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Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, determining the amount and timing of snowmelt runoff especially in the Euphrates basin, where large dams are located, is an important task in order to use the water resources of the country in an optimal manner.The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km 2 on the headwaters of Euphrates river for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in upper Euphrates basin operating in real-time. Since ground-based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) optical satellite. In the first part of the study, an automatic model parameter estimation method, Shuffled Complex Evolution, University of Arizona (SCE-UA), is utilized to calibrate the HBV model parameters with a multi-variable criteria using runoff as well as snow-covered area (SCA) to ensure the internal validity of the model. Results show that calibrations against SCA in addition to discharge simulate discharge nearly as well as calibrations against discharge only, but further suggest that longer time periods and more study catchments should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from Mesoscale Model 5 (MM5) for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting driven by numerical weather prediction data for flood mitigation, reservoir operation and dam safety.

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Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).