GC064
High-Resolution Regional Earth System Modeling: Hydroclimate Extremes, Impacts, and Technology-Driven Solutions
Session ID#: 252515
Session Description:
High-resolution regional Earth system models are essential for addressing global change, offering policy-relevant insights for climate adaptation and mitigation. These models complement global efforts by focusing on region-specific challenges. However, key hurdles remain in capturing complex processes across scales and translating results into actionable tools for stakeholders. This session explores advances in modeling hydroclimate variability and extremes (e.g., heatwaves, droughts, floods, wildfires, cold snaps) and their impacts on energy, agriculture, ecosystems, water, and infrastructure. We highlight inclusive approaches using IoT, remote sensing, data assimilation, and AI/ML to enhance early warning systems and model accuracy. These tools harness big data from observations, crowd-sourcing, and ensemble simulations to improve predictions and participatory modeling. We invite contributions on CMIP6 assessments, Sub-seasonal to Decadal Prediction Large Ensembles, and high-resolution climate experiments. We also welcome work on regional model development, long-term observations, machine learning, and innovative methods for engaging stakeholders and informing local-to-regional climate planning.
Co-Sponsor(s):
Index Terms:
1843 Land/atmosphere interactions [HYDROLOGY]
3355 Regional modeling [ATMOSPHERIC PROCESSES]
4313 Extreme events [NATURAL HAZARDS]
4343 Preparedness and planning [NATURAL HAZARDS]
Primary Convener: Zong-Liang Yang, University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States
Conveners: Min-Hui Lo, National Taiwan University, Department of Atmospheric Sciences, Taipei, Taiwan, Vasco Manuel Mantas, University of Coimbra, Department of Earth Sciences, Coimbra, Portugal and Li Dan, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Student/Early Career Convener: Sanjiv Kumar, Auburn University, College of Forestry, Wildlife and Environment, Auburn, United States
Quantifying vegetation health exposure risk and spatial inequality along with their impact on vegetation (1849139)
Peng Zhou1, Jieming Chou1, Mengting Sun2, Weixing Zhao1, Yuanmeng Li1, Hongze Pei1, Haofeng Jin1 and Muhammad Bilal3, (1)Beijing Normal University, Beijing, China, (2)Xinjiang University, Urumqi, China, (3)King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
Mechanistic Role of Regional Air-Sea Coupling in Modulating East Asian Summer Precipitation and Temperature: Insights from a New Regional Earth System Model (RIEMS v4.0) (1871066)
Kai Li1, Li Dan2, Liwei Zou3, Jianping Tang4, Hui Zheng3, Zhongfeng Xu5, Prof. Jing Peng, PhD6, Fuqiang Yang1, Wei Pan7 and Zong-Liang Yang8, (1)Institute of Atmospheric Physics, Chinese Academy of Sciences, CAS Key Laboratory of Regional Climate and Environment for Temperate East Asia, Beijing, China, (2)Inst. of Atmospheric Physics, Beijing, China, (3)Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, (4)Nanjing University, School of Atmospheric Sciences, Nanjing, China, (5)Key Lab of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, (6)Institute of Atmospheric Physics, RCE-TEA, Beijing, China, (7)Organization Not Listed, Washington, DC, United States, (8)University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States
Toward High-Resolution Modeling of Human-Altered Terrestrial Water Cycles across Global River Networks (Invited) (1883713)
Peirong Lin1, Ziyun Yin2, Haomei Lin2, Fenghe Zhang2 and Jinxuan Guo2, (1)Peking University, Beijing, China, (2)Peking University, Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Beijing, China
Variation characteristics and driving factors of nitrogen concentration at key monitoring sections of the middle route of the South-to-North Water Diversion Project from 2018 to 2024 (1912006)
