Global Yield Assessment: Description and data requirements of the global dynamic vegetation model LPJmL


Published on

Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Global Yield Assessment: Description and data requirements of the global dynamic vegetation model LPJmL

  1. 1. Global Yield Assessment Description and data requirements of the global  dynamic vegetation model LPJmL Katharina Waha Workshop Beyond Diagnostics: Insights and Recommendations for  Remote Sensing, 14./15. December 2013
  2. 2. 1 – LPJmL in Short LPJmL Processes • process‐based dynamic vegetation model, originates from EPIC and BIOME  models • simulates plant responses to climate and  climate change in natural and agriculture ecosystems Climate Respiration NPP Allocation • high spatial and temporal resolution Photosynthesis Soil water
  3. 3. 2 – Main features / modules Climate, Soil, Land Use Land use change • Regular grid (67.420 grid cells 0.5°x 0.5°) Crop Biomass, Harvest, Water Use • 13 crops + managed grassland + bioenergy  plants:  ‐ wheat, rice, maize, millet, pulses,  sugarbeet, cassava, sunflower, groundnuts,  soybean, rapeseed, sugar cane, other crops
  4. 4. 2 – Main features / modules (cont.) Carbon pools and fluxes Water balance • Regular grid (67.420 grid cells 0.5°x 0.5°) biochemical leaf photosynthesis model (Farquhar et al. 1980  /Haxeltine & Prentice 1996) Daily allocation driven by phenology,  stress and production ‐ Farquhar, G.D. et al. 1980. A Biochemical  Model of Photosynthetic CO2 Assimilation  in Leaves of C3 Species. Planta. 149, 78‐90. ‐ Haxeltine, A.,Prentice, I.C., 1996. BIOME3:  An equilibrium terrestrial biosphere model  based on ecophysiological constraints,  resource availability, and competition  among plant functional types. Global  Biogeochemical Cycles. 10, 693‐709.
  5. 5. 3 – Crop management • Management modules (climate‐driven, input‐driven) + often more important than climate and soils ‐> Computed internally + Planting dates (Waha et al. 2012)  + Available irrigation water (Biemans et al. 2011) + Variety characteristics (Bondeau et al. 2008, van Bussel 2011) ‐> Prescribed + Annual land‐use patterns (Fader et al. 2010) + Irrigation (yes/no) + Intercrops  + Residue management + Management  Intensity (Fader et al. 2010) Simulated sowing date for maize in 2000 (Waha et al. 2012)
  6. 6. 4 – Model Input • Soils + FAO Harmonized Soil Database (13 soil texture classes ‐> water holding capacity) • Climate + current and past climate:  monthly: CRU TS 3.21 (1901‐2012) daily: GPCC (1901‐2007), WATCH (1901‐2001) + future climate: climate projections from GCMs via CMIP5 project • Landuse + generated from 3 land use data sets + rainfed and irrigated cropland in  1700 – 2005 for 13 crops CRU ‐ Climate Research Unit, University of East Anglia,  GPCC ‐ Global Precipitation Climatology Centre           WATCH ‐ WATCH Forcing Data 20th Century                     GCM ‐ General Circulation Model                                      CMIP5 ‐ Coupled Model Intercomparison Project Phase 5  Compilation procedure of the land‐use dataset for LPJmL  (Fader et al. 2010)
  7. 7. 5 – Model Output: Crop Yields National and grid‐cell yields Simulated grid‐cell wheat yields (t/ha) in 2000 Simulated mean area‐weighted national wheat yield (t/ha) in 2000 Rainfall Simulated mean area‐ weighted national  maize yield 1961‐2000  (t/ha) in Burkina Faso Yield Interannual variability
  8. 8. 5 – Model Output: Crop yields under climate change With CO2 fertilization Without CO2 fertilization Mean climate change impact (%) on (sub‐) national crop yields in 2050 relative to 2000. Climate change impacts are  shown as simulated with LPJmL with climate projections from 5 general circulation models and 3 emission scenarios  (Müller et al. 2009). Müller, C., Bondeau, A., Popp, A., Waha, K.,Fader, M., 2009. Climate Change Impacts on Agricultural Yields.  Background note to the World Development Report 2010. World Bank, Washington D.C.
  9. 9. 6 – Under development and future plans (examples) • Refine management modules (irrigation, rainwater harvesting and vapor shift techniques, multiple cropping) • Add more crops (potato, cotton, date palm, citrus, …) • Continue development of bioenergy plants • Add nitrogen cycle • Understand uncertainty in CO2 fertilization effect (coupled effects from increased temperatures and CO2) • Improve grassland management and representation of livestock • Revise simulated impacts of extreme temperature and precipitation
  10. 10. Thank you http://www.pik‐ Dr Katharina Waha Climate Impacts & Vulnerabilities tel: +49 331 288 26 27  e‐mail: katharina.waha@pik‐
  11. 11. Literature key model components, LPJmL as LPJ‐DGVM: • Collatz, G.J., Ribas‐Carbo, M.,Berry, J.A. 1992. Coupled Photosynthesis‐Stomatal Conductance Model for Leaves of C4 Plants,  pp. 519‐538, Vol. 19. • Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke,  K.,Venevsky, S., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology. 9, 161‐185. agricultural vegetation: • Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze‐Campen, H., Müller, C., Reichstein,  M.,Smith, B., 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change  Biology. 13, 679‐706. hydrology, river routing: • Biemans, H., Haddeland, I., Kabat, P., Ludwig, F.,Hutjes, R.W.A., 2011. Impact of reservoirs on river discharge and irrigation water supply during the 20th century. Water Resources Research. 47, W03509. • Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W.,Sitch, S., 2004. Terrestrial vegetation and water balance ‐ hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology. 286, 249‐270. water management in agricultural, virtual water, land‐use data set and management intensity: • Fader, M., Rost, S., Müller, C., Bondeau, A.,Gerten, D., 2010. Virtual water content of temperate cereals and maize: Present and  potential future patterns. Journal of Hydrology. 384, 218‐231. • Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J.,Schaphoff, S., 2008. Agricultural green and blue water consumption and  its influence on the global water system. Water Resources Research. 44, W09405 (17pp). permafrost, soil hydrology update: • Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J.,Lucht, W., 2013. Contribution of permafrost soils to the global  carbon budget. Environmental Research Letters. 8, 014026. crop phenology, sowing and harvest dates • Van Bussel, L.G.J., 2011. From field to globe: upscaling of crop growth modelling., Dissertation, Wageningen University,  Wageningen. • Waha, K., van Bussel, L.G.J., Müller, C.,Bondeau, A., 2012. Climate‐driven simulation of global crop sowing dates. Global  Ecology and Biogeography. 21, 247–259. bioenergy: • Beringer, T.I.M., Lucht, W.,Schaphoff, S., 2011. Bioenergy production potential of global biomass plantations under environmental and agricultural constraints. GCB Bioenergy.