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Accounting for sediment sources and sinks in the linear regression analysis of the suspended sediment load of streams: The Rio Puerco, New Mexico, as an example

¹ Department of Geological and Environmental Sciences, Youngstown State University, Youngstown, Ohio 44555; ieamin{at}ysu.edu
² Department of Geological and Environmental Sciences, Youngstown State University, Youngstown, Ohio 44555

Isam E. Amin is an associate professor and director of the Environmental Studies Programs at Youngstown State University in Ohio. He received his B.Sc. degree (1977) in geology from the University of Khartoum, Sudan, his M.S. degree (1983) in hydrogeology from the New Mexico Institute of Mining and Technology, Socorro, New Mexico, and his Ph.D. (1987) in hydrogeology from the University of Nevada-Reno.

Alan M. Jacobs is professor and chair of the Department of Geological and Environmental Sciences at Youngstown State University. Jacobs received his B.S. degree in geology at City College of New York (1963), and his M.A. degree (1965) and his Ph.D. (1967) at Indiana University.

This study addresses the effects of sediment sources and sinks on the simulation of the suspended sediment load in streams by linear regression. The study investigates the sediment load of the Rio Puerco, an ephemeral stream in central New Mexico, which produces one of the highest suspended sediment loads in the United States and the world.

The suspended sediment load in the Rio Puerco is simulated by linear regression on a daily, monthly, and annual basis. The monthly regression relationship yielded the highest correlation coefficient (0.93) compared to the daily (0.87) and annual (0.90) relationships. Hence, the monthly relationship is chosen to investigate the impact of sources and sinks on the suspended sediment load.

The effects of sources and sinks are determined by fitting three regression lines through the data. This has allowed the calculation of the amounts of sediment gained from sources and those lost to sinks. Accounting for the effects of sources and sinks has resulted in a significant increase in the correlation coefficient of the two regression variables, the monthly suspended sediment load (Y) and the monthly water discharge (X). The correlation coefficient has increased from 0.93 to 0.98. This, in turn, has led to substantial improvement in the accuracy of the prediction of the suspended load using linear regression. The absolute value of the percent deviation of the annual predicted loads from the observed values has a median of 12.9% and a mean of 20.2% for the proposed approach, which considers sources and sinks, compared to a median of 38.5% and a mean of 45.7% for the existing traditional approach, which does not account for sources and sinks.