Abstract
Remotely sensed (RS) observations are becoming prevalent for hydrological model calibration in sparsely monitored regions. In this study, the parameter uncertainty associated with a hydrological model calibrated with RS evapotranspiration (ET) and soil moisture (SM) is investigated in detail using a Markov chain Monte Carlo (MCMC) approach. The daily Commonwealth Scientific and Industrial Research Organization (CSIRO) Moderate Resolution Imaging Spectrometer (MODIS) ReScaled potential ET (CMRSET) and SM retrievals from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calibrate a simplified Australian Water Resource Assessment Landscape (AWRA-L) model at 10 small catchments in Eastern Australia. The study inspects the changes in parameter uncertainty with respect to different RS observations and catchment rainfall conditions and the impact of parameter uncertainty on model predictions. Results suggest that uncertainty in posterior parameter distributions increases from high- to low-rainfall catchments due to the intricate nonlinear relationship between rainfall and runoff in low-yielding catchments. Uncertainty is narrower for ET calibrations than SM calibrations, representing higher uncertainty associated with SM data processing. The study concluded that quantification of parameter uncertainty alone is not enough to provide satisfactory prediction uncertainty.
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Data Availability Statement
The streamflow data that support the findings of this study are openly available from respective Australian state water-monitoring portals, including http://data.water.vic.gov.au/static.htm, https://realtimedata.waternsw.com.au/water.stm, and https://water-monitoring.information.qld.gov.au/. The forcing AWAP data for the model are available through http://www.csiro.au/awap/ upon request. The reanalysis CMRSET and AMSR-E soil mositure data supporting the findings of the study are openly available from the National Computational Infrastructure (NCI), Australia (http://remote-sensing.nci.org.au/u39/public/html/wirada/) and the National Snow and Ice Department Centre (NSIDC) (source: https://nsidc.org/data/ae_land3), respectively.
Acknowledgments
The authors would like to acknowledge CSIRO and BoM for providing data. They also would like to express their sincere gratitude to the editor and two anonymous reviewers, whose valuable comments/suggestions helped to improve this manuscript.
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Journal of Hydrologic Engineering
Volume 26 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
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Received: May 28, 2020
Accepted: Oct 26, 2020
Published online: Dec 18, 2020
Published in print: Mar 1, 2021
Discussion open until: May 18, 2021
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