Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model

Zhang

et al. 2019

Vadose Zone Journal

Groundwater level data require EnRML for better interpretation. Different methods perform similarly when assimilating surface soil moisture data. Soil water pressure head data are the most valuable in terms of parameter estimation. MCMC performs well in homogeneous soil but degrades in heterogeneous soil. The EnKF method relies more on a relatively large number of ensembles than does EnRML. In the past few decades, different data assimilation methods have been proposed to estimate soil parameters. It is not clear whether a straightforward sampling approach is sufficient or whether a linear filter or even an advanced nonlinear filter is needed to interpret the potential information carried by different data types (e.g., pressure head and water content data from multiple depths, easily available surface soil moisture data, and groundwater level data). In this study, three classical data assimilation methods, i.e., the ensemble Kalman filter (EnKF), the ensemble randomized maximum likelihood filter (EnRML), and the Markov chain Monte Carlo (MCMC), were investigated numerically in terms of the utility to cope with three different types of observations. Results show that, compared with the EnKF approach, EnRML is a superior method to extract the parameter information from observations. The MCMC approach performs well in homogeneous soil but not in heterogeneous soil. Regardless of the data assimilation methods and the soil heterogeneity, point‐scale soil water pressure head data are the most valuable in terms of soil parameter estimation, followed by groundwater level data, which require a nonlinear filter to interpret. A smaller observation error for groundwater level data leads to obviously improved parameter estimates by using EnRML with a slight improvement by using EnKF. The stable performance of the EnKF method relies more heavily on a relatively large number of ensembles than the EnRML method, whereas only a few ensemble members are needed for EnRML in a homogeneous soil column.

Section: Resultsmentioning

confidence: 99%

Investigation of Data Assimilation Methods for Soil Parameter Estimation with Different Types of Data

Zhang

et al. 2019

Vadose Zone Journal

“…alternate parameterizations for land surface-atmosphere coupling or atmospheric convection (Davin et al 2016. Options for reducing these uncertainties, such as using observational data to constrain simulations (Stegehuis et al 2013), data assimilation (Zhang et al 2018, Naz et al 2019, and finer model discretization are becoming more feasible as computational power continues to increase and may provide a path towards reduced uncertainty in future work.…”

Section: Synthesizing Local and Remote Sensitivity To Lulc Changementioning

confidence: 99%

Land use change impacts on European heat and drought: remote land-atmosphere feedbacks mitigated locally by shallow groundwater

Zipper

Keune

Kollet³

2019

Environ. Res. Lett.

Heat and drought are projected to increase globally but may be mitigated or exacerbated by land use/land cover (LULC) change. Here, we show that remote land-atmosphere feedbacks caused by historical European LULC change led to widespread changes in the energy and water balances, drought, and heat. Using a continental-scale bedrock-to-atmosphere model, we find that LULC change following the Soviet Union collapse and European Union formation may have substantially increased cloud cover and decreased incoming shortwave radiation in western Europe, even in locations where LULC did not change. These changes to the water and energy balances had spatially heterogeneous impacts on drought and heat, including drying in the Mediterranean and Eastern Europe regions. The response of the water and energy balances to remote feedbacks was lessened in areas with shallow groundwater, indicating that local-and continental-scale responses to LULC change are influenced by the coupling between the subsurface, land surface, and atmosphere.

“…Wanders et al [20] applied a dual state and parameter EnKF to calibrate the hydrological LISFLOOD model and used the discharge and remotely sensed soil moisture to improve the estimation of groundwater and routing parameters. Zhang et al [21] examined the benefits of assimilating groundwater levels using different EnKF-based methodologies to improve the predictions of root zone soil moisture with an integrated terrestrial system model.…”

Section: Introductionmentioning

confidence: 99%

Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Jiang

Huang

Li³

et al. 2019

Water

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.