GC068
Hyper-resolution Land Surface Modeling: Innovations and Implementations for Global Environmental Challenges
Session ID#: 252027
Session Description:
This session explores hyper-resolution land surface modeling, essential for accurately monitoring and predicting Earth's terrestrial water, energy, and biogeochemical cycles. The coarse spatial granularity in traditional approaches limits their ability to address critical global environmental challenges, including implications for extreme events, water security, food production, and ecosystem resilience. Hyper-resolution modeling demands resolutions as fine as 1 km globally—and finer at regional scales. Meeting this challenge requires innovations across multiple fronts: physical process development, integration of technologies like machine learning and data assimilation, and creation of high-resolution datasets for model forcing and validation. We welcome contributions addressing both process-level evaluation of model physics at fine scales and development of novel methods, datasets, and processes that enable hyper-resolution modeling applications. Submissions may cover diverse terrestrial processes—from atmosphere-land interactions to surface hydrology and river dynamics, including land-ocean interfaces.
Co-Sponsor(s):
Index Terms:
1402 - Critical Zone [CRITICAL ZONE]
1622 Earth system modeling [GLOBAL CHANGE]
1631 Land/atmosphere interactions [GLOBAL CHANGE]
4303 Hydrological [NATURAL HAZARDS]
Primary Convener: Dr. Lingcheng Li, PhD, Pacific Northwest National Laboratory, Richland, WA, United States
Conveners: Donghui Xu, Pacific Northwest National Laboratory, Richland, WA, United States, Gautam Bisht, Pacific Northwest National Laboratory, Richland, United States and Ming Pan, Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
Student/Early Career Convener: Yifan Cheng, University at Buffalo, Department of Earth Sciences, Buffalo, United States
Diurnal T/RH hysteresis and VPD dynamics revealed by hourly observations and 2km WRF simulations over montane forests (Invited) (1860120)
Min-Hui Lo1, Mr. Ching-Hung Shih1, Rong-Yu Gu2 and Yi-Shin Jang1, (1)National Taiwan University, Department of Atmospheric Sciences, Taipei, Taiwan, (2)Columbia University, Department of Earth and Environmental Engineering, New York, United States
Multi-scale Earth System Modeling Framework Reveals the Importance of Topographic Connectivity in Compound Flooding Dynamics at Actionable Scales (1879990)
Donghui Xu1, Gautam Bisht2, Dongyu Feng2, Zeli Tan1, Dalei Hao2, Darren Engwirda3, James J Benedict4, Vinh Tran5, Mukesh Kumar6, Valeriy Yu Ivanov7 and L. Ruby Leung2, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Laboratory, Richland, United States, (3)Los Alamos National Laboratory, New York City, United States, (4)Los Alamos National Laboratory, Los Alamos, NM, United States, (5)University of Michigan, Ann Arbor, United States, (6)University of Alabama, Civil, Construction and Environmental Engineering, Tuscaloosa, AL, United States, (7)University of Michigan, Civil and Environmental Engineering, Ann Arbor, United States
Multi-Satellite-Based Image Fusion Methods Using Deep Learning (1906725)
Dohee Han, Jeonbuk National University, Jeonju, Republic of Korea, Korea, South, SOUTH KOREA, Suhwan Kim, Jeonbuk National University, Jeonju, Korea, South, SOUTH KOREA, Yejin Lee, Jeonbuk National University, Jeonju, Jeonbuk State, SOUTH KOREA and Jong-Min Yeom, Jeonbuk National University, Jeonsu-si, South Korea
Quantifying the Benefit of High-Resolution Precipitation for Field-Scale Soil Moisture Modeling (1941894)
Vishnu U Krishnan, Indian Institute of Technology Bombay, Center for Climate Studies, Mumbai, India, Noemi Vergopolan, Rice University, Earth, Environmental and Planetary Sciences, Houston, United States, Bhupendra Singh, Indian Institute of Tropical Meteorology, Pune, India, Karthikeyan Lanka, Indian Institute of Technology Bombay, Center of Studies in Resource Engineering, Mumbai, India and J. Indu, Indian Institute of Technology Bombay, Civil Engineering, Mumbai, India
Developing Workflow for High, Mixed and Irregular Resolution E3SM Land Model Coupled with 3D Watershed Flow and Reactive-Transport Models (1948616)
Fengming Yuan1, Ethan Coon1, Scott L Painter2, Benjamin N Sulman3, Daniel M Ricciuto2, Teri O'Meara3 and Peter E Thornton2, (1)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States, (3)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, United States
Improving Modeling of Surface Fluxes: Leveraging Anisotropy-Generalized MOST in CTSM (1954319)
Tyler Waterman1, Ivana Stiperski2, Megan Devlan Fowler3 and Marc Calaf1, (1)University of Utah, Salt Lake City, UT, United States, (2)University of Innsbruck, Innsbruck, Austria, (3)University of California Irvine, Irvine, United States
Enhancing Climate Monitoring: High-Resolution Albedo Estimation with Sentinel-2 using a Machine Learning Algorithm (1972502)
Varun Tiwari, Clark University, Geospatial for Analytics, Worcester, United States, Hamed Alemohammad, PhD, Massachusetts Institute of Technology, Cambridge, United States and Christopher A Williams, Clark University, Worcester, United States
