Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales

<p>SUPREHILL is a new (2020) and first Croatian critical zone observatory (CZO), focused on local scale agricultural e.g., vineyard hillslope processes. The experimental setup includes an extensive sensor-based network accompanied by weighing lysimeters and instruments for surface and subsurface hydrology measurement. The field measurements are supported by novel laboratory and numerical quantification methods for the determination of water flow and solute transport. This combined approach will allow the research team to accurately determine soil water balance components (soil water flow, preferential flow/transport pathways, surface runoff, evapotranspiration), the temporal origin of water in hillslope hydrology (isotopes), transport of agrochemicals, and to calibrate and validate numerical modeling procedures for describing and predicting soil water flow and solute transport. First results from sensors indicate increased soil moisture on the hilltop, which is supported by precipitation data from rain gauges and weighing lysimeters. The presence of a compacted soil horizon and compacted inter-row parts (due to trafficking) of the vineyard seems to be highly relevant in regulating water dynamics. Wick lysimeters confirm the sensor soil moisture data, while showing a significant difference in its repetitions which suggests a possibility of a preferential flow imposed by local scale soil heterogeneity. Measured values of surface and subsurface runoff suggest a crucial role of these processes in the hillslope hydrology, while slope and structure dynamics additionally influence soil hydraulic properties. We are confident that the CZO will give us new insights in the landscape heterogeneity and substantially increase our understanding regarding preferential flow and nonlinear solute transport, with results directly applicable in agricultural (sloped agricultural soil management) and environmental (soil and water) systems. Challenges remain in characterizing local scale soil heterogeneity, dynamic properties quantification and scaling issues for which we will rely on combining CZO focused measurements and numerical modeling after substantial data is collected.</p>

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Impact of Bias-Correction Methods on Effectiveness of Assimilating SMAP Soil Moisture Data into NCEP Global Forecast System Using the Ensemble Kalman Filter

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2806092 ◽

2018 ◽

Vol 15 (5) ◽

pp. 659-663 ◽

Cited By ~ 7

Author(s):

Jifu Yin ◽

Xiwu Zhan

Keyword(s):

Soil Moisture ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Bias Correction ◽

Global Forecast System ◽

Forecast System ◽

Soil Moisture Data ◽

Ncep Global Forecast System

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

10.5194/hess-2020-106 ◽

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.

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

10.5194/hess-2020-672 ◽

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.

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