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This paper proposes a novel deterministic methodology for estimating the optimal sampling frequency (SF) of water quality monitoring systems. The proposed methodology is based on employing two-dimensional contaminant transport simulation models to determine the minimum SF, taking into consideration all the potential changes in the boundary conditions of a water body. Two-dimensional contaminant transport simulation models (RMA4) were implemented to estimate the distribution patterns of some effective physiochemical parameters within the Al-Hammar Marsh in the southern part of Iraq for 30 cases of potential boundary conditions. Using geographical information system (GIS) tools, a spatiotemporal analysis approach was applied to the results of the RMA4 models to determine the minimum SF of the monitoring stations with a monitoring accuracy (MA) level of detectable change in contaminant concentration ranging from the standard level to 50% (stepwise 5%). For the study area, the proposed methodology specified a minimum and maximum SF for each monitoring station (MS) that ranged between 12 and 33 times per year, respectively. An exponential relationship between SF and MA was obtained. This relationship shows that increasing the MA to ±10%, ±25%, and ±50% increases the SF by approximately 14%, 28%, and 93%, respectively. However, the proposed methodology includes all the potential values and cases of flow and contaminant transport boundary conditions, which increases the certainty of monitoring the system and the efficiency of the SF schedule. Moreover, the proposed methodology can be effectively applied to all types of surface water resources.

Deterministic Methodology for Determining the Optimal Sampling Frequency of Water Quality Monitoring Systems