H53L:
Hydrologic Data Assimilation II


Session ID#: 10544

Session Description:
Hydrologic and land data assimilation (DA) studies will be reported. Focus of contributions include: advances in methods to estimate/reproduce errors and model-observation integration; applications of DA systems on land/atmospheric/water quality state/flux/parameter estimation; implications in decision support systems; integrating observations at diverse spatial and temporal scales; operational applications such as flood forecasting/inundation and drought forecasting. We also encourage contributions related to hydrology missions (e.g., GRACE, SMOS, AMSR2, SMAP, SWOT, GPM, etc.) and ground sensor networks (e.g., COSMOS, OzNet, DTS) as well as simultaneous use of multiple observation types (e.g., multi-satellite, multi-scale, conjunctive use of ground, airborne and satellite observations).
Primary Convener:  Barton A Forman, University of Maryland College Park, College Park, MD, United States
Conveners:  Valentijn R N Pauwels, Monash University, Melbourne, Australia, Dongryeol Ryu, University of Melbourne, Parkville, VIC, Australia and Susan C Steele-Dunne, Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, Netherlands
Chairs:  Barton A Forman, University of Maryland College Park, College Park, MD, United States and Valentijn R N Pauwels, Monash University, Melbourne, Australia
OSPA Liaison:  Dongryeol Ryu, University of Melbourne, Parkville, VIC, Australia
Index Terms:

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

Abstracts Submitted to this Session:

Establishing the Framework for Land Data Assimilation of GRACE Terrestrial Water Storage Information (79361)
Carly Sakumura1, Srinivas V Bettadpur1, Zong-Liang Yang2, Himanshu Save3 and Christopher M McCullough4, (1)Center for Space Research, Austin, TX, United States, (2)University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States, (3)The University of Texas at Austin, Center for Space Research, Austin, United States, (4)Jet Propulsion Laboratory, Pasadena, CA, United States
An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater (65242)
Manuela Girotto, University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, CA, United States, Gabrielle J.M. De Lannoy, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Rolf H Reichle, NASA Goddard Space Flight Center, Greenbelt, United States and Matthew Rodell, NASA GSFC HSL, Greenbelt, United States
Impact of state updating and multi-parametric ensemble for streamflow hindcasting in European river basins (77881)
Seong Jin Noh, Kumoh National Institute of Technology, Gumi, South Korea; Kumoh National Institute of Technology, Civil Engineering, Gumi, South Korea, Oldrich Rakovec, Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany, Rohini Kumar, Helmholtz Centre for Environmental Research GmbH – UFZ, Leipzig, Germany, Computational Hydrosystems, Leipzig, Germany and Luis Samaniego, Helmholtz Centre for Environmental Research UFZ Leipzig, Computation Hydrosystems, Leipzig, Germany
Improved Large-Scale Hydrological Modelling Through The Assimilation Of Streamflow And Downscaled Satellite Soil Moisture Observations (77636)
Patricia Lopez1,2, Niko Wanders3, Jaap Schellekens2, Luigi J Renzullo4, Edwin Sutanudjaja5 and Marc FP Bierkens6, (1)Utrecht University, Physical Geography, Faculty of Geosciences, Utrecht, Netherlands, (2)Deltares, Delft, Netherlands, (3)Utrecht University, Utrecht, Netherlands, (4)CSIRO Land and Water, Canberra, Australia, (5)Utrecht University, Physical Geography, Utrecht, Netherlands, (6)Utrecht University, Department of Physical Geography, Utrecht, Netherlands
Improving Hydrologic Prediction at the Basin Scale through State Updating (Invited) (60866)
Albrecht Weerts1,2, Arno Kockx3, Jaap Schellekens3, Niels Drost4, Darja Tretjakova5, Jianning Ren6, Patricia Lopez7 and Rolf Hut8, (1)Deltares, Operational Water Management & Early Warning, Delft, Netherlands, (2)Wageningen University and Research, Hydrology and Quantitative Water Management Group, Wageningen, Netherlands, (3)Deltares, Delft, Netherlands, (4)Netherlands eScience Center, Amsterdam, Netherlands, (5)Wageningen UR,, Hydrology and Quantitative Water Management Group, Wageningen, Netherlands, (6)UNESCO-IHE� Institute for Water Education, Delft, Netherlands, (7)Organization Not Listed, Washington, DC, United States, (8)Delft University of Technology, Water Management, Delft, Netherlands
Simultaneous Assimilation of Multiple Data into a Conceptual Rainfall-Runoff Model using Variational Methods for Hydrological Forecasting Applications (65494)
Rodolfo Alvarado Montero, University of Duisburg-Essen, Essen, Germany, Dirk Schwanenberg, Deltares, Delft, Netherlands, Aynur Sensoy Sorman, Eskisehir Technical University, Civil Engineering, Eskisehir, Turkey and Peter Krahe, Federal Institute of Hydrology, Koblenz, Germany
Synthetic tests of passive microwave brightness temperature assimilation over snow covered land using machine learning algorithms (79241)
Barton A Forman, University of Maryland College Park, College Park, MD, United States
An Extensible Global Land Data Assimilation System Based on NCAR's Community Land Model (CLM) and Data Assimilation Research Testbed (86600)
Zong-Liang Yang1, Yongfei Zhang2, Yonghwan Kwon2, Peirong Lin1, Long Zhao2, Timothy J Hoar3, Jeffrey L Anderson4, Ally Mounirou Toure5 and Matthew Rodell6, (1)University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States, (2)University of Texas at Austin, Austin, TX, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States, (4)University Corporation for Atmospheric Research, Boulder, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)NASA GSFC HSL, Greenbelt, United States

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