The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.

Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.

We have used the Kolmogorov complexities and the Kolmogorov complexity spectrum to quantify the randomness degree in river flow time series of seven rivers with different regimes in Bosnia and Herzegovina, representing their different type of courses, for the period 1965–1986. In particular, we have examined: (i) the Neretva, Bosnia and the Drina (mountain and lowland parts), (ii) the Miljacka and the Una (mountain part) and the Vrbas and the Ukrina (lowland part) and then calculated the Kolmogorov complexity (KC) based on the Lempel–Ziv Algorithm (LZA) (lower—KCL and upper—KCU), Kolmogorov complexity spectrum highest value (KCM) and overall Kolmogorov complexity (KCO) values for each time series. The results indicate that the KCL, KCU, KCM and KCO values in seven rivers show some similarities regardless of the amplitude differences in their monthly flow rates. The KCL, KCU and KCM complexities as information measures do not “see” a difference between time series which have different amplitude variations but similar random components. However, it seems that the KCO information measures better takes into account both the amplitude and the place of the components in a time series.

UAV technologies provide a time- and cost-efficient framework for a variety of environmental monitoring domains. It also increases data resolution and provides new insights into observed objects and phenomena, especially within the difficult-to-access and complex for monitoring aquatic habitats. The objective of this study was to develop UAV-based acquisition and GIS-based image processing guidelines for aquatic macrophyte detection and monitoring in large temperate rivers. According to the European standard CEN EN -14184:2014, the assessment of aquatic macrophytes should be performed using the transect approach. Large rivers, such as the Danube, represent an exception and should be evaluated using 1km transects. Therefore, seven transects of the Middle Danube in Serbia were simultaneously surveyed using traditional field methods and novel UAV technology. UAV images were acquired using RGB and multispectral cameras carried by a fixed-wing drone. The images were processed and orthomosaics were classified using Object Based Image Analysis (OBIA), to create digital GIS maps of the river transects. During the traditional monitoring approach, the relative abundance of 22 macrophyte species was recorded along the transects. Using the UAV technology and OBIA approach eight macrophyte classes were distinguished based on dominant macrophyte taxa or plant life form traits. Aquatic macrophytes were 'almost perfectly' distinguished from the orthomosaics, achieving a high classification accuracy of 96 % / 88 % / 0.84 for RGB and 94 % / 97 % / 0.95 Producers /Users accuracy/Kappa index for the multispectral approach. Individual macrophyte classes accuracy varied between 0.5 and 1 Kappa and were generally higher for the multispectral imagery approach. Although the resolution of the taxonomic data is lower, UAV monitoring provided the necessary spatial context of macrophytes distribution and absolute area occupied by macrophytes. It also provided information on the diversity and distribution of habitats along the river. Therefore, the UAV-assisted monitoring approach described in this study can be effectively integrated into macrophyte monitoring during large river expeditions such as the JDS.

The use of geospatial technology at lower levels of education has become a global tendency. However, Bosnia and Herzegovina is a country that is virtually uncharted in this regard, and the present paper has the main purpose to change that fact. Results of a survey conducted among geography teachers place this country within the entry category. Different attitudes among various groups of teachers (based on gender, age and regional distribution) have also been tested. Although the general hypotheses could not be confirmed, certain specific differences have been found, such as in the use of virtual globes, theoretical knowledge of GIS and willingness to attend educational training courses.

Solar ultraviolet (UV) radiation is a significant health hazard in the warm part of the year. In order to assess the level of hazard and the effects of UV radiation on the living world, long-term measured or estimated data are needed. In Novi Sad, the measurement of UV radiation has been performed since 2003, while ozone measurements have been made since 2007. However, those data sets are too short for assessing long-term biological effects. Therefore, several techniques for reconstruction of UV radiation doses have been developed. Reconstruction techniques are based on using available ground-based measurements of the meteorological data and satellite measurements of the total ozone column. It is shown that techniques that use ozone data show better performance than those that use only ground-based meteorological measurements. However, the difference between the performances of the methods is smaller when it comes to the monthly values, indicating that the techniques which use only ground-based meteorological measurements are roughly as good as the ozone-based techniques for assessing long-term changes in the surface UV radiation. The statistically significant increasing long term-trend of annual mean erythemal UV doses (ERY) and the decreasing trend in the total ozone column in Novi Sad since 1981 have been noticed. An increase in ERY has been noticed in all the seasons except in autumn and it is the highest in winter. The analysis showed that the increase in the ERY in the period 1981-1996 was mainly caused by the total ozone column, while the increase after 1996 is largely caused by cloudiness.