Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies
Suspended sediment rating-curves are low cost and reliable tools used all around the world to estimate river suspended sediment concentrations (SSC) based on either linear or non-linear regression with a second variable, such as the river discharge. The aim of this paper is to undertake an evaluatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/9/2382 |
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| author | Bárbara M. Jung Elisa H. Fernandes Osmar O. Möller Felipe García-Rodríguez |
| author_facet | Bárbara M. Jung Elisa H. Fernandes Osmar O. Möller Felipe García-Rodríguez |
| author_sort | Bárbara M. Jung |
| collection | DOAJ |
| description | Suspended sediment rating-curves are low cost and reliable tools used all around the world to estimate river suspended sediment concentrations (SSC) based on either linear or non-linear regression with a second variable, such as the river discharge. The aim of this paper is to undertake an evaluation of four different suspended sediment rating-curves for three turbid large river tributaries flowing into the largest choked coastal lagoon of the world, a very turbid system. Statistical parameters such as Nash–Sutcliffe efficiency coefficient (NSE), percent of bias (PBIAS) and a standardized root-mean-square error (RMSE), referred to as <i>RSR</i> (RMSE-observations standard deviation ratio) were used to calibrate and validate the suspended sediment rating-curves. Results indicated that for all tributaries, the non-linear approach yielded the best correlations and proved to be an effective tool to estimate the SSC from river flow data. The tested curves show low bias and high accuracy for monthly resolution. However, for higher temporal resolution, and therefore variability, an ad hoc data acquisition to capture extreme rating-curve values is required to reliably fill gaps of information for both performing modeling approaches and setting monitoring efforts for long-term variability studies. |
| first_indexed | 2024-03-10T16:50:04Z |
| format | Article |
| id | doaj.art-b837418c421148779c5e9d37b710c724 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T16:50:04Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-b837418c421148779c5e9d37b710c7242023-11-20T11:20:00ZengMDPI AGWater2073-44412020-08-01129238210.3390/w12092382Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability StudiesBárbara M. Jung0Elisa H. Fernandes1Osmar O. Möller2Felipe García-Rodríguez3Instituto de Oceanografia, Universidade Federal do Rio Grande (FURG), CP 474, Rio Grande-RS CEP 96201-900, BrazilInstituto de Oceanografia, Universidade Federal do Rio Grande (FURG), CP 474, Rio Grande-RS CEP 96201-900, BrazilInstituto de Oceanografia, Universidade Federal do Rio Grande (FURG), CP 474, Rio Grande-RS CEP 96201-900, BrazilInstituto de Oceanografia, Universidade Federal do Rio Grande (FURG), CP 474, Rio Grande-RS CEP 96201-900, BrazilSuspended sediment rating-curves are low cost and reliable tools used all around the world to estimate river suspended sediment concentrations (SSC) based on either linear or non-linear regression with a second variable, such as the river discharge. The aim of this paper is to undertake an evaluation of four different suspended sediment rating-curves for three turbid large river tributaries flowing into the largest choked coastal lagoon of the world, a very turbid system. Statistical parameters such as Nash–Sutcliffe efficiency coefficient (NSE), percent of bias (PBIAS) and a standardized root-mean-square error (RMSE), referred to as <i>RSR</i> (RMSE-observations standard deviation ratio) were used to calibrate and validate the suspended sediment rating-curves. Results indicated that for all tributaries, the non-linear approach yielded the best correlations and proved to be an effective tool to estimate the SSC from river flow data. The tested curves show low bias and high accuracy for monthly resolution. However, for higher temporal resolution, and therefore variability, an ad hoc data acquisition to capture extreme rating-curve values is required to reliably fill gaps of information for both performing modeling approaches and setting monitoring efforts for long-term variability studies.https://www.mdpi.com/2073-4441/12/9/2382rating-curveregression analysisriver dischargesuspended sediment |
| spellingShingle | Bárbara M. Jung Elisa H. Fernandes Osmar O. Möller Felipe García-Rodríguez Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies Water rating-curve regression analysis river discharge suspended sediment |
| title | Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies |
| title_full | Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies |
| title_fullStr | Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies |
| title_full_unstemmed | Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies |
| title_short | Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies |
| title_sort | estimating suspended sediment concentrations from river discharge data for reconstructing gaps of information of long term variability studies |
| topic | rating-curve regression analysis river discharge suspended sediment |
| url | https://www.mdpi.com/2073-4441/12/9/2382 |
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