Evaluation of European Meteorological Observations gridded data  of air temperature and precipitation amount over Danube River Basin (1990-2022) 

• DOI: 10.5194/egusphere-egu25-16995
• Publication Date: 2025-03-15T04:22:26Z
• Authors: Vlad Amihăesei; Sorin Cheval; Zenaida Chitu; Andrei Radu; Catalina Petre; Tamás Ács; Zsolt Kozma; Máté György; Viorel Chendeș

Large-scale hydrological models simulate the water cycle for regions, countries, and continents. The choice of input data directly impacts the accuracy of these models' final output and the spatial and temporal pattern, as well as the quality of data (air temperature and precipitation), influences the quality and pattern of water availability estimates. It is essential to acknowledge that the accuracy of these estimates depends on the input quality of the data used.In this regard, precipitation and air temperature gridded European Meteorological Observations (EMO1) datasets specifically used for hydrological modeling inputs (CWATM) are evaluated over the Danube River Basin (DRB). The observation data (air temperature and precipitation) from 9 different countries within the DRB are used for the EMO1’s evaluation.  The performance of the datasets was evaluated at daily, monthly, and annual scales, using Pearson Correlation (r), root mean square errors (RMSE), mean absolute errors (MAE), Nash Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion.The results showed the range of temperature differences varies between approximately -3°C and +2°C. This reflects both underestimations and overestimations by EMO1 compared to observations. The median differences are close to 0 for most months, indicating the EMO1 model is generally unbiased or well-calibrated overall. Larger variability and more outliers occur in warmer months (e.g., May–August), suggesting the model may struggle with accurately capturing summer temperature dynamics. For precipitation, the median is slightly positive, suggesting a systematic overestimation of precipitation during the summer months. This could be due to the model overestimating convective rainfall. By identifying the periods where the EMO-1 deviates most from observations, researchers can target specific processes for calibration or refinement, which is especially important for hydrological  applicationsAcknowledgment This work was supported as part of DANUBE WATER BALANCE, an Interreg Danube Region Programme project co-funded by the European Union.