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Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances.

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions.

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy.