Minimum detectable change in water quality in nonpoint source control programs
Reduction of agricultural nonpoint source (NPS) pollution by implementation of Best Management Practices (BMPs) on a watershed basis is being monitored by 21 Rural Clean Water Program (RCWP) projects throughout the US. The concept of determining the Minimum Detectable Change (MDC) for trend detection for these monitoring programs is developed in this dissertation. A measured change in water quality is statistically significant if it exceeds a value defined in this research as the MDC. Given a particular monitoring scheme, the water quality observations and their variability can be used to calculate the MDC required in the geometric mean pollutant concentration over time. Methods of MDC calculations and the impact of factors affecting MDC are compared using grab sample monitoring data from the Rock Creek, Idaho and Taylor Creek-Nubbin Slough, Florida RCWP projects. The factors affecting MDC include: monitoring design, sampling frequency, duration of monitoring, system variability, meteorological variability, hydrologic variability, temporal variability, spatial variability, and the statistical analysis performed. Autocorrelation was significant in these time series and it was necessary to incorporate an AR(1) autocorrelation structure into the statistical trend models to obtain value estimates of the MDC.
- Research Organization:
- North Carolina State Univ., Raleigh, NC (United States)
- OSTI ID:
- 5090632
- Resource Relation:
- Other Information: Thesis (Ph.D.)
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
WATER POLLUTION CONTROL
RESEARCH PROGRAMS
WATERSHEDS
WATER POLLUTION
AGRICULTURE
FLORIDA
HYDROLOGY
IDAHO
MONITORING
POLLUTION SOURCES
STATISTICAL MODELS
CONTROL
DEVELOPED COUNTRIES
FEDERAL REGION X
MATHEMATICAL MODELS
NORTH AMERICA
POLLUTION
POLLUTION CONTROL
USA
540320* - Environment
Aquatic- Chemicals Monitoring & Transport- (1990-)