Fitting sediment rating curves using regression analysis: a case study of Russian Arctic rivers
Published suspended sediment data for Arctic rivers is scarce. Suspended sediment rating curves for three medium to large rivers of the Russian Arctic were obtained using various curve-fitting techniques. Due to the biased sampling strategy, the raw datasets do not exhibit log-normal distribution, w...
| Published in: | Proceedings of the International Association of Hydrological Sciences |
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| Main Author: | |
| Format: | Text |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/piahs-367-193-2015 https://piahs.copernicus.org/articles/367/193/2015/ |
| Summary: | Published suspended sediment data for Arctic rivers is scarce. Suspended sediment rating curves for three medium to large rivers of the Russian Arctic were obtained using various curve-fitting techniques. Due to the biased sampling strategy, the raw datasets do not exhibit log-normal distribution, which restricts the applicability of a log-transformed linear fit. Non-linear (power) model coefficients were estimated using the Levenberg-Marquardt, Nelder-Mead and Hooke-Jeeves algorithms, all of which generally showed close agreement. A non-linear power model employing the Levenberg-Marquardt parameter evaluation algorithm was identified as an optimal statistical solution of the problem. Long-term annual suspended sediment loads estimated using the non-linear power model are, in general, consistent with previously published results. |
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