Earth Observations (EO) have become popular in hydrology because they provide valuable information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EO’s into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved forecasting of different hydrometeorological variables. When assimilated, the data exert additional controls on the quality of the forecasts; it is hence important to apportion the effects according to model forcings and the assimilated data. Here, we investigate the hydrological response and seasonal predictions over the snow-melt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven by two meteorological forcings: (i) a down-scaled GCM product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Ensemble Streamflow Prediction (ESP) technique. Six datasets are assimilated comprising of four EO products (fractional snow cover, snow water equivalent, and the actual and potential evapotranspiration) and two in-situ measurements (discharge and reservoir inflow). We finally assess the impacts of the meteorological forcing data and the assimilated data on the quality of streamflow and reservoir inflow seasonal forecasting skill for the period 2001-2015. The results show that all assimilations generally improve the skill but the improvements vary depending on the season and assimilated variable. The lead times until when the data assimilations influence the forecast quality are also different for different datasets and seasons; as an example, the impact from assimilating snow water equivalent persists for more than 20 weeks during the spring. We finally show that the assimilated datasets exert more control on the forecasting skill than the meteorological forcing data, highlighting the importance of initial hydrological conditions for this snow-dominated river system.


Data assimilation, seasonal forecasting, E-HYPE


Musuuza, J. L., Crochemore, L., and Pechlivanidis, I. G.: The impact of assimilating Earth Observation and in-situ data on seasonal hydrological predictions, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-225, https://doi.org/10.5194/iahs2022-225, 2022.