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, o€er 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 km Yadkin River basin in North Carolina, USA. DLM regressions were estimated between logtransformed volume-weighted sediment concentration as a response and log-transformed rainfall erosivity and river ̄ow, respectively, as input variables. A similar regression between log-transformed river ̄ow and log-transformed basin averaged rainfall was also analyzed. The dynamic regression coecient which re ̄ects the erodibility of the basin decreased signi®cantly 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. Ó 2000 Elsevier Science Ltd. All rights reserved.