H093 – Machine Learning in Hydrologic Modeling

https://agu.confex.com/agu/fm19/prelim.cgi/Session/75246

NG004 – Data Assimilation meets Machine Learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/77631

IN033 – Incorporating physics and domain knowledge to improve interpretability, explainability, reliability and generalization of Machine Learning Models (MLM) in the geosciences

https://agu.confex.com/agu/fm19/prelim.cgi/Session/82358

Domain-specific problems

H045 – Domain-Aware Machine Learning for Subsurface Applications

https://agu.confex.com/agu/fm19/prelim.cgi/Session/85474

H138 – Utility of Artificial Intelligence/Machine Learning Approaches in Soil Hydrological Processes and Agriculture

https://agu.confex.com/agu/fm19/prelim.cgi/Session/74487

H002 – Advances and Applications of Data Integration, Inverse Methods, and Machine Learning in Hydrogeophysics

https://agu.confex.com/agu/fm19/prelim.cgi/Session/71766

S015 – Extracting Information from Geophysical Signals with Machine Learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80960

S017 – Geophysical inversion, inference and imaging in the age of machine learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80845

U009 – Data Analytics and Machine Learning Innovation for Climate and Earth Surface Processes

https://agu.confex.com/agu/fm19/prelim.cgi/Session/83451

EP027 – Machine Learning Applications in Earth Surface Processes Research

https://agu.confex.com/agu/fm19/prelim.cgi/Session/75302

In other fields

GC006 – AI and Machine Learning for Climate and Extreme Weather Prediction

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81937

A137 – Use of Machine Learning and Causal Discovery to Advance Knowledge in the Atmospheric Sciences – Methods, Limitations and Trade-offs

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76723

A091 – Machine Learning for Climate Modeling and Inference

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78836

GC076 – Solar Radiation Monitoring and Forecast from Satellite Observations and Models: Physical and Machine Learning Perspectives

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81059

NG007 – Machine Learning in Space Weather

https://agu.confex.com/agu/fm19/prelim.cgi/Session/72833

SH015 – Machine Learning and Data Assimilation as Emerging Tools for Characterization and Forecasting of Solar Variability and Space Weather Events

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80120

P022 – Machine Learning for Planetary Science

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78481

OS019 – Innovation and Exploration with Machine Learning in Ocean and Atmospheric Sciences: Global and Regional Applications

https://agu.confex.com/agu/fm19/prelim.cgi/Session/85463

Communicating Uncertainty

H144 – Water and Society: Communication, Decision Support and Stakeholder Engagement to improve Policy and Management in an Uncertain World.

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78608

H146 – Water and Society: Water Resources Management and Policy in a Changing World

https://agu.confex.com/agu/fm19/prelim.cgi/Session/77235

H142 – Water and Society: Adaptive Short-Term Management of Coupled Human-Natural Systems Confronting Long-Term Global Change

https://agu.confex.com/agu/fm19/prelim.cgi/Session/74575

B130 – Water and Society: Adapting institutions to address future water resource challenges

https://agu.confex.com/agu/fm19/prelim.cgi/Session/83562

Domain-specific problems

H145 – Water and Society: Enhancing and Communicating Hydroclimatic Forecasts for Water Resources Decision-making

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78810

H036 – Co-management of Floods and Droughts under Climate Change

https://agu.confex.com/agu/fm19/prelim.cgi/Session/77223

H030 – Assessment of Vulnerability and Sustainability of Water Resources under Climate Change for Small Scale Watersheds

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78267

GC074 – Resilience in an era of climate change…what does that even mean?

https://agu.confex.com/agu/fm19/prelim.cgi/Session/82457

In other fields

IN035 – Making Data Uncertainty Information FAIR: Findable, Accessible, Interoperable, and Reusable

https://agu.confex.com/agu/fm19/prelim.cgi/Session/82569

PA057 – Scientist Citizens:  Shaping Science-Informed Policy

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80982

PA040 – Optimizing natural hazard risk assessment: a showcase of how utilizing decision and risk analysis methods from other sectors can help improve natural hazard planning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/83749

