Conference Agenda

Multiple global water cycle related satellite products (soil moisture, vegetation optical depth etc.) are available and explored by a growing community. Since the launch of the ESA Soil Moisture Ocean Salinity (SMOS), it has provided L-band monitoring of soil moisture, vegetation and ocean salinity in the past decade. The SMAP satellite, also equipped with L-band sensors a, can provide very good collaborative observations. The global mapping of soil moisture and vegetation needs to be continued, while the temporal-spatial resolution and accuracy of SMOS products, combining with SMAP and the Chinese Fengyun series satellites, needs to be refined for a wider global water cycle study.

This project is dedicated to improving the accuracy and temporal-spatial resolution of SMOS products related to water cycle, including soil moisture, vegetation optical depth, through the synergy use of multi-sources satellite observations from European and Chinese Earth observation data. It is aimed to: (1) Enhance the retrieval performance by developing a multi-channel collaborative algorithm suitable for SMOS multi-angular observations, (2) Generate long-term physical consistency and high temporal coverage of soil moisture products, through the inter-calibration of TBs and the introducing of parameter relationships from SMOS multi-angular TBs in SMOS-SMAP synergy; (3) Bridging spatio-temporal discontinuities in satellite retrievals by coupling physics in deep learning; (4) Value-added products by improving the resolution through the fusion of optical and active/passive microwave observations. Meanwhile, new satellite missions should be studied to combine the advantages of current satellite design, and continue the multi-frequency microwave observation from space.

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), quantify blue runoff), green (evapotranspiration), and white (sublimation) water fluxes in high elevation catchments. We will apply the land surface model 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). By inferring climatological biases through an inversion, we will shortcut the laborious and computationally expensive effort to correct meteorological forcing in data-scarce regions, enabling a generalizable assessment of cryospheric and vegetation dynamics across regional scales. 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. We will extend our previous efforts across HMA to additionally focus on catchments affected by the Pamir-Karakoram Anomaly, where the scarcity of in situ data has limited cryospheric modelling. This effort is possible through the combined expertise in remote sensing (Chinese PI, European co-PI) with atmospheric (Chinese co-PI) and land-surface modelling (European PI), with synergies due to existing projects and partnerships in the region.

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. High-resolution satellite data of glacier mass balance, surface albedo, and vegetation phenology will constrain model applications, while land-cover, surface water, and glacier velocities provide evaluation datasets. The focus of remote sensing data analysis will be 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. Particular attention will be dedicated to spatial and temporal variability of glacier surface elevation using high spatial resolution satellite data. 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 will allow independent evaluation of the model results, where available. Our team of European and Chinese scientists will: i) provide an advanced characterization of glacier and hydrological processes from remote sensing datasets; ii) reduce meteorological forcing biases by combining land-surface modelling and earth observation; iii) apply a state-of-the-art hyper-resolution land surface model to simulate the complex high mountain water budget and its changes. The proposed work is supported by many complementary research grants.