Biome-BGC is an effective model that uses site description data, meteorological data, and vegetation physiological and ecological parameters to simulate daily-scale carbon, water, and nitrogen fluxes. The spatial scale of its research can be extended from the point scale to terrestrial ecosystems.
In the Biome-BGC model, for carbon biomass accumulation, the photosynthetic enzymatic reaction mechanism model is used to calculate the daily primary productivity (GPP), and the products after subtracting growth respiration and maintenance respiration are allocated to leaves, branches, stems and root. The carbon of living organisms enters the litter carbon pool in the form of litter at a certain proportion every day; for the water transport process, the water cycle process simulated by this model includes rainfall, snowfall, canopy interception, penetrating precipitation, trunk runoff, and canopy evaporation. , snowmelt, snow sublimation, canopy transpiration, soil evaporation, evapotranspiration, surface runoff and soil moisture changes, and water use by plants; for soil processes, the model considers the process of litter decomposition into soil organic carbon pools and the mineralization process of soil organic matter. and the transport relationship of water between soil layers based on the barrel model; for energy balance, the model also considers processes such as net radiation, sensible heat flux, and latent heat flux.
This time, we will teach how to use the China Regional Surface Meteorological Element Driven Dataset (CMFD) and CN05.1 climate data gridded meteorological data to drive Biome-BGC to perform regional simulations. During the simulation process, it is necessary to comprehensively use some small tools such as Linux and Python to complete the pre-processing and post-processing of the model.
| arrange |
content |
| first part Mode explanation |
Introduction to Biome-BGC
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| the second part Course Basics |
Linux application lAchieve batch creation of files, deletion of files and folders lParallel execution of programs CDO tool application lUse the cdo tool to merge netCDF files l Filter time and variables and crop them into small areas Python application l Python loop statements, logical statements, lCreate Numpy array and perform statistical calculations; l Use Matplotlib to create scatter plots and contour plots; lUse scattered data Pandas to create data and production time lUse X array to read netCDF files and write netCDF files; implement interpolation work |
| the third part data processing |
Use cdo and xarray data to prepare the required data on Linux. 1Static data preparation: lTerrain data: GTOPO 30 S 1 km lLand use data: GLCC 1 km lSoil data: FAO l GPP data: MODIS data
2 Drive data preparation: l CN 05 . 1 Data processing l CMFD data processing 3 Ecological data MODIS GPP |
| fourth part Single point simulation |
1Pre-processing l Interpolation from spatial grid data (netCDF format) to sites lConfigure Biome-BGC run file lPrepare meteorological data used to drive Biome-BGC 2Run the BGC model 3 Parameter adjustment Using the MODIS GPP product as the observation value, use the Python library to adjust the parameters of the Biome-BGC model in parallel lAdjust the start and end of the growing season
4 post-processing lRead Biome-BGC ascii files and binary files lResult statistical calculation Results visualization |
| the fifth part Regional Simulation-1 |
Regional simulation is performed by calculating each grid point in the region separately. In the case of this section, a high-resolution simulation will be performed on a smaller province and a coarse-resolution simulation will be performed on China. The following steps are involved in the simulation process: lStatic geographic data preparation lMeteorological driven data preparation lDistribute data lRun in parallel Merge single point results into spatial data |
| Part VI Long time series simulation case |
CMIP6 future climate change downscaled data using ERA5 as the downscaled observational data. l Downscale meteorological data to obtain temperature, humidity, precipitation and downward shortwave radiation. lSoil data and vegetation database query lPrepare meteorological data and static data lPost -processing simulation result data
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| Part 7 analyze |
On the basis of single point and spatial simulation data, the following analysis is performed: l Sensitivity analysis: Use sensitivity analysis method ( SAL ib library) to analyze the impact of main simulation parameters on GPP l Attribution analysis: Use the path analysis method ( semopy library), combined with meteorological elements, to analyze the impact on GPP and ET |
| Requires basic hardware requirements |
CPU: 8 cores, 16 threads and above (space simulation requires computing resources) Memory: 16G and above Hard disk: Computer local hard disk 100GB or above (storage of virtual machine + data) Before the course, you will be assisted in deploying and configuring the VirtualBox virtual machine (Python's running environment) |