ED027 – Evaluation of Education, Outreach, and Communication: Learning from our Work and telling the Story of Learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/79881

ED045 – Science to Action: Learning Ecosystems as a Pathway to Resilient Communities and a Diverse Geoscience Workforce

https://agu.confex.com/agu/fm19/prelim.cgi/Session/74447

Improving modelling and watershed science

H098 – Modeling hydrological processes and changes under a changing environment

https://agu.confex.com/agu/fm19/prelim.cgi/Session/77780

H023 – Advancing watershed science using machine learning, diverse data, and mechanistic modeling

https://agu.confex.com/agu/fm19/prelim.cgi/Session/83085

Domain-specific problems

H092 – Long-Term Climate Modeling and Hydrological Projection

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78073

H067 – GRACE / GRACE-FO Applications in Terrestrial Hydrology

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80807

H064 – Geogenic Contamination in the Groundwater System: Advances (and Challenges) in Assessment, Water Quality Modeling, Uncertainty, and Water Supply Management

https://agu.confex.com/agu/fm19/prelim.cgi/Session/85455

H013 – Advances in quantifying impacts and extents of land-use/land-cover change on hydrology.

https://agu.confex.com/agu/fm19/prelim.cgi/Session/72615

GC057 – Multi-scale, Multi-Region, and Multi-Actor Interactions for Sustainability and Resilience in the Food-Energy-Water Nexus

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80748

H102 – Nonstationary Impacts on Urban Watersheds

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76386

H126 – Statistical Methods for Modeling Changes in Flood Characteristics: Novel Approaches and Comparisons

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81731[CX1]

In other fields

GC014 – Climate Science in the Age of Deep Learning and Machine Learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/75997

A055 – Deep Learning for improving short-term atmospheric modeling and prediction

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81500

B081 – Narrowing uncertainty in GHG emissions using UAV (unmanned aerial vehicle) platforms

https://agu.confex.com/agu/fm19/prelim.cgi/Session/79284

A111 – Reducing uncertainty in aerosol effects on climate

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76712

A136 – Urban Climate Informatics — from sourcing to (deep) learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81563

IN002 – Advanced Geospatial Cyberinfrastructure for Deep Learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/74455

H038 – Conventional and Enhanced Geothermal Systems: Characterization, Integration, Stimulation, Simulation, and Induced Seismicity

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80861

GC039 – Four Decades and Counting: NASA Earth Observations and Agricultural Monitoring

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80239

A116 – Seasonal to Multi-Seasonal Earth System Prediction and its Applications

https://agu.confex.com/agu/fm19/prelim.cgi/Session/81965

GC068 – Quantifying impacts in socio-environmental systems through the application of remote sensing and machine learning

https://agu.confex.com/agu/fm19/prelim.cgi/Session/77863

Uncertainty and Sensitivity Analysis: Methodological Advances

H044 – Diagnostics, Sensitivity, and Uncertainty Analysis of Earth, Hydrologic and Environmental Models, Including Multiple Hypotheses and Extremes

https://agu.confex.com/agu/fm19/prelim.cgi/Session/85481

H002 – Advances and Applications of Data Integration, Inverse Methods, and Machine Learning in Hydrogeophysics

https://agu.confex.com/agu/fm19/prelim.cgi/Session/71766

Domain-specific problems

A069 – Extreme Weather Events: Forecast skill, Uncertainty Quantification and Impact Modeling

https://agu.confex.com/agu/fm19/prelim.cgi/Session/80909

H071 – Heterogeneity, upscaling and uncertainty quantification in subsurface flow and transport

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76176

B078 – Multi-hypothesis studies of ecological and hydrological systems

https://agu.confex.com/agu/fm19/prelim.cgi/Session/82170

In other fields

NG001 – Advances in Data Assimilation, Predictability and Uncertainty Quantification

https://agu.confex.com/agu/fm19/prelim.cgi/Session/72944[CX1]

