Open Access Open Access  Restricted Access Subscription or Fee Access

Record Length Needed to Predict Suspended Sediment Load from Stream Discharge Data Using Linear Regression

Isam E. Amin, Alan M. Jacobs

Abstract



Although it has been established that suspended sediment load of streams can be derived from stream water discharge data, it is assumed that the longer the record length, the better the correlation. Using linear regression analysis, we applied this technique to four diverse US rivers, with sediment record lengths of 30 to 54 years. High correlation coefficients (0.72 to 0.97) showed high accuracy whereas predicted minus observed suspended sediment loads for each river re-calculated for records in increasing increments of 5 to 10 years in length showed high precision regardless of the record length. Consequently, record lengths more than 5 to 10 years were considered unnecessary.

Keywords


Linear regression analysis, suspended sediment load, river discharge, correlation coefficients, predicted vs. observed sediment load, accuracy vs. precision

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.