Climate gridded datasets are highly needed and useful in conducting data analysis for research and practical purposes. They provide long-term information on various climatic variables for large areas worldwide, making them suitable for use at any spatial level. It is essential to assess the accuracy of gridded data by comparing the data to measured values, especially when they are used as input parameters for hydro-climatic models. From the multitude of databases available for North-western Romania, we selected three, particularly the European Climate Assessment and Dataset (E-OBS), the Romanian Climatic Dataset (ROCADA), and the Climate of the Carpathian Region (CARPATCLIM). In this paper, we analyse the extreme precipitation and temperature data derived from the above-mentioned datasets over a common 50-year period (1961–2010) and compare the data with raw values to estimate the degree of uncertainty for each set of data. The observation data, recorded at two meteorological stations located in a complex topography region, were compared to the output of the gridded datasets, by using descriptive statistics for the mean and extreme annual and seasonal temperature and precipitation data, and trend analyses. The main findings are: the high suitability of the ROCADA gridded database for climate trend analysis and of the E-OBS gridded database for extreme temperature and precipitation analysis.

The interaction between precipitation and vegetation plays a significant role in the formation of runoff, and it is still a widely discussed issue in hydrology. The difficulty lies in estimating the degree to which a forest influences runoff generation, especially flood peaks, on the one hand, due to the small amount of information regarding the evolution of the forest area and density, and, on the other hand, the correlations between these indicators and the runoff and precipitation values. The analysis focuses on a small basin in the mountain region of Romania, the upper basin of the Ruscova River located in northwestern Romania. In this river basin, there is no significant anthropic influence, other than the intense deforestation and reforestation actions. Using satellite images captured by Landsat missions 5, 7 and 8 for the period 1985–2019, the forest canopy density vegetation index was extracted. Using a gridded precipitation dataset, a hydrological model was calibrated based on three scenarios to assess the impact of forest vegetation on the runoff. Analysis of the results of these models conducted on scenarios allowed us to deduce a simple equation for estimating the influence of forest area on maximum river flows. The analysis showed that even small differences in the forest surface area exert an influence on the peak flow, varying between −5.28% and 8.09%.