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The results indicate that the estimated monthly streamflow data compares favorably well with the observed data and this generated monthly streamflow data has been used to generate corresponding daily flow data.
The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran.
CET is tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics.
The continuous wavelet transform spectra of monthly streamflow data revealed consistent significant 6- and 12-month periodicities, which are likely associated with strong seasonality factors.
Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models.
The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study.
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In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data.
Monthly and annual streamflow data with a record length of 55 years were used to capture streamflow variability at intra-annual, inter-annual and inter-decadal scales.
The temporal downscaling technique as described by Wood et al. (2004) has been used to downscale the modeled streamflow data from monthly to daily values.
Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing.
From streamflow data, determinations can be made of the minimum, maximum, average daily, and average monthly flows; the size of dams, spillways, and downstream channel; and the seasonal and carry-over storage needed.
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