Abstract
Abstract. The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from ∼ 0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.
Highlights
In recent decades, a number of large-scale hydrological and land-surface models have been developed to quantify the global water cycle components, to analyse the human influence on the global water balance, to study climate change impact on water resources and to assess global hydrological extremes, such as drought and flood risk
The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in root mean squared error (RMSE))
Several meteorological forcing data sets at a global scale are available, including the European Centre for Medium-Range Weather Forecasts – ECMWF ERAInterim – global atmospheric reanalysis data (Dee et al, 2011), the Climatic Research Unit Time Series – CRU TS – (Mitchell and Jones, 2005), the NASA reanalysis ModernEra Retrospective Analysis for Research and Applications – MERRA – (Rienecker et al, 2011) and the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data – WFDEI – (Weedon et al, 2014)
Summary
A number of large-scale hydrological and land-surface models have been developed to quantify the global water cycle components, to analyse the human influence on the global water balance, to study climate change impact on water resources and to assess global hydrological extremes, such as drought and flood risk. Several meteorological forcing data sets at a global scale are available, including the European Centre for Medium-Range Weather Forecasts – ECMWF ERAInterim – global atmospheric reanalysis data (Dee et al, 2011), the Climatic Research Unit Time Series – CRU TS – (Mitchell and Jones, 2005), the NASA reanalysis ModernEra Retrospective Analysis for Research and Applications – MERRA – (Rienecker et al, 2011) and the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data – WFDEI – (Weedon et al, 2014). More research is required to understand the gain that can be obtained using these higher-spatialresolution forcing data for uncalibrated global hydrological models at finer spatial resolutions
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