H127
Stochastic Modeling of the Hydrosphere and Biosphere
Session ID#: 81288
Session Description:
Stochastic models can provide parsimonious and mathematically tractable numerical frameworks for characterizing the response of the hydrosphere and biosphere to environmental changes and anthropogenic disturbances. This session welcomes contributions that advance understanding and capability of stochastic modeling frameworks in hydrology, ecology, and biogeochemistry. Submissions may address external sources of stochasticity in drivers such as climatic forcing, as well as internal system variability and uncertainties. The latter can be related to incomplete process understanding, parameter upscaling (e.g., for spatially heterogeneous soils and land-cover), and temporally dynamic properties (e.g., plant trait plasticity and adaptation). We seek studies that demonstrate how rigorous, probabilistic representations can help identify risks, make predictions that are robust to scenario unpredictability, and quantify model sensitivity to parameter uncertainty or non-stationarity. We welcome submissions that facilitate communication of probabilistic outcomes to resource managers, and the integration of the hydrosphere and biosphere into stochastic Earth system modeling frameworks.
Index Terms:
1813 Eco-hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1869 Stochastic hydrology [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
Primary Convener: G. H. Crystal Ng, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States
Conveners: Xue Feng, University of Minnesota Twin Cities, Civil, Environmental, Geo-Engineering, Minneapolis, MN, United States, Christoforos Pappas, ETH Zurich, Zurich, Switzerland and Sally E Thompson, University of California, Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, United States
Primary Liaison: G. H. Crystal Ng, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States
Using pressure change to stochastically disaggregate non-stationary hourly precipitation series from monthly temperature projections in the northeast of the US (487013)
Ziwen Yu1, Franco A Montalto2, Stefan Jacobson3, Upmanu Lall4, Daniel Bader5 and Radley M Horton4, (1)Philadelphia, PA, United States, (2)Drexel University, Philadelphia, PA, United States, (3)Drexel University, CAEE, Philadelphia, PA, United States, (4)Columbia University, New York, NY, United States, (5)Columbia University, CCSR, New York, NY, United States
Succession to Halophytic Species Reduces Coastal Wetlands Potential for Sealevel-rise Mitigation (494818)
Annalisa Molini, Masdar Institute, Khalifa University of Science and Technology, Civil Infrastructure and Environmental Engineering, Abu Dhabi, United Arab Emirates, Saverio Perri, Masdar Institute at Khalifa University of Science and Technology, Civil Infrastructure and Environmental Engineering, Abu Dhabi, United Arab Emirates and Amilcare M Porporato, Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States
Using a Stochastically Driven Model of Plant Hydraulics to Design More Resilient Rehabilitated Landscapes in Arid Ecosystems (500339)
Jean V Wilkening, University of California Berkeley, Civil and Environmental Engineering, Berkeley, CA, United States, Sebastian C Lamoureux, Okane Consultants Pty Ltd, Perth, Australia, Erik J Veneklaas, University of Western Australia, School of Biological Sciences, Perth, Australia; University of Western Australia, School of Agriculture and Environment, Perth, Australia, Xue Feng, University of Minnesota Twin Cities, Civil, Environmental, and Geo-Engineering, Minneapolis, MN, United States; University of Minnesota Twin Cities, St. Anthony Fall's Laboratory, Minneapolis, MN, United States and Sally E Thompson, University of Western Australia, Department of Civil, Environmental and Mining Engineering, Perth, Australia
Stochastic simulation of climate and climate change near the land surface (509200)
Michael Bliss Singer, Cardiff University, School of Earth & Ocean Sciences, Cardiff, United Kingdom, Owen Jones, Cardiff University, School of Mathematics, Cardiff, United Kingdom, Katerina Michaelides, University of Bristol, School of Geographical Sciences, Bristol, BS8, United Kingdom, Roberto Quaglia, University of Bristol, Department of Geographical Sciences, Bristol, United Kingdom and Rafael Rosolem, University of Bristol, Civil Engineering, Bristol, United Kingdom
Stochastic model of water table depth in snow-dominated and hydrologically connected ecosystems (512036)
Rachel Sophia Lonchar1, Tyler D Roman2, Stephen D Sebestyen3, G. H. Crystal Ng4, Shaoqing Liu4, Randy Kolka2 and Xue Feng1, (1)University of Minnesota Twin Cities, Civil, Environmental, and Geo-Engineering, Minneapolis, MN, United States, (2)USDA Forest Service Northern Research Station, Grand Rapids, MN, United States, (3)USDA Forest Service, Northern Research Station, Vallejo, CA, United States, (4)University of Minnesota, Twin Cities, Department of Earth Science, Minneapolis, MN, United States
A Tree-based Direct Sampling Method for Stochastic Modeling in Surface and Subsurface Hydrology (514000)
Chen Zuo1,2, David Zhen Yin1, Zhibin Pan2, Emma Mackie3 and Jef Caers1, (1)Stanford University, Department of Geological Sciences, Stanford, CA, United States, (2)Xi'an Jiaotong University, Faculty of Electronic and Information Engineering, Xi'an, China, (3)Stanford University, Department of Geophysics, Stanford, CA, United States
Modeling the Impact of Spatiotemporally Variable Vegetation on Groundwater Recharge (517589)
Harsh Anurag1, G. H. Crystal Ng2, Kathy Tokos3 and Robert Tipping1, (1)University of Minnesota Twin Cities, Minneapolis, MN, United States, (2)University of Minnesota, Twin Cities, Department of Earth Science, Minneapolis, MN, United States, (3)University of Minnesota, Minneapolis, MN, United States
Stochastic simulation and inference of complex ecohydrologic processes with uncertainty quantification in an Amazonian catchment (534753)
Valeriy Yu Ivanov1, M. Chase Dwelle2, Khachik Sargsyan3 and Jongho Kim1, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)University of Michigan, Ann Arbor, MI, United States, (3)Sandia National Laboratories, Albuquerque, NM, United States
Quantifying uncertainty in E3SM land surface model predictions using surrogate modeling approaches (Invited) (621652)
Daniel M Ricciuto1, Dan Lu2, Khachik Sargsyan3 and Anthony W King1, (1)Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)Sandia National Laboratories, Albuquerque, NM, United States
