H026
Applications of Hydrologic Data Assimilation

Session ID#: 74259

Session Description:
Applications and developments of hydrologic and land data assimilation (DA) studies will be reported. Focus of contributions include: advances in methods to estimate/reproduce errors; model-observation integration; integrating observations at diverse spatial and temporal scales; applications of DA systems on land/atmospheric/water and state/flux/parameter estimation; implications in decision support systems; and operational applications for floods or droughts. We also encourage contributions related to hydrology missions (e.g., AirMOSS, AMSR2, GPM, GRACE-FO, NISAR, SMAP, SMOS, SnowEx, SWOT, ECOSTRESS, etc.) and ground sensor networks (e.g., COSMOS, DTS, OzNet, etc.) as well as simultaneous use of multiple observation types (e.g., multi-satellite, multi-scale, conjunctive use of ground, airborne and satellite observations).
Index Terms:

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

Primary Convener:  Manuela Girotto, UC Berkeley, Berkeley, CA, United States
Convener:  Barton A Forman, University of Maryland, College Park, MD, United States
Primary Liaison:  Manuela Girotto, UC Berkeley, Berkeley, CA, United States
 
A high-resolution soil moisture reanalysis over Europe (491567)
Bibi S Naz1, Stefan J Kollet1, Harrie-Jan Hendricks Franssen1, Carsten Montzka1 and Wolfgang Kurtz2, (1)Agrosphere Institute (IBG-3), Research Centre Juelich, Juelich, Germany, (2)Leibniz Supercomputing Centre, Munich, Germany
 
Assimilation of MODIS and GLASS Leaf Area Index in a Land Data Assimilation System at the Global Scale (492499)
Azbina Rahman1, Viviana Maggioni2, Xinxuan Zhang1, Paul Houser1, Timothy Sauer1, Sujay V Kumar3 and David M Mocko4, (1)George Mason University Fairfax, Fairfax, VA, United States, (2)George Mason University Fairfax, Sid and Reva Dewberry Dept. of Civil, Environmental & Infrastructure Engineering, Fairfax, VA, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)SAIC, Greenbelt, MD, United States
 
Soil water content as landslide predictor: the effectiveness of observations, simulations and data assimilation results (492677)
Anne Felsberg, Katholieke Universiteit Leuven, Department of Earth and Environmental Sciences, Leuven, Belgium, Gabrielle De Lannoy, KU Leuven, Department of Earth and Environmental Sciences, Heverlee, Belgium, Manuela Girotto, UC Berkeley, Berkeley, CA, United States, Rolf H Reichle, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Jean William Poesen, Katholieke Universiteit Leuven, Leuven, Belgium
 
Toward a Universal and Scalable Data Assimilation in Hydrology (Invited) (493472)
Hamid Moradkhani1, Peyman Abbaszadeh1, Keyhan Gavahi2 and Mahkameh Zarekarizi3, (1)The University of Alabama, Tuscaloosa, AL, United States, (2)University of Alabama, Civil, Construction and Environmental Engineering, Tuscaloosa, AL, United States, (3)Penn State University, Earth and Environmental Systems Institute, State College, PA, United States
 
Integrating satellite and ground-based observations with airborne lidar snow pattern assimilation in mountainous terrain (497238)
Justin M Pflug, University of Washington, Seattle, WA, United States, Mimi Hughes, NOAA/ESRL/PSD, Boulder, CO, United States and Lundquist Jessica, University of Washington, Seattle, United States
 
A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile (502553)
Parisa Heidary1, Leila Farhadi1 and Muhammad Umer Altaf2, (1)George Washington University, Washington, DC, United States, (2)King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
 
Leveraging the Community WRF-Hydro Modeling System via Multivariate Assimilation of Satellite Soil Moisture and Streamflow observations (508345)
Peyman Abbaszadeh, University of Alabama, Tuscaloosa, AL, United States, Keyhan Gavahi, University of Alabama, Civil, Construction and Environmental Engineering, Tuscaloosa, AL, United States and Hamid Moradkhani, The University of Alabama, Tuscaloosa, AL, United States
 
Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration into Noah-MP Land Surface Model (508440)
Keyhan Gavahi, Peyman Abbaszadeh and Hamid Moradkhani, The University of Alabama, Tuscaloosa, AL, United States
 
A data assimilation framework for generating space-time continuous SWOT river discharge products (515053)
Dongyue Li, University of California Los Angeles, Los Angeles, CA, United States, Konstantinos Andreadis, University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States, Steven A Margulis, UCLA, Department of Civil and Environmental Engineering, Los Angeles, CA, United States and Dennis P Lettenmaier, UCLA, Department of Geography, Los Angeles, CA, United States
 
Towards a Multi-Variate, Multi-Sensor Assimilation Framework over Snow-Covered Terrain in Western Colorado (520646)
Lizhao Wang1, Barton A Forman1, Yonghwan Kwon2 and Sujay V Kumar3, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA Goddard Space Flight Center / UMD ESSIC, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States
 
Model-Independent Data Assimilation Package for Estimation of High Dimensional Model Parameters and Dynamic States Fields (525981)
Ayman Alzraiee, USGS California water Science Center, Sacramento, CA, Sacramento, California, United States, Jeremy White, US Geological Survey, Lutz, FL, United States, Michael N Fienen, USGS, Middleton, WI, United States and Randall Hunt, U.S. Geological Survey, Upper Midwest Water Science Center, Middleton, WI, United States
 
