The response in floodplain respiration of an alpine river to experimental inundation under different temperature regimes

The respiratory potential [i.e. electron transport system activity (ETSA)] of soils and sediments from five floodplain habitats (channel, gravel, islands, riparian forest and grassland) of the Urbach River, Switzerland, and actual respiration rate (R) of the same samples exposed to experimental inundation were measured. Measurements were carried out at three incubation temperatures (4°C, 12°C and 20°C), and ETSA/R ratios (i.e. exploitation of the overall metabolic capacity) were investigated to better understand the effects of temperature and inundation on floodplain functional heterogeneity. Furthermore, ETSA/R ratios obtained during experimental inundation were compared with ETSA/R ratios from field measurements to investigate the exploitation in total metabolic potential at different conditions. Lowest ETSA and R were measured in samples from channel and gravel habitats, followed by those from islands. Substantially higher values were measured in soils from riparian forest and grassland. Both ETSA and R increased with increasing temperature in samples from all habitats, while the ETSA/R ratio decreased because of a rapid response in microbial community respiration to higher temperatures. The metabolic capacity exploitation (i.e. ETSA/R) during experimental inundation was lowest in predominantly terrestrial samples (riparian forest and grassland), indicating the weakest response to wetted conditions. Comparison of experimentally inundated and field conditions revealed that in rarely flooded soils, the metabolic capacity was less exploited during inundation than during non‐flooded conditions. The results suggest high sensitivity in floodplain respiration to changes in temperature and hydrological regime. ETSA/R ratios are considered good indicators of changes in metabolic activity of floodplain soils and sediments, and thus useful to estimate the impact of changes in hydrological regime or to evaluate success of floodplain restoration actions. Copyright © 2015 John Wiley & Sons, Ltd.     

Chemical and isotopic interpretation of major ion compositions from precipitation: a one-year temporal monitoring study in Wrocław,SW Poland

The chemical compositions (Na⁺, NH₄ ⁺, K⁺, Mg²⁺, Ca²⁺, Cl^?, NO₂ ^?, NO₃ ^?, SO₄ ^2?, HCO₃ ^?) of wet precipitation and nitrogen isotope compositions δ¹⁵N(NH₄ ⁺) were studied from January to December 2010 in Wroc?aw (SW Poland). Results of a principle component analysis show that 82 % of the data variability can be explained by three main factors: 1) F1 (40 %) observed during vegetative season (electrical conductivity, HCO₃ ^?, NO₃ ^?, NO₂ ^?, NH₄ ⁺ and SO₄ ^2?), mainly controlling rainwater mineralization; 2) F2 (26 %) observed during vegetative and heating seasons (K⁺, Ca²⁺ and Mg²⁺), probably representing a combination of two processes: anthropogenic dusts and fertilizers application in agricultural fields, and 3) F3 (16 %) reported mainly during heating season (Na⁺ and Cl^?) probably indicating the influence of marine aerosols. Variations of δ¹⁵N(NH₄ ⁺) from ?11.5 to 18.5?‰ identify three main pathways for the formation of NH₄ ⁺: 1) equilibrium fractionation between NH₃ and NH₄ ⁺; 2) kinetic exchange between NH₃ and NH₄ ⁺; 3) NH₄ ⁺ exchange between atmospheric salts particles and precipitation. The coupled chemical/statistical analysis and δ¹⁵N(NH₄ ⁺) approach shows that while fossil fuels burning is the main source of NH₄ ⁺ in precipitation during the heating season, during the vegetative season NH₄ ⁺ originates from local sewage irrigation fields in Osobowice or agricultural fertilizers.     

Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.