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We develop interesting projects aimed at addressing challenging problems related to surface water.

Global Discharge Mapping

We develop high-accuracy discharge products for global rivers by integrating land surface model, hydrologic model, remote sensing, mass conserved flow law inversion (McFLI) discharge algorithms, data assimilation, and big data computation.

Funding: NASA and NSF

Major CollaboratorsDr. Colin Gleason (UMass, Amherst), Dr. Xiao Yang (UNC), Dr. Peirong Lin (Princeton), Dr. Ming Pan (UCSD), and Yuta Ishitsuka (UMass, Amherst).

Relevant Publications:

Feng D., Gleason C.J., Lin P., Yang X., Pan M., and Ishitsuka Y., 2021, Recent changes to Arctic river discharge, Nature Communications (link).

Ishitsuka, Yuta; et al., (2021) Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed.Water Resources Research (link)


River water quality

We develop methods and algorithms to quantify river water quality (sediment and nutrients) in the Mississippi River basin by leveraging satellites, land surface models, surface water quality models, and machine learning techniques.

Funding: NASA Terrestrial Hydrology Program

Principal Investigator: Dr. Dongmei Feng

Relevant Publications: Ongoing (2022-2025)

Remote Sensing of Discharge


Satellites observe the hydraulic conditions of global rivers, which can be used to estimate river discharge. We are interested in using various satellites, including government satellites (Landsat and Sentinel) and commercial satellites (e.g., Planet), to extract hydraulic data for the estimation of river discharge. We provided the first assessment of the suitability of CubeSat satellite data for river discharge estimation. Our work laid the methodological foundation for NASA's upcoming mission Surface Water and Ocean Topography (SWOT) planned to launch in 2022.

Funding: NASA and NSF.

Major CollaboratorsDr. Colin Gleason (UMass, Amherst), Dr. Xiao Yang (UNC), Dr. Tamlin Pavelsky (UNC), and Craig Brinkerhoff (UMass, Amherst).

Relevant Publications:

Feng, D., C. Gleason, X. Yang and T. Pavelsky, 2019, Comparing discharge estimates made via the BAM algorithm in high-order Arctic rivers derived solely from optical CubeSat, Landsat, and Sentinel-2 data, Water Resources Research (link).

Brinkerhoff, C. B.; Gleason, C. J.; Feng, D.; Lin, P. (2020. ) Constraining Remote River Discharge Estimation Using Reach-Scale Geomorphology. Water Resources Research (link). 

Greenland Meltwater Transport

We use in situ measurement, climate models, and a hydrologic model to simulate the transport of Greenland Ice Sheet (GrIS) meltwater and to advance our understanding of supraglacial hydrology.

Major CollaboratorsDr. Colin Gleason (UMass, Amherst), Dr. Kang Yang (Nanjing U.), Dr. Laurence Smith (Brown U.)

Relevant Publications:

Gleason C.J., Yang K., Feng D., Smith L.C., Liu K., Pitcher L.H., Chu V., Cooper M.G., Overstreet B.,Rennermalm A., and Ryan J., 2021. Hourly surface meltwater routing on the Greenland ice sheet across hillslopes and through a dense topological channel network, The Cryosphere (link).


(Image credits: Colin Gleason)

Hydrologic impacts of climate change


Hydrological processes are sensitive to climatic variations. Hydrologic response to climate change is complicated and shows substantial spatial heterogeneity. We develop interdisciplinary research to address questions: e.g., How will hydrologic fluxes (e.g., runoff, river discharge) respond to climate alterations? and How will hydrologic changes impact humans and the environment (e.g., flood risks and fish migration)? 

Funding: NSF and NOAA.

Relevant Publications:

Feng, D., R.E. Beighley, et al., 2019. Propagation of Future Climate Conditions into Hydrologic Response from Coastal Southern California Watersheds, Climatic Change (link)

Feng, D., et al., 2020. Future climate impacts on the hydrology of headwater streams in the Amazon River Basin: Implications for migratory goliath catfishes, Hydrological Processes (link)

Feng, D. and R.E. Beighley, 2020, Identifying uncertainties in simulated streamflow from hydrologic process models for climate change impact assessments, Hydrology and Earth System Sciences (link).

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