Dynamic modeling of long-term sedimentation in the Yadkin River basin

Modeling of sediment transport in relation to changing land-surface conditions against a background of considerable natural variability is a challenging area in hydrology. Bayesian dynamic linear models (DLMs) however, offer opportunities to account for non-stationarity in relationships between hydrologic input and basin response variables. Hydrologic data are from a 40 years long record (1951-1990) from the 5905 km2 Yadkin River basin in North Carolina, USA. DLM regressions were estimated between log-transformed volume-weighted sediment concentration as a response and log-transformed rainfall erosivity and river flow, respectively, as input variables. A similar regression between log-transformed river flow and log-transformed basin averaged rainfall was also analyzed. The dynamic regression coefficient which reflects the erodibility of the basin decreased significantly between 1951 and 1970, followed by a slowly rising trend. These trends are consistent with observed land-use shifts in the basin. Bayesian DLMs represent a substantial improvement over traditional monotonic trend analysis. Extensions to incorporate multiple regression and seasonality are recommended for future applications in hydrology. (C) 2000 Elsevier Science Ltd. All rights reserved.

Duke Scholars

Author Daniel D. Richter Earth and Climate Sciences