Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana
- 1Department of Earth Sciences, University of Bristol, Bristol, UK
- 2Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
- 3Laboratoire des Sciences du Climat et de l' Environnement, UMR 8212, CEA-CNRS-UVSQ, CEA-orme des Merisiers, 91191 Gif-sur-Yvette, France
- 4Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden
- 5KlimaCampus, University of Hamburg, Hamburg, Germany
- 6Nature Conservation and Plant Ecology Group, Department of Environmental Sciences,Wageningen University, Wageningen, The Netherlands
- 7FastOpt, Hamburg, Germany
- 8Max-Planck-Institute for Biogeochemistry, Jena, Germany
- 9European Commission, Joint Research Center, Ispra, Italy
Abstract. Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS).
Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.