Sample based water quality monitoring of coastal seas: How significant is the information loss in patchy time series compared to continuous ones?

The high temporal and spatial variability of tidal dominated coastal areas poses a challenge for characterising water quality. Water quality monitoring relies often on information collected by water sampling from a vessel or by satellites, and covers limited time periods and therefore limited tidal and meteorological conditions. To assess the loss of information from discrete sampling, continuous time series of one year (suspended particulate matter (SPM) concentration, SPM flux and Chlorophyll a (Chl) concentration) were used. Eight different schemes of sampling into these time series were applied that are typical for many monitoring programs. They differ in the time between sampling events (synodic or half-synodic) and the duration of the sampling (tidal cycle, half a tidal cycle, one or more samples). The information loss was quantified by applying a bootstrap method to calculate the mean and standard deviation over the considered period. These were then compared with the true mean calculated from the continuous series. The probability to match the true mean within a certain margin depends on the sampling period and the season, but it is always low, especially if the allowed uncertainty is stringent (e.g., ±2.5 % about the true mean). For the SPM concentration this probability is lower than 10 % and for Chl concentration lower than 20 %. Similarly, conclusions arise for the detection of trends in a 20 year time series of SPM concentration with an artificial yearly increase of 0.5 %. None of the sampling schemes was able to assess statistical significant interannual trends with probabilities above 60 %. Further, the significant trends overestimated the increase by a factor 2 to 8. Here, present modus operandi is thus inadequate for basic trend detection, but may be acceptable for the more marine, lower turbid areas where higher probabilities were obtained in this study.