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Krzyszczak, J. R., Baranowski, P., & Sławiński, C. (2014). CO2 flux measurements in the vegetation period of winter wheat in Lubelskie province. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The assessment of net ecosystem exchange and respiration of ecosystem of terrestrial ecosystems is necessary to improve our knowledge about carbon cycle in nature. Here we present measurements of CO2 fluxes for a winter wheat temperate climate ecosystem (buckwheat in the previous years) located in the Lubelskie province (eastern Poland) using a closed dynamic chamber system over a 2013 vegetation season. Measurements of carbon dioxide emission from soils and its assimilation by plants were carried out on a typical for Lubelskie highland arable land located in the Stany Nowe (N50o49’17.0555”, E22o16’28.51”, height 243m above sea level) using the set of two chambers (transparent and dark). Carbon dioxide fluxes have been measured by EGM-4 PP Systems sensor during fixed stages of the plant growing season. During the experiment carbon emission from soil ranged from 151 to 764 mg C·m-2·h-1 and its assimilation by plants ranged from -148 (emission) to 1585 mg C·m-2·h-1. We found substantial differences in emission and assimilation of carbon in the winter wheat ecosystem. This, along with other measurements (meteorological factors and soil and plant parameters) carried out in the Stany Nowe can be used as a high quality data to verify various models of emission of greenhouse gases. The chamber technique occurs to be a useful tool for determining carbon dioxide exchange between ecosystem surface and the atmosphere.
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Baranowski, P. (2015). Multifractal analysis of meteorological time series to assess climate impact on chosen regions of Europe (Vol. 5).
Abstract: Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label
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Bojar, W., Knopik, L., Żarski, J., Sławiński, C., Baranowski, P., & Żarski, W. (2014). Impact of extreme climate changes on the forecasted agriculture production. Acta Agrophysica, 21(4), 415–431.
Abstract: The paper presents general characteristics of resources and outputs of agriculture in the Kujawsko-Pomorskie and Lubelskie Regions, based on statistical databases and literature review. Some specific features of the regions, with special consideration for the predicted extreme climate changes, are also included. Next, some statistically significant dependencies between the climatic parameters and yields of selected important crops in the abovementioned regions were worked out on the basis of empirical survey conducted in the University of Technology and Life Sciences, Bydgoszcz, and the Institute of Agrophysics in Lublin. Creating an appropriate method of forecasting long series of ten days without precipitation was necessary to find the desired dependencies. Third, some efforts were taken to make integrated assessments of forecast agricultural outputs influenced by climate extreme phenomena on the basis of the yield-precipitation relations obtained and on the data coming from wide area model regional outputs such as prices of farmland and produce.
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Murat, M., Malinowska, I., Hoffmann, H., & Baranowski, P. (2016). Statistical modelling of agrometeorological time series by exponential smoothing. International Agrophysics, 30(1), 57–65.
Abstract: Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.
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Krzyszczak, J., Baranowski, P., & Slawinski, C. (2014). Chamber system measurements of carbon dioxide fluxes from winter wheat field in a Lubelskie province..
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