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%0 Thesis
%A Li, Fang
%T Assimilation of groundwater level and cosmic-ray neutron sensor soil moisture measurements into integrated terrestrial system models for better predictions
%V 650
%I RWTH Aachen University
%V Dissertation
%C Jülich
%M RWTH-2024-11825
%@ 978-3-95806-796-7
%B Schriften des Forschungszentrums Jülich. Reihe Energie & Umwelt
%P 1 Online-Ressource (xvii, 172 Seiten) : Illustrationen, Diagramme, Karten
%D 2024
%Z Druckausgabe: 2024. - Onlineausgabe 2024 - Auch veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2025
%Z Dissertation, RWTH Aachen University, 2024
%X Groundwater and soil moisture (SM) play a crucial role in the hydrological cycle, and therefore the dynamics of these two variables need to be accurately quantified on spatial and temporal scales. In situ observation networks can provide direct and accurate information on groundwater level (GWL) and SM. However, observations from observation networks are not sufficient to fully represent the Earth’s hydrological system without the help of models. Integrated models such as the Terrestrial System Modelling Platform (TSMP) can simulate the hydrological system from the subsurface to the atmosphere and accurately capture the full terrestrial hydrological cycle. Current model estimates of GWL and SM are highly uncertain due to data limitations and model uncertainties. The main sources of uncertainty are related to atmospheric forcings, model structural errors, and uncertain parameterization. Data assimilation (DA) can merge numerical models with observations, resulting in a correction of hydrological states and fluxes and improved parameter estimates.Different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of first study is to investigate the impact of assimilating groundwater data into TSMP for improving hydrological modelling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before DA. Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5 km, 5 km and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA (i.e., open loop (OL) runs). The best results at the assimilated locations are obtained for a localization radius of 10km, with an 81
%F PUB:(DE-HGF)11 ; PUB:(DE-HGF)3
%9 Dissertation / PhD ThesisBook
%R 10.18154/RWTH-2024-11825
%U https://publications.rwth-aachen.de/record/999042