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  • 标题:Salinity Trends, Variability, and Control in the Northern Reach of the San Francisco Estuary
  • 本地全文:下载
  • 作者:Enright, Christopher ; Culberson, Steven D.
  • 期刊名称:San Francisco Estuary and Watershed Science
  • 印刷版ISSN:1546-2366
  • 出版年度:2009
  • 卷号:7
  • 期号:2
  • DOI:10.15447/sfews.2009v7iss2art3
  • 出版社:San Francisco Bay-Delta Science Consortium and the John Muir Institute of the Environment
  • 摘要:The State and federal water projects decoupled long-term trends in annual mean outflow and salinity from long-term trends in precipitation. The water projects also dampen seasonal and annual outflow and salinity variability. Despite this, both seasonal and annual timescale outflow and salinity are generally more variable in the water project era concordant with watershed precipitation. We re-constructed monthly time series of precipitation, outflow, and salinity for the northern reach. These include salinity at Port Chicago (since 1947), Beldons Landing (since 1929), and Collinsville (since 1921), Delta outflow (since 1929), and a San Francisco Estuary watershed precipitation index (since 1921). We decomposed data into seasonal, decadal, and trend components to clarify the superposition of variability drivers. With the longest time series over 1000 months, these are the longest data records in the estuary save for Golden Gate tide. We used the precipitation index to compare trends and variability in climate forcing to outflow and salinity trends before and after construction of the water projects and the Suisun Marsh Salinity Control Gate. We test the widely held conceptual model that water project reservoir and Delta export operations reduce seasonal and annual outflow variability. We found that the water projects influence the trend of the annual and some monthly means in outflow and salinity, but exert far less influence on variability. We suggest that climate is the primary variability driver at timescales between one-month and ~20 years. We underscore the understanding that identifying trends and mechanisms requires data sets that are longer than the timescale of the lowest frequency forcing mechanism.
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