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- Title
Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model.
- Authors
Meyer Oliveira, Aline; van Meerveld, H. J.; Vis, Marc; Seibert, Jan
- 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. Plain Language Summary: Hydrological models are important for water resources management. The parameters for these models are estimated in a calibration process. Usually, calibration is based on streamflow data from gauging stations. However, for many catchments there are no streamflow data and therefore the calibration of hydrological models is difficult. In this study, we tested whether satellite data that shows the area that is covered by water can be used to calibrate the parameters of a hydrological model for Brazilian catchments. First, we tested if satellite data would be useful if the water extent was perfectly correlated to streamflow and available for every day, month, or month for half of the year due to cloud cover. For two thirds of the catchments, daily observations would be helpful for model calibration, but both monthly data sets were also informative. When we used actual satellite images to calibrate the model for a subset of 76 large rivers, only 30% of them benefitted from these data. This is probably due to inaccuracies in the water extent from satellite images and its coarse spatial resolution. We expect that newer higher‐resolution satellite data will be more useful for model calibration, especially when they become available for longer time periods. Key Points: Synthetic (i.e., perfect) daily water extent time series were informative for model calibration for two thirds of the Brazilian study catchmentsReduction of the temporal resolution to monthly time series did not limit the value of the synthetic water extent data for model calibrationActual remotely sensed water extent data was helpful for calibration for only one third of the subset of 76 catchments with large rivers
- Subjects
WATER management; HYDROLOGIC models; WATERSHEDS; STREAM-gauging stations; CALIBRATION; STREAM measurements; REMOTE-sensing images
- Publication
Water Resources Research, 2023, Vol 59, Issue 11, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2023WR034875