Comparison of sequential and variational assimilation methods to improve hydrological predictions in snow dominated mountainous catchments

Snowmelt modeling is vital issue for proper operational flow forecasting systems but still challenging in upstream mountainous basins due to limited observations and associated uncertainties. Data assimilation is a valuable tool to improve model state estimates to improve hydrological forecast. While assimilation of snow has been increasingly implemented in research, particularly using sequential data assimilation methods, less attention has been dedicated to compare different types of assimilation techniques. This study aims at comparing the wellknown Ensemble Kalman Filter (EnKF) sequential data assimilation and the less known Moving Horizon Estimation (MHE) variational assimilation. Initial conditions of the hydrological model states are improved by assimilating in-situ streamflow and remotely sensed snow covered area data into the Hydrologiska Byrans Vattenbalansavdelning (HBV) conceptual rainfall-runoff model.The study is applied to improve runoff predictions over two mountainous basins: one in the uppermost catchment of the Karasu river, in Eastern Turkey, and the second in an alpine basin located in the headwaters of the Genil River in the northern flank of the Sierra Nevada Mountain in Southern Spain. The forecast models are tested in a hindcasting condition, i.e. closed loop environment, which simulates real-time forecasting systems. The results are analyzed using forecast verification metrics. Results showed that streamflow and snow state predictions using MHE outperform the commonly used EnKF based on the continuous ranked probability score (CRPS).

Automated summary

This study compares the application of Ensemble Kalman Filter (EnKF) and Moving Horizon Estimation (MHE) for data assimilation in mountainous basins. The results show that MHE outperforms EnKF in streamflow and snow state predictions.