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| Titel |
A data assimilation framework for constraining upscaled cropland carbon flux seasonality with MODIS |
| VerfasserIn |
O. Sus, M. Williams |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250067650
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| Zusammenfassung |
| Agroecosystem models are strongly dependent on information on land management patterns
for regional applications. Land management practices play a major role in determining global
yield variability, and add an anthropogenic signal to the observed seasonality of atmospheric
CO2 concentrations. However, there is still little knowledge on spatial and temporal
variability of important farmland activities such as crop sowing dates, cultivar selection, and
fertilisation application, and thus these remain rather crudely approximated within carbon (C)
cycle studies.
In this study, we present a data assimilation framework allowing for spatiotemporally
resolved simulation of cropland C fluxes under observational constraints on sowing dates and
canopy greenness. MODIS 250 m vegetation index data were assimilated both
variationally (for sowing date estimation) and sequentially (for improved model state
estimation, using the EnKF) into a crop C mass balance model (SPAc). In doing so,
we are able to accurately quantify the multiannual (2000–2006) regional C flux
seasonality of maize-soybean crop rotations surrounding the Bondville (IL, US)
Ameriflux EC site, averaged over 104 pixel locations within the wider area (32 km à 25
km).
We find that MODIS-derived sowing dates allow for highly accurate simulations of
growing season C cycling at locations for which ground-truth sowing dates are
not available. Resulting simulations provide an envelope on upscaled cropland
phenology, with significant deviations from plot-scale observations at Bondville: study
area average growing season length is ~20 days longer than observed, primarily
because of an earlier estimated start of season. Relative spatial variability of net
ecosystem exchange (NEE) of C ranges from ~7% to ~10%, but variability in net
biome productivity is considerably larger (~24% to ~32%). Differences between
Bondville and upscaled NEE are especially large in years with non-optimal weather
conditions for sowing. This shows that regional patterns of land management are
important drivers of agricultural C cycling and major sources of uncertainty if not
properly accounted for. Our framework enables modellers to accurately simulate
current (i.e. last 10 years) C cycling of major agricultural regions with sufficient
individual field patch sizes. DA methodology applied in this study can help to isolate an
anthropogenic signal from natural variability in atmospheric CO2 concentration time series. |
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