Long‑term monitoring programs provide data essential to understand waterway condition and direct management actions. However, due to the often-substantial operating costs associated with these programs, they are increasingly under financial and operational pressures. In addition, methodologies tailored to support adaptive management and optimisation of stream monitoring networks post-establishment are limited.
We present a practical, data‑driven approach to adapt long-term monitoring to the changing environmental, social and economic contexts in which they operate, and optimise the utility of the data collected within these constraints. The approach aims to address the question faced by managers of long-term monitoring programs:
When there are changes required to a network (e.g. sites go offline, budget reductions/increases, temporary disruptions) how do we best respond?
Our approach integrates monitoring data with a river-based geostatistical model to assess how the inclusion or removal of observation locations (sites) in an existing monitoring network affects model predictive performance and to identify optimal network designs. The approach is tailored to address specific adaptive management challenges faced by waterway managers: (1) Eliminate redundancy and reduce the number of sites in a network, (2) Efficiently acquire additional sites to improve model prediction, and (3) Effectively prioritise responses to sampling disruptions.
In this presentation we demonstrate the approach using a case-study examining monitoring of stream temperature over the Greater Melbourne region. Our findings indicate that many long-term networks will likely have some sites that are redundant in the network, and moving these sites may improve overall model prediction. And that when site changes are required, that model uncertainty can be used as a reliable predictor to move sites to improve overall prediction performance.
We will also discuss limitations and caution required when using such an approach and how it may be applied to monitoring networks more broadly.