Abstract
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.
Highlights
Hyperresolution land modeling is expected to improve the simulation of terrestrial water, energy, and carbon cycles, which is crucially important for meteorological, hydrological, and ecological applications
This study aims at clarifying whether surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models by a minimalist numerical experiment
Despite the uncertainty in rainfall and hydraulic conductivity, root-mean-square error (RMSE) is reduced by data assimilation directly under the observation, and the lower part of the slope where it does not rain
Summary
Hyperresolution land modeling is expected to improve the simulation of terrestrial water, energy, and carbon cycles, which is crucially important for meteorological, hydrological, and ecological applications (see Wood et al, 2011, for a comprehensive review). Previous works indicated that a lateral transport of water strongly controls latent heat flux and the partitioning of evapotranspiration into base soil evaporation and plant transpiration (e.g., Maxwell and Condon, 2016; Ji et al, 2017; Fang et al, 2017). This effect of a lateral transport of water on land–atmosphere interactions has been recognized (e.g., Williams and Maxwell, 2011; Keune et al, 2016). Data assimilation has contributed to improving the performance of LSMs by fusing simulation and observation. In previous works on the conventional 1-D LSMs, many land data
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