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Main features of the Biome-BGC MuSo model Zoltán BARCZA, Dóra HIDY Training Workshop for Ecosystem Modelling studies Budapest, 29-30 May 2014.

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Presentation on theme: "Main features of the Biome-BGC MuSo model Zoltán BARCZA, Dóra HIDY Training Workshop for Ecosystem Modelling studies Budapest, 29-30 May 2014."— Presentation transcript:

1 Main features of the Biome-BGC MuSo model Zoltán BARCZA, Dóra HIDY Training Workshop for Ecosystem Modelling studies Budapest, May 2014

2 Biome-BGC Typical process-based biogeochemical model with some shortcomings -no management - problematic phenology -for grasslands - very simple soil hydrology - some parameters are ‘burned in’ within the source code - no drought effect - general PFT parameterization is not applicable at site level

3 Drought and heat in 2003 over Europe: response of Biome-BGC was consistent with other models but cause of this was not plausible (respiration increased in spite of drought – measurements do not support this result)

4 Biome-BGC MuSo – multilayer soil module improved phenology – HSGSI method, combination of heatsum and GSI index (Jolly et al., 2005 GCB) multilayer soil module [soil temperature is also simulated layer by layer] effect of long lasting drought on plant mortality [leaf senescence] stomatal conductance control – now with relative soil moisture content root profile is simulated + management is implemented [not exclusively for herbaceous vegetation]

5 Management mowing [hay meadows] grazing typical agricultural practices [ploughing, sowing, harvest, use of organic or inorganic fertilizers] forest thinning is also implemented

6 Management Hegyhátsál grassland: mowing 1-2 times per year mowing

7 Management Prior to MuSo v1.3 management was the same in each simulation year [set by the INI file]. Starting with MuSo v1.3 there is an option to control the normal simulation phase with ancillary files that define annually varying management. Example for mowing [this is a separate file!]:

8 Soil hydrology MuSo v2.2.1 soil layer depths : 0-10, 10-30, 30-60, , , , and cm; soil layer thickness is calculated (7 layers!)

9 Soil hydrology Exponential root profile

10 Nagy et al., 2007 AGEE Drought effect on biogeochemical cycles Bugac – sandy grass [drought is typical]

11 Drought induced plant mortality at Bugac

12 Hungarian grasslands – satellite view Credit: Anikó Kern, Space Research Group, ELU

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14 Effect of excess water - elevated groundwater [flooded areas] are typical in many lowland ecosystems - measurements clearly show the effect of soil saturation on fluxes

15 Effect of excess water – previous work

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17 Effect of excess water – our approach - water table depth is prescribed [model does NOT calculate water table depth] - another model [watershed model?] is needed - daily data is needed for the entire normal simulation period - with the latest MuSo groundwater can be prescribed during spinup [1 year of data is needed] - prior to MuSo v2.2.1 groundwater effect was step-wise; now rising water table causes smooth transition as a function of depth and affected soil layer

18 Biome-BGC MuSo v2.2.1 developments

19 Biome-BGC MuSo v2.2.1 developments

20 Jastrebarsko pendunculate oak forest effect of groundwater on simulated GPP vs. measurement Credit: Maša Ostrogović, Hrvoje Marjanović

21 Issues with storage/transfer pools In Biome-BGC MuSo v2.2.1 specific management types (e.g. grazing, mowing and havest) affects (decrease) the storage/transfer pools and also fine roots. RRM defines the ratio of the belowground and aboveground pool decline due to grazing, mowing and harvest. RRM is set to 0.1 in the current model version. This means that e.g. in case of removing 50% of aboveground plant material (actual pools of leaf) due to cutting causes 5% decrease in both the leaf and root storage/transfer pools, and also the root pool itself. New parameter: Ratio of belowground/ aboveground management related mortality (RRM)

22 Other developments correction of bug related to the calculation of daylight average temperature standing dead biomass [drought related leaf senescence – intact, turnover might be slow] annually varying ecophysiological parameters: e.g. implementation of annually varying whole plant mortality (dynamic mortality) – forests C4 photosynthesis is improved [Di Vittorio et al. 2010]

23 Unresolved issues soil carbon content is too high after spin-up [recalcitrant SOM is overestimated] if soil carbon is reduced by e.g. increased mortality during spin-up then fluxes are underestimated this is caused by N limitation caused by reduced SOM LAI is overestimated multilayer soil module might need improvement soil organic matter profile is not simulated

24 Current version of Biome-BGC MuSo It is version installed on the Demo Grid - MuSo 2.2 is available at the Desktop Grid [will be replaced with 2.2.1]

25 Is MuSo better than the original model? - better performance on eddy-covariance sites - management seems to be a more important driver of the carbon balance than climate! - we have additional parameters + more complicated ini, so practical application of the model became more complex - but thanks to BioVeL now we have great infrastructure, which will be maintained after BioVeL ended

26 Carpathian grasslands - soil control on NPP is dominant – precipitation and temperature has less effect on NPP!

27 Legacy effect of climate variability

28 Documentation

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30 Biome-BGC MuSo v2.2.1

31 Biome-BGC MuSo Models can never be finished, but way may decide to stop the development at some point

32 Thank you for your attention


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