Identification of Extreme Weather Events Using Meteorological and Hydrological Indicators in the Laborec River Catchment, Slovakia

Comparison between flood damages evaluation methods and selection of the appropriate method for application in Slovakia

Flood damages come immediately with flood events. There is a wide range of models and methods that can be used for its evaluation, which bring satisfactory but not exact results. The rate of flood damages estimation can differ significantly from the real damages measured after the real flood. The current paper presents three methodologies for assessing flood damages including INSYDE model, AGRIDE-c model and RASOR platform. These methodologies are compared and their suitability for application to available data is analysed. The available data includes the number of damaged elements due to flood as well as the extent of the flooded area. Results of comparison shows that the INSYDE model and RASOR platform could be possibly modified and applied to the available data, but there are significant changes and further analysis needed. The analysis of selected methods shows the data that are needed must be first obtained from other source, then modified, and then could be applied. But we do not have the same data, so the further studies need significant changes.

Forecasting of Flash Floods Peak Flow for Environmental Hazards and Water Harvesting in Desert Area of El-Qaa Plain, Sinai

Water resources in arid and semi-arid regions are limited where the demands of agriculture, drinking and industry are increasing, especially in drought areas. These regions are subjected to climate changes (CC) that affect the watershed duration and water supplies. Estimations of flash flooding (FF) volume and discharge are required for future development to meet the water demands in these water scarcity regions. Moreover, FF in hot deserts is characterized by low duration, high velocity and peak discharge with a large volume of sediment. Today, the trends of flash flooding due to CC have become very dangerous and affect water harvesting volume and human life due to flooding hazards. The current study forecasts the peak discharges and volumes in the desert of El-Qaa plain in Southwestern Sinai, Egypt, for drought and wet seasons by studying the influence of recurrence intervals for 2, 5, 10, 25, 50 and 100 years. Watershed modeling system software (WMS) is used and applied for the current study area delineation. The results show that the predictions of peak discharges reached 0, 0.44, 45.72, 195.45, 365.91 and 575.30 cubic meters per s (m³ s⁻¹) while the volumes reached 0, 23, 149.80, 2,896,241.40, 12,664,963.80 and 36,681,492.60 cubic meters (m³) for 2, 5, 10, 25, 50 and 100 years, respectively, which are precipitation depths of 15.20, 35.30, 50.60, 70.70, 85.90 and 101 mm, respectively. Additionally, the average annual precipitation reached 13.37 mm, with peak flow and volume reaching 0 m³ s⁻¹ where all of water harvesting returned losses. Moreover, future charts and equations were developed to estimate the peak flow and volume, which are useful for future rainwater harvesting and the design of protection against flooding hazards in drought regions due to CC for dry and wet seasons. This study provides relevant information for hazard and risk assessment for FF in hot desert regions. The study recommends investigating the impact of recurrence intervals on sediment transport in these regions.

Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)

Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.

Patterns of Past and Future Droughts in Permanent Lowland Rivers

The problem of droughts is acute due to climate change. The study aims to assess the temporal and spatial drought patterns in Lithuanian lowland rivers in the past and to project these phenomena according to climate scenarios and models. Drought analysis was based on Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). To evaluate the past patterns, the hydrometeorological data of 17 rivers were used from 1961–2020. Future drought changes were analyzed in 2021–2100 according to the selected RCPs (Representative Concentration Pathways) using the hydrological model HBV. There were different patterns of droughts in three hydrological regions of Lithuania (Western, Central and Southeastern). The Southeastern region was more prone to extreme summer hydrological droughts, and they had a shorter accumulation period compared to the other two regions. SPI and RDI indices showed that the number of dry months and the minimum value of the index increased, extending the accumulation period. The highest correlation was recorded between RDI-12/SPI-12 and SDI-12. The amplitude between extremely wet and dry values of river runoff will increase according to RCP8.5. The projections indicated that hydrological drought intensity in the Central region is expected to increase under both analyzed RCPs.

The Analysis of Long-Term Trends in the Meteorological and Hydrological Drought Occurrences Using Non-Parametric Methods—Case Study of the Catchment of the Upper Noteć River (Central Poland)

The study aims to identify long-term trends in the changes of drought occurrences using the Mann-Kendall (MK) test and the Theil-Sen estimator. Trend research was carried out on the example of the catchment area of the Upper Noteć River, which covers an agricultural area of Poland with some of the lowest water reserves. The meteorological droughts were identified based on the Standardized Precipitation Index (SPI), while the hydrological droughts were determined on the basis of the Standardized Runoff Index (SRI) in various time scales (1, 3, 6, 9 and 12 months) in the period of 1981–2016. The relationship between SPI and SRI was determined on the basis of the Pearson correlation analysis. The results showed that statistically significant trends (at the significance level of 0.05) were identified at 3 out of 8 meteorological stations (downward trend at Kłodawa station and upward trend for drought at Sompolno and Kołuda Wielka stations). Statistically significant hydrological droughts showed an increase in occurrences at the Łysek station, while a downward trend was noted at the Noć Kalina station. No trend was found at the Pakość station. The analysis of the correlation between meteorological and hydrological droughts showed a strong relationship in dry years. The maximum correlation coefficient was identified in longer accumulation periods i.e., 6 and 9 months. The example of the catchment of the Upper Noteć River points to the necessity of using several indicators in order to assess the actual condition of the water reserves.