Improving Ecological Response Monitoring of Environmental Flows

Alison J. King, Ben Gawne, Leah Beesley, John D. Koehn, Daryl L. Nielsen, Amina Price

Environmental flows are now an important restoration technique in flow-degraded rivers, and with the increasing public scrutiny of their effectiveness and value, the importance of undertaking scientifically robust monitoring is now even more critical. Many existing environmental flow monitoring programs have poorly defined objectives, nonjustified indicator choices, weak experimental designs, poor statistical strength, and often focus on outcomes from a single event. These negative attributes make them difficult to learn from. We provide practical recommendations that aim to improve the performance, scientific robustness, and defensibility of environmental flow monitoring programs. We draw on the literature and knowledge gained from working with stakeholders and managers to design, implement, and monitor a range of environmental flow types. We recommend that (1) environmental flow monitoring programs should be implemented within an adaptive management framework; (2) objectives of environmental flow programs should be well defined, attainable, and based on an agreed conceptual understanding of the system; (3) program and intervention targets should be attainable, measurable, and inform program objectives; (4) intervention monitoring programs should improve our understanding of flow-ecological responses and related conceptual models; (5) indicator selection should be based on conceptual models, objectives, and prioritization approaches; (6) appropriate monitoring designs and statistical tools should be used to measure and determine ecological response; (7) responses should be measured within timeframes that are relevant to the indicator(s); (8) watering events should be treated as replicates of a larger experiment; (9) environmental flow outcomes should be reported using a standard suite of metadata. Incorporating these attributes into future monitoring programs should ensure their outcomes are transferable and measured with high scientific credibility.