2. Extreme events: modeling

Qualitative and quantitative analysis of extreme events can be conducted through land surface/hydrological modeling. To achieve this goal, key parameters and physical processes need to be identified and uncertainty needs to be documented. A physically-based and validated model is important for improving predictive skill of extreme events. Modeling capabilities to deal with responses to climatic and social-economic impacts need to be assessed.

Chair: John Nielsen-Gammon, Texas A&M University
Keynote 1: Barton Forman, University of Maryland: “Towards Multisensor Snow Assimilation: A Simultaneous Radiometric and Gravimetric Framework”
Snow is a critical resource and serves as the dominant freshwater supply for 1+ billion people worldwide. Recent events in California, for example, highlight the importance of snow and its impact on extreme drought. Accurate measurements of snow are vital for predicting (and mitigating) the effects of extreme drought. However, global estimates of snow mass (a.k.a. snow water equivalent [SWE]) contain significant uncertainty and are often unavailable in regions of the globe where SWE is greatest. Further, satellite-based remote sensing products of SWE are severely limited when the snow pack contains liquid water, internal ice layers, surface ice crusts, or is overlain by forest canopy. Recent advances in data assimilation offer the potential to improve our estimates of global SWE. In particular, the merger of passive microwave remote sensing (e.g., AMSR-E) with satellite-based gravimetric retrievals (e.g., GRACE) offers unique opportunities to bridge remote sensing scales in space and time, “see” deeper into the snow pack, and add vertical resolution to the gravimetric retrievals that currently does not exist. A discussion of current and emerging data assimilation techniques as applied to snow is presented with an emphasis on regional- and continental-scale SWE estimation.
Keynote 2: David Maidment, University of Texas at Austin: “National Flood Interoperability Experiment”
The National Weather Service (NWS) has opened a new National Water Center on the Tuscaloosa campus of the University of Alabama.   The NWS intends that this center be operated in conjunction with its partners in IWRSS (Integrated Water Resources Science and Services), which are the US Geological Survey and the US Army Corps of Engineers, with the Federal Emergency Management Agency (FEMA) in the process of joining the partnership.  The NWS also intends to gradually synthesize the activity of its thirteen regional river forecast centers to provide a national river forecasting function through the National Water Center.   As this new center begins its functions, it provides an opportunity to propose and compare new approaches for high spatial resolution, near-real-time flood simulation and forecasting that can be applied on a national scale.   A National Flood Interoperability Experiment (NFIE) has been proposed that will be led by the academic community coordinated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI), and will be supported by the National Weather Service.  The NFIE will operate from September 2014 through August 2015 in two phases: a mobilization phase from September 2014 to May 2015 in which components of the proposed system will be made operational at the National Water Center, followed by a Summer Institute from June to August 2015, when students and faculty from the University of Alabama and from CUAHSI institutions elsewhere, will work together at the National Water Center to test and assemble a new shared set of national services for flood data, modeling, forecasting and inundation mapping.
Oral 1: Zhicong Yin, Beijing Meteorological Bureau, China: “Numerical Simulation of Urban Ponding and Its Application in Beijing”
Oral 2: Li Dan, IAP/Chinese Academy of Sciences, China: “Hydrological Projections over the 3H Region of China Using Climate Change Scenarios”
Oral 3: Yong-Fei Zhang, The University of Texas at Austin, USA: “Assimilation of MODIS Snow Cover and GRACE Terrestrial Water Storage Data through DART/CLM4”
Oral 4: Peirong Lin, The University of Texas at Austin, USA: “Implementing a vector-based river routing scheme within the WRF-Hydro modeling system”