Mining information from in-situ, remote sensing, and modeling datasets with advanced numerical methods to fill the gaps in our hydrologic knowledge system and to provide solutions to challenging water-related problems.


Remote sensing (RS) measures Earth surface attributes and dynamics using sensors aboard unmanned, airborne, and spaceborne platforms. It provides an accurate big-picture measurement from local to continental scale.

My work on RS mostly focuses on developing new processing algorithms for existing RS observations to increase their utilities in inferring hydrology fluxes, and on finding new ways in which RS data can be collected and be used to study hydrology.

Li et al. 2012, 2014, 2019, Cai et al. 2017, Margulis et al. 2019

Surface processes modeling

Surface processes modeling ingests meteorological inputs and simulates the physical processes that control the water-mass and energy exchanges among the atmosphere, land surface, and subsurface to quantitatively estimate and predict hydrologic fluxes across spatial and temporal scales.

My work on modeling mainly involves: (1) improving the representation of physical processes in models for enhanced modeling accuracy, (2) conducting basin-scale to continental-scale simulations with computing resources from laptop to supercomputer.

Li et al. 2014, 2015, 2019, Schaperow et at. 2019


Data assimilation (DA) refers to a set of mathematical methods that merge the information from modeling and observations based on Bayes' theorem. Given both observed and modeled datasets, DA serves as a "smart" system that can automatically derive a more accurate estimation of the variable of interest than any single dataset possibly can, essentially by weighing the relative errors/uncertainties of the two datasets. Land DA is a young research area compared with the more mature DA in atmospheric and ocean sciences, due to the complex land surface processes and human activities. A nice introduction of DA is available in this Youtube video here.

I develop DA systems for better estimates of key hydrologic and thermal fluxes and hydraulic properties in snow hydrology and river hydrology.

Li et al. 2017, 2019a, 2019b, Kim et al., 2019, Margulis et al., 2019


Snow is a critical water resource; snowmelt directly provides water supply for over 1 billion people worldwide. Snow is a key modulator of the energy balance due to its high albedo. Declining snowpack in the warming climate threats the water security and the balance of ecosystem in many regions.

I am interested in large-scale quantification of the water equivalent of seasonal snowpacks, forecasting the volume and timing of snowmelt runoff, and better understanding the mechanisms that control the distribution and evolution of seasonal snowpacks. I am keen to use snow as an interface for interdisciplinary research of the broader ecosystem.

Li et al. 2014, 2015, 2017, 2019a, 2019b, Margulis et al. 2016, Kim et al, 2019


River is a critical element in the water cycle and the carbon cycle. Large rivers bred human civilizations many thousands of years ago, but until today, humans still do not have a coherent global observation or understanding of even the most basic hydrologic properties of rivers (e.g., discharge, depth).

My work on rivers is mostly around the forthcoming SWOT satellite mission. I help develop continuous river data products from discontinuous SWOT observations. I also use in-situ and remotely sensed data to better understand hydraulic properties of rivers, lakes, and reservoirs.

Li et al. 2019, Schaperow et al. 2019, Mehran et al.

hydrologic extremes and Water resources predictions

Accurate forecasts of water availability help facilitate timely coordinated efforts to mitigate the impact of water shortages and floods on the socio-economic wellbeing.

My work focuses on understanding the mechanisms of the large flooding and drought events, and on developing numerical methods to increase the accuracy and timeliness of water resources and hazards forecasting.

Li et al 2019a, 2019b, Margulis et al., 2016, Huang et al., Tarouilly et al.