Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types.

Climate change is affecting freshwater ecosystems globally, particularly those in semi-arid and arid regions. The Central Anatolian Ecoregion (CAE) in Türkiye has a semi-arid climate and is home to numerous endemic fish species. We used species distribution modelling to elucidate the distribution of sixteen endemic fish species in CAE and predicted their potential distributions for 2041–2060 and 2081–2100 based on the CMIP6 climate model. Half of the species are predicted to experience a significant loss of climatically suitable areas. Anatolichthys fontinalis, Gobio gymnostethus, Gobio hettitorum, and Pseudophoxinus burduricus will face a complete loss of suitable areas by 2081–2100 under a high emissions climate scenario, whereas Cobitis bilseli, Egirdira nigra, Gobio intermedius, and Squalius anatolicus will experience a significant loss. The other eight species can potentially benefit from climate warming if all other stressors remain equal. Anthropogenic stressors, such as water abstraction for irrigation, pollution, invasive species introductions, and dam construction, are already putting endemic fish populations in CAE under extreme pressure. Climate change is expected to exacerbate these threats. Regular monitoring of freshwater ecosystems and fish fauna in the CAE and protecting the region from key anthropogenic stressors are recommended to successfully conserve these endemic freshwater fishes under climate change.

Timely and consistent information on the seasonal snow cover is critical for various scientific studies and operational applications, especially for hydrological purposes. Snow water equivalent (SWE) is a significant seasonal snow parameter, which serves as a key input for many hydrological and climatological models. H13 is a SWE product supplied within the frame of EUMETSAT’s H-SAF project based on the processing of passive microwave radiometer data. The basic aim of this study is to perform a validation of H13 over Turkey for the 2020-2021 snow season by using in-situ snow depth measurements. The validation covers the period between January and March 2021, and it includes 1282 ground-based observations. According to the results, annual RMSE of the H13 SWE product is obtained as 40.00 mm, which lies within the acceptable limits of the required product compliance. The minimum and maximum snow depth measurements within the validation period are 2.80 cm and 95.34 cm, respectively. The results obtained in this validation study clearly indicate the usability of the H13 SWE product in hydrological and climatic studies.

Snow cover is a key variable for climate monitoring and hydrological applications, yet optical satellite observations are strongly limited by cloud contamination, particularly during winter. The EUMETSAT H SAF H34 snow product derived from MSG-SEVIRI uses the unique 15-minute temporal resolution over the full SEVIRI disc to clear the clouds, but persistent clouds still cause substantial data gaps. In this study, we present a cloud-gap reconstruction framework that combines Numerical Weather Prediction data with machine learning to infer snow presence beneath cloud-covered pixels in the H34 product. Skin temperature, snow depth, and snow temperature fields from the Integrated Forecast System (IFS) were used as physically consistent predictors and resampled to the H34 grid, together with elevation information from SRTM. An XGBoost-based model was trained using cloud-free H34 snow observations and applied exclusively to cloud-contaminated pixels to estimate the probability of underlying snow presence. Pixels exceeding an 80% probability threshold were reclassified as snow. The approach was applied to the winter seasons of 2024 and 2025 and validated over the European Alps using in-situ snow observations from World Meteorological Organization (WMO) stations. Evaluation using probability of detection (POD), false alarm ratio (FAR), and overall accuracy (ACC) shows a clear improvement in snow detection under cloudy conditions, with a significant reduction in missing observations. Compared to conventional temporal gap-filling methods, the proposed framework reduces reliance on temporal interpolation by directly exploiting physically meaningful meteorological information, while preserving the high temporal resolution advantage of MSG-SEVIRI.

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EUMETSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2021, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Türkiye, and the conterminous U.S. For Türkiye, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Türkiye yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.