Current ozone levels threaten gross primary production and yield of Mediterranean annual pastures and nitrogen modulates the response

Pastures are among the most important ecosystems in Europe considering their biodiversity and dis- tribution area. However, their response to increasing tropospheric ozone (O 3 ) and nitrogen (N) deposi- tion, two of the main drivers of global change, is still uncertain. A new Open-Top Chamber (OTC) experiment was performed in central Spain, aiming to study annual pasture response to O 3 and N in close to natural growing conditions. A mixture of six species of three representative families was sowed in the fi eld. Plants were exposed for 40 days to four O 3 treatments: fi ltered air, non- fi ltered air (NFA) repro- ducing ambient levels and NFA supplemented with 20 and 40 nl l � 1 O 3 . Three N treatments were considered to reach the N integrated doses of “ background ” , þ 20 or þ 40 kg N ha � 1 . Ozone signi fi cantly reduced green and total aboveground biomass (maximum reduction 25%) and increased the senescent biomass (maximum increase 40%). Accordingly, O 3 decreased community Gross Primary Production due to both a global reduction of ecosystem CO 2 exchange and an increase of ecosystem respiration. Nitrogen could partially counterbalance O 3 effects on aboveground biomass when the levels of O 3 were moderate, but at the same time O 3 exposure reduced the fertilization effect of higher N availability. Therefore, O 3 must be considered as a stress factor for annual pastures in the Mediterranean areas.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.