Bioavailability of engineered metal nanoparticles affects uptake in plants, impacts on ecosystems, and phytoremediation. We studied uptake and translocation of Ti in plants when the main source of this metal was TiO2 nanoparticles. Two crops (Phaseolus vulgaris (bean) and Triticum aestivum (wheat)), a wetland species (Rumex crispus, curly dock), and the floating aquatic plant (Elodea canadensis, Canadian waterweed), were grown in nutrient solutions with TiO2 nanoparticles (0, 6, 18 mmol Ti L(-1) for P. vulgaris, T. aestivum, and R. crispus; and 0 and 12 mmol Ti L(-1) for E. canadensis). Also examined in E. canadensis was the influence of TiO2 nanoparticles upon the uptake of Fe, Mn, and Mg, and the influence of P on Ti uptake. For the rooted plants, exposure to TiO2 nanoparticles did not affect biomass production, but significantly increased root Ti sorption and uptake. R. crispus showed translocation of Ti into the shoots. E. canadensis also showed significant uptake of Ti, P in the nutrient solution significantly decreased Ti uptake, and the uptake patterns of Mn and Mg were altered. Ti from nano-Ti was bioavailable to plants, thus showing the potential for cycling in ecosystems and for phytoremediation, particularly where water is the main carrier.

This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

Thorough characterization of the response of finite water resources to climatic factors is essential for water monitoring and management. In this study, groundwater level data from U.S. Geological Survey Ground-Water Climate Response Network wells were used to analyze the relationship between selected drought indices and groundwater level fluctuation. The drought episodes included in this study were selected using climate division level drought indices. Indices included the Palmer Drought Severity Index, Palmer Hydrological Drought Index, and Standardized Precipitation Index 9,12,24). Precipitation and the average temperature were also used. SPI-24 was found to correlate best with groundwater levels during drought. For 17 out of 32 wells, SPI-24 showed the best correlation amongst all of the indices. For 12 out of 32 wells, SPI-24 showed correlation coefficients of −0.6 or stronger; and for other wells, reasonably good correlation was demonstrated. The statistical significance of SPI-24 in predicting groundwater level was also tested. The correlation of average monthly groundwater levels with SPI-24 does not change much throughout the timeframe, for all of the studied wells. The duration of drought also had a significant correlation with the decline of groundwater levels. This study illustrates how drought indices can be used for a rapid assessment of drought impact on groundwater level.

An understanding of drought occurrences and their characteristics such as intensity, duration, frequency, and areal coverage, and their variations on different spatial scales, is crucial to plan for droughts in different regions and in different sized areas. This study investigated the variations of spatio-temporal characteristics of droughts under selected spatial scales: National (Contiguous U.S.), regional (High Plains), state (North Dakota, ND), climatic division (South Central, ND), and county (Grant, ND). Weekly drought area coverage data for the period spanning the years 2000-2014 from the U.S. Drought Monitor of the National Drought Mitigation Center were used. The study captured the areal coverages and occurrence frequency of droughts with different intensity levels for the years 2000 to 2014 for the contiguous U.S. Year to year variability in spatial distribution of the areal coverages of droughts with different intensity levels were also analysed. The study further investigated how the weekly percentage area under different intensity categories varied along time, and extracted the spatio-temporal characteristics of different drought intensity categories at different spatial scales. The study identified areas that are frequently affected by droughts of different intensity categories in the U.S. at the national scale, and reported the spatial scale dependence of drought characteristics.

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