Gap-filling workshop, Jena 09/2006Markus Reichstein Gap-filling: What, why, how? - an Introduction Gap-filling Comparison Workshop, September 18-20, 2006 Max Planck Institute for Biogeochemistry Biogeochemical Model-Data Integration Group M. Reichstein (Biogeochemical Model-Data Integration Group, Max- Planck Intstitute for Biogeochemistry, Jena)

Gap-filling workshop, Jena 09/2006Markus Reichstein Why are we here – a short historical perspective ? 2001: Falge et al. FLUXNET (AMERIFLUX, EUROFLUX) 2002: MDS online gap-filling tool MIND 2003: Boost of gap-filling methods FLUXNET 2004: Eddy QC/QA/ GF/FP workshop CARBOEUROPE Today: Comp.of 15 methods + spin-offs from gap-filling

Gap-filling workshop, Jena 09/2006Markus Reichstein What is a gap ? “Gap is a synonym for any hole or opening; a chasm. Many uses of the word are either literally or figuratively based on this meaning.” (wikipedia.org) “A gap is a series of missing data of eddy-covariance flux data (and/or meteorology) caused by instruments failure, unfavorable measurement conditions or removal of data point during the quality control.” –Univariate gaps (e.g. only NEE) –Multivariate flux gaps (e.g. all fluxes missing  sonic failure) –Flux and meteo gaps (e.g. all missing  storm or Xmas) –Length of a gap?

Gap-percentage varies Falge et al CE database ~30% (without ½ year gaps)

Gap-filling workshop, Jena 09/2006Markus Reichstein Gaps abundance Length of gap [days] Frequency [log(year -1 )] Length of gap [log(day)]

Gap-filling workshop, Jena 09/2006Markus Reichstein Days affected by gaps Length of gap [days] Total days affected [days/year] Length of gap [days] Cumulative percentage affected

Gap-filling workshop, Jena 09/2006Markus Reichstein Why ? ‘Annual sums’ Model validation at daily to monthly scale Syntheses at monthly time scale Model parameterization at hourly to daily scale Statistical time-series analysis Uncertainty estimation Modellers Data analysts

Gap-filling workshop, Jena 09/2006Markus Reichstein Available data at daily and monthly scale before and after gap-filling

Gap-filling workshop, Jena 09/2006Markus Reichstein How? Gap-filling requirements –Conservation of annual sums –Conservation of fluxes at other time integrals –Minimum of a-priori theoretical assumptions –Usage of a much as possible information from data –Applicability with available data –Conversation of statistical time-series properties –Availability of conditional error estimate

Development of gap-filling methods

Gap-filling workshop, Jena 09/2006Markus Reichstein Classification of gap-filling methods With vs. without meteorological drivers Data-oriented versus process-oriented approaches Incorporation vs. ignorance of autocorrelation Smooth versus non-smooth methods Look-up tables vs. regressions vs. neural networks

Gap-filling workshop, Jena 09/2006Markus Reichstein Gap-filling methods characterized Meteo infoAuto- correlation Theo. assumption Conservation of error MDV-x-- LUTx--- MDS, MLUTxx-(x) NLIN, DAx-X- SDPxx(x)x ANNx(x)--

Gap-filling workshop, Jena 09/2006Markus Reichstein Conclusions Gap-filling is important from different perspectives Annual NEE is not the only target Existence of vast majority of methods with different characteristics –  need for a characterization and cross- comparison

Gap-filling workshop, Jena 09/2006Markus Reichstein Conclusions II Open questions Can we transfer methods established for NEE also to energy fluxes and meteorological data ? How can discontinuous systems be gap-filled ? How critically do gap-filling methods affect the statistical properties of the time-series? How can gap-filling be used for uncertainty estimation of flux data ? And for flux-partitioning?