H21J:
Stochastic Modeling of the Hydrosphere and Biosphere I Posters

Submit an Abstract to this Session



Session ID#: 27087

Session Description:
Stochastic models are ideal for characterizing the response of hydrological, ecological, and biogeochemical systems to natural and human-caused random disturbances. The complexity of stochastic models ranges from parsimonious and analytical to high-dimensional and numerical; they can be implemented through closed-form distributions as well as Monte Carlo approaches. This session welcomes proposals that advance understanding and capability of stochastic modeling frameworks in hydrology, ecology, and biogeochemistry. Submissions may address external sources of stochasticity, such as random climatic forcing, as well as internal system variability, such as in hydraulic conductivity, vegetation parameters, and biogeochemical rates. We seek studies that demonstrate how probabilistic representations can help identify threshold-based 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.
Primary Convener:  Gene-Hua Crystal Ng, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States
Conveners:  Xue Feng, University of Minnesota Twin Cities, St. Anthony Falls Laboratory, Minneapolis, United States; University of Minnesota Twin Cities, Department of Civil, Environmental, and Geo-Engineering, Minneapolis, United States, Shaoqing Liu, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States and David Dralle, USDA Forest Service, Pacific Southwest Research Station, Davis, United States
Chairs:  Gene-Hua Crystal Ng, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States, Xue Feng, University of Minnesota Twin Cities, Department of Civil, Environmental, and Geo-Engineering, Minneapolis, United States; University of Minnesota Twin Cities, St. Anthony Falls Laboratory, Minneapolis, United States, David Dralle, USDA Forest Service, Pacific Southwest Research Station, Davis, United States and Shaoqing Liu, Purdue University, West Lafayette, IN, United States
OSPA Liaison:  Gene-Hua Crystal Ng, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States

Cross-Listed:
  • B - Biogeosciences
  • GC - Global Environmental Change
  • NG - Nonlinear Geophysics
Index Terms:

0466 Modeling [BIOGEOSCIENCES]
1813 Eco-hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1869 Stochastic hydrology [HYDROLOGY]

Abstracts Submitted to this Session:

Macroscopic behavior and fluctuation-dissipation response of stochastic ecohydrological systems (Invited) (275973)
Amilcare M Porporato, Duke University, Civil and Environmental Engineering, Durham, NC, United States
Towards a Stochastic Predictive Understanding of Ecosystem Functioning and Resilience to Environmental Changes (Invited) (211833)
Christoforos Pappas, University of Montreal, Montreal, QC, Canada
What Do They Have in Common? Physical Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes at Ungauged Locations in the Contiguous United States (238811)
Andrea Betterle, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, Mario Schirmer, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Department of Water Resources and Drinking Water, Duebendorf, Switzerland and Gianluca Botter, University of Padua, Department of Civil, Environmental and Architectural Engineering, Padua, Italy
The Generation of a Stochastic Flood Event Catalogue for Continental USA (223354)
Niall Quinn1,2, Oliver Wing1,3, Andrew Smith2,4, Christopher Charles Sampson1,2, Jeffrey C Neal1,5 and Paul D Bates2,4, (1)Fathom, Bristol, United Kingdom, (2)University of Bristol, School of Geographical Sciences, Bristol, United Kingdom, (3)University of Bristol, Bristol, BS8, United Kingdom, (4)Fathom Ltd, Bristol, United Kingdom, (5)University of Bristol, School of Geographical Sciences, Bristol, BS8, United Kingdom
Multiple Point Statistics algorithm based on direct sampling and multi-resolution images (235921)
Straubhaar Julien, University of Neuchâtel, Neuchâtel, Switzerland, Philippe Renard, University of Neuchâtel, Centre for Hydrogeology and Geothermics, Neuchâtel, Switzerland and Tatiana Chugunova, Total SA, Pau, France

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