Conference Agenda

This project leverages the ESA and NRSCC opportunity to access satellite observations of Earth’s surface to assess precipitation and temperature biases in climate reanalyses, and, building on our previous project (ID 59199), to quantify blue runoff), green (evapotranspiration), and white (sublimation) water fluxes in high elevation catchments. We are applying the land surface models Tethys & Chloris (T&C), validated by independent observations, to deepen our understanding of the cryosphere and water cycle of key water towers in High Mountain Asia (HMA). The T&C model allows us to bridge the disciplinary gaps between snow, permafrost, and glaciers, which generate the runoff that ultimately feeds major rivers, and to consider downstream vegetation, which buffers, delays or amplifies that runoff.

Our main aim is to use Earth Observation data to constrain glacio- and eco-hydrological processes, in order to quantify the interplay of blue, green, and white water fluxes in glacierized catchments across High Mountain Asia. The focus of remote sensing data analysis is the observation and understanding of the relation between climate forcing through the surface energy balance of snow and ice and the cryosphere dynamics in terms of both mass and dynamics of snowpack and glaciers. The validated T&C model can assess how ecosystems and vegetation enhances or reduces glacier contributions to streamflow under climate change in HMA. The glacierized study sites span a range of climatic regimes, with several sites in the Pamir-Karakoram Anomaly domain. In addition to the remote sensing observations, field measurements allow independent evaluation of the model results, where available.
We determined seasonal and interannual variations in catchment snowline altitudes from multiple satellite imagers. We also determined seasonal and interannual changes in land surface temperature (Ren et al, 2024a) and snow/ice albedo. Ice thickness change data are being actively collected for Pamir locations.
A proof-of-concept of the method to estimate biases in climate reanalysis through ecohydrological modelling has been completed and tested for 4 sites with differing climates, using a Bayesian particle filter.
The hyper-resolution eco-hydrological modelling system has been improved, particularly as regards computationally efficiency. This has enabled long-term simulations with a dynamic glacier geometry module.
We developed an improved method to retrieve glacier surface velocity with an optimized image pair selection scheme and post-processing methods. We used the near-infrared images (10 m spatial resolution) acquired by the Sentinel-2 MSI L2A products and the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) method to retrieve the glacier velocity in Yanong glacier between 2020 and 2024.
We used daily China surface climate data and five machine learning methods to classify precipitation phases, and evaluated their performance in the temperature range (-7℃～10℃) that is difficult to accurately identify with traditional methods. By training and testing different models, their recognition accuracy was analyzed and compared with traditional empirical methods.
We determined daily 500 m river ice cover changes in six major high-latitude river basins in the Northern Hemisphere region from 2002 to 2022 and produced a daily 12.5 km river ice coverage dataset and a 20-year dataset on river ice phenology.