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, UMR8212, CEA-CNRS-UVSQ, CEA-orme des Merisiers, 91191 Gif-sur-Yvette, France
4KlimaCampus, University of Hamburg, Hamburg, Germany
5Department of Meteorology and Climatology, Aristotelian University of Thessaloniki, Thessaloniki, Greece
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 mechanisms controlling ecological and hydrogical 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 either separately or simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurement, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from Sea-viewing Wide Field-of-view Sensor (SeaWiFS).
Assimilation of both 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 closest agreement is found for each observed data stream when only the same data stream is assimilated. The mean uncertainty reduction (relative to the prior) over all parameters is 16.1% for the simultaneous assimilation of LHF and FAPAR, 9.2% for assimilating LHF only, and 7.8% for assimilating FAPAR only. Furthermore, the set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction is found for a parameter describing maximum plant-available soil moisture for all three cases. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.