This study attempts to extend the prevailing definition of informational entropy, where entropy relates to the amount of reduction of uncertainty or, indirectly, to the amount of information gained through measurements of a random variable. The approach adopted herein describes informational entropy not as an absolute measure of information, but as a measure of the variation of information. This makes it possible to obtain a single value for informational entropy, instead of several values that vary with the selection of the discretizing interval, when discrete probabilities of hydrological events are estimated through relative class frequencies and discretizing intervals. Furthermore, the present work introduces confidence limits for the informational entropy function, which facilitates a comparison between the uncertainties of various hydrological processes with different scales of magnitude and different probability structures. The work addresses hydrologists and environmental engineers more than it does mathematicians and statisticians. In particular, it is intended to help solve information-related problems in hydrological monitoring design and assessment. This paper first considers the selection of probability distributions of best fit to hydrological data, using generated synthetic time series. Next, it attempts to assess hydrometric monitoring duration in a netwrok, this time using observed runoff data series. In both applications, it focuses, basically, on the theoretical background for the extended definition of informational entropy. The methodology is shown to give valid results in each case.

Land use and land cover change (LULCC) is considered one of the major drivers of climate change, although climate change can also foster direct or indirect influences leading to LULCC. The objective of the presented study is to offer a strategic observation frame as the land use and land cover (LULC) transitions are grouped to define the cover flows (CFs). The Küçük Menderes River Basin (KMRB), which is located in the west of Turkey was examined as the case study. Through LULCC modelling via the employment of multi-layer perceptron (MLP), cellular automata (CA), and Markov Chain methods, future LULC maps were projected up to the horizon of 2050. Hydrologic responses of the basin to LULCC were determined by the developed hydrologic model, which is generated by the Soil and Water Assessment Tool (SWAT). The superimposed impacts of the examined effects of LULCC have been investigated by the CF types. This way, the individual impacts of the CFs have been assessed. In the case of the KMRB, projected annual runoffs for the year 2050 cover map represent a 9.06% reduction and the major responsible CF type for this reduction is the conversion from forest to non-irrigated agricultural land cover by 22.90%.