TY - JOUR
T1 - Evaluation of state and bias estimates for assimilation of SMOS retrievals into conceptual rainfall-runoff models
AU - Pauwels, Valentijn R.N.
AU - Hendricks Franssen, Harrie Jan
AU - De Lannoy, Gabriëlle J.M.
N1 - Funding Information:
This work was supported by computational resources provided by the Australian Government through the National Computational Infrastructure under the National Computational Merit Allocation Scheme. We also thank the Bureau of Meteorology for the discharge, rainfall, and evapotranspiration data. We express our gratitude to Dr. Stefania Grimaldi for making Figure 1. Funding. For this study, VP was funded by ARC Future Fellow grant FT130100545.
Funding Information:
For this study, VP was funded by ARC Future Fellow grant FT130100545.
Publisher Copyright:
Copyright © 2020 Pauwels, Hendricks Franssen and De Lannoy.
PY - 2020/3/17
Y1 - 2020/3/17
N2 - For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.
AB - For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.
KW - bias
KW - discharge
KW - ensemble Kalman filter
KW - hydrology
KW - SMOS
UR - http://www.scopus.com/inward/record.url?scp=85119872342&partnerID=8YFLogxK
U2 - 10.3389/frwa.2020.00004
DO - 10.3389/frwa.2020.00004
M3 - Article
AN - SCOPUS:85119872342
SN - 2624-9375
VL - 2
JO - Frontiers in Water
JF - Frontiers in Water
M1 - 4
ER -