on HWB. Finally, the spatio-temporal scales, the links of the services with HWB and climate-dependence are further discussed. The research of CSs adopt interdisciplinary approach to address the formation mechanisms of CSs and their dynamic relationships with HWB, which is poised to provide a better understanding of the cryosphere’s role in human society and help enhance socio-ecological sustainability and HWB over cryosphere-affected areas. Notably, most CSs have been deteriorating under global warming and cryosphere shrinkage, further leading to negative impacts on associated HWB. Therefore, great attention should be paid to the changes in CSs and their cascading risks.

As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades⁻¹, p < .10) and spring (−0.184 cm·decades⁻¹, p < .05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas.

The origin of bottled water can be identified via its stable isotope signature because of the spatial variation of the stable isotope composition of natural waters. In this paper, the spatial pattern of δ²H and δ¹⁸O values were analyzed for a total of 242 bottled water samples produced at 137 sites across China that were randomly purchased during 2014–2015. The isotopic ratios of bottled water vary between −166‰ and −19‰ for δ²H, and between −21.6‰ and −2.1‰ for δ¹⁸O. Based on multiple regression analyses using meteorological and geographical parameters, an isoscape of Chinese bottled water was created. The results showed that altitude among spatial parameters and precipitation amount and air temperature among meteorological parameters were major natural factors determining the isotopic variation of bottled water. Our findings indicate the potential and the significance of the use of stable isotopes for the source identification of bottled water. An analysis of different origin types (spring, glacier and unmarked) and several different brands of bottled water in the same location reflected different production processes and source signatures.

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.