No. 3 (2025): HYDROMETEOROLOGY AND ENVIRONMENTAL MONITORING

The article presents a statistical analysis of precipitation amount series based on 32 years of data from five meteorological stations located in the Tashkent region. During the study period, a slight decrease in precipitation was observed in the Chirchik River basin, while the number of days with heavy precipitation tended to increase. As a result, the probability of mudflows and floods in the basin may increase

The article evaluates the impact of the ENSO phenomenon on precipitation variability across Uzbekistan using rainfall data from 50 meteorological stations during all El Niño and La Niña events observed between 1963 and 2024. The results showed that during El Niño periods seasonal precipitation in all meteostations exceeded normal levels, while during La Niña periods, it was below average. This pattern became particularly pronounced with increasing altitude

This article examines several meteorological parameters that influence astronomical observations at Maidanak astronomical observatory: the amount of nighttime with clear and partly cloudy weather in mountainous areas, and the speed and direction of wind in the surface layer. Based on ground-based observations at Uzhydromet meteorological stations, an analysis of the dynamics of meteorological parameters over a long period (1981-2020) was conducted and a comparison of these values ​​with other locations in Uzbekistan. Trends in time series changes in the ratio of clear and partly cloudy weather at night to the total amount of nighttime were examined using the statistical nonparametric Mann-Kendall test

The study analyzes the dynamics of snow cover changes in the upper part of the        Amudarya river basin above the Kerki hydrological post during 2001-2025, based on data obtained using the MODSNOW program. Snow cover variations were analyzed both monthly for the entire basin and by elevation zones. Snow cover change trends were determined using the Mann-Kendall statistical test.

This article examines options for genetic analysis of suspended sediment runoff in mountain rivers, including: dissection of the chronological graph of river water turbidity; thermal analysis of suspended sediment runoff in rivers; and statistical assessment of multifactorial relationships between suspended sediment runoff in rivers and natural factors. Each option is characterized and their applications in hydrological calculations are indicated

This article explores the possibilities of forecasting water discharge in the Piskom River basin based on meteorological data using Machine Learning (ML) models. The study establishes relationships between the Piskom River flow and meteorological factors using Random Forest, XGBoost, and LSTM models, with their accuracy compared through various evaluation metrics (MAE, RMSE, R², and NSE). The analysis demonstrates that the Random Forest model provides the highest accuracy in forecasting the water discharge of the Piskom River. The research results indicate that ML models can serve as an effective tool for preliminary assessment of river flow and water resource management

Data on vegetation cover over vast territories cannot be obtained based on traditional in-kind measurements due to the lack of a specialized observation network. Point measurements of vegetation parameters at different times do not allow tracking its dynamics, where the “from the particular to the general” method is not applicable. Under these conditions, the only source of information on the state of vegetation cover is space imagery data, which makes it possible to simultaneously monitor the state of vegetation cover over large areas and periodically repeat these observations. Conservation of biological diversity, ensuring a sustainable state and scientifically based use of vegetation cover objects is possible only with the availability of operational vegetation monitoring. The article describes the developed technology of automated monitoring of vegetation cover based on data delivered from the NOAA/AVHRR series satellite

This review synthesizes evidence‑based advances in weather modification achieved during 2010–2025, with emphasis on precipitation management and fog dissipation. It assesses results from randomized and/or instrumentally verified field campaigns (e.g., SNOWIE, WWMPP), recent electric (reagent‑free) approaches, contemporary operational‑planning methods, and reproducible fog experiments. Priorities for adaptation in Uzbekistan are outlined

The article considers the quality of atmospheric air in the regions of the Republic of Uzbekistan in 2024. Atmospheric air quality was studied according to the data of the Uzhydromet observation network for the main pollutants (suspended particles (dust), nitrogen dioxide, sulfur dioxide, carbon monoxide). It has been shown that the average monthly concentrations of the main pollutants in the atmospheric air in most regions of Uzbekistan were lower than quality standards, only in the atmospheric air of some cities in the regions there was an excess of the average daily maximum permissible concentrations (MPC). The atmospheric air pollution index (API) in the cities of the republic was in the range of 0.67-7.23