Enhancing terrestrial snow mass estimation via assimilation of AMSR-E brightness temperature spectral differences using the Catchment land surface model and support vector machine regression (541308)
Jing Wang, University of Maryland College Park, College Park, MD, United States and Barton A Forman, University of Maryland, College Park, MD, United States
 
Impacts of improving the observability of land surface model soil moisture estimates on data assimilation performance (542884)
Sujay V Kumar, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Joseph A Santanello Jr, NASA, Greenbelt, MD, United States, Mahdi Navari, UCLA, Irvine, CA, United States, Soni Yatheendradas, NASA/GSFC and UMD/ESSIC, Greenbelt, MD, United States and Christa D Peters-Lidard, NASA GSFC, Greenbelt, MD, United States
 
Estimation of soil states and parameters of integrated subsurface-land surface models by data assimilation (553290)
Ching Pui Hung1, Bernd Schalge2, Gabriele Baroni3, Daniel Erdal4, Emilio Sanchez4, Tobias Finn5,6, Olaf A Cirpka7, Felix Ament5,6, Stefan J Kollet8, Insa Neuweiler9, Clemens Simmer10 and Harrie-Jan Hendricks Franssen11, (1)Forschungszentrum Julich GmbH, IBG-3: Agrosphere, Julich, Germany, (2)University of Bonn, Meteorological Institute, Bonn, Germany, (3)University of Bologna, Department of Agricultural and Food Sciences, Bologna, Italy, (4)University of Tübingen, Center for Applied Geoscience, Tübingen, Germany, (5)University of Hamburg, Meteorological Institute, Hamburg, Germany, (6)Max Planck Institute for Meteorology, Hamburg, Germany, (7)University of Tübingen, Tübingen, Germany, (8)Forschungszentrum Jülich GmbH, Jülich 52428, Germany, (9)University of Hannover, Hannover, Germany, (10)University Bonn, Bonn, Germany, (11)Forschungszentrum Jülich GmbH, Julich, Germany
 
Pitfalls and perils of machine learning-based passive microwave brightness temperature data assimilation over terrestrial snow in High Mountain Asia (568867)
Yonghwan Kwon, NASA Goddard Space Flight Center / UMD ESSIC, Greenbelt, MD, United States, Barton A Forman, University of Maryland, College Park, MD, United States, Jawairia Ashfaq Ahmad, University of Maryland, Department of Civil and Environmental Engineering, College Park, MD, United States, Sujay V Kumar, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Yeosang Yoon, NASA GSFC/SAIC, Greenbelt, MA, United States
 
Simultaneous Assimilation of Water Levels from Stage Gauges and Satellite Derived Flood Extents for Improving the Performances of a 2d Flood Routing Model (569311)
Fabio Castelli1, Antonio Annis2 and Fernando Nardi2, (1)University of Florence, Florence, Italy, (2)University for Foreigners Perugia, Perugia, Italy
 
Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment (570532)
Manuela Girotto, UC Berkeley, Berkeley, CA, United States, Rolf H Reichle, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Matthew Rodell, NASA GSFC, Greenbelt, MD, United States and Viviana Maggioni, George Mason University Fairfax, Sid and Reva Dewberry Dept. of Civil, Environmental & Infrastructure Engineering, Fairfax, VA, United States
 
Assimilation of L-band Brightness Temperatures into a Global Land Surface Model to Improve Groundwater Table Estimates in Northern Peatlands (Invited) (575908)
Michel Bechtold1,2, Gabrielle De Lannoy1, Rolf H Reichle3, Dirk Roose2, Nicole Balliston4, Iuliia Burdun5, Kevin Devito6, Mateusz Grygoruk7, Juliya Kurbatova8, Tariq M Munir9 and Evgeny A. Zarov10, (1)KU Leuven, Department of Earth and Environmental Sciences, Heverlee, Belgium, (2)KU Leuven, Department of Computer Science, Heverlee, Belgium, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)University of Waterloo, Wetlands Hydrology Research Group, Waterloo, Canada, (5)University of Tartu, Department of Geography, Tartu, Estonia, (6)University of Alberta, Biological Sciences, Edmonton, AB, Canada, (7)Warsaw University of Life Sciences, Warsaw, mazowieckie, Poland, (8)A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia, (9)University of Calgary, Calgary, AB, Canada, (10)Yugra state university, UNESCO chair Environmental dynamic and global climate changes, Khanty-Mansiysk, Russia
 
Streamflow data assimilation for hydrologic river routing: advances and challenges (612912)
Seong Jin Noh, University of Texas at Arlington, Arlington, TX, United States, Hak Su Lee, LEN Tech, Silver Spring, MD, United States and Dong-Jun Seo, University of Texas at Arlington, Civil Engineeing (Water Resources), Arlington, TX, United States
 
Improving Streamflow Data Assimilation and Flood Forecasting Using Adaptive Conditional Bias-Penalized Ensemble Kalman Filter (627463)
Haojing Shen, University of Texas at Arlington, Arlington, TX, United States, Hak Su Lee, LEN Technologies, Oak Hill, VA, United States and Dong-Jun Seo, University of Texas at Arlington, Civil Engineeing (Water Resources), Arlington, TX, United States
 
Learning to Assimilate - Machine Learning Based Data Assimilation for Operational Hydrology Forecasts (630410)
Alden Keefe Sampson1, Grey Stephen Nearing2, David Lambl1 and Eliza Hale1, (1)Natel Energy Inc, Upstream Tech, Alameda, CA, United States, (2)University of Alabama, Geological Sciences, Tuscaloosa, AL, United States
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