H032
Advancing Measurements and Modeling of Invisible Components of Water Budget: Challenges, Opportunities, and Applications
Session ID#: 249966
Session Description:
The “invisible components” of the water budget, such as evapotranspiration (ET), infiltration, soil moisture recharge, sublimation, percolation, and groundwater flow, can provide a better understanding of ecosystem functioning. However, they are often challenging to quantify. For example, ET, which comprises biotic canopy transpiration (T) and abiotic direct evaporation (E), is typically quantified in a lumped form. Land surface models (LSMs) also face challenges in accurately partitioning water budget components due to uncertainties in their structure and parameterization. We welcome contributions that advance the theories, measurements, modeling, and application of water budget partitioning (e.g., ET, groundwater recharge, soil moisture) using different methods, including remote sensing, machine learning, and data assimilation in LSMs and hydrologic models. We also encourage submissions that demonstrate the utility of partitioned products in answering interesting science questions, such as ecosystem resilience, drought responses, or climate feedback.
Co-Sponsor(s):
Index Terms:
1818 Evapotranspiration [HYDROLOGY]
1830 Groundwater/surface water interaction [HYDROLOGY]
1838 Infiltration [HYDROLOGY]
1866 Soil moisture [HYDROLOGY]
Primary Convener: Nishan Bhattarai, University of Oklahoma Norman Campus, Department of Geography and Environmental Sustainability, Norman, United States
Conveners: Pradeep Wagle, USDA-ARS, Grazinglands Research Laboratory, El Reno, United States and Pushpendra Raghav, University of Alabama, Tuscaloosa, AL, United States
Student/Early Career Convener: Afshin Shayeghi, University of Oklahoma, Department of Geography and Environmental Sustainability, Norman, United States
Coupling Eddy Covariance and CubeSat Observations with Machine Learning Models to Improve Satellite-Based Evapotranspiration Estimates (1876241)
Muhammad Umar Akbar1, Ali Mirchi1, Pradeep Wagle2, Abubakarr Mansaray3, Muhammad Farooq4 and Aimen Iqbal5, (1)Oklahoma State University, Department of Biosystems and Agricultural Engineering, Stillwater, United States, (2)USDA-ARS, Grazinglands Research Laboratory, El Reno, United States, (3)North Carolina A & T State University, Natural Resources and Environmental Design, Greensboro, United States, (4)Government College University Faisalabad, Department of Information and Technology, Faisalabad, Pakistan, (5)Government College University, Faisalabad, Department of Computer Science, Faisalabad, Pakistan
What Drives Day-to-Day Changes in Evapotranspiration? Partitioning the relative importance of radiative, atmospheric, and surface conditions (1880548)
Bowen Wang, Massachusetts Institute of Technology, Cambridge, MA, United States, Kaighin A. McColl, Harvard University, Department of Earth and Planetary Sciences and School of Engineering and Applied Sciences, Cambridge, United States, Dan Gianotti, Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, United States and Dara Entekhabi, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, United States
Improving Drought-Time ET Estimates Using Plant Hydraulics Coupled with MODFLOW Groundwater Modeling (1904716)
Dr. Lucas Emilio Hoeltgebaum1, Yanlan Liu2, Howard W Reeves3, Dr. Lingcheng Li, PhD4 and Meng Zhao1, (1)University of Idaho, Moscow, ID, United States, (2)The Ohio State University, School of Earth Sciences, Columbus, United States, (3)U.S. Geological Survey, Upper Midwest Water Science Center, Lansing, United States, (4)Pacific Northwest National Laboratory, Richland, WA, United States
An analytical thermodynamic framework to explain daily variations and decadal trends in the diurnal air temperature range (1908985)
Sarosh Alam Ghausi1, Kaighin A. McColl2, Erwin Zehe3 and Axel Kleidon1, (1)Max Planck Institute for Biogeochemistry, Jena, Germany, (2)Harvard University, Department of Earth and Planetary Sciences and School of Engineering and Applied Sciences, Cambridge, United States, (3)SMNK Natural History Museum Karlsruhe, Karlsruhe, Germany
Soil Moisture as a Superior Calibration Target for Groundwater Recharge Estimation: Evidence from the Chongwe River Catchment, Zambia (1914329)
Majid Taie Semiromi1, Brindha Karthikeyan2 and Christoph Merz1,3, (1)Freie Universität Berlin, Hydrogeology Group, Berlin, Germany, (2)IHE Delft Institute for Water Education, Department of Water Resources and Ecosystems, Delft, Netherlands, (3)Leibniz Centre for Agricultural Landscape Research (ZALF), Working Group “Lowland Hydrology and Water Management”, Müncheberg, Germany
Monitoring Field-Scale Crop Water Use and Productivity in Oklahoma Using an Optimized Evapotranspiration (ET) Modeling Framework (1915806)
