气候变化领域集成服务门户

Applications of Total Maximum Daily Load (TMDL) criteria for complex estuarine systems like Chesapeake Bay have been limited by difficulties in estimating precisely how changes in input loads will impact ambient water quality. A method to deal with this limitation combines the strengths of the Chesapeake Bay's Water Quality Sediment Transport Model (WQSTM), which simulates load response, and the Chesapeake Bay Program's robust historical monitoring dataset. The method uses linear regression to apply simulated relative load responses to historical observations of water quality at a given location and time. Steps to optimize the application of regression analysis were to: (1) determine the best temporal and spatial scale for applying the WQSTM scenarios, (2) determine whether the WQSTM method remained valid with significant perturbation from calibration conditions, and (3) evaluate the need for log transformation of both dissolved oxygen (DO) and chlorophyll a (CHL) datasets. The final method used simple linear regression at the single month, single WQSTM grid cell scale to quantify changes in DO and CHL resulting from simulated load reduction scenarios. The resulting linear equations were applied to historical monitoring data to produce a set of scenario-modified DO or CHL concentration estimates. The utility of the regression method was validated by its ability to estimate progressively increasing attainment in support of the 2010 Chesapeake Bay TMDL.