Submitted to: ASAE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 4/1/2003
Publication Date: 7/27/2003
Citation: KING, K.W. DEVELOPMENT AND SENSITIVITY ANALYSIS OF A METHOD TO SELECT WATER QUALITY SAMPLING STRATEGIES. 2003. AMERICAN SOCIETY FOR AGRICULTURAL ENGINEERS. PAPER NO. 03-2047 Interpretive Summary:
Technical Abstract: The number of research/monitoring projects involving water quality data is increasing as a result of 1) increases in competition for high quality water supply; 2) public awareness and demand; and 3) legislative mandates including total maximum daily loads (TMDLs). Water quality monitoring programs often form the basis from which related legislation is derived. Currently, a protocol or method is not available for selecting a sampling strategy when initiating a monitoring program. An analytical approach for selecting a sampling strategy was conceived and developed based on dimensionless unit hydrograph technology and documented correlation of measured pollutant concentrations to the hydrograph. Time-based sampling was most sensitive to those parameters used to derive the hydrograph time to peak with a primary sensitivity to curve number. Similarly, flow-proportional sampling was most sensitive to those parameters used to obtain peak flow with a primary sensitivity to 10 yr 1 hr precipitation. Optimal mean predicted time and volumetric flow depth increased with an increase in acceptable error while the mean predicted number of samples required to obtain a specified error decreased with an increase in error. Trends of these basic statistics suggest that the methodology is valid, however, statistical significance of the methodology could not be evaluated due to the absence of, and difficulty in obtaining, measured field data. The outcome of the method is the selection of a sampling strategy based on expected error and sampler constraints. Use of this method should facilitate the selection of water quality sampling strategies for field and watershed scale studies and enhance the findings from a sampling strategy by providing more confidence in the load estimates.