Satellite-derived Water Storage
Characterizing the amount of available freshwater is of paramount importance to freshwater resource management. Recent advances in satellite-based remote sensing enable the measurement of the anomalies in the Earth's gravitational field. These measurements can be used to better quantify the amount (or mass) of water stored as snow, ice, lakes, soil moisture, and groundwater.
Downwelling shortwave (SW) and longwave (LW) radiation are the dominant energetic inputs to the land surface. Our research involves the use of geostationary and polar-orbiting satellite platforms to characterize the keys modes of variability in the land surface energy forcing, which drives much of the variability in the latent heat and sensible heat fluxes emanating from the terrestrial environment.
Passive Microwave Prediction
More than one billion people globally are dependent on freshwater runoff that originates as snow and ice. Space-based measurements of microwave radiation emitted from snowpack provide an important information source that can be used to improve our knowledge of the mass of snow within a snowpack, and hence improve our understanding of available freshwater resources.
Hydrologic Data Assimilation
Data assimilation is a general technique where model estimates are merged with observations to ultimately yield an estimate that is superior to either the model or observations alone. In the context of hydrology, this technique can be applied to satellite-derived estimates of soil moisture, water surface elevation, vegetation growth, snow depth, land surface temperature, and groundwater storage.
Machine Learning Applications
Machine learning applications, including neural networks and support vector machines, provide unique capabilities in the prediction of nonlinear processes. The skill and efficiency of these predictions enhance our knowledge of hydrologic processes operating across regional and continental scales. Examples include the prediction of satellite-based measurements related to snow and soil moisture estimation.
High Performance Computing
Application and development of advanced computer models utilizing satellite-borne observations requires advanced computing techniques such as parallel processing and petabyte storage. The Deepthought2 and Bluecrab supercomputers at UMD employ >10,000 CPUs to provide the computing power necessary to study hydrologic processes operating across a range of spatial and temporal scales.
(professor and group leader)
Research interests include terrestrial hydrology, satellite-based remote sensing, machine learning applications, and data assimilation
Research interests include satellite-based remote sensing, data assimilation, and machine learning
Research interests include soil moisture remote sensing and distributed hydrologic modeling
Research interests include hydraulic modeling, water quality modeling and low impact urban development
Research interests include climate change impacts on water availability and water temperature as applied to the electrical generation sector
Research interests include rain-on-snow event detection using passive microwave measurements collected by satellite-based instrumentation
Hydrologic quantities are typically estimated from observations or via physical models. The emerging field of “data assimilation” is a general technique whereby observations and physical models are optimally merged. The goal of data assimilation is to derive the most utility from two disparate data streams. (click here for syllabus)
Explores the role of hydrology in the climate system, precipitation and evaporation processes, atmospheric radiation, the exchange of mass, heat, and momentum between the soil and vegetative surface and the overlying atmosphere, and the flux and transport of water within the turbulent boundary layer. (click here for syllabus)
Examines the theoretical bases for fluid statics and dynamics, including the conservation of mass, energy and momentum. Modeling of hydraulic systems is introduced. Pipe flow and open-channel hydraulics are emphasized with application to real-world problems. (click here for syllabus)
Introduces basic concepts of remote sensing in water resource management. Discussion of measurements related to soil moisture, snow, groundwater, precipitation, and river discharge. Application of remote sensing datasets in the characterization and quantification of global freshwater. (click here for syllabus)
Engineers Without Borders
More than a dozen household-scale (~10 people/house) rainwater collection systems have been constructed since the inception of this project. Each concrete system collects and stores clean, drinking water during the rainy season so that residents have safe, drinking water during the dry season when water is scarce. Click here to watch a video discussing the project approach.
A liquid drip chlorination system was successfully installed in January 2014 and is currently undergoing monitoring. The system was designed to disinfect fecal coliform from the water supply. The overarching goal is to deliver safe, pathogen-free drinking water to the residents of Compone, Peru, a small village about 20 miles from Machu Picchu.
An assessment trip was conducted in July 2013 to evaluate a recently-constructed stormwater runoff system as well as other recently-completed projects conducted by EWB-UMD. Our dedicated partnership with the community of Addis Alem will continue during the next phase of our project which started Fall 2013.