Satellite remote sensing is widely used to estimate snow depth and snow water equivalent (SWE) which are two key parameters in global and regional climatic and hydrological systems. Remote sensing techniques for snow depth mainly include passive microwave remote sensing, Synthetic Aperture Radar (SAR), Interferometric SAR (InSAR) and Lidar. Among them, passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency. Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer (SMMR) freely provided multi-frequency passive microwave data. SAR was found to have ability to detecting snow depth in 1980s, but was not used for satellite active microwave remote sensing until 2000. Satellite Lidar was utilized to detect snow depth since the later period of 2000s. The estimation of snow depth from space has experienced significant progress during the last 40 years. However, challenges or uncertainties still exist for snow depth estimation from space. In this study, we review the main space remote sensing techniques of snow depth retrieval. Typical algorithms and their principles are described, and problems or disadvantages of these algorithms are discussed. It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography. With increasing number of freely available SAR data, future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.

For most mountain glaciers, chemical components in snowfall are subject to the elution process under the influences of meltwater before they are preserved in ice, creating difficulties for interpreting ice core records. To understand the formation process of ice core records and analyze the influences of meteorological factors on the ice core resolution, we measured ion concentrations of snowpacks from 2003 to 2006 in the PGPI (Program for Glacier Processes Investigation) site of Urumqi Glacier No. 1. The ion concentration variation in snowpack exhibits apparent seasonality. In summer, the higher snowmelt rates due to air temperature rise intensify dilution and lead to an exponential decrease in ion concentrations as the accumulated positive temperature increases. In winter, the snow ion concentrations are stable and low as a result of reduced temperature and rare precipitation. Many ions from summer precipitation are leached out by meltwater, and only the precipitation that occurs at the end of the wet season can be preserved. Through tracking the evolution of magnesium ion peaks in the snowpack, it is concluded that the ice core resolution is one year on Urumqi Glacier No. 1, albeit 70% of the concentration information is lost.

Soil organic carbon (SOC) and total nitrogen (TN) stocks are usually calculated with samples collected using core samplers. Although the calculation considers the effects of gravel in soil samples, other coarse fragments such as stones or boulders in soil may not be collected due to the restricted diameter of core samplers. This would cause an incorrect estimation of soil bulk density and ultimately SOC and TN stocks. In this study, we compared the relative volume of coarse fragment and bulk density of fine earth determined by large size soil sampler with three core samplers. We also investigated the uncertainties in estimation of SOC and TN stocks caused by this soil sampler procedure in three typical alpine grasslands on the northeast edge of the Qinghai-Tibetan Plateau (QTP), China. Results show that (1) the relative volume and size of coarse fragment collected by large size sampler were significantly (p <0.05) higher and larger than those of core samplers, while bulk density of fine earth, SOC and TN stocks show opposite patterns in all grassland types; (2) SOC and TN stocks determined by core samplers were 17%-45% and 18%-46% higher than larger size sampler for three typical alpine grasslands; and (3) bulk density of fine earth, SOC and TN stocks exponentially decreased with the increasing of coarse fragment content. We concluded that core sampler methods significantly underestimated the volume occupied by coarse fragment but overestimated SOC and TN stocks. Thus, corrections should be made to the results from core samplers using large size samplers on regions with gravel and stone-rich soils in future studies.

Soil fungi play a key role in soil functional performance and ecological restoration. To understand the diversity and composition of culturable fungi in soils of Horqin Sandy Land, China, mobile dune, semi-fixed dune, fixed dune and sandy grassland were selected to investigate the soil fungal diversity using a traditional culture-dependent approach. ITS sequencing was applied to identify the fungal strains. The counts of culturable fungi increased significantly from mobile dune to sandy grassland along the gradient of sandy land restoration. The Shannon-Wiener, Simpson and Evenness indices of culturable fungi ranged from 1.26-1.71, 0.22-0.37 and 0.83-0.87, respectively. A total of 27 fungal strains were isolated using dilution plate cultural technique. The 27 fungal isolates were clustered into three groups: Ascomycota, Basidiomycota and Mucoromycota at phylum level, indicating that Ascomycota was the dominant fungal phylum (88.9% of the total). The isolated fungi were grouped into 3 phyla, 5 classes, 6 orders, 11 families and 13 genera. The results show that culturable fungi were diverse in sandy land soils and fungal isolates have potential function in lipid turnover, cellulose degradation and ethanol, glucose and fatty acid production. Future studies should be carried out to explore their ecological and biological function for degraded sandy land restoration.

In saline soil areas, there are a large number of ions in soil or water environments, such as Cl^- and SO₄^2-, which have strong corrosive interactions with buildings. To study the deterioration of non-dispersible underwater concrete in sulfate, chloride, and mixed salt environments, the compressive strength and deterioration resistance coefficient of the studied concrete mixed with different amounts of ground granulated blast-furnace slag (GGBS) were analyzed in this paper. At the same time, the micro morphology and corrosion products distribution of the studied concrete were observed by means of SEM, plus XRD diffraction, TG-DTG and FT-IR analyses to explore the influence of corrosive solutions on the hydration products of concrete. We also analyzed the mechanism of improving the deterioration resistance of the studied concrete by adding GGBS in a saline soil environment. The results show that the compressive strength of the studied concrete in a chloride environment was close to that in a fresh water environment, which means that chloride has no adverse effect on compressive strength. The deterioration of the studied concrete was most serious in a sulfate environment, followed by mixed salt environment, and the lowest in a chloride environment. In addition, by adding GGBS, the compressive strength and deterioration resistance of the studied concrete could be effectively improved.

Three deterministic prediction evaluation methods, including the standard deviation, root-mean-square error, and time correlation coefficient, and three extreme temperature indices were used to assess the performance of the BCC_CSM2_MR model from CMIP6 in simulating the climate of Northwest China based on monthly grid air temperature data from ground stations. The model performance was evaluated using the daily mean temperature, daily minimum temperature, and daily maximum temperature from 1961 to 2014 and future temperature changes in Northwest China under different radiative forcing scenarios. The BCC_CSM2_MR model reproduces well the seasonal changes, spatial distribution, and other characteristics of the daily mean temperature in Northwest China, especially in the Tarim Basin, the Kunlun and Qilian mountains, and Shaanxi. There is still some deviation in the simulation of the daily mean temperature in the high terrains of the Tianshan, Kunlun, and Altai mountains. The model better simulates the daily minimum temperature than the daily maximum temperature. The simulation error is smallest in summer, followed by autumn and winter, and largest in spring. In terms of extreme temperature indices, the deviations are smaller for cold nights, warm nights, and the annual maximum daily minimum temperatures. Furthermore, the model can capture the increase in warm events and the decrease in cold events. Under different forcing scenarios, there is a general warming trend in Northwest China, with the greatest warming in Xinjiang.