Abstract
This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.
Highlights
The results showed that leaf area index (LAI) data assimilation (DA) improves the estimation of key water budget terms (i.e., soil moisture, evapotranspiration (ET), terrestrial water storage, and streamflow) and carbon fluxes when validated with ground-based reference datasets, but the improvement in surface soil moisture was still limited
The Global Land Surface Satellite (GLASS) LAI DA and the joint (GLASS LAI and SMAP soil moisture) DA show higher values of LAI in most areas of Continental U.S (CONUS) when compared to OL, whereas SMAP DA presents lower values of LAI
ET and NEE show larger differences with the original OL run after the application of GLASS LAI DA and the joint DA. This is due to the fact that these two vegetation variables are directly related to LAI in Noah-MP
Summary
Vegetation and soil moisture both play very important roles in land surface modeling by supporting critical functions in the global and regional water and energy cycles. Vegetation impacts soil characteristics, e.g., its water content, chemistry, and texture, that have feedback on vegetation characteristics, including productivity and structure [1]. Soil moisture represents the land storage for water and energy, effectively controlling the balance between sensible and latent heat flux at the land–atmosphere interface. Soil moisture content impacts atmospheric processes, such as cloud coverage and rainfall, affecting hydrological processes such as runoff and plant transpiration [2,3]. Soil moisture content impacts atmospheric processes, such as cloud coverage and rainfall, affecting hydrological processes such as runoff and plant transpiration [2,3]. 4.0/).
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