Which session to submit to? Hydrologic Uncertainty at AGU Fall Meeting 2017

Uncertainty is a multi-faceted topic. To help in choosing a session to submit to at the AGU Fall Meeting 2017, we've put together a shortlist of sessions related to characterizing uncertainty, living with uncertainty, and reducing uncertainty.

Comments and questions about specific sessions are welcome, including any we may have missed.

The early abstract submission deadline is 26th July 2017.

Characterizing uncertainty

Uncertainty analysis (UA) and Sensitivity Analysis (SA) methods

• H042: Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models
• H128. Understanding the Interface between Models and Data 

Parameter estimation and data assimilation

• H039. Data integration, inverse methods, and data valuation across a range ofscales in hydrogeophysics
• H066. Hydrologic Data Assimilation
• S010. Frontiers of uncertainty quantification in geoscientific inversion
• NG001: Advances in Data Assimilation, Predictability and Uncertainty Quantification

Ensemble methods

• H008: Advances in Hydrologic Prediction to Support Water and Energy Applications
• H146. Weather/Climate Ensembles and Statistical Downscaling for Hydrologic prediction systems: Methods, Process Understanding and Applications 

Stochastic modelling

• H116. Stochastic Modeling of the Hydrosphere and Biosphere
• NG011. Stochastic Modelling in Atmosphere, Ocean, and Climate Dynamics

Living with uncertainty

Risk management and Robust Decision Making

• H110: Science to Action: What is the Role of Climate Science and the Climate Scientist in Robust Decisions?
• H135. Water and Society: Implications of Climate and Hydrologic Forecasting in Risk Mitigation and Management
• PA029: Science to Action: Resilient Decision Making in the Midst of Uncertainty
• H139. Water and Society: Water Resources Management and Policy in a Changing World

Uncertainty in Decision Support Systems

• H133. Water and Society: Advances in Decision Support for Climate Adaptation in Environmental Systems
• H099. Quantifying uncertainty in assessment of freshwater, ecosystem and agricultural sustainability under climate change and urbanization (eLightning)

Reducing uncertainty

Data-based, machine learning modelling approaches

• H022: Applications of machine learning in hydrology
• H031. Computational Intelligence and Machine Learning Methods in Water and Environmental Management 
• H082. Machine Learning Applications in Earth Science and Remote Sensing 
• H089. Multiscale, multifidelety, and hybrid machine-learning methods for flow and transport in hydrologic systems
• H094. Prediction of Hydrologic Behavior from Sparse Information Using Statistical and Machine Learning Techniques 
• A028. Combining Physical Simulation and Machine Learning across Geophysical Sciences 

Advancing process-based modelling

• H067. Hydrologic Dynamics, Complexity and Predictability: Physical and Analytical Approaches for Improving System Understanding and Prediction
• H073. Advances in experimental techniques, validation of modelling tools and uncertainty in predictions from pore to field scale
• H088. Multi-hypothesis modeling of ecological and hydrological systems
• H106. Role of Process Representations on Prediction of Hydrologic States and Fluxes