Journal
ADVANCES IN WATER RESOURCES
Volume 23, Issue 8, Pages 881-892Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0309-1708(00)00013-0
Keywords
dynamic linear models; Bayesian time-series methods; hydrology; sedimentation; land-use change; Yadkin River basin
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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 km(2) 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.
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