H125
Statistical Characterization and Modeling of Precipitation Variability Across Scales
Session ID#: 76334
Session Description:
Quantifying and modeling precipitation variability are crucial tasks to improve water resources management, monitoring and prediction of extreme weather events, hydrologic modeling, and infrastructure design. This session welcomes contributions that use statistical methods to quantify and/or model precipitation variability across scales, in current and future climate scenarios. Particularly welcome are contributions where statistical analyses and modeling of precipitation are guided by a better understanding of the underlying physical processes, or are used to advance knowledge on such processes. Contributions focusing on one or more of the following topics are welcome:
- Assessment and modeling of precipitation variability–including extremes–across multiple spatiotemporal scales utilizing different products;
- Analysis and/or modeling of precipitation non-stationarity;
- Precipitation studies based on innovative statistical methods, including deep learning;
- Use of climate model outputs and reanalysis products to improve precipitation stochastic models.
Index Terms:
1817 Extreme events [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1854 Precipitation [HYDROLOGY]
1869 Stochastic hydrology [HYDROLOGY]
Primary Convener: Giuseppe Mascaro, Arizona State University, Tempe, AZ, United States
Conveners: Shih-Chieh Kao, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Elena Yegorova, Nuclear Regulatory Commission, Washington, MD, United States and Ardehsir Ebtehaj, Georgia Institute of Technology Main Campus, Atlanta, GA, United States
Primary Liaison: Giuseppe Mascaro, Arizona State University, Tempe, AZ, United States
Spatial characteristics of precipitation fields and their relation with atmospheric predictors: Application to a stochastic downscaling model (490245)
Joseph Bellier, CIRES, Boulder, United States; NOAA/ESRL Physical Sciences Division, Boulder, United States, Michael Scheuerer, CIRES, NOAA/ESRL Physical Sciences Division, Boulder, CO, United States and Thomas Hamill, NOAA Boulder, ESRL/PSD, Boulder, CO, United States
Investigating daily categorized rainfall at global scale (496491)
Francesco Viola1, Stefano Farris2, Giuseppe Mascaro3 and Roberto Deidda1, (1)University of Cagliari, Department of Civil, Environmental and Architectural Engineering, Cagliari, Italy, (2)University of Cagliari, Cagliari, Italy, (3)Arizona State University, Tempe, AZ, United States
Quantifying Future Changes in Extreme Precipitation Using a Max-Stable Process Model (556872)
Charlotte Anne Love, University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States, Brian E Skahill, Engineer Research and Development Center, Coastal and Hydrualic Laboratory, Vicksburg, MS, United States and Amir AghaKouchak, University of California, Irvine, Civil and Environmental Engineering, Irvine, CA, United States
Temporal Rainfall Disaggregation Technique for Future Rainfall Quantile Estimation (583331)
Jeonghoon Lee1, Sangdan Kim2 and Ungtae Kim1, (1)Cleveland State University, Civil and Environmental Engineering, Cleveland, OH, United States, (2)Pukyong National University, Busan, Korea, Republic of (South)
A Watershed-based Assessment of Precipitation Areal Reduction Factors in the Ohio River Basin (597292)
Scott DeNeale1, Shih-Chieh Kao2, Elena Yegorova3, Joseph Kanney3 and Meredith L Carr4, (1)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)Nuclear Regulatory Commission, Washington, MD, United States, (4)Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, Washington, DC, United States
Uncertainty analysis on sub-daily precipitation extremes from global datasets (619970)
Lanxin Hu1, Efthymios Nikolopoulos2, Francesco Marra3, Efrat Morin3 and Emmanouil N Anagnostou2, (1)University of Connecticut, Groton, CT, United States, (2)University of Connecticut, Civil and Environmental Engineering, Storrs, CT, United States, (3)Hebrew University of Jerusalem, Institute of Earth Sciences, Jerusalem, Israel
CoSMoS: A Platform for Complete Stochastic Modelling Solution (634154)
Simon Michael Papalexiou1, Filip Strnad2, Ioannis Markonis2, Chandra Rupa Rajulapati3, Salma Hobbi4 and Martin Hanel2, (1)University of Saskatchewan, Civil, Geological and Environmental Engineering, Saskatoon, SK, Canada, (2)Czech University of Life Sciences, Department of Water Resources and Environmental Modelling, Prague, Czech Republic, (3)Indian Institute of Science, Bangalore, India, (4)University of Saskatchewan, Civil, Geological & Environmental Engineering, Saskatoon, SK, Canada
