MET Nordic Reanalysis data improves the performance of catchment-level hydrological models

High-quality input data is the foundation for good model performance, including catchment level hydrological models. The resolution and quality of meteorological data has a direct impact on modelling results and as such strongly influences the outcomes of scenario analyses of different types. Nowadays one can choose between different meteorological products when setting up a mathematical model, including direct measurements and reanalyses. The goal of this study was to test the ability of MET Nordic data, a reanalysis product from Met Norway, on improving the simulations of hydrological models.  The MET Nordic Reanalysis Dataset consists of post-processed products that (a) describe the current and past weather (reanalysis), and (b) gives a best estimate of the weather in the short-term future (forecasts). The products integrate output from MetCoOp Ensemble Prediction System (MEPS) as well as measurements from various observational sources, including crowdsourced weather stations. 

Two different catchment models were set up and calibrated against measured discharge data. The SWAT+ model was applied in two Norwegian and one Danish catchment, while the CWatM model was tested in one Norwegian catchment. The model’s performance was compared when using input datasets from measuring stations and MET Nordic reanalysis data. We concluded that applying reanalysis data can significantly improve the performance of the tested models, therefore the use of these data in hydrological modelling is highly recommended.