Runoff calculations for ungauged river basins of the Russian Arctic region

• Main Author: Ayzel Georgy
• Format: Conference Object
• Language: unknown
• Published: Zenodo 2016
• Subjects: arctic; runoff; modeling; ungauged basins; machine learning; HBV; Arctic; Arctic Ocean
• Online Access: https://doi.org/10.5281/zenodo.61066

Arctic coastal systems are very sensitive to the freshwater budget mainly formed by river runoff. Great biases in estimation of total river runoff load to the Arctic Ocean proposed by the number of various scientific groups and insufficiency of physically-based, short-term, spatially diverse runoff predictions lead to strong necessity of state-of-art hydrological techniques implementation. At the moment the most powerful tools for the land hydrological cycle modeling are physically-based, conceptual or data-driven models. Better model – wider sources of hydrometeorological and landscape-related information we need to use to perform robust calculations. Severe climatic conditions of Arctic coastal region have led to weak river runoff monitoring net and a high level of uncertainties related to difficulties of direct measurements. There is the reason we need to develop modern techniques that allow providing effective runoff predictions by state-of-art models in the case of strong research data scarcity (for ungauged basins). Early stage of research aimed to coupling of conceptual hydrological model, cutting edge machine learning techniques and various sources of geographical data will be proposed with the call for intensification of cross-disciplinary research activities for the Arctic region sustainable development and safety.