In order to assess the nature of climate change, it is important to analyze the indicators of climate variability in different scales: spatial and temporal. The analysis at different scales can lead to understanding of the nature of variations. Climate change studies are essential for comprehending the nature of global processes, to refine global climate patterns and also develop further research for natural processes (Meinke, Stone, 2005; Hulme et al., 1999). Processes in nature are united, continuous and in constant interaction. Variance of interaction types are immeasurable, types can be connected with different scales and science fields, for example, biological, ecological, physical etc. If interaction is taking place between the land and atmosphere, it is defined as hydrological interaction. As water is significantly important for many purposes on the Earth, it is relevant to analyze precipitation and water runoff on a local scale. In the territory of Latvia, the amount of precipitation exceeds the level of evapotranspiration. Long-term monitoring data show that precipitation leads to average runoff of 250 mm per year (Ziverts, 2004). The monitoring data collected at three research sites located in Latvia was used for this research including Berze (Lielupe river basin, meteorological station in Dobele), Mellupite (Venta river basin, meteorological station in Saldus monitoring) and Vienziemite (Gauja river basin, meteorological station in Zoseni). The results from this study show that there is a pronounced interaction between runoff and precipitation with an average of 53 to 82%.

The goal of this research is to explore sentence structures expressed by parts of speech. Due to a small amount of data, a problem of sparse data has arisen, which was solved by recording the annotated sentences and considering a “framework” of a sentence made up from a verb and a noun, which was conditionally called a code. The code of a sentence is created by changing each word of a sentence by a symbol (letter or number) that encodes one or other property of that word as a constituent of the sentence. Zipf’s law describes sentences, encoded like that, rather well. If we ‘learn’ well to identify and analyze (annotate, translate, etc.) sentences of the simplest structure, we can automatically process quite a large part of text sentences. It is possible to identify at least 17% of sentences consisting of the simplest structure.

In rural Kazakhstan, the credit and insurance services are limited and the state support is weak. Therefore, households’ saving is crucial to provide an insurance against the economic and social shocks. The main goal of this study is to contribute to the literature on financial literacy in emerging economies, namely, the effect of financial literacy on saving rates of rural population. Being well educated not always means to be financial literate and make efficient decisions regarding one’s own finance. People with a lower formal education level but with better experience in consuming financial products could be better prepared for making financial decisions including those related to savings. In this paper other socio-economic determinants of saving rates were taken into account, such as an income level, family size and an employment status. This research was carried out in Pavlodar region of Kazakhstan, and the data collection took place in spring 2014. In total, 405 households were surveyed. Results of the analysis show that if a respondent gives at least one correct answer, it positively affects the saving rates as well as one can observe that the higher the financial literacy level, the higher are the saving rates. Availability of state supported financial education programs for rural people will significantly contribute to the financial literacy improvement. At the same time, providing various and appropriate financial products in rural areas will motivate rural people to search for new knowledge and require authorities to intensify activities in this field.