Most Slovak rivers have increasing spring flow followed by a period or two of low flow in the summer, autumn, and, in some cases, winter. The flow rate fluctuations in two different streams in Slovakia are being investigated in this study. The study focused on an under-mountain and a lowland-highland river to investigate the low and peak flow periods and to identify the trends in monthly and annual mean flows for both rivers. Analysing daily mean discharge data from two different types of streams requires the use of a robust normalization approach to verify the comparability between the chosen streams. On both streams, a broad statistical low-flow analysis was performed over different study periods, as well as a hydrological drought analysis utilizing the water-bearing coefficient approach over the period 2010-2020. The evaluation for the foothill river in Slovakia demonstrates that snow melting has a significant impact on annual runoff in the spring months, and both rivers have a low flow period in August, September, and October. Despite the considerable variations in the catchment area, geographical, and hydrological characteristics, drought analysis for the years 2010 to 2020 found a lack of normality and a dry hydrological situation in both streams.

Drought is one of many critical problems that could arise as a result of climate change as it has an impact on many aspects of the world, including water resources and water scarcity. In this study, an assessment of hydrological drought in the Gidra River is carried out to characterize dry, normal, and wet hydrological situations by using the Slovak Hydrometeorological Institute (SHMI) methodology. The water bearing coefficient is used as the index of the hydrological drought. As machine and deep learning are increasingly being used in many areas of hydroinformatics, this study is utilized artificial neural networks (ANNs) and support vector machine (SVM) models to predict the hydrological drought in the Gidra River based on daily average discharges in January, February, March, and April of the corresponding year. The study utilized in total 58 years of daily average discharge values containing 35 normal and wet years and 23 dry years. The results of the study show high accuracy of 100% in predicting hydrological drought in the Gidra River. The early classification of the hydrological situation in the Gidra River shows the potential of integrating water management with the deep and machine learning models in terms of irrigation planning and mitigation of drought effects.

Climate change is affecting every aspect of the world including water resources and water scarcity. Drought is one of many big problems associated with climate change that could occur all over the world. Moreover, hydrological drought is one form of drought that relates to decreased river discharges, below-normal groundwater level, declining the area of wetlands and low water level in lakes or reservoirs. In this study, an assessment of hydrological drought in Gidra river is conducted to characterize dry and normal hydrological years according to Slovak Hydrometeorological Institute (SHMI) Methodology. Furthermore, making benefit of machine learning and artificial intelligence in this field is applicable now, as data of many types are being recorded every day. Deploying machine learning algorithms for the purpose of drought prediction is one way to regulate many operations of water management to prevent irrigation problems. By catching patterns through historical data and deploying machines to learn from those patterns, it is possible to use the values of daily average discharges for January, February, March, and April to correctly predict the hydrological situation in Gidra river whether it is dry or normal, knowing that normal situation refers to wet or normal hydrologically assessed years as the optimal goal in this study is drought assessment and prediction of Gidra river.

This study examines the effect of drought on the discharge seasonality of the Topľa River from 1988 to 2020. Each year is classified into dry, normal, or wet years using the water-bearing coefficient as a drought index. The Seasonal and Trend decomposition using the Loess time series decomposition method was used to compare discharge patterns between these groups. The results demonstrate a significant impact of drought on the seasonal discharge of the Topľa River, with substantially lower discharge and affected seasonality during dry years. The study findings demonstrate that the impact of the drought is altering the seasonal discharge pattern of the river. This highlights the importance of considering the effects of drought in water management and resource planning, particularly in the face of climate change and increasing water scarcity. These findings provide valuable insights for informing water management policies and practices in the region and can guide future research on the impact of drought on river systems.