The influence of assimilating leaf area index in a land surface model on global water fluxes and storages
| Autor: | Viviana Maggioni, Yuan Xue, Azbina Rahman, Sujay V. Kumar, Xinxuan Zhang, Timothy Sauer, David Mocko, Paul R. Houser |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:GE1-350
010504 meteorology & atmospheric sciences lcsh:T 0208 environmental biotechnology Water storage lcsh:Geography. Anthropology. Recreation 02 engineering and technology Atmospheric sciences 01 natural sciences lcsh:Technology lcsh:TD1-1066 020801 environmental engineering Carbon cycle Water balance Data assimilation lcsh:G Evapotranspiration Environmental science Ensemble Kalman filter Leaf area index lcsh:Environmental technology. Sanitary engineering Water content lcsh:Environmental sciences 0105 earth and related environmental sciences |
| Zdroj: | Hydrology and Earth System Sciences, Vol 24, Pp 3775-3788 (2020) |
| ISSN: | 1607-7938 1027-5606 |
| Popis: | Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations. |
| Databáze: | OpenAIRE |
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