HR: 0800h
AN: H31B-1310    [Abstracts]
TI: Space/Time Assessment Of Water Quality Along The River Network In New Jersey
AU: * Money, E
EM: emoney@email.unc.edu
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AU: Akita, Y
EM: akita@email.unc.edu
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AU: Carter, G
EM: GCARTER@dep.state.nj.us
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AU: Serre, M
EM: marc_serre@unc.edu
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AB: States are mandated by the federal Clean Water Act to provide an assessment of water quality along all streams and rivers. These assessments are used to identify river segments not attaining water quality standards and to establish pollutant budgets (TMDLs) that will bring these waters into compliance. However due to budget and scientific limitations a large fraction of river miles have currently not been adequately assessed. Therefore there is a need to develop a method that can use the partial monitoring information available to estimate water quality along the unmonitored network of streams and rivers. The research proposed will address this need by developing and applying the Bayesian Maximum Entropy (BME) method of modern spatiotemporal Geostatistics to assess water quality along all stream reaches. BME provides a rigorous Bayesian Framework to process historical data, expert knowledge and hydraulic laws available, and produces a more accurate assessment of water quality in unmonitored reaches than can be obtained with classical statistical methods. In this work we present results we have obtained in the development of a river metric used to better model the space/time variability of surface water quality processes. A synthetic case study demonstrates that using a river metric may lead to substantial improvements in mapping accuracy over a classical approach using a Cartesian metric. Additionally we present a framework to account for the composite space and time variability of water quality data. We apply this framework on a case study in New Jersey concerned with the spatiotemporal non-attainment assessment analysis of the surface water quality standard for tetrachloroethene along all river segments of the state. A cross validation comparison with the classical approach using a purely spatial analysis demonstrates that the space/time framework presented here leads to a better accuracy of concentration estimation, and a reduction of the number of non-assessed miles in New Jersey.
DE: 1819 Geographic Information Systems (GIS)
DE: 1839 Hydrologic scaling
DE: 1871 Surface water quality
DE: 1873 Uncertainty assessment (3275)
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: Fall Meeting 2005