Abstract
Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydro-meteorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average.
Highlights
Motivated by the impact of climate change on the scarcity or excess of water in many areas around the world, and following the recommendations of the Sendaï framework for disaster risk reduction (UNISDR, 2015), several agencies and research institutions have put substantial efforts into better monitoring of and prediction of the hydrologic cycle at a global scale
Considering that in our set-up, the representation of the evapotranspiration is rather simplistic as it is based on the Hamon formula; this could explain the poorer performance of the model in the western part of the basin
Result 4 can be explained considering the input data used for running Community Microwave Emission Modelling (CMEM) concerning the fraction of the grid cell that is covered by surface water
Summary
Motivated by the impact of climate change on the scarcity or excess of water in many areas around the world, and following the recommendations of the Sendaï framework for disaster risk reduction (UNISDR, 2015), several agencies and research institutions have put substantial efforts into better monitoring of and prediction of the hydrologic cycle at a global scale. To reduce uncertainty in model simulations, an advanced solution that has gained increased interest over the last few decades is the integration of remote sensing data into models (Andreadis and Schumann, 2014; Hostache et al, 2018; De Lannoy and Reichle, 2016b) This approach pursues an optimal combination of hydro-meteorological modelling and remote sensing, for example by using satellite measurements as forcing or calibration data and/or for regular updates of the model states or parameters (Moradkhani, 2007). In forecasting mode, such data assimilation approaches allow one to keep the predictions on track, while in hind-casting mode they enable improved simulations of measured fluxes and states of the past
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