Book/Dissertation / PhD Thesis

FZJ-2018-04324

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Improved characterization of root zone soil moisture by assimilating groundwater level and surface soil moisture data in an integrated terrestrial system model

Zhang, H. (Corresponding author)FZJ*

2018
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich
ISBN: 978-3-95806-335-8

Please use a persistent id in citations: http://hdl.handle.net/2128/19414  urn:nbn:de:0001-2018080300

Abstract: Soil moisture is an important variable for the cycling of water and energy at the catchment/regional/global scale. Soil moisture content is usually simulated by land surface models, monitored by ground-based sensors, or observed by remote sensing techniques. However, land surface models often have high uncertainties due to simplified parameterizations and uncertainties from input forcing data and hydraulic parameters. For example, in most land surface models, the interaction between groundwater and root zone soil moisture is neglected. The availabilities of in situ monitoring networks are limited because of high costs. Remote sensing can provide soil moisture at the global scale but is limited to the top 5 cm and resolution is coarse. Data assimilation can take advantage of these three different sources of information by assimilating the observations (e.g. from the ground sensors or remote sensing data) into the land surface models to improve soil moisture predictions in both space and time. Furthermore, soil hydraulic parameters in land surface models can also be estimated jointly with soil moisture by data assimilation to further improve soil moisture characterization. In order to make land surface models more robust, integrated land surface-subsurface models have been developed which consider the effect of groundwater on root zone soil moisture in a fully two-way coupled fashion. In this work, we firstly compared four data assimilation methods in terms of joint estimation of soil moisture and soil hydraulic parameters for two land surface models. The four assimilation methods included Ensemble Kalman Filter (EnKF) with state augmentation (EnKF-AUG) or dual estimation (EnKF-DUAL), the residual resampling Particle Filter (RRPF) and the MCMC-based parameter resampling method (PMCMC). The two land surface models used were the Variable Infiltration Capacity Model (VIC) and the Community Land Model (CLM version 4.5). Real world data (soil properties, soil moisture measurements at 5, 20 and 50 cm depth, climate forcing data) from the Rollesbroich site located in the western Germany were used. We evaluated the usefulness and applicability of the four different data assimilation methods for joint parameter and state estimation of the VIC and the CLM using a 5-month calibration (assimilation) period of the soil moisture measurements. The performance of the “calibrated” VIC and CLM were investigated using water moisture measurements of a 5-month evaluation period. Results from the first study showed that all of the four assimilation methods were able to improve the model predictions of soil moisture after soil hydraulic parameters (for VIC) or sand/ clay/ organic matter fraction (for CLM) were jointly estimated with soil moisture. Overall, EnKF (EnKF-AUG and EnKF-DUAL) performed better than PF (RRPF and PMCMC). The differences between the soil moisture simulations of VIC and CLM were much larger than the discrepancies among the four data assimilation methods. CLM performed better than VIC in the soil moisture simulations at 50 cm depth. The large systematic underestimation of water storage at 50cm depth in VIC is most probably related to the fact that groundwater is not well represented in VIC. [...]

Note: RWTH Aachen, Diss., 2018

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2018

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Database coverage:
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