Download Environmental response functions

1Karlsruhe
Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
2Chinese Academy of Sciences, IAP-LAPC, Beijing, China
3National Ecological Observatory Network, FIU, Boulder, U.S.A.
4University of Colorado, INSTAAR, Boulder, U.S.A.
5Technical University of Ilmenau, Programming Languages and Compilers, Ilmenau, Germany
6University of Bayreuth, Department of Micrometeorology, Bayreuth, Germany
Environmental response functions – relating eddy-covariance
flux measurements to ecosystem drivers
Wolfgang Junkermann1, Stefan Metzger1,2,3,4 ([email protected]), Matthias Mauder1, Klaus Butterbach-Bahl1,
Baltasar Trancon y Widemann5, Jeffrey Taylor3,4, Henry Loescher3,4, Xunhua Zheng2, Hans Peter Schmid1, Thomas Foken6
Setting
Goal: Scaling the exchange of sensible and
latent heat to the entire Xilin River Catchment.
Landscape is variable in time and space.
Ground based / airborne flux measurements integrate in time / space.
2% 1%
In the summer of 2009 a total of 36
research flights were conducted.
6%
1%
1% 5%
10% 5%
Fig. 1: Location of the Xilin River Catchment in the Inner Mongolia autonomous
region, northeastern China. Topography (shading) and land cover (colors)
vary on multiple spatial scales. Straight lines indicate the flight lines. The
aircraft measurements intend to (i) understand the influence of this
heterogeneity on the fluxes and (ii) enable to integrate it in the quantification
of the regional sensible and latent heat exchange.
71%
Distribution
Fig. 2: Catchment land cover distribution, and detail of flight line O12 with footprint
effect level rings. The actual surface state can be represented by MODIS
land surface temperature (LST) and enhanced vegetation index (EVI).
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Scientific questions
How does one scale the exchange of heat from
heterogeneous flight transects to the catchment?
Can airborne flux measurements at 50 m above
the ground be related to surface properties?
How does one achieve high horizontal resolution
without increasing the systematic error?
Fig. 3: Background – The minimum measurement altitude is 50 m above ground.
This altitude is a compromise of (i) flight safety in a hilly landscape under
thermal turbulence, and (ii) proximity to the heat exchange processes at the
ground.
50 m
Fig. 4: Top – Wavelet cross-scalogram of the latent heat flux (LE) and its sectional
integration over all turbulent scales along the flight line O12 (Metzger et al.,
2012b). Bottom – Concurring Bowen ratio (Bo) and fractional contributions of
land cover, LST and EVI in the footprint of each observation along the flight
line O12. How does one disentangle the influence of meteorological forcing
and varying surface properties on the flux observations?
The LTFM method
The wavelet cross-scalogram resolves the flux
along the flight line without longwave losses.
What is LTFM?
1. Low-level flux measurement;
2. Time-frequency analysis;
3. Footprint modeling;
4. Machine learning.
A footprint algorithm quantifies the contribution of
different surfaces to the measurement.
Boosted regression trees (BRTs) extract the
relationships between continuous environmental
drivers and flux observations (responses).
Fig. 5: Top and left – LTFM does not require pre-stratification of the observations
(e.g., Hutjes et al., 2010). BRTs are used to fit an environmental response
function using all 8446 observations. The resulting function is visualized as
partial dependence plots, which show the effect of each individual driver on
the response. Bottom right – the agreement of observations and LTFM is
excellent, few outliers are related to intermittent solar irradiance.
Fig. 6: Background – Eddy Covariance setup on the microlight aircraft (Metzger et
al., 2011, 2012a): data from the nose boom and infrared gas analyzer are
recorded at 10 Hz, the horizontal resolution of the observations is 2.6 m.
Fig. 5
Fig. 6 (background)
Results and outlook
The resulting environmental response function
can be used to;
isolate and quantify relevant exchange processes;
bridge observational scales;
assess spatial representativeness;
design, constrain and evaluate flux algorithms;
distinguish anthropogenic and natural sources/sinks…
Fig. 7: The environmental response function is used to extrapolate the sensible(top) and latent heat exchange (bottom) throughout the Xilin River
Catchment. Numerical tests show that the predictions are accurate to ≤18%,
and, when summarized for land cover classes, precise to ≤5% (Metzger et
al., 2012b). Despite LTFM does not use the land cover classification, several
landscape units are clearly recognizable in the flux maps.
Fig. 8: Summary of LTFM predicted catchment-wide Bo for individual land covers
throughout the flight campaigns. Top – Comparison among subsequent
flights – noontime Bo can be interpreted as a land cover specific property.
Bottom – Time-series of land cover specific noontime Bo. The 1σ error bars
are dominated by spatial variability within each land cover class.
Fig. 7
Fig. 8
Acknowledgements:
References:
Recognition has to be given to Frank Neidl1, who programmed and continuously maintained the data acquisition system. The MODIS data were obtained through the online Data Pool at the
NASA Land Processes Distributed Active Archive Centre, USGS/Earth Resources Observation and Science Centre (https://lpdaac.usgs.gov/get data). The flight campaign in Inner Mongolia was
funded by the German Research Foundation, research group FOR 536 MAGIM, “Matter fluxes in grasslands of Inner Mongolia as influenced by stocking rate”, and the National Natural Science
Foundation of China, grant number 41021004. Stipend funding by the German Academic Exchange Service, Helmholtz Association of German Research Centers, China Scholarship Council and
the European Union under the Science and Technology Fellowship China is acknowledged. The National Ecological Observatory Network is a project solely sponsored by the National Science
Foundation and managed under cooperative agreement by NEON, Inc. This material is based upon work supported by the National Science Foundation under the grant DBI-0752017. Any
opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Hutjes, R. W. A., Vellinga, O. S., Gioli, B., and Miglietta, F.: Dis-aggregation of airborne flux measurements using footprint analysis, Agric. For. Meteorol., 150, 966-983, 2010.
Metzger, S., Junkermann, W., Butterbach-Bahl, K., Schmid, H. P., and Foken, T.: Measuring the 3-D wind vector with a weight-shift microlight aircraft, Atmos. Meas. Tech., 4, 1421-1444, 2011.
Metzger, S., Junkermann, W., Mauder, M., Beyrich, F., Butterbach-Bahl, K., Schmid, H. P., and Foken, T.: Eddy-covariance flux measurements with a weight-shift microlight aircraft, Atmos.
Meas. Tech., 5, 1699-1717, 2012a.
Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancón y Widemann, B., Neidl, F., Schäfer, K., Wieneke, S., Zheng, X. H., Schmid, H. P., and Foken, T.: Spatial resolution and
regionalization of airborne flux measurements using environmental response functions, Biogeosciences Discuss., 9, 15937-16003, 2012b.