Assimilation of remotely sensed vegetation data
Synopsis: 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
Synopsis: 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
Synopsis: 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
Synopsis: 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
Synopsis: 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
Synopsis: 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
Synopsis: 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
Synopsis: 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