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Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved forecasting of different 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 datasets. Here, we investigate the hydrological response and seasonal predictions over the snowmelt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven by two meteorological forcings: (i) a downscaled GCM meteorological product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Extended Streamflow Prediction (ESP) technique. Six datasets are assimilated consisting 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 EO and in-situ 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 improvement varies 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.
Jude Lubega Musuuza; Louise Crochemore; Ilias G. Pechlivanidis. What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality? 2021, 1 .
AMA StyleJude Lubega Musuuza, Louise Crochemore, Ilias G. Pechlivanidis. What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality? . 2021; ():1.
Chicago/Turabian StyleJude Lubega Musuuza; Louise Crochemore; Ilias G. Pechlivanidis. 2021. "What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality?" , no. : 1.
The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.
Jude Lubega Musuuza; Louise Crochemore; David Gustafsson; Rafael Pimentel; Ilias Pechlivanidis. Impact of satellite and in situ data assimilation on hydrological predictions. 2020, 1 .
AMA StyleJude Lubega Musuuza, Louise Crochemore, David Gustafsson, Rafael Pimentel, Ilias Pechlivanidis. Impact of satellite and in situ data assimilation on hydrological predictions. . 2020; ():1.
Chicago/Turabian StyleJude Lubega Musuuza; Louise Crochemore; David Gustafsson; Rafael Pimentel; Ilias Pechlivanidis. 2020. "Impact of satellite and in situ data assimilation on hydrological predictions." , no. : 1.
The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.
Jude Lubega Musuuza; David Gustafsson; Rafael Pimentel; Louise Crochemore; Ilias Pechlivanidis. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions. Remote Sensing 2020, 12, 811 .
AMA StyleJude Lubega Musuuza, David Gustafsson, Rafael Pimentel, Louise Crochemore, Ilias Pechlivanidis. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions. Remote Sensing. 2020; 12 (5):811.
Chicago/Turabian StyleJude Lubega Musuuza; David Gustafsson; Rafael Pimentel; Louise Crochemore; Ilias Pechlivanidis. 2020. "Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions." Remote Sensing 12, no. 5: 811.