Dear colleagues,
We would like to invite you to submit abstracts to our session in
this year's AGU Fall Meeting (11-15 Dec, 2017) in New Orleans,
Louisiana, titled "Stochastic Modeling of the Hydrosphere and
Biosphere." So far invited speakers for this session include Amilcare
Porporato(Duke university). You can submit your work here.
https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session27087
Session ID: 27087
Session Title: H116. Stochastic Modeling of the Hydrosphere and
Biosphere
Section/Focus Group: Hydrology
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.
Abstract Submission Deadline: 26 July (late submission: 2 August)
Looking forward to a great AGU meeting!
Shaoqing Liu
on behalf of the session organizers:
Crystal C Ng, University of Minnesota Twin Cities, Minneapolis, MN,
United State
Xue Feng, University of Minnesota Twin Cities, Minneapolis, MN, United
Stat
David Dralle, University of California Berkeley, Berkeley, CA, United
States
Dear colleagues,
We would like to invite you to submit abstracts to our session in
this year's AGU Fall Meeting (11-15 Dec, 2017) in New Orleans,
Louisiana, titled "Stochastic Modeling of the Hydrosphere and
Biosphere." So far invited speakers for this session include Amilcare
Porporato(Duke university). You can submit your work here.
https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session27087
Session ID: 27087
Session Title: H116. Stochastic Modeling of the Hydrosphere and
Biosphere
Section/Focus Group: Hydrology
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.
Abstract Submission Deadline: 26 July (late submission: 2 August)
Looking forward to a great AGU meeting!
Shaoqing Liu
on behalf of the session organizers:
Crystal C Ng, University of Minnesota Twin Cities, Minneapolis, MN,
United State
Xue Feng, University of Minnesota Twin Cities, Minneapolis, MN, United
Stat
David Dralle, University of California Berkeley, Berkeley, CA, United
States
