Mining information from observed and modeled datasets using advanced numerical methods to fill the gaps in our knowledge system and provide solutions to challenging problems. 


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

My work on RS focuses on: (1) developing new processing algorithms to increase the utilities of the processed data in inferring hydrology fluxes, and (2) assimilating RS data into models to optimize hydrologic model estimation and prediction.

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

Surface process modeling

Surface process modeling ingests meteorological inputs and simulates the physical processes that control the water 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 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 science and ocean science due to the complex land surface processes and human activities. A nice introduction to DA is available in this Youtube video here.

I develop various DA systems to better estimate 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 ecosystems 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 climate system and 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 socio-economic wellbeing.

My work focuses on understanding the mechanisms of severe flooding and drought events and developing numerical methods to increase the accuracy and timeliness of forecasting.  

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