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This study compares the flood regime of rivers in Ukraine and Austria over the last decades. We used data from mountain and lowland watersheds, where floods are caused by different processes. In order to identify possible shifts in the day of occurrence of annual flood maxima, we apply the kernel density method to the time series of two subperiods (1960–1987 and 1988–2015). We use the Mann Kendall test at a 5% significance level to identify significant positive or negative trends in the series of annual maximum discharges. In Austria, we observe an increasing trend in summer floods associated with increasing precipitation. In the lowland areas of Ukraine, a clear reduction in spring floods is observed, linked to shallower snow packs in a warming climate. In the Ukrainian Carpathians, on the other hand, where floods occur throughout the year, an increase in the portion of liquid precipitation during the cold period of the year leads to earlier floods and an increase in the probability of flooding in winter.

This article focuses on water security in Mykolaiv, a city of 0.5 million inhabitants in southern Ukraine, in the situation of scarcity of usable water resources caused by climate change and military operations. This problem arose after the Dnipro-Mykolaiv water pipeline was destroyed in April 2022 as a result of military operations and the supply of drinking water to the city was cut off. To ensure that the city's population has constant access to sufficient water of acceptable quality, a search for alternative water sources and a climate risk assessment were carried out for the new municipal water supply system from the Southern Bug River. The possible change in flow and its intra-annual distribution under the influence of climate change was modeled using the WaterGAP2 hydrological model and climate projections under the SSP1-2.6 and SSP5-P8.5 scenarios. It was found that under the SSP1-2.6 scenario, the reduction in river flow will be insignificant (up to a maximum of 14% in the far future) and there will be no restrictions on the city's water supply from this section of the river in the near (2021-2050) and far (2051-2080) period. The maximum water withdrawal for municipal water supply and the minimum environmental flow will reach their maximum value only in August (56% of the projected flow), which is not critical. Under the SSP5-8.5 scenario, in the long-term perspective of 2051-2080, the largest decrease in runoff will occur from May to October, and the water withdrawal will increase to 40-79% of the projected flow. The use of the research results not only in water management, but also in municipal administration, and their dissemination in territorial communities will contribute to the successful adaptation of socioeconomic and environmental processes in the region and can bring successful benefits not only to the economy, but also to communities. [ABSTRACT FROM AUTHOR]

We employ a high‐resolution Köppen climate classification dataset to examine shifts in Tundra zones within the Alps and Asia. Our analysis shows substantial reductions in Tundra areas by the mid‐21st century under different Shared. Socioeconomic pathways (SSP1‐2.6, SSP3‐7.0, SSP5‐8.5). Tundra zones in the Alps and the Tibetan Plateau are crucial for their unique climates and role as water reservoirs. Characterized by short, mild summers and long, severe winters, these zones are vital for the glaciers and perennial snow. The projected climate instability may significantly reduce alpine snow cover by mid‐century with irreversible consequences. A 2°C temperature increase from the 1981–2010 baseline could eliminate the Tundra climate in the Alps and reduce it by over 70% in Asia. This is particularly concerning given that rivers from the Tibetan Plateau sustain nearly 40% of the global population. [ABSTRACT FROM AUTHOR]

Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow‐depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year−1) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter‐comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5‐8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow‐melting leads to an increase in the run‐off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil‐fueled development. The models' uncertainty increases for the far‐future time‐slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins. [ABSTRACT FROM AUTHOR]

Due to the long memory of snow processes, statistical seasonal streamflow predictions in snow-dominated catchments typically rely on snowpack estimates. Using mountainous catchments in Central Asia as a case study, we demonstrate how seasonal hydrological forecasts benefit from incorporating large-scale climate oscillations (COs). First, we examine the teleconnections between the major COs and peak precipitation season in eight catchments across the Pamir and Tian-Shan mountains from February to June. We then employ a machine learning framework that incorporates snow water equivalent (SWE) and dominant COs indices as predictors for mean discharge from April to September. Our workflow leverages an ensemble technique that uses multiple SWE estimates from near-time global data sources and diverse types of explainable machine-learning models. We find that the winter states of the El Niño-Southern Oscillation and the North Atlantic Oscillation enhance SWE-based forecasts of seasonal discharge in the study catchments. We identify three instances in which the inclusion of COs as additional predictors could be instrumental for snowpack-based seasonal streamflow forecasting: 1) when forecasts are issued at extended lead times and accumulated SWE is not yet representative of seasonal terrestrial water storage; 2) when climate variability during the forecasted season plays a larger role in shaping seasonal discharge; and 3) SWE estimates for a catchment are subject to larger uncertainty. Our approach provides a novel way to reduce uncertainties in seasonal discharge predictions in data-scarce snowmelt-dominated catchments. [ABSTRACT FROM AUTHOR]

To estimate future climate change impacts, usually high-resolution climate projections are necessary. Statistical and dynamical downscaling or a hybrid of both methods are mostly used to produce input datasets for impact modelers. In this study, we use the regional climate model (RCM) COSMO-CLM (CCLM) version 6.0 to identify the added value of dynamically downscaling a general circulation model (GCM) from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and its climate change projections' signal over Central Asia (CA). We use the MPI-ESM1-2-HR (at 1° spatial resolution) to drive the CCLM (at 0.22° horizontal resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three different shared socioeconomic pathways (SSPs): SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset, we evaluate the CCLM performance over the historical period using a simulation driven by ERAInterim reanalysis. CCLM's added value, compared to its driving GCM, is significant over CA mountainous areas, which are at higher risk of extreme precipitation events. Furthermore, we downscale the CCLM for future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with CMIP6 GCMs ensemble. Our analysis shows a significant increase in heavy precipitation intensity and frequency over CA areas that are already at risk of extreme climatic events in the present day. Finally, applying our single model high-resolution dynamical downscaling, we train a convolutional neural network (CNN) to map the low-resolution GCM simulations to the dynamically downscaled CCLM ones. We show that applied CNN could emulate the GCM-CCLM model chain over large CA areas. However, this specific emulator has shortcomings when applied to a new GCM-CCLM model chain. Our downscaling data and the pre-trained CNN model could be used by scientific communities interested in downscaling CMIP6 models and searching for a trade-off between the dynamical and statistical methods. [ABSTRACT FROM AUTHOR]