DOI: 10.5194/hess-24-3881-2020
论文题名: Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?
作者: Sawada Y.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 8 起始页码: 3881
结束页码: 3898
语种: 英语
Scopus关键词: Flow of water
; Groundwater
; Hydraulic conductivity
; Kalman filters
; Soil moisture
; Surface waters
; Topography
; Background-error covariances
; Ensemble Kalman Filter
; Land data assimilation
; Land surface models
; Lateral water flow
; Saturated hydraulic conductivity
; Surface water flows
; Synthetic experiments
; Soil surveys
; data assimilation
; ensemble forecasting
; hydrological modeling
; innovation
; Kalman filter
; land surface
; performance assessment
; simulation
; soil moisture
; topography
英文摘要: 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. © 2020 IOS Press. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162624
Appears in Collections: 气候变化与战略
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作者单位: Sawada, Y., Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
Recommended Citation:
Sawada Y.. Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?[J]. Hydrology and Earth System Sciences,2020-01-01,24(8)