Zhen Li1, Nan Yang1, Ruijue Liu1, Nuo Ma2, Ziyan Zheng1, Zuoqiang Han3, Yajie Qi4, Qiudan Dai1, Yi Fang1, Baozhu Ge1, Li Dan1 and Tianbao Zhao1, (1)Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, (2)Xinjiang Uygur Autonomous Region Meteorological Service, Beijing, China, (3)Yellow River Conservancy Commission of the Ministry of Water Resources, Beijing, China, (4)Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
Extreme Precipitation Intensity Scaling with Surface Conditions Under Climate Change Scenarios (1927524)
Hongchen Qin1, Faisal Mohammad Alvee2, Xiaoming Sun3, Huancui Hu4, Ruby Leung5 and Guiling Wang2, (1)University of Connecticut, Department of Civil and Environmental Engineering, Groton, CT, United States, (2)University of Connecticut, School of Civil and Environmental Engineering, Storrs, United States, (3)Los Alamos National Laboratory, Los Alamos, United States, (4)Pacific Northwest National Laboratory (DOE), Richland, United States, (5)Pacific Northwest National Laboratory, Richland, United States
A Pixel‑Scale Monte Carlo Framework for Attributing Hydrological Changes to Land‑Use Change (1929668)
Jibin Joseph, Purdue University, Lyles School of Civil and Construction Engineering, West Lafayette, IN, United States, Sanjiv Kumar, COLA, Boulder, United States and Venkatesh Merwade, Purdue University, Lyles School of Civil and Construction Engineering, West Lafayette, United States
Evaluation of Convective-Permitting S2S Forecast Skill of Monsoon Precipitation Extremes in the Southwest U.S. (1931243)
M. del R. Lourdes Mendoza Fierro, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States, Hsin-I Chang, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, United States and Christopher L. Castro, National Center for Atmospheric Research, Research Applications Laboratory, Boulder, United States
Ensemble Projections of Mississippi River Discharge Using a High-Resolution Land Surface Model (1932213)
Xiaochen Zhao1,2, Z. George Xue3,4, Charles Philip Johnson5, Paul Miller5, Annalisa Molini6 and Mijael Vargas Godoy6, (1)Louisiana State University, Center for Computation and Technology, Baton Rouge, United States, (2)Louisiana State University, Department of Oceanography & Coastal Sciences, Baton Rouge, United States, (3)Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, LA, United States, (4)Louisiana State University, Center for Computation and Technology, Baton Rouge, LA, United States, (5)Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, United States, (6)Tulane University, Department of River-Coastal Science & Engineering, New Orleans, United States
The Impact of Westerly Wind Anomalies on Vegetation Growth Across Eurasia (1933578)
Liufeng Liao1, Li Dan2, Mei Huang3 and Ying Duan1, (1)Guizhou Ecological Meteorology and Agrometeorology Center, GuiYang, China, (2)Inst. of Atmospheric Physics, Beijing, China, (3)IGSNRR Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China
A Machine Learning-based Prediction-to-Map Framework for Rapid and Accurate Spatial Flood Prediction (1935781)
Daoyang Bao1, Z. George Xue2, Matthew R Hiatt3, Kehui Xu2, Dr. Courtney Kay Harris, Ph.D.4 and Jill C. Trepanier5, (1)Louisiana State University, Oceanography and Coastal Sciences, Baton Rouge, LA, United States, (2)Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, LA, United States, (3)LSU, Oceanography & Coastal Sciences, Baton Rouge, United States, (4)Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, United States, (5)Louisiana State University, Geography & Anthropology, Baton Rouge, LA, United States
Evaluating Bias-Corrected CMIP6 Precipitation Products over Southern Africa Using In-Situ Observations (1949166)
Gizaw Mengistu Tsidu1, Amarech Alebie Addisuu2, Sydney Samuel3, Simon Ageet4, Simbarashe Govere3 and Andreas H. Fink5, (1)Botswana International University of Science and Technology, Gaborone, Botswana, (2)Botswana International University of Science and Technology, Department of Earth and Environmental Sciences, Palapye, Botswana, (3)Botswana International University of Science and Technology, Department of Sustainable Natural Resources, Palapye, Botswana, (4)Karlsruhe Institute of Technology, Institute for Meteorology and Climate, Karlsruhe, Germany, (5)Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Karlsruhe, Germany