GC047 – Interpretation and Uncertainty Quantification of Climate, Earth System, and Integrated Assessment Models

https://agu.confex.com/agu/fm19/prelim.cgi/Session/72457

NG011 – Uncertainty Quantification and Error Characterization in Remote Sensing

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76526

V055 – Volcanic Hazard Modeling, Risk Assessment and Uncertainty Quantification

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78179

SM039 – Uncertainty Quantification and Statistical Best Practices for Space Weather

https://agu.confex.com/agu/fm19/prelim.cgi/Session/78947

G004 – Geodetic InSAR Time-series Analysis; Applications and Uncertainty Quantification

https://agu.confex.com/agu/fm19/prelim.cgi/Session/82055

A010 – Assessing Atmospheric Instrumental Uncertainty and Measurement Consistency

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76153

NH033 – Quantifying Uncertainty in Probabilistic Storm Surge Estimates – Recent Advances in Research and Applications

https://agu.confex.com/agu/fm19/prelim.cgi/Session/83417

GC087 – Uncertainty in Recent and Future Sea-Level Change

https://agu.confex.com/agu/fm19/prelim.cgi/Session/76965

As AGU is celebrating its 100th anniversary, researchers around the world are invited to another AGU Fall Meeting in San Francisco, home of AGU’s Fall Meeting for nearly half a century, to celebrate their own contributions to science. The newly renovated Moscone Center welcomes all researchers interested in geophysical sciences, to enjoy Centennial presentations and special events that will bring the life to the past, present and the future of our science. The details of this event could be found on our Event page and also on AGU’s official website.

Hydrologic Uncertainty Technical Committee is proud to invite all the researchers contributing to the field of hydrologic uncertainty, to submit abstracts of their current state-of-the-art research subjects.

The Communication and Social Media sub-committee of the TC has compiled lists of sessions related to our three big questions. Below you can find the link to each list:

1. Sessions related to “Characterizing Uncertainty”
2. Sessions related to “Reducing Uncertainty”
3. Sessions related to “Communicating Uncertainty”
4. Sessions related to “Machine Learning”

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

The 2014 John Dalton Medal is awarded to Hoshin V. Gupta for seminal contributions to systems approaches to hydrologic science, for training a large number of outstanding young scientists, and for unselfish stewardship of hydrologic science and practice on a global scale.

Hoshin V. Gupta is largely responsible for, and is the most visible symbol of, the evolution of systems approaches in hydrologic and land surface modelling. Gupta developed the first global optimisation algorithm specifically suited for the particular characteristics of hydrological models. Gupta was among the first to use the notion of the perceptual, conceptual, mathematical and numerical stages of model development, and the first to present a generalised representation of model structure, which then allowed a single evaluative framework for considering data assimilation, parameter optimisation and ensemble modelling.

Gupta was one of the first to identify the need for multi-objective optimisation of watershed scale hydrologic models. He brought artificial neural networks into hydrologic modelling and demonstrated how they can be used for continuous rainfall-runoff modelling. He developed new Bayesian stochastic approaches for uncertainty analysis. Gupta also proposed a new theory of diagnostic model evaluation, which, for the first time, explicitly included hydrologic process understanding, in the form of runoff signatures, in the formulation of objective functions. His contributions to hydrologic science, especially modelling, have been phenomenal and consistently of excellent quality maintained over three decades: Gupta is one of most visible hydrologists at the international level.

Gupta has collaborated widely and has mentored a large number of outstanding young scientists. He is an acknowledged world leader, with a long sustained international record of leadership of and service to the discipline via international committees and editorial activities. Few scientists have left a deeper footprint in hydrology than him. For Gupta’s pioneering, seminal and sustained contributions to systems approaches to watershed hydrologic modelling, for his role in the training of a large number of young scientists, and for his unselfish stewardship of hydrologic science and practice on a global scale, Gupta is awarded the John Dalton Medal.