Assimilation of Groundwater Level and Soil Moisture Data in an Integrated Land Surface‐Subsurface Model for Southwestern Germany

Abstract

Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface‐subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state‐only/state‐hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance.

Water table depth assimilation in integrated terrestrial system models at the larger catchment scale

“…In our study, in order to ensure stability and avoid the occurrence of anomalous pressure values in the unsaturated zone related to updating pressure in the DA step, a weakly coupled approach was followed, which implies that only pressure in saturated layers is updated during assimilation. Hung et al (2022) found that the weakly coupled approach outperformed the fully coupled approach for assimilating WTD measurements in TSMP. In the OL run, the vertical division between the unsaturated and saturated zones will differ among ensemble members.…”

“…TSMP has been applied in a series of studies (e.g., Shrestha et al, 2015;Keune et al, 2016;Furusho-Percot et al, 2019). The combination of PDAF and TSMP has been used for the assimilation of different hydrological variables (e.g., soil moisture and groundwater) at different scales (e.g., hillslope, catchment, and continental scale) (Kurtz et al, 2016;Zhang et al, 2018;Gebler et al, 2019;Naz et al, 2019Naz et al, , 2020Hung et al, 2022). Zhang et al (2018) demonstrated in synthetic experiments with only four grid cells that the joint assimilation of groundwater level and soil moisture data has great potential to improve root zone soil moisture characterization.…”

“…Zhang et al (2018) demonstrated in synthetic experiments with only four grid cells that the joint assimilation of groundwater level and soil moisture data has great potential to improve root zone soil moisture characterization. Hung et al (2022) assimilated groundwater levels at the large catchment scale in a synthetic study that mimicked the Neckar catchment, and the results showed that groundwater level assimilation can lead to a large scale improved characterization of groundwater levels, also between groundwater wells, but the impact of groundwater level assimilation on other compartments of the terrestrial system was limited, except for the deep vadose zone. However, there is still a lack of studies with integrated land surface-subsurface models to investigate assimilation of real groundwater measurements at the larger catchment scale.…”

Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

“…As a matter of fact, the use of physics-based models has also been pivotal to the success of atmospheric DA in NWP to propagate information to unobserved areas (Kalnay, 2002). At the same time, several studies with current state-of-the-art land surface models also reported limited success (Crow et al, 2020;Hung et al, 2022) in propagating information from one compartment to another, which suggests that the modeling (parameterization) of the coupling and fluxes between land compartments as well as DA strategies need further research.…”

Recent advances and opportunities in data assimilation for physics-based hydrological modeling

“…In addition to improved estimates of the driving forces, as detailed above, an accurate, detailed and spatio-temporal assessment of groundwater fluxes requires also a reliable estimate of soil and aquifer parameters, to avoid errors in parameters leading to over-or underestimation of fluxes. Starting from prior parameter distributions that can be extracted from globally available datasets (e.g., Batjes, 1997;Hengl et al, 2017), data assimilation allows for their dynamic update, which can lead to improved model predictions (e.g., Hung et al, 2022).…”