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首页> 外文期刊>Biosystems Diversity >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
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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

机译：根据全球气候变化模型模拟Askania Nova羊茅草草原的总降水量和地上净初级生产力的动态根据全球气候变化模拟Askania Nova羊茅草草原的总降水量和地上净初级生产量的全球气候变化情景

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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.
机译：本文讨论了在赫尔松地区生物圈保护区“ Askania-Nova”的“ Stara”研究地点，草原（干旱草地生态系统）植物物种的地上净初级生产力（ANPP）与上一年总降水量变化的关系（乌克兰）。为了研究降水与地上净初级生产之间的联系，选择了相关分析，并基于1988-2012年的数据集进行了时间序列回归分析。发现前一个秋冬-春季期间（AWSP）的NPP对降水量的依赖性比前12个月的时期更为显着。提出了ANPP对AWSP依赖性的回归模型。 2013年至2016年在“ Stara”研究中心收集的ANPP作者样本进一步验证了该模型。回归模型显示干旱草地秋冬春季的带状和带状植物代数的净初级生产力和总降水量的非线性（二次）依赖性，测定系数等于0.54，显着性水平小于0.05。使用非线性最小二乘回归方法，可以通过抛物线曲线可视化这些关系的非线性方程。该数据集基于所计算的预测值并使用所计算的方程式，具有与ANPP上的历史数据类似的动态，但是该模型无法预测关键值。因此，对于关键的降水事件还需要进行其他研究。根据回归分析进行的非线性响应揭示了植物生长的最佳区域，具体取决于植被高峰之前的总降水量。对于主要草种为草皮草的研究区，Stipa ucrainica P. Smirn。，S。capillata L.，S。？lessingiana Trin。 ＆Rupr。，Festuca valesiaca Gaudin，Koeleria cristata（L.）Pers。）发现在AWSP和ANPP浓度为350？g / m2时，最佳降水速率为350–400 mm。在回归模型和当前降水率变化的预测的基础上，我们对四种气候变化情景（RCP2.6，RCP4.5，RCP6和RCP8.5）中描述的植物群落净初级生产力进行了预测。政府间气候变化专门委员会（IPCC）第五次评估。为此，对10种主要气候模型（社区气候系统模型版本4（CCSM4），GISS-E2-R，HadGEM2-AO，HadGEM2-ES，IPSL-CM5A-LR，MIROC-ESM-CHEM，MIROC）进行了生物气候预测分析了用于准备IPCC报告的-ESM，MIROC5，MRI-CGCM3，NorESM1-M），并将其导入到地理信息系统软件包QGIS中。 QGIS建模软件用于Askania-Nova和邻近干旱草原的GIS层的地理分析和计算。根据预测的CO2水平，针对四种IPCC情景中的每一种，对10种气候模型的建模结果进行了比较和分析。提出的建模结果表明，直到2040年至2060年的所有情景，AWSP降水和NPP都有增长的趋势。 RCP2.6，RCP4.5，RCP6情景预测了2040–2060年当前植物多样性的最佳降水区，RCP8.5情景预测了2080年之后的最佳降水区峰值。研究证实了监测水稻生产力的重要性。乌克兰干旱草原生态系统中的草本群落。

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