Floodplain vegetation communities have been heavily impacted by altered flow regimes due to river regulation and water extraction for off-stream use. These communities require specific inundation regimes (extent, duration, timing) to persist, but in many regions those inundation regimes no-longer occur, leading to declines in vegetation condition. Projected climate change will exacerbate hydrologic change and is predicted to cause further declines in the ‘hydrological health’ of river ecosystems. As a mitigating measure, environmental watering may be an effective way of maintaining and improving the health of floodplain vegetation communities. However, determining the flow-regime required to reverse declines in health, as well as assessing the feasibility of delivering such flows is a major management challenge.
Previous modelling approaches addressing the dynamic nature of vegetation on the floodplain have been via expert elicitation resulting in deterministic rules defining vegetation response to hydrological inundation events. These models currently do not incorporate variability in the response of vegetation condition to inundation events and requires validation via measured vegetation condition. Here we assess how variable the condition of river red gum forest communities (Eucalyptus camaldulensis) is at different inundation thresholds within the Barmah-Millewa Forest.
To do this we modelled the change in remotely sensed fractional cover information describing vegetation condition over time, