NDLI: Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies

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Estimating Suspended Sediment Concentrations from River Discharge Data for Reconstructing Gaps of Information of Long-Term Variability Studies

Content Provider	MDPI
Author	Osmar, Möller Felipe, García-Rodríguez Jung, Bárbara Fernandes, Elisa
Copyright Year	2020
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 RSR (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.
Starting Page	2382
e-ISSN	20734441
DOI	10.3390/w12092382
Journal	Water
Issue Number	9
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2020-08-25
Access Restriction	Open
Subject Keyword	Water Marine Engineering Rating-curve Regression Analysis River Discharge Suspended Sediment
Content Type	Text
Resource Type	Article

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