Reeya Shrestha, University of Oklahoma, Norman, OK, UNITED STATES, Nishan Bhattarai, University of Oklahoma, Department of Geography and Environmental Sustainability, Norman, United States, Pradeep Wagle, USDA-ARS, Grazinglands Research Laboratory, El Reno, United States, Sumit Sharma, Oklahoma State University, Plant & Soil Sciences, Stillwater, United States and Renee A McPherson, University of Oklahoma, South Central Climate Adaption Science Center, Norman, United States
Developing a Historical Daily Reservoir Evaporation Dataset and Assessing Long-term Trends for Water Management in Texas (1933366)
Yinuo Zhu1, Anshul Yadav2, Deep Shah3, Dr. Bingjie Zhao4, Nelun Fernando5, John Zhu5 and Huilin Gao3, (1)Texas A&M University College Station, Zachry Department of Civil Engineering, College Station, United States, (2)Indian Institute of Technology Kanpur, Kanpur, India, (3)Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, United States, (4)Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, TX, United States, (5)Texas Water Development Board, Water Availability, Austin, TX, United States
HyGBR-Baseflow: A Hybrid Machine Learning-Based Approach for Baseflow Separation Across Major Indian River Basins (1961–2021) (1945736)
Rajesh Singh, Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Ahmedabad, 382, India, Hiren Solanki, Indian Institute of Technology Gandhinagar, Earth Sciences, Ahmedabad, India and Vimal Mishra, Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Gandhinagar, India
Estimation of Water Losses and Recharge in Agricultural Water Balance Modeling using in-situ Field-Installed Sensors (1954858)
Tejinder Singh, Utah State University, Plants, Soils & Climate, Logan, UT, United States, Matt Yost, Utah State University, Plants, Soils & Climate, Logan, United States, Burdette Barker, Utah State University, Department of Civil and Environmental Engineering, Logan, United States, Scott B Jones, Utah State University, Logan, United States, Gaylon Campbell, METER, Pullman, United States and Leo Daniel Rivera, Meter Group, Inc., Research and Development, Pullman, United States
Consumptive use and water budget of a commercial sod (grass) field in a rapidly urbanizing high desert landscape: in-situ measurements to scaled-up models (1955347)
Meetpal Kukal, University of Idaho, Soil and Water Systems, Boise, United States, Kendra Kaiser, Idaho Water Resources Research Institute, Soil and Water Systems, Boise, United States; University of Idaho, Boise, United States, Clarence Robison, University of Idaho, Kimberly, United States and Nick McKellip, McKellip Sod Farm, Nampa, United States
From Sensors to Simulations: Understanding the Field Water Balance in Irrigated Agriculture Using In-Situ Data and HYDRUS 1D Modeling (1958538)
Vengus Panhwar1, Thijs Kelleners2, Ginger B Paige1 and Joseph Cook3, (1)University of Wyoming, Ecosystem Science and Management, Laramie, United States, (2)University of Wyoming, Department of Ecosystem Science & Management, Laramie, United States, (3)University of Wyoming, Ecosystem Science & Management, Laramie, United States
Reconstructing Monthly Groundwater Depth Dataset in India (2002–2021) using Cluster-Based Machine Learning and Temporal Disaggregation Approach (1961131)
Tapis Samaiya, Indian Institute of Technology BHU (Varanasi), Electrical Engineering, Varanasi, India, Himanshu Singh, Indian Institute of Technology Gandhinagar, Civil Engineering, Gandhinagar, India, M Niranjan Naik, Indian Institute of Science Education and Research Bhopal, Earth and Environmental Sciences, Bhopal, India, Rajesh Singh, Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Ahmedabad, 382, India, Hiren Solanki, Indian Institute of Technology Gandhinagar, Earth Sciences, Ahmedabad, India and Vimal Mishra, Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Gandhinagar, India
Evapotranspiration Dynamics in Juniper-Encroached Rangelands: A Comparison of Estimated and Measured Values (1980409)
Ross Shipley1, Carlos G Ochoa2, María Iglesias-Thome3, Daniel Gómez3 and Derek Godwin4, (1)Oregon State University, Corvallis, OR, United States, (2)Oregon State University, Department of Animal and Rangeland Sciences, Corvallis, OR, United States, (3)Oregon State University, Corvallis, United States, (4)Oregon State University, Biological and Ecological Engineering, Corvallis, OR, United States
Quantifying temporal trends in net inflow: A key step in assessing the future of heavily pumped aquifers (1991186)
Gaisheng Liu1, Geoffrey Bohling2, Blake B Wilson2, Donald O Whittemore2 and James J Butler Jr3, (1)University of Kansas, Lawrence, KS, United States, (2)University of Kansas, Kansas Geological Survey, Lawrence, KS, United States, (3)University of Kansas, Kansas Geological Survey, Lawrence, United States
Dynamic, High-Resolution Retrievals of Transpiration, Evaporation, and Carbon Exchange (1999281)
Nishan Bhattarai, University of Oklahoma Norman Campus, Department of Geography and Environmental Sustainability, Norman, United States and Xiangming Xiao, School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, United States
