{'fraction_non_alphanumeric': 0.01168770453482936, 'fraction_numerical': 0.007947639083683964, 'mean_word_length': 5.584615384615384, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves

Abstract. In many simulations of historical daily streamflow distributional bias
arising from the distributional properties of residuals has been noted. This
bias often presents itself as an underestimation of high streamflow and an
overestimation of low streamflow. Here, 1168 streamgages across the
conterminous USA, having at least 14 complete water years of daily
data between 1 October 1980 and 30 September 2013, are used to explore a
method for rescaling simulated streamflow to correct the distributional bias.
Based on an existing approach that separates the simulated streamflow into
components of temporal structure and magnitude, the temporal structure is
converted to simulated nonexceedance probabilities and the magnitudes are
rescaled using an independently estimated flow duration curve (FDC) derived
from regional regression. In this study, this method is applied to a pooled
ordinary kriging simulation of daily streamflow coupled with FDCs estimated
by regional regression on basin characteristics. The improvement in the
representation of high and low streamflows is correlated with the accuracy
and unbiasedness of the estimated FDC. The method is verified by using an
idealized case; however, with the introduction of regionally regressed FDCs
developed for this study, the method is only useful overall for the upper
tails, which are more accurately and unbiasedly estimated than the lower
tails. It remains for future work to determine how accurate the estimated
FDCs need to be to be useful for bias correction without unduly reducing
accuracy. In addition to its potential efficacy for distributional bias
correction, this particular instance of the methodology also represents a
generalization of nonlinear spatial interpolation of daily streamflow using
FDCs. Rather than relying on single index stations, as is commonly done to
reflect streamflow timing, this approach to simulation leverages
geostatistical tools to allow a region of neighbors to reflect streamflow timing.
