Abstract

For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat-based surface water extent observations can inform the calibration of a lumped bucket-type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April-October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely-available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher-resolution imagery will be helpful for model calibration for more sites, especially when time series length increases.

Keywords

Landsat; CAMELS Brazil; hydrological model calibration; remote sensing data; value of data; large scale hydrology

Published in

Water Resources Research
2023, Volume: 59, number: 11, article number: e2023WR034875
Publisher: AMER GEOPHYSICAL UNION

SLU Authors

• Seibert, Jan

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    
  • University of Zürich

Sustainable Development Goals

Ensure availability and sustainable management of water and sanitation for all


UKÄ Subject classification

Remote Sensing
Oceanography, Hydrology, Water Resources


Publication identifier

DOI: https://doi.org/10.1029/2023WR034875

Permanent link to this page (URI)

https://res.slu.se/id/publ/127040