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Research Themes and Projects

Assimilation of remotely sensed vegetation data

topographic mapSynopsis: The spatial distribution of vegetation tells us something about the availability of soil moisture available to plants. Platforms like Landsat and the MODIS sensor observe vegetation properties in the visible and infrared portion of the electromagnetic spectrum from space at high resolution. These data should help us make better predictions of soil moisture when they are ingested into a hydrologic model that simulate coupled  vegetation and soil moisture dynamics.

Researchers: Lejo Flores

Funding sources: US Army Research Office, National Science Foundation

Improving multiscale soil moisture estimation

Soil moisture read outsSynopsis: Soil moisture is an essential hydrologic state variable and many applications, like military trafficability and mobility assessment, depend on accurate and timely predictions of soil moisture. We are using a variety of models and data to improve our ability to predict soil moisture across a large range of scales. One component of this project downscales snowmelt outputs from a coarse-scale snow model to hillslope scales to drive the tRIBS integrated hydrologic model.

Researchers: Reggie Walters, Esther Contreras

Collaborators: Warren Barrash, Rafael Bras, Jim McNamara, John Bradford

Funding sources: US Army Research Office

Retrieving vegetation water content from space

vegetation mapSynopsis: In semiarid environments, water contained in vegetation can be a significant proportion of water storage in the near-surface environment, particularly during the dry summer. It reflects the availability of soil moisture, the health or stress of plants, and is important for wildland fire fuel assessment. Vegetation water content information can improve microwave soil moisture retrievals. To constrain this important land surface parameter, we combine field data collected in Dry Creek Experimental Watershed with Lansat and MODIS data.

Researchers: Ricci Loughridge, Paige LaPorte

Collaborators: Andreas Colliander, Jet Propulsion Laboratory

Funding sources: NASA Idaho Space Grant Constortium

Multi-scale, physically based snow modeling

base snow mapSynopsis: Up to 60 million people in the Western US and up to 1 billion people globally depend on runoff from snow to supply water. Despite the importance of snow as a water resource and its sensitivity to climate change, accurate knowledge of the spatiotemporal dynamics of mountain snowpacks remain elusive. We are evaluating the snow components of a number of macro-scale snow models contained in the Land Information System (LIS) and developing a physically based, snow mass and energy balance component for tRIBS-VEGGIE.

Researchers: Lejo Flores, Katelyn Watson, Miguel Aguayo

Collaborators: Christa Peters-Lidard, Sujay Kumar, NASA Goddard Space Flight Center, Jim McNamara, Venkataramana Sridhar

Funding sources: NASA Idaho Space Grant Consortium, National Weather Service

Assimilating radar observations for snow storage estimation

radar colorsSynopsis: In addition to modeling of snow mass and energy balance, attributes of snow can be measured from space. We are particularly interested in using radar instruments in the X- and Ku-bands (consistent with the planned ESA CoReH2O and NASA SCLP satellites) to obtain estimates of snow water equivalent in mountain watersheds. We are developing methods to assimilate these observations into complex models of snow mass and energy balance (see above) to obtain value-added SWE estimates at resolutions of individual hillslopes.

Researchers: Miguel Aguayo, Katelyn Watson, Austin Hopkins

Collaborators: HP Marshall, Jim McNamara, John Bradford, Kasper van Wijk

Funding sources: NASA EPSCoR

Improving long lead-time forecasts of natural streamflow

natural streamflow mapSynopsis: In the western US, water resource security depends upon accurate forecasts of streamflow with sufficiently long lead-times (months or longer) to manage water resource infrastructure adaptively. We are developing and testing a number of models to predict streamflow and other key decision-support hydrologic variables by exploiting correlations between streamflow and measures of the intensity of the oceanic and atmospheric drivers of Idaho climate. We are also trying to understand how and when these models fail, and how they may be sensitive to climate change.

Researchers: Mel Kunkel

Funding sources: US Bureau of Reclamation

Stable isotopes to improve hydrologic modeling

stable isotopes graphSynopsis:  The stable isotopes of water in all its phases tell amazing stories about that parcel of water; where it came from, what to meteoric conditions were there, how long it’s been where it was sampled, etc. In our group, we are using stable isotopes to tell us about the hydrology of the Dry Creek Experimental Watershed. Further, we are seeking to use the data collect about the stable isotopes of water we find to inform hydrologic models to improve their predictive performance.

Researchers: Danny Tappa, Kimi Smith

Collaborators: Shawn Benner, Matt Kohn

Funding sources: USGS via Idaho Water Resources Research Institute

Optimizing hydrologic monitoring networks for data assimilation

hydrologic monitoring mapSynopsis: Hydrologic monitoring networks are the backbone of hydrologic data collection. Increasingly data from hydrologic monitoring networks is being assimilated into complex hydrologic models. But can these networks be designed in a way that maximized the impact of the information delivered to the data assimilation system? In a project for the South Florida Water Management District, we developed a synthetic experiment-based approach to designing monitoring networks to improve the predictions that arise from their hydrologic models.

Researchers: Lejo Flores

Funding sources: South Florida Water Management District