Modelling the dynamics of total precipitation and aboveground net primary production of fescue-feather grass steppe at Askania Nova according to global climate change scenariosModelling the dynamics of total precipitation and aboveground net primary production of fescue-feather grass steppe at Askania Nova according to global climate change scenarios


  • S. O. Belyakov National University of Kyiv-Mohyla Academy
  • O. P. Gofman Falz-Fein Biosphere Reserve “Askania-Nova”
  • I. G. Vyshenska National University of Kyiv-Mohyla Academy
Keywords: primary production, fescue-feather grass steppe, climate change, regression model, IPCC scenarios, BIOCLIM

Abstract

This article discusses modelling of Aboveground Net Primary Production (ANPP) of steppe (arid grassland ecosystems) plant species in relation to changes in total precipitation over the previous year at the “Stara” study site, Biosphere Reserve “Askania-Nova”, Khersonregion (Ukraine). To investigate linkages between precipitation and Aboveground Net Primary Production, correlation analysis was chosen and a time series regression analysis was based on the data set for the period 1988–2012. The NPP dependence on quantity of precipitation was found to be more significant for the previous autumn-winter-spring period (AWSP) than for the previous 12 month period. A regression model of ANPP’s dependence on AWSP is proposed. This model was further validated by the authors’ samples of ANPP, collected at the “Stara” study site in 2013–2016. The regression model showed a non-linear (quadratic) dependence of net primary production of zonal and intrazonal plant coenoses and total precipitation for the autumn-winter-spring period for arid grasslands with a coefficient of determination equal to 0.54 and significance level less than 0.05. The non-linear equation for these relations, visualized by a parabola curve, was calculated using the Nonlinear Least-Squares Regression Method. The data set, based on calculated predicted values, using the calculated equation, had a similar dynamic to the historical data on ANPP, but the model could not predict critical values. For this reason, additional studies are required for critical precipitation events. Non-linear response, investigated according to regression analysis, reveals optimal zones of plant growth, depending on the total precipitation level before the vegetation peak. For research areas where the dominant species are the turf grasses Stipa ucrainica P. Smirn., S. capillata L., S. lessingiana Trin. & Rupr., Festuca valesiaca Gaudin, Koeleria cristata (L.) Pers.) the optimal precipitation rates were found to be 350–400 mm during AWSP with ANPP at 350 g/m2. On the basis of the regression model and current forecasts of changes in precipitation rates we made a forecast of net primary production of plant communities for four climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) described in the Fifth Assessment of Intergovernmental Panel on Climate Change (IPCC). For this purpose, bioclimate projections of 10 major climate models (The Community Climate System Model Version 4 (CCSM4), GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MRI-CGCM3, NorESM1-M), used for preparation of the IPCC report, were analyzed and imported to the geographical information system package QGIS. QGIS modelling software was used for geoanalysis and calculation of GIS-layers for Askania-Nova and adjacent arid grasslands. The results of modelling with the 10 climate models were compared and analyzed for each of the four IPCC scenarios, depending on predicted CO2 levels. The presented modelling results showed a trend to growth in AWSP precipitation and NPP for all scenarios up to 2040–2060. The scenarios RCP2.6, RCP4.5, RCP6 predicted the optimum precipitation zone for current plant diversity for the period of 2040–2060 and scenario RCP8.5 predicted an optimum zone peak after 2080. The study confirmed the importance of monitoring the productivity of herbaceous communities in dry steppe ecosystems ofUkraine.

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Published
2017-01-31
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