scholarly journals Joint State and Parameter Estimation of Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter

2016 ◽  
Author(s):  
Hongjuan Zhang ◽  
Harrie-Jan Hendricks Franssen ◽  
Xujun Han ◽  
Jasper Vrugt ◽  
Harry Vereecken
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Particle Filter ◽  
Water Content ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Land Surface Models ◽  
Surface Models ◽  
Dual Estimation

Abstract. Land surface models (LSMs) contain a suite of different parameters and state variables to resolve the water and energy balance at the soil-atmosphere interface. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. In this paper, we use the Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM) to simulate temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany. Four different data assimilation (DA) methods are used to jointly estimate the spatially distributed water content values, and hydraulic and/or thermal properties of the resolved soil domain. This includes the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month data period, and subsequently evaluated for another five month period. Our results show that all the different DA methods improve the fit of the VIC and CLM model to the observed water content data, particularly if the maximum baseflow velocity (VIC), soil hydraulic (VIC) properties and/or soil texture (CLM) are jointly estimated along with the model states. In the evaluation period, the augmentation and dual estimation method performed slightly better than RRPF and MCMCPF. The differences in simulated soil moisture values between the CLM and VIC model were larger than variations among the data assimilation algorithms. The best performance for the Rollesbroich site was observed for the CLM model. The strong underestimation of the soil moisture values of the third VIC-layer are likely explained by an inadequate parameterization of groundwater drainage.

scholarly journals State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

2017 ◽  
Vol 21 (9) ◽  
pp. 4927-4958 ◽  
Author(s):  
Hongjuan Zhang ◽  
Harrie-Jan Hendricks Franssen ◽  
Xujun Han ◽  
Jasper A. Vrugt ◽  
Harry Vereecken
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Model Parameters ◽  
Land Surface Models ◽  
Evaluation Period ◽  
Community Land Model ◽  
Surface Models ◽  
State Augmentation ◽  
Dual Estimation

Abstract. Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Parallel Data ◽  
Surface Models

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.

scholarly journals Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

2020 ◽  
Author(s):  
Yohei Sawada
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Pressure Head ◽  
Surface Flow ◽  
State Variables ◽  
Background Error ◽  
Water Flows

Abstract. It is expected that hyperresolution land modeling substantially innovates the simulation of terrestrial water, energy, and carbon cycles. The major advantage of hyperresolution land models against conventional one-dimensional land surface models is that hyperresolution land models can explicitly simulate lateral water flows. Despite many efforts on data assimilation of hydrological observations into those hyperresolution land models, how surface water flows driven by local topography matter for data assimilation of soil moisture observations has not been fully clarified. Here I perform two minimalist synthetic experiments where soil moisture observations are assimilated into an integrated surface-groundwater land model by an ensemble Kalman filter. I discuss how differently the ensemble Kalman filter works when surface lateral flows are switched on and off. A horizontal background error covariance provided by overland flows is important to adjust the unobserved state variables (pressure head and soil moisture) and parameters (saturated hydraulic conductivity). However, the non-Gaussianity of the background error provided by the nonlinearity of a topography-driven surface flow harms the performance of data assimilation. It is difficult to efficiently constrain model states at the edge of the area where the topography-driven surface flow reaches by linear-Gaussian filters. It brings the new challenge in land data assimilation for hyperresolution land models. This study highlights the importance of surface lateral flows in hydrological data assimilation.

scholarly journals Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis

2021 ◽  
Author(s):  
Tobias Sebastian Finn ◽  
Gernot Geppert ◽  
Felix Ament
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Atmospheric Boundary Layer ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Model Systems ◽  
Surface Component ◽  
Moisture Analysis

