# Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast
## 4 Discussion
This is because the underlying assumption of independence in observation-level modelling (standard linear regression) is not fulfilled in spatially clustered data [33]. This is the case even if there is no significant variation between groups of observations, in this case between the sampling sites [33]. In addition, after incorporating suitable regional-level predictor variables into the models, they would be suitable for predictive water property modelling. The results from our multilevel modelling suggest that both the overall level and the seasonal development of water properties vary at the regional (but not local) scale. In multilevel modelling, allowing the regression coefficients to vary between sites reveals important processes, which may be masked in observation-level examination. For example, only the best multilevel model for temperature allows the effect of depth to vary between sites, revealing potentially important vertical-temporal processes. Conversely, the depth profiles of salinity, pH and chlorophyll-a are similar among the sites. Preliminary tests using second- and third-order models perform reasonably well for temperature, salinity and pH, and capture the main seasonal trends (see Appendix B). The more complex models (including up to seventh-order terms) are able to also capture the smaller temporal fluctuations in water properties. These preliminary findings suggest that the simpler second- and third-order models could be more suitable starting points for the predictive modelling of water properties (temperature, salinity and pH), i.e., when transferring the results into other areas and other years. The higher-order models seem to capture small-scale temporal variability of the particular observation period. These are potentially influenced by factors such as weather conditions and hydrological and oceanographic conditions. It should be noted that national monitoring programs are designed to provide an overview of entire sea areas, and therefore they should not be expected to reveal regional-scale details. However, for example, the Finnish national program covers two (temporal and vertical) of the three relevant dimensions identified in this study: a small number of key sites (NAU2361 in our study area; see Figure 1) are sampled frequently throughout the year, and along the depth gradient. In addition, the Finnish national program has been running uninterrupted for decades, thereby offering long time-series data that enable inter-annual comparisons. Regarding regional-scale spatial detail, our results indicate that a single sampling site does not reliably represent water properties--apart from temperature--in the entire archipelago, not even at the nearest sites included in the national monitoring network. Thus, the temporally frequent sampling at one site only represents the very local (\(10^{2}\) m) surroundings at the site. In addition, national monitoring programs may involve a spatially dense but temporally sparse sampling of water properties, such as in Finland. The results of one spatially dense sample cannot be generalised to other periods of time, since the seasonal development patterns are dissimilar among the sites. The data and methodology used in this study provide solid statistical evidence on the issue of horizontal, vertical and temporal scalability of water property measurements. The methods are applicable in coastal areas anywhere to locally examine the scalability of in situ sampling. The predictability of water properties in other years based on the multivariate models for a single year is somewhat restricted, due to year-to-year variability in weather conditions and in the Atlantic forcing. To a degree, it can be assumed that the main spatio-temporal trends persist from year to year, but the lack of detailed data for other growing seasons sets a limitation to these conclusions. Further research on this topic would benefit from multi-annual in situ time series data from a few selected sites, as well as combining high-quality remotely sensed data to in situ data [47; 48; 49; 50].