H53N:
Hydrologic Data Assimilation and Analysis Tools for Large Data Sets and Their Application in Hydrology I

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Session ID#: 34493

Session Description:
Applications and developments of hydrologic and land data assimilation (DA) studies are reported. Focus of contributions include: advances in methods to estimate/reproduce errors; integrating observations across diverse spatial and temporal scales; applications of DA systems on land/atmospheric/water and state/flux/parameter estimation; and implications in decision support systems. We encourage contributions related to hydrology missions and ground sensor networks as well as simultaneous use of multiple observation types (e.g., multi-satellite, multi-scale, conjunctive use of ground, airborne and satellite observations). Studies using collaborative field experiments that collect extensive datasets will also be reported along with the development of effective analysis tools. Examples of tools include novel data sampling and statistical methods, data fusion techniques, data mining approaches, and visualization schemes for large datasets. Potential applications of these tools include modeling evapotranspiration and other earth system processes, assessing flood and drought risk, characterizing the spatial variability of precipitation, and water resources management.
Primary Convener:  Barton A Forman, University of Maryland College Park, College Park, MD, United States
Conveners:  Joseph G Alfieri, USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Manuela Girotto, University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, CA, United States and Valentijn R N Pauwels, Monash University, Melbourne, Australia
Chairs:  Joseph G Alfieri, USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States and Barton A Forman, University of Maryland, College Park, MD, United States
OSPA Liaison:  Valentijn R N Pauwels, Monash University, Melbourne, Australia

Cross-Listed:
  • A - Atmospheric Sciences
  • B - Biogeosciences
  • C - Cryosphere
  • EP - Earth and Planetary Surface Processes
Index Terms:

1805 Computational hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1910 Data assimilation, integration and fusion [INFORMATICS]

Abstracts Submitted to this Session:

Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management (252535)
Fangni Lei1, Wade T Crow2, William P Kustas2, Yun Yang3 and Martha Anderson4, (1)USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, United States, (2)USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States, (3)University of Maryland College Park, ESSIC, College Park, MD, United States, (4)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States
Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions (289474)
Catalina M Oaida Taglialatela1, Konstantinos Andreadis2, John T Reager II3, James S. Famiglietti4 and Steven Levoe4, (1)Raytheon, Jet Propulsion Laboratory/PODAAC, Pasadena, CA, United States, (2)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States, (3)Jet Propulsion Laboratory, Pasadena, CA, United States, (4)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
The value of high-resolution and spatiotemporally continuous snow information to California streamflow estimates (238080)
Dongyue Li, UCLA, Department of Geography, Los Angeles, CA, United States, Dennis P Lettenmaier, University of California Los Angeles, Los Angeles, CA, United States, Steven A Margulis, University of California, Los Angeles, Department of Civil and Environmental Engineering, Los Angeles, CA, United States and Konstantinos Andreadis, University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States
Assimilation of flood extent data with 2D flood inundation models for localised intense rainfall events (Invited) (250832)
Jeffrey C Neal1, Melissa Wood2, Maria Bermudez3, Renaud Hostache4, Jim E Freer5, Paul D Bates1 and Gemma Coxon1, (1)University of Bristol, School of Geographical Sciences, Bristol, United Kingdom, (2)CRP Gabriel Lippmann, Belvaux, Luxembourg, (3)Universidade da Coruña, A Coruña, Spain, (4)Luxembourg Institute of Science and Technology, Environmental Research and Innovation, Belvaux, Luxembourg, (5)University of Saskatchewan Coldwater Laboratory, Canmore, AB, Canada
Data assimilation of citizen collected information for real-time flood hazard mapping (263967)
Takahiro Sayama, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan and Kaoru Treasure Takara, 606-8306, ​​Graduate School of Advanced Integrated Studies in Human Survivability, Kyoto, Japan
Ensemble streamflow assimilation with the National Water Model. (279136)
Arezoo Rafieeinasab1, James L McCreight2, Seongjin Noh3, Dong-Jun Seo3 and David Gochis2, (1)NSF National Center for Atmospheric Research, Research Applications Laboratory, Boulder, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States, (3)University of Texas at Arlington, Arlington, TX, United States

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