Characteristics of two complex rock and glacier avalanches and RAMMS simulation of a long-runout disaster in the Sedongpu Basin of the Yarlung Zangbo River downstream in October 2018, Tibet, China

On October 17 and 29, 2018, two rock and glacier avalanches occurred on the western slope of the Sedongpu Basin upstream of the Yarlung Zangbo River in the Tibetan Plateau, forming the disaster chains and causing damage to many bridges and roads. Based on the comparative analysis of multiple pre-and post-remote sensing images, the initial sliding body, which was composed of rock and glacial material, was located on a steep slope above an elevation of 6000 m. Under the coupling effect of multiple factors such as gravity, rainfall, and weather changes, the initial sliding body detached from the source zone and then transformed into a debris flow after impact and fragmentation. The debris flow traveled downstream and scraped loose glacial till in its path, causing the volume of the sliding body to increase. In addition, the debris flow traveled 10 km under low frictional resistance, as a result of the lubrication via early rainfall and glacial meltwater. Eventually, the debris flow rushed out onto the valley floor, forming a landslide dam and blocking the Yarlung Zangbo River. The deposit volumes on October 17 and 29 were 20.4 million m3 and 10.1 million m3, respectively, with a total mean thickness of ~22m. This study provides an insight into the dynamic process as they unfolded, through multitemporal satellite imagery and numerical simulation. Furthermore, we also discuss the potential cause of rock/ice avalanche and disaster scenarios, as well as the tendency of the rock and glacier avalanches are discussed.

A high-accuracy rainfall dataset by merging multiple satellites and dense gauges over the southern Tibetan Plateau for 2014–2019 warm seasons

Tibetan Plateau (TP) is well known as Asia's water tower from where many large rivers originate. However, due to complex spatial variability in climate and topography, there is still a lack of a high-quality rainfall dataset for hydrological modeling and flood prediction. This study therefore aims to establish a high-accuracy daily rainfall product through merging rainfall estimates from three satellites, i.e., GPM-IMERG, GSMaP and CMORPH, based on a high-density rainfall gauge network. The new merged daily rainfall dataset with a spatial resolution of 0.1∘ focuses on warm seasons (10 June–31 October) from 2014 to 2019. Statistical evaluation indicated that the new dataset outperforms the raw satellite estimates, especially in terms of rainfall accumulation and the detection of ground-based rainfall events. Hydrological evaluation in the Yarlung Zangbo River basin demonstrated high performance of the merged rainfall dataset in providing accurate and robust forcings for streamflow simulations. The new rainfall dataset additionally shows superiority to several other products of similar types, including MSWEP and CHIRPS. This new rainfall dataset is publicly accessible at https://doi.org/10.11888/Hydro.tpdc.271303 (Li and Tian, 2021).

Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.

Impacts of Climate Change on Blue and Green Water Resources in the Middle and Upper Yarlung Zangbo River, China

The Yarlung Zangbo River is the largest river on the Tibetan Plateau and a major international river in South Asia. Changes in the blue and green water resources in its basin are of great importance to the surrounding local and Asian regions in the context of global warming. This research used the Soil and Water Assessment Tool model to estimate blue and green flows (BWF and GWF) and analyze the spatial-temporal distribution characteristics under different hypothetical climate change scenarios. The results show that (1) the multi-year average BWF in the middle and upper reaches of the Yarlung Zangbo River Basin is 176.2 mm, the GWF is 213.1 mm, and the difference between precipitation and total water resources is only 5.4 mm; (2) both BWF and GWF in this basin showed a slightly increasing trend from 1980 to 2010, but the distribution of subbasins from upstream to downstream is decreasing; and (3) GWF has a positive correlation with both precipitation and temperature, but BWF only increases with precipitation and decreases with increasing temperature. Moreover, the change in blue and green water resources is more sensitive to the changes in precipitation than to changes in the temperature.

A comprehensive runoff variation evaluation system of the Yarlung Zangbo River based on vague sets

Runoff processes are the basis for maintaining the safety of river ecosystems. The Yarlung Zangbo River (YZR) faces changes in flow regimes due to the impacts of human activities and climate change#which may threaten its fragile ecosystem. In this study#a new comprehensive system for evaluating runoff variation was constructed to investigate the degree of runoff alternation in the YZR. Based on the data from the primary hydrological stations in the YZR from 1956 to 2000#the assessment indicators of runoff variation were selected by considering the flow#sediment#and water temperature processes. Furthermore#a comprehensive evaluation system for runoff variation was constructed via multiple hydrological analysis methods and vague sets. The results showed that the variation index of the YZR from 2010 to 2013 was 0.15–0.20 compared with the flow regimes of the YZR before 2000#which were within a reasonable range#indicating that the comprehensive runoff conditions of the YZR were not greatly disturbed by human activities such as reservoir construction and river regulation during this period. These results provide a tool for evaluating the runoff change in the YZR and new references for researching runoff variation in other similar watersheds.