Optimising sampling frequency for change detection of variables in lake monitoring programs

• Optimized sampling regime can match restoration timeline with improvements in water quality.
• Duration and frequency of the optimized sampling regime affect operational monitoring costs.
• Detecting improvement in chlorophyll-a within 5 years will cost 16 times more than the current costs.
• Detecting improvement in chlorophyll-a within 20 years will cost 3 times more than the current costs.
• A realistic framework to assess monitoring costs relative to investments in restoration.

A lake monitoring regime optimised to detect natural and anthropogenic variations in water quality is critical for supporting and understanding the impact of lake restoration actions. It can support efficient investment in monitoring by minimising the time required for change detection of lake water quality attributes.

The objective of this study was to develop tools and resources to support the development of optimised lake monitoring programs tailored for individual monitored and unmonitored lakes in New Zealand, targeted to detecting change in key water quality attributes in response to restoration actions. We used monitoring data collected routinely from 148 lakes and modelled variability of chlorophyll-a (chl-a), total nitrogen (TN), total phosphorus (TP), and Secchi disk depth (SDD). Using a gradient boosted regression tree (BRT) procedure, we estimated the number of samples per year required over a 5-year and 20-year period to detect improvement in water quality equivalent to meeting thresholds for minimum acceptable water quality states in national policy.

At monitored sites, chl-a monitoring to detect improvements within five years was estimated to increase current costs by over 16 times and over 3 times to detect improvements within 20 years.

Monitoring to document the effects of restoration actions is important for justifying investments in restoration. Our assessment indicates the importance of monitoring frequency for the level of investment and duration with what is possible to detect the resulting changes in water quality.