Value of uncertain streamflow observations for hydrological modelling
2018
Abstract. Previous studies have shown that hydrological models can be parameterised using a
limited number of streamflow measurements. Citizen science projects can
collect such data for otherwise ungauged catchments but an important question
is whether these observations are informative given that these streamflow
estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a
simple bucket-type runoff model for six Swiss catchments. We pretended that
only a few observations were available and that these were affected by
different levels of inaccuracy. The level of inaccuracy was based on a
log-normal error distribution that was fitted to streamflow estimates of 136
citizens for medium-sized streams. Two additional levels of inaccuracy, for
which the standard deviation of the error distribution was divided by 2 and
4, were used as well. Based on these error distributions,
random errors were added to the measured hourly streamflow data. New time
series with different temporal resolutions were created from these synthetic
streamflow time series. These included scenarios with one observation each
week or month, as well as scenarios that are more realistic for crowdsourced
data that generally have an irregular distribution of data points throughout
the year, or focus on a particular season. The model was then calibrated for
the six catchments using the synthetic time series for a dry, an average and
a wet year. The performance of the calibrated models was evaluated based on
the measured hourly streamflow time series. The results indicate that
streamflow estimates from untrained citizens are not informative for model
calibration. However, if the errors can be reduced, the estimates are
informative and useful for model calibration. As expected, the model
performance increased when the number of observations used for calibration
increased. The model performance was also better when the observations were
more evenly distributed throughout the year. This study indicates that
uncertain streamflow estimates can be useful for model calibration but that
the estimates by citizen scientists need to be improved by training or more
advanced data filtering before they are useful for model calibration.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
49
References
19
Citations
NaN
KQI