利用基于数据的机制模型重建历史水库沉积速率

J.S. Rowan , L.E. Price , C.P. Fawcett , P.C. Young
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引用次数: 13

摘要

本文报道了基于数据的机制(DBM)建模在储层沉积研究中的应用。在Wyresdale公园水库的集水区进行了详细的监测计划,以校准双组分DBM沉积模型。第一个组件是一个非线性降雨-悬沙负荷(SSL, kg s−1)模型,第二个组件处理沉积物路径和水库的捕集器效率。利用1911-1996年的日降水资料模拟了水库的沉积历史。合成的吸积序列证明了气候强迫的影响,并与137Cs独立定年的湖床沉积物岩心进行了比较。合成叠加与观测到的吸积数据基本一致。模型性能的偏离很可能反映了由于土地利用和水库调节的局部变化而导致的系统的非平稳性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstructing historic reservoir sedimentation rates using data-based mechanistic modelling

This paper reports an application of data-based mechanistic (DBM) modelling to the study of reservoir sedimentation. A detailed monitoring programme in the catchment of the Wyresdale Park reservoir was used to calibrate a two component DBM sedimentation model. The first component was a non-linear rainfall to suspended sediment load (SSL, kg s−1) model, the second dealt with sediment routing and the trap efficiency of the reservoir. Daily precipitation data for the period 1911–1996 were used to simulate the sedimentation history of the reservoir. The synthetic accretion sequence evidences the effects of climatic forcing and was compared to lake-bed sediment cores independently dated using 137Cs. The synthetic stack showed general agreement with the observed accretion data. Departures in model performance most likely reflect non-stationarity in the system due to local changes in land use and reservoir regulation.

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