Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands

Lokupitiya, Erandathie; Denning, Scott; Paustian, Keith; Baker, Ian; Schaefer, Kevin; Verma, Shashi B.; Meyers, Tilden P.; Bernacchi, Carl J.; Suyker, Andrew E.; Fischer, M. L.

doi:10.5194/bg-6-969-2009

Cited by 160 publications

(139 citation statements)

References 102 publications

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“…An exception are the C 4 vegetation classes, grasses and crops, which were projected onto the MODIS biomes from (Wang et al, 2006). The crop characterization is admittedly simple and more work is currently being done to incorporate more accurate crop maps and more realistic crop modeling into SiB (Lokupitiya et al, 2009). SiB has traditionally calculated fPAR, which defines the fraction of photosynthetically available radiation that is absorbed by the plant canopy, and Leaf Area Index (LAI) using satellite derived NDVI fields.…”

Section: Prior Flux Model and Transportmentioning

confidence: 99%

A regional high-resolution carbon flux inversion of North America for 2004

et al. 2010

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Abstract.Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO 2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from Correspondence to: A. E. Schuh (aschuh@atmos.colostate.edu) the RAMS and the Parameterized Chemical Transport Model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO 2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO 2 . RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America. We perform the inversion with two independently derived boundary inflow conditions and calculate jackknife-based statistics to test the robustness of the model results. We then compare final results to estimates obtained from the CarbonTracker inversion system and at the Southern Great Plains flux site. Results are promising, showing the ability to correct carbon fluxes from the biosphere models over annual and seasonal time scales, as well as over the different GPP and ER components. Additionally, the correlation of an estimated sink of carbon in the South Central United States with regional anomalously high precipitation in an area of managed agricultural and forest lands provides interesting hypotheses for future work.

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Section: Prior Flux Model and Transportmentioning

confidence: 99%

A regional high-resolution carbon flux inversion of North America for 2004

et al. 2010

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“…They are intensively managed to avoid resource constraints, for example by tilling, irrigation, fertilization, and weeding. Eddy covariance measurements over growing maize show net ecosystem exchange (NEE, expressed as ecosystem respiration minus photosynthesis) of CO 2 as high as 75 lMol m -2 s -1 at midday (Lokupitiya et al 2009), more than three times the typical rate over the Amazon rainforest (Saleska et al 2003). As a result, seasonal drawdown of about 35 ppm of CO 2 in the atmospheric boundary layer was observed by a network of instrumented towers in the central US Corn Belt; it was the strongest seasonal CO 2 cycle ever observed (Miles et al 2012).…”

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confidence: 99%

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

et al. 2016

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Croplands are highly productive ecosystems that contribute to land-atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization Author's personal copy for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.Keywords Carbon and energy fluxes Á Cropland ecosystems Á Land-atmosphere exchange Á Modeldata comparison Á Cropland carbon and energy exchange

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“…Nonetheless, the readings displayed in Fig. 1 The different phenology not only has an impact on the primary productivity during the growing season but also on the energy and matter fluxes such as evapotranspiration, sensible heat flux or long-and shortwave outgoing radiation as well as on CO 2 fluxes or soil moisture (Lokupitiya et al, 2009). This must be taken into account when modelling the processes on the land surface.…”

Section: Heterogeneity Of Arable Landmentioning

confidence: 99%

“…According to CLC, arable land accounts for 46% of the study area and thereby represents the class with the largest proportion of all land cover classes in central Europe. However, croplands include a variety of species with different phenology and physiology (Lokupitiya et al, 2009).…”

Section: Heterogeneity Of Arable Landmentioning

confidence: 99%

Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

Zabel

Hank

Mauser

2010

Hydrol. Earth Syst. Sci.

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Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

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Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands

Cited by 160 publications

References 102 publications

A regional high-resolution carbon flux inversion of North America for 2004

A regional high-resolution carbon flux inversion of North America for 2004

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

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