SIAM Colloquium Winter Term 2018

In the winter term 2018 we continue the SIAM Colloquium. The schedule is:

 

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Representation of soil water movement

Dr. Hannes Bauser / Research Group: Terrestrial Systems & Chaotic, Complex, and Evolving Environmental Systems

23rd October 2018 - 4 pm c.t. INF 205, Conference Room (5th floor)

 

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Explainable Machine Learning

Prof. Dr. Ullrich K├Âthe / Research Group: Visual Learning Lab Heidelberg

20th November 2018 - 4 pm c.t. INF 205, Conference Room (5th floor)

 

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Uncertainty Quantification and Multiscale Problems

Prof. Dr. Robert Scheichl / Research Group: Numerical Analysis and Uncertainty Quantification

22nd January 2019 - 4 pm c.t. INF 205, Conference Room (5th floor)

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Talks

Representation of soil water movement

Representation of soil water movement

Dr. Hannes Bauser

Soil water movement is a key process in ecosystem services, such as biomass production, fresh water retention, climate regulation, or water buffering and filtering. However, the quantitative description of soil water movement on all relevant scales from meters to the global scale, remains an open challenge. In this talk I focus on the meter scale, where soil water movement can still be described with the process based Richards equation. Nevertheless, the mathematical representation of soil water movement exhibits uncertainties in all model components. This means that the representation of uncertainties in each model component becomes an integral part of the model formulation. The goal is then an optimal consistent representation with minimal uncertainties. Data assimilation methods, which combine models and data, are a key tool for this task. In this talk I present an application on a real-world case. We assessed the key uncertainties for the specific hydraulic situation of a 1-D soil profile with TDR (time domain reflectometry)-measured water contents. We employed a data assimilation method, the ensemble Kalman filter (EnKF), with an augmented state to represent and reduce all key uncertainties (initial condition, soil hydraulic parameters, small-scale heterogeneity, and upper boundary condition), except for an intermittent violation of the local equilibrium assumption by the Richards equation. To bridge this time, we employed a closed-eye period, which pauses the parameter estimation and only guides the states through this time. This ensured constant parameters throughout the whole estimation, suggesting that we achieved a more consistent description and limited the incorporation of errors into parameters.