ASSESSING FUTURE CONSECUTIVE DRY AND WET DAYS IN CENTRAL INDIA : A CMIP6 DOWNSCALING APPROACH (1957997)
Amey Pathak, Indian Institute of Technology Kharagpur, Kharagpur, India, Bikash Ranjan Behera, Indian Institute of Technology Kharagpur, Agricultural and Food Engineering, Kharagpur, India and Kulkarni Shashikanth, Osmania University, University College of Engineering, Civil Engineering, Hyderabad, India
Evaluating a Regionally Refined Climate Model for Simulating Precipitation Extremes over the United States (1960224)
Ramalingam Saravanan, Texas A&M University College Station, College Station, TX, United States, Chi-Jui Chen, Texas A&M University, College Station, United States, Xue LIU, Texas A&M University, Department of Oceanography, College Station, TX, United States and Ping Chang, Texas A&M University, Department of Oceanography, College Station, United States
Growing Floods but Diminished Annual Runoff over the 21st Century in California’s Sierra Nevada (1961649)
Lu Su1, Daniel R Cayan1, Ming Pan2 and David W Pierce1, (1)University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States, (2)University of California San Diego, Scripps Institution of Oceanography, Center for Western Weather and Water Extremes, La Jolla, United States
Integrating Multi‑Scale Empirical Mode Decomposition to Elucidate Spatio‑Temporal Changes of Extreme Precipitation in the Colorado River Basin, Texas (1969076)
Chieh-Chen Lee, University of Texas at Austin, Austin, TX, United States, Zong-Liang Yang, University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States, Chenxi Hu, The University of Texas at Austin, Department of Earth and Planetary Sciences, Austin, United States and Zhaohua Wu, Florida State University, Tallahassee, United States
Diagnosing the ability of k-scale Earth system models to capture sub-hourly urban rainfall extrema associated with landfalling tropical cyclones (1971986)
Colin M. Zarzycki, Pennsylvania State University Main Campus, University Park, PA, United States, Kyle Dailey, Pennsylvania State University Main Campus, University Park, United States and Paul Ullrich, Lawrence Livermore National Laboratory, Livermore, United States
Assessing future U.S. convective extremes under global warming and stratospheric aerosol injection using a convection-permitting WRF model (1983876)
Lantao Sun1, James W Hurrell2, Kristen Rasmussen3, Bali Summers4, Erin Sherman3 and Ben Kravitz5, (1)Colorado State University, Department of Atmospheric Science, Fort Collins, CO, US, (2)Colorado State University, Department of Atmospheric Science, Fort Collins, CO, United States, (3)Colorado State University, Department of Atmospheric Science, Fort Collins, United States, (4)Colorado State University, Atmospheric Science, Fort Collins, United States, (5)Indiana University, Bloomington, United States
Advancing Drought Prediction in Midwest US using Remote Sensing, Machine Learning Models and Explainable Artificial Intelligence (XAI) (1987203)
Mirza Md Tasnim Mukarram, University of Iowa, School of Earth, Environment and Sustainability, Iowa City, United States, Marc A Linderman Sr., University of Iowa, Iowa City, United States and Quazi Umme Rukiya, University of Iowa, School of Earth, Environment and Sustainability, Iowa City, IA, United States
Towards NoahMP-AI: Enhancing Land Surface Model Prediction with Deep Learning (2001626)
Mahmoud Mbarak, University of Texas at Austin, Austin, TX, United States, Naveen Sudharsan, The University of Texas at Austin, Austin, United States, Manmeet Singh, University of Texas, Austin, United States and Zong-Liang Yang, University of Texas at Austin, Austin, United States
