Dr Xueying Li

Dr Xueying Li is a Marie Curie Fellow working at the Hydro-Climate Extremes research group, closely in collaboration with Prof. Louise Slater. She has been working on multiscale changes of the hydrological cycle and the associated effects on human society, using multisource satellite remote sensing and different modelling skills such as hybrid and machine learning methods.

Dr Li’s research focuses on water sustainability and hydrological processes under a changing environment, mainly including: (1) climatic and anthropogenic impacts on the water cycle; (2) hydro-climate extremes and water vulnerability; (3) generalizable AI models in hydrological analysis; and (4) earth observations for diagnosing hydrological changes across different scales.

Before joining the School of Geography and the Environment at the University of Oxford, Dr Li obtained her PhD in Hydrology at Tsinghua University (China), and conducted her postdoctoral research at the Helmholtz Centre for Environmental Research – UFZ in Germany. She acts as a reviewer for international journals in the fields of hydrology and remote sensing. She is a member of several leaned societies including the American Geophysical Union (AGU), the European Geosciences Union (EGU), and the Asia Oceania Geosciences Society (AOGS).

Notable Awards

• 2024: Marie Curie Postdoc Fellowship
• 2023: Outstanding PhD Graduates and PhD Thesis (Tsinghua University)
• 2022: Outstanding Paper Award (Chinese Youth in Water Sciences)
• 2022, 2019: National Scholarship for Graduates (China)
• 2018: National Outstanding Graduates in Hydrologic Engineering (China)

Dr Xueying Li is currently leading the 2 year European Marie Curie Fellowship: Global Hotspots of Runoff-Storage Water Stress (GLOSTRESS). The project investigates the compound effects of water storage and runoff on sustainable water supply at the global scale. By leveraging the strengths of remote sensing techniques, advanced modelling, and heuristic indicators, it aims to provide new insights into adaptive strategies for vulnerable regions by jointly treating storage and runoff as integrated sources of water supply.

The key contributions of Dr Li’s research include the following, which has been reported over 160 international media outlets:

1. Identifying climatic mechanisms driving historical and future water storage changes across cryospheric and high-mountain regions.
2. Developing hybrid and data-driven models (e.g., Soil Moisture to Runoff, SM2R) that improve runoff estimation and flood analysis in poorly gauged basins.
3. Advancing data fusion and machine-learning approaches to generate high-resolution and spatiotemporally continuous hydrological datasets.
4. Assessing compound water stress arising from joint limitations in water storage and runoff globally, with implications for drought and flood risk.

Research Projects

• 2025–2027: PI, European Commission, Marie Skłodowska-Curie Actions Postdoctoral Fellowships, Global Hotspots of Runoff-Storage Water Stress (GLOSTRESS).
• 2025–2028: Co-PI, European Space Agency & Ministry of Science and Technology of China, ESA-MOST Dragon 6 Co-operation Project, Quantifying the Impacts of Compound Hot-Dry Extremes on Agriculture and Water Resources from Earth Observation (AGRIWATER)

• Li, X. et al. (2025) Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities. Fundamental Research, In press.
• Li, L. et al. (2025) Fingerprint-based attribution and constrained projection of global risk of daily compound hot extremes. Journal of Geophysical Research: Atmospheres, 130(13): e2024JD041986.
• Guan, Y. et al. (2024) Human-induced intensification of terrestrial water cycle in dry regions of the globe. npj Climate and Atmospheric Science, 7(1): 45.
• Meng, X. et al. (2024) Validation and expansion of the soil moisture index for assessing soil moisture dynamics from AMSR2 brightness temperature. Remote Sensing of Environment, 303: 114018.
• Long, D. et al. (2024) Spatial disparity in runoff variability between Southwestern China’s River Basin headwaters during 1981‒2020. Chinese Science Bulletin, 69:1-10.
• Li, X. et al. (2023) Soil Moisture to Runoff (SM2R): A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics. Water Resources Research, 59(3): e2022WR033597.
• Li, X. et al. (2022) Climate change threatens terrestrial water storage over the Tibetan Plateau. Nature Climate Change, 12: 801-807.
• Long, D. and Li, X. (2022) Water loss over the Tibetan Plateau endangers water supply security for Asian populations. Nature Climate Change, 12: 785-786. (Research Briefing)
• Long, D. et al. (2022) Remote sensing retrieval of water storage changes and underlying climatic mechanisms over the Tibetan Plateau during 2000‒2020. Advances in Water Sciences, 33(03): 375-389.
• Abowarda, A.S. et al. (2021) Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale. Remote Sensing of Environment, 255: 112301.
• Hong, Z. et al. (2021) Generation of an improved precipitation data set from multisource information over the Tibetan Plateau. Journal of Hydrometeorology, 22(5): 1275-1295.
• Li, X. and Long, D. (2020) An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach. Remote Sensing of Environment, 248: 111966.
• Long, D. et al. (2020) Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach. Remote Sensing of Environment, 246: 111863.
• Sun, Z. et al. (2020) Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins. Water Resources Research, 56(4): e2019WR026250.
• Li, X. et al. (2019) Evapotranspiration estimation for Tibetan Plateau headwaters using conjoint terrestrial and atmospheric water balances and multisource remote sensing. Water Resources Research, 55(11): 8608-8630.
• Li, X. et al. (2019) Spatiotemporal soil moisture variations associated with hydro-meteorological factors over the Yarlung Zangbo River basin in Southeast Tibetan Plateau. International Journal of Climatology, 40(1): 188-206.
• Li, X. (2017) A new evaluation for water transfer optimal schemes with the consideration of reliability, stability, and severity. Water Resources Management, 31(9): 2823-2836.
• Li, X. et al. (2016) Simulation of reservoir sediment flushing of the Three Gorges Reservoir using an artificial neural network. Applied Sciences, 6(5): 148.