Abstract. We revise the potential of assimilating atmospheric boundary layer observations into the soil moisture. Previous studies often stated a negative assimilation impact of boundary layer observations on the soil moisture analysis, but recent developments in physically-consistent hydrological model systems and ensemble-based data assimilation lead to an emerging potential of boundary layer observations for land surface data assimilation. To explore this potential, we perform idealized twin experiments for a seven-day period in Summer 2015 with a coupled atmosphere-land modelling platform. We use TerrSysMP for these limited-area simulations with a horizontal resolution 1.0 km in the land surface component. We assimilate sparse synthetic 2-metre-temperature observations into the land surface component and update the soil moisture with a localized Ensemble Kalman filter. We show a positive assimilation impact of these observations on the soil moisture analysis during day-time and a neutral impact during night. Furthermore, we find that hourly-filtering with a three-dimensional Ensemble Kalman filter results in smaller errors than daily-smoothing with a one-dimensional Simplified Extended Kalman filter, whereas the Ensemble Kalman filter additionally allows us to directly assimilate boundary layer observations without an intermediate optimal interpolation step. We increase the physical consistency in the analysis for the land surface and boundary by updating the atmospheric temperature together with the soil moisture, which as a consequence further reduces errors in the soil moisture analysis. Based on these results, we conclude that we can merge the decoupled data assimilation cycles for the land surface and the atmosphere into one single cycle with hourly-like update steps.

scholarly journals Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

2020 ◽  
Vol 24 (8) ◽  
pp. 3881-3898
Author(s):  
Yohei Sawada
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Pressure Head ◽  
Surface Flow ◽  
State Variables ◽  
Background Error ◽  
Water Flows

Abstract. It is expected that hyperresolution land modeling substantially innovates the simulation of terrestrial water, energy, and carbon cycles. The major advantage of hyperresolution land models against conventional 1-D land surface models is that hyperresolution land models can explicitly simulate lateral water flows. Despite many efforts on data assimilation of hydrological observations into those hyperresolution land models, how surface water flows driven by local topography matter for data assimilation of soil moisture observations has not been fully clarified. Here I perform two minimalist synthetic experiments where soil moisture observations are assimilated into an integrated surface–groundwater land model by an ensemble Kalman filter. I discuss how differently the ensemble Kalman filter works when surface lateral flows are switched on and off. A horizontal background error covariance provided by overland flows is important for adjusting the unobserved state variables (pressure head and soil moisture) and parameters (saturated hydraulic conductivity). However, the non-Gaussianity of the background error provided by the nonlinearity of a topography-driven surface flow harms the performance of data assimilation. It is difficult to efficiently constrain model states at the edge of the area where the topography-driven surface flow reaches by linear-Gaussian filters. It brings the new challenge in land data assimilation for hyperresolution land models. This study highlights the importance of surface lateral flows in hydrological data assimilation.

Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX

2017 ◽  
Vol 200 ◽  
pp. 295-310 ◽  
Author(s):  
Carlos Román-Cascón ◽  
Thierry Pellarin ◽  
François Gibon ◽  
Luca Brocca ◽  
Emmanuel Cosme ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Models ◽  
Soil Moisture Data ◽  
Surface Models

Assessing the Performance of the Ensemble Kalman Filter for Land Surface Data Assimilation

2006 ◽  
Vol 134 (8) ◽  
pp. 2128-2142 ◽  
Author(s):  
Yuhua Zhou ◽  
Dennis McLaughlin ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Conditional Moments ◽  
Surface Data ◽  
Distributional Information ◽  
Importance Resampling ◽  
Efficient Option

Abstract The ensemble Kalman filter provides an easy-to-use, flexible, and efficient option for data assimilation problems. One of its attractive features in land surface applications is its ability to provide distributional information about variables, such as soil moisture, that can be highly skewed or even bimodal. The ensemble Kalman filter relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects of these approximations can be evaluated by comparing the conditional marginal distributions and moments estimated by the ensemble Kalman filter with those obtained from a sequential importance resampling (SIR) particle filter, which gives exact solutions for large ensemble sizes. Comparisons for two land surface examples indicate that the ensemble Kalman filter is generally able to reproduce nonnormal soil moisture behavior, including the skewness that occurs when the soil is either very wet or very dry. Its conditional mean estimates are very close to those generated by the SIR filter. Its higher-order conditional moments are somewhat less accurate than the means. Overall, the ensemble Kalman filter appears to provide a good approximation for nonlinear, nonnormal land surface problems, despite its dependence on normality assumptions.

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