TY  - GEN
TI  - Knowledge Fusion in Soil Hydrology
Y1  - 2018///
AV  - public
ID  - heidok24713
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/24713/
A1  - Bauser, Hannes Helmut
N2  - The mathematical representation of soil water movement exhibits uncertainties in all model components. Data assimilation methods, like the ensemble Kalman filter (EnKF), combine models and measurements into an improved representation and can ? at least in principle ? account for all uncertainties. However, a proper description of the uncertainties is required, which is particularly difficult in soil hydrology, where model errors typically vary rapidly in space and time. Inflation methods can account for unrepresented model errors. To improve the EnKF performance, I designed an inflation method specifically for soil hydrology, that is capable of adjusting inflation factors to spatiotemporally varying model errors. For the application on a real-world case, I assessed the key uncertainties for the specific hydraulic situation of a 1-D soil profile with TDR (time domain reflectometry)-measured water contents. With the EnKF, I directly represented and reduced 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, I introduced a closed-eye period, which ensures constant parameters and improves the EnKF towards the goal of knowledge fusion ? the consistent aggregation of all information pertinent to some observed reality.
ER  -