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Hannes H. Bauser
PhD, Physics, Ruprecht-Karls-Universität Heidelberg, Germany, 2018
M.Sc., Physics, Ruprecht-Karls-Universität Heidelberg, Germany, 2014
Title & Affiliation:
Postdoctoral Research Associate, Biosphere 2, University of Arizona
The aim of my research is to improve our understanding and prediction of soil hydraulic processes by combining information from models and measurements, including their uncertainties. I employ data assimilation methods to achieve a consistent process-based description of soil water movement at LEO.
The mathematical representation of soil water movement exhibits uncertainties in all model components. Especially, the soil properties and their heterogeneity are highly uncertain. Data assimilation methods combine models and measurements into an improved representation and can – at least in principle – account for all uncertainties. The extensive sensor and sampler network at LEO offers a unique possibility to improve our understanding and to successfully achieve a detailed and consistent description of soil water movement, including its heterogeneity, at the hillslope scale.
Berg, D., Bauser, H. H., and Roth, K. (2019): Covariance resampling for particle filter – state and parameter estimation for soil hydrology, Hydrology and Earth System Sciences, 23(2), 1163-1178, doi: 10.5194/hess-23-1163-2019
Bauser, H. H., Berg, D., Klein, O., and Roth, K. (2018): Inflation method for ensemble Kalman filter in soil hydrology, Hydrology and Earth System Sciences, 22(9), 4921-4934, doi: 10.5194/hess-22-4921-2018
Bauser, H. H., Jaumann, S., Berg, D., and Roth, K., (2016): EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology, Hydrology and Earth System Sciences, 20(12), 4999-5014, doi: 10.5194/hess-20-4999-2016
Challenges with effective representations of heterogeneity in soil hydrology based on local water content measurements . Bauser, H. H., Riedel, L., Berg, D., Troch, P. A., Roth, K. (2020): Vadose Zone Journal 19(1): e20040.
Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network . Kreyenberg, P. J., Bauser, H. H., and Roth, K. (2019): Water Resources Research 55(8): 7275-7293.