Multi criteria hydrological modelling and forecasting study in upper aras basin

Changing precipitation-flow relationships and the need for water and energy due to increasing population indicate an efficient and effective use of water resources. For this reason, hydrological model supported planning and forecasting studies in basins with high snow and water potential including dam reservoirs gain importance. In such areas, setting up early warning systems may minimize flood/drought occurrences as well as increase efficiency in reservoir operations. The first step in water resources management is to model and estimate the water coming from the basin in the most accurate way. In this study, a conceptual hydrological model (HBV) is used to determine precipitation-flow modeling in the Upper Aras Basin. However, it is important that the internal functioning of the model works correctly rather than simply representing the runoff as output. One of the most reliable methods to verify these steps is remote sensing. A multi-criteria hydrological modeling structure is formed utilizing data from snow and soil moisture satellite images. The most suitable model parameter sets are found by automatically calibrating satellite and ground observations using Genetic Algorithm optimization within fuzzy logic framework. Afterwards, short-term runoff prediction is conducted in the pilot basin with the integration of numerical weather forecast data and hydrological model. The study performed and the results achieved serve as an example of the conceptual hydrological modeling on the selected region and have the potential to lead for similar snow dominated basins.