Titel Gap Filling of CO 2 Fluxes of Frequently Cut Grassland Christof Ammann Agroscope ART Federal Research Station, Zürich Gap Filling Comparison Workshop, Jena, 2006 Federal Research Station Agroscope Reckenholz-Tänikon ART

Motivation Motivation and Contents  Most gap-filling algorithms up to now have been optimized and evaluated based mainly on forest NEE datasets  Managed grassland sites (as other agricultural sites) can experience very rapid changes (discontinuities) in vegetation cover and soil conditions  Not only annual NEE but also management/event-related NEE (over few weeks/months) is of interest.  We applied a specific gap filling algorithm that should be able to reproduce fast changes and yield an adequate seasonal course of NEE. CONTENT  Short description of Swiss grassland site (with specific problems)  Description of gap filling method  Performance of gap filling for examplary events

Site/Region Measurement Site near Oensingen 2002   2008

Site plots Oensingen Site: Experimental Plots Intensively managed grassland mineral fertilizer and manure (ca. 200 kg/ha/y total N) 4-5 cuts per year Extensively managed grassland no fertilizer 3 cuts per year Various crops (rotation) Flux measurement systems (1.2 m above ground) local W  E scale [m] local S  N scale [m] Highway annual distribution of wind directions

CO2 night Low Wind Conditions during the Night  intermittent/no turbulence  mostly identified with stationarity and/or integral turbulence criteria

Data selection Quality Control and Data Coverage

Management Vegetation Development and Management Events

Gap Method 1 Applied Gap-Filling Method  Low data coverage (mostly short gaps: 1 hour...2 days)  Rapidly changing vegetation cover during the entire growing season  Highly adaptive gap-filling  3-day (5-day/7-day) moving window  Best use of available data  non-linear regression functions: NEE = R(T soil ) – A(Q PAR )  To keep the method simple and robust, only R 10 and A 2000 are fitted with the moving window  T 0 and  /A 2000 are kept konstant (determined by an overall regression)

Gap Method 2 Applied Gap Filling Method  Respiration R(T soil ) was only fitted to nighttime data.  Daytime assimilation was calculated as NEE–R(T soil )  Overall fit of A(Q PAR ) was made with selected dataset (canopy height > 20 cm)

Gap Result Normalized Assimilation and Respiration... resulting from the gap filling procedure

NEE topo Seasonal and Diurnal CO 2 Exchange (INT ) CO 2 flux [  mol m -2 s -1 ]

cumul NEE Cumulative NEE for Different Years and Management

Examp: Cut Example of Gap-Filled Time Series: Cutting Event

Examp: Winter Example of Gap-Filled Time Series: Freezing

R_SWC_1 Respiration during Summer soil temperature (-5cm) [°C] nocturnal respiration [mmol m -2 s ]

R_SWC_2 Nocturnal Respiration and Soil Moisture

R_SWC_3 Nocturnal Respiration and Soil Moisture

Conclusions Conclusions  The applied gap filling method is relatively simple and well suited for rapidly changing conditions and a low data coverage (with short gaps)  Larger gaps can be filled by interpolation of R 10 and A 2000 or by using default values (long-term means).  Potential improvement: Time dependent fit for all functional parameters (partly with larger window size?)  Further activities: Comparison with other methods (test performance on discontinuities)

END Thank You!

Gap Method 3 observed flux NEE[t] (with gaps) daytime flux nighttime flux R[t] assimilation A[t] 3..7-day moving average filter for R 10 [t] complete time series NEE[t] (Eq.2) A[t]R[t] R 10 [t] A 2000 [t] 3..7-day moving average filter for A 2000 [t] time-independent fit of param. T 0 time-independent fit of ratio  /A 2000 for h c >20cm

C-budg avg Carbon Budget for Intensive and Extensive Management ( )  C/  t CO 2 Harvest Manure