Jaromir Krzyszczak

Relationship between assimilable-nutrient content and physicochemical properties of topsoil

Abstract In the years 2008-2011, an environmental study was conducted for Polish soils, focusing on the south-eastern Poland soils, as they exhibit significant acidification. This study aimed at assessing the current pHKCl and the supply of basic macro- (P, K, Mg and S-SO4) and microelements (B, Cu, Fe, Mn and Zn) in the collected soil samples, and also at determining their relationship with the soil agronomic category, humus content and pH class. Soil reaction and humus and macronutrient content were positively correlated with the amount of colloidal clay and particles < 0.02 mm. In the majority of cases, the macro-element content in the soil was positively correlated with soil pH and humus content. As for microelements, a usually significant and positive correlation was found between the soil agronomic category and the content of manganese, iron and zinc, whereas for the content of boron and copper, no such relationship was observed. A significant and positive correlation between soil reaction and the content of manganese, iron and boron was also found. Such correlations were not observed in relation to copper and zinc content. Statistical analysis indicated that the content of boron and manganese depended to the greatest extent on the investigated physicochemical properties.

Analysis of Climate Dynamics Across a European Transect Using a Multifractal Method

Climate dynamics were assessed using multifractal detrended fluctuation analysis (MF-DFA) for sites in Finland, Germany and Spain across a latitudinal transect. Meteorological time series were divided into the two subsets (1980–2001 and 2002–2010) and respective spectra of these subsets were compared to check whether changes in climate dynamics can be observed using MF-DFA. Additionally, corresponding shuffled and surrogate time series were investigated to evaluate the type of multifractality. All time series indicated underlying multifractal structures with considerable differences in dynamics and development between the studied locations. The source of multifractality of precipitation time series was two-fold, coming from the width of the probability function to a greater extent than for other time series. The multifractality of other analyzed meteorological series was mainly due to long-range correlations for small and large fluctuations. These results may be especially valuable for assessing the change of climate dynamics, as we found that larger changes in asymmetry and width parameters of multifractal spectra for divided datasets were observed for precipitation than for other time series. This suggests that precipitation is the most vulnerable meteorological quantity to change of climate dynamics.

Forecasting daily meteorological time series using ARIMA and regression models

Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.