Glacial lakes are widely distributed across glaciated mountainous regions such as Southeast Tibet (ST). As a result of climate change, glacial lakes have expanded continually in ST over recent five decades, and ST is seriously suffering from glacial lake outburst floods (GLOFs). Hence, glacial lake investigations and hazard assessments of GLOFs are very necessary to perform and are of great practical significance for this area. The region running from Ranwu to Tongmai (RTR) in ST was selected as a study area. To assess hazard degrees of GLOFs, nine factors (F1–F9) were selected from glacial characteristics: the scale of glacial lakes, meteorological conditions, moraine dam parameters and downstream gullies. Average monthly (May to September) land surface temperatures (LSTs), temperature change rates, and average annual precipitation levels were first calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) LST product and Tropical Rainfall Measuring Mission (TRMM) precipitation data. Then, glacial lake areas, downstream gully slopes, distances to glacial lakes, upper glacier areas, average gradients of upper glaciers, and terminal moraine dam widths were obtained based on Thematic Mapper (TM) and Operational Land Imager (OLI) images and Digital Elevation Model (DEM) data. Finally, the fuzzy comprehensive assessment (FCA) method was used to assess hazard degrees of GLOFs in the study area. According to the results, 135 glacial lakes existed during the study periods, five new glacial lakes formed due to lake collapse and snow and ice melt, and one disappeared due to GLOFs. The number of glacial lakes in the area increased from 130 in 1986 to 134 in 2015, and the total area of glacial lakes increased from 832.77 ha in 1986 to 850.51 ha in 2015 due to lake expansion and GLOFs. Of the 134 glacial lakes present in 2015, 10 are identified as high hazardous lakes, eight are identified as medium hazardous lakes, two are considered low hazardous lakes, and 114 are considered extremely low hazardous lakes. The results illustrate hazard levels of GLOFs in ST, which can help guide future hazard prevention in the area.

Determining the contribution of gully erosion to the total sediment yield is important for enhancing decision-making regarding sediment abatement and soil conservation measures. The dry-hot valley region of the Jinsha River is one of the most intensive areas of gully erosion in China. However, the contribution of gully erosion to total sediment production in this region is unclear. Instead of using the source tracing technique commonly employed in other studies, this study provides a method of rapidly reconstructing historical sediment yields based on the properties of sediment that accumulated in an undrained pond at a small gully watershed outlet. We produced a three-dimensional model in geographical information system software to calculate the volume of each layer of accumulated pond deposits from 2006 to 2011. Moreover, we estimated sheet erosion amounts using the Universal Soil Loss Equation. The results showed that sheet erosion accounted for between 14.28 and 23.39% of the total sediment yield in the watershed in the case that the sediment delivery ratio was 100%. Therefore, gully erosion is the major source of total soil loss in this area, and effective soil measures of gully erosion mitigation are crucial for managing the sediment yield.

The 2008 Wenchuan Earthquake induced landslides that destroyed large swaths of mountain vegetation. Presently, the damaged vegetation areas are exhibiting various stages of recovery depending on environments. A spatial analysis of earthquake-damaged and recovered vegetation can provide useful information for understanding landslide processes. The mountainous watersheds of the Minjiang River Upstream, near Yinxiu Town (one of the highest seismic intensity zones during the Wenchuan earthquake) were selected. A DSAL (digital elevation model (DEM), slope, aspect and lithology) spatial zonation method was established to detect natural features of the vegetation survival environments, and damaged and recovered vegetation areas were extracted using the normalized difference vegetation index (NDVI) changes form multi-temporal (2001–2014) Landsat Thematic Mapper/Enhanced Thematic Mapper/Operational Land Imager (TM/ETM/OLI) images. Statistical results show that the vegetation growth was mainly controlled by its survival environments, and vegetation has coupling relations with slope stability. Then, the slope stability model was developed through multivariate analysis of earthquake-damaged vegetation and its controlling factors (i.e., topographic environments and material properties). Application to the Mianyuan River and Subao River basins validated the proposed model, showing that monitoring the vegetation (using the remote sensing images) can be used to assess the slope stability, and model results show what vegetative conditions with its survival environments are susceptible to landslide processes, although the predicted values may be higher than the actual values in the most mountainous basins. Our modeling approach may also be valuable for use in other regions prone to landslide hazards.