A practical approach to improve the statistical performance of surface water monitoring networks

Abstract

The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying the uncertainties in the status class estimation. We estimated the temporal and spatial variance components, as well as the effect of sampling allocation to the precision and confidence of chlorophyll- a and total phosphorus. Our results suggest that almost 70% of the lake and coastal waterbodies, and 27% of the river waterbodies, were classified without sufficient confidence in these variables. On the other hand, many of the waterbodies produced unnecessary precise metric means. Thus, reallocation of sampling effort is needed. Our results show that, even though the studied variables are among the most monitored status metrics, the unexplained variation is still high. Combining multiple data sets and using fixed covariates would improve the modeling performance. Our study highlights that ongoing monitoring programs should be evaluated more systematically, and the information from the statistical uncertainty analysis should be brought concretely to the decision-making process.

A Study on the Determination Methods of Monitoring Point for Inundation Damage in Urban Area Using UAV and Hydrological Modeling

“…Most of the studies on monitoring in urban areas have focused on the influence of air pollution by altitude, sampling frequency, and observation of monitoring points [49][50][51][52][53][54][55][56][57][58][59]. In some studies, monitoring was performed, and a method of selecting optimal points in consideration of parameters or improving quality was presented [60][61][62]70].…”

“…It is possible to measure related parameters if a network capable of identifying the observation elements of monitoring is set for each point [59][60][61][62]. There are no studies focusing on the methodology for selecting monitoring points, despite a number of investigations involving urban area monitoring.…”