H091
Remote Sensing, Modeling and Data Assimilation of the Terrestrial Water Cycle
Session ID#: 158823
Session Description:
Hydrologic models and remotely-sensed observations are essential tools in the study of the terrestrial water cycle while data assimilation allows to systematically merge those observations to yield an estimate superior to either alone. The collective use of these techniques helps bridge spatiotemporal scales as well as limitation gaps between observations and models. This session presents the latest developments in hydrologic modeling, remote sensing, and data assimilation to better understand the spatial and temporal dynamics of the terrestrial water cycle and freshwater resources. We invite multi-scale, multi-variable, and multi-sensor studies that aim to assess spatiotemporal variability in water resources, improve errors and uncertainty estimation, and those that integrate models with observations across a range of spatial and temporal scales. A particular focus is given to ground-based, airborne, and space-based hydrology missions such as AirMOSS, AMSR2, AIRS/AMSU, ECOSTRESS GCOM-W, GPM, GRACE-FO, NISAR, MetOp, TRMM, TOPEX, SMAP, SMOS, SnowEx, SWOT, CYGNESS.
Index Terms:
1836 Hydrological cycles and budgets [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1855 Remote sensing [HYDROLOGY]
1910 Data assimilation, integration and fusion [INFORMATICS]
Primary Convener: Iliana E Mladenova, USDA Washington DC, Washington, DC, United States
Conveners: Venkataraman (Venkat) Lakshmi, University of Virginia, Engineering Systems and Environment, Charlottesville, United States, Manuela Girotto, University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, United States and Sayed M. Bateni, University of Hawaii at Manoa, Department of Civil and Environmental Engineering and Water Resources Research Center, Honolulu, United States
Reconstruction of the SMAP-based 12-hourly soil moisture product over the CONUS through water balance budgeting (1064169)
Runze Zhang, University of Virginia, Engineering Systems and Environment, Charlottesville, VA, United States, Seokhyeon Kim, Kyung Hee University, Department of Civil Engineering, Yongin, South Korea, Hyunglok Kim, USDA ARS, Beltsville, United States, Bin Fang, University of South Carolina, School of Earth Ocean and Environment, Columbia, United States, Ashish Sharma, University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia and Venkataraman (Venkat) Lakshmi, University of Virginia, Engineering Systems and Environment, Charlottesville, United States
Restoring Terrestrial Water Storage Anomalies from GRACE Spherical Harmonic Coefficients Using Convolutional Neural Network (1064858)
Zhang Qingquan1, Yun Pan1, Chong Zhang1, Yalin Ma1, Huixiang Li1 and Huili Gong2, (1)Capital Normal University, College of Resources Environment & Tourism, Beijing, China, (2)Capital Normal University, Beijing, China
Evaluation of Data-Driven Estimates of Evapotranspiration for Physically-Based Water Balance Assessment in a West African Region (1065876)
Fabian Merk, Technical University of Munich, Chair of Hydrology and River Basin Management, Department of Civil, Geo and Environmental Engineering, Munich, Germany, Markus Disse, Technical University of Munich, Chair of Hydrology and River Basin Management, School of Engineering and Design, Munich, Germany, Jean-Martial Cohard, Université Grenoble Alpes, The Institute for Geosciences and Environmental research (IGE), Grenoble, France and Ossénatou Mamadou, Institut of Mathematics and Physical Sciences, Porto-Novo, Benin
A Sustainable Approach to Evaluate the Non-Heuristic Drivers of Urban Flooding: A Case Study on Sub-Catchment of Mumbai, India (1066263)
Shray Pathak, Assistant Professor, Netaji Subhas University of Technology, Civil Engineering Department, New Delhi, India and Min Liu, East China Normal University, Key Laboratory of Geographic Information Sciences, Ministry of Education, School of Geographic Sciences, Shanghai, China
Towards Sustainable Irrigation: Spatio-Temporal Simulation of Soil Moisture by Integrating Satellite Remote Sensing Data into Hydrological Models (1071841)
Shirin Moradi, Forschungszentrum Jülich, Institute of Bio and Geosciences (Agrosphere, IBG-3), Jülich 52428, Germany, Haojin Zhao, Agrosphere Institute (IBG-3) Forschungszentrum Jülich, Deutschland, Germany, Harry Vereecken, Forschungszentrum Julich, Institute of Bio and Geosciences (Agrosphere, IBG-3), Julich, Germany and Carsten Montzka, Forschungszentrum Jülich, Institute of Bio and Geosciences (Agrosphere, IBG-3), Jülich, Germany
Sequential EnKF Assimilation of Sensitive Soil Moisture Observations to Improve Streamflow Estimation (1077844)
R Visweshwaran, Indian Institute of Technology Bombay, Mumbai, India, Raaj Ramsankaran, Indian Institute of Technology Bombay, Civil Engineering, Mumbai, India and TI Eldho, Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India
Global snow water equivalent distribution analysis of observational needs (1084073)
Eunsang Cho1,2, Carrie Vuyovich1, Sujay V Kumar3, Melissa Wrzesien2,3, Rhae Sung Kim3,4, Justin M Pflug2,3 and Goutam Konapala1,4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)University of Maryland College Park, College Park, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)University of Maryland Baltimore County, Baltimore, United States
A Direct Insertion Technique to Assimilate Sea Surface Height in a Storm Surge Model (1089599)
Soelem Aafnan Bhuiyan, George Mason University Fairfax, Fairfax, VA, United States, Viviana Maggioni, George Mason University Fairfax, Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, Fairfax, VA, United States, Celso Ferreira, George Mason University, Sid and Reva Dewberry Dept of Civil, Environmental & Infrastructure Engineering, Fairfax, VA, United States and Azbina Rahman, University of Maryland College Park, College Park, MD, United States
Benchmarking multimodel terrestrial water storage against GRACE observations over major global river basins (1099724)
Sadia Bibi, ZJU-UIUC Institute, International Campus, Zhejiang University, China, Haining, China and Tingju Zhu, Zhejiang University, ZJU-UIUC Institute, International Campus, Haining, China
The Particle Batch Smoother as a Downscaling Technique over Complex Mountainous Environments: A Synthetic Snow Data Assimilation Experiment (LA-UR 22-27508) (1101103)
Claire Bachand, University of California Berkeley, Computing, Data Science, and Society, Berkeley, United States; Los Alamos National Laboratory, Los Alamos, United States, Elias Charbel Massoud, University of California Berkeley, Environmental Science, Policy, & Management, Berkeley, CA, United States and Manuela Girotto, University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, United States
A Hybrid Machine Learning (ML) Model to Downscale GRACE Data for Improved Quantification of Water Storage Changes in the Indus Basin (1122375)
Arfan Arshad1,2, Ali Mirchi1 and Saleh Taghvaeian1, (1)Oklahoma State University, Biosystems and Agricultural Engineering, Stillwater, United States, (2)University of Agriculture Faisalabad, Department of Irrigation and Drainage, Faculty of Agricultural Engineering and Technology, Faisalabad, Pakistan
Assimilation of SMAP Soil Moisture for Advancing Integrated Groundwater-Surface Water Modeling (1130374)
Xiaoyong Xu, University of Toronto Mississauga, Department of Chemical and Physical Sciences, Mississauga, ON, Canada, Steven K. Frey, Aquanty, Waterloo, ON, Canada and Aruna Kumar Nayak, University of Toronto at Mississauga, Department of Chemical and Physical Sciences, Mississauga, ON, Canada
Comparison of Univariate Assimilation using GRACE / GRACE-FO Retrievals, GPS Surface Deformation, and Leaf Area Index Retrievals in Enhancing Land Surface Model Performance (1131352)
Alireza Moghaddasi, University of Maryland College Park, College Park, MD, United States and Barton A Forman, University of Maryland, College Park, MD, United States
Estimating Storage and Evaporation Dynamics for Large Global Lakes (1137514)
Deep Shah1, Gang Zhao2, Yao Li1, ChiHsiang Huang1, Shuai Zhang1 and Huilin Gao1, (1)Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, United States, (2)Carnegie Institution for Science Stanford, Department of Global Ecology, Stanford, United States
The Global Gravity-based Groundwater Product (G3P) (1141336)
Andreas Guntner1, Ehsan Sharifi2, Julian Haas2, Wouter Dorigo3, Adrian Jaeggi4, Claudia Ruz Vargas5 and the G3P team, (1)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Hydrology, Potsdam, Germany, (2)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, (3)Vienna University of Technology, Department for Geodesy and Geoinformation, Vienna, Austria, (4)University of Bern, Astronomical Institute, Bern, Switzerland, (5)International Groundwater Resources Assessment Centre, Delft, Netherlands
Tempered Particle Filter in a Real World Experiment. (1150198)
Concetta Di di Mauro1, Renaud Hostache2,3, Patrick Matgen4, Peter Jan van Leeuwen5, Nancy Nichols5 and Guenter Bloeschl6, (1)Luxembourg Institute of Science and Technology, Environmental Research and Innovation, Esch-sur-alzette, Luxembourg, (2)Luxembourg Institute of Science and Technology, Environmental Research and Innovation, Belvaux, Luxembourg, (3)Institut de recherche pour le développement (IRD), Marseille, France, (4)List Luxembourg Institute of Science and Technology, ERIN, Belvaux, Luxembourg, (5)University of Reading, Reading, United Kingdom, (6)Vienna University of Technology, Institute of Hydraulic Engineering and Water Resources Management, Vienna, Austria
Surface water detection from passive microwave data during flood events using deep learning (1156223)
Rohit Mukherjee1, Zhijie Zhang2, Matthew Purri3, Bin Fang4, Venkataraman (Venkat) Lakshmi5, Upmanu Lall6 and Beth Tellman1, (1)University of Arizona, School of Geography, Development and Environment, Tucson, AZ, United States, (2)University of Arizona, School of Geography, Development and Environment, Tucson, United States, (3)University of Arizona, School of Geography, Development, and Environment, Tucson, United States, (4)University of South Carolina, School of Earth Ocean and Environment, Columbia, United States, (5)University of Virginia, Engineering Systems and Environment, Charlottesville, United States, (6)Columbia University, Earth and Environmental Engineering, New York, United States
North American Land Data Assimilation (NLDAS) Data and Services at NASA GES DISC (1176748)
Ashley Heath1,2, Stephanie Grace Stettz1,2, Carlee F Loeser1,2 and David M Mocko3,4, (1)NASA/GSFC/GES DISC, Greenbelt, United States, (2)ADNET Systems Inc. Greenbelt, Greenbelt, United States, (3)SAIC, Greenbelt, United States, (4)NASA/GSFC Hydrological Sciences Laboratory, Greenbelt, United States
The Impact of Extreme Precipitation Events on The Terrestrial Water Storage in The Arabian Peninsula (1181529)
Hadi Karimi1, Mohamed Sultan1, Mustafa Kemal Emil1, Hossein Sahour2, Karem Abdelmohsen1 and Hassan Saleh2, (1)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, United States, (2)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, MI, United States
ParFlow-LIS: An Advanced Hydrologic Data Assimilation System for Studying Surface and Subsurface Hydrologic Processes (1194186)
Peyman Abbaszadeh1, Fadji Zaouna Maina2, Chen Yang3, Sujay V Kumar2, Dan Rosen4 and Reed M Maxwell5, (1)Princeton University, Department of Civil and Environmental Engineering, Princeton, NJ, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)Princeton University, Civil & Environmental Engineering, Princeton, United States, (4)National Center for Atmospheric Research (NCAR), Boulder, United States, (5)Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States
Leveraging advances in deep learning and hyper-resolution soil moisture data assimilation for S2S hydroclimate applications (1199728)
Noemi Vergopolan1, Nathaniel W. Chaney2, Sergey Malyshev3 and Elena Shevliakova3, (1)Princeton University, Princeton, NJ, United States, (2)Princeton University, Atmospheric and Oceanic Sciences, Princeton, United States, (3)NOAA/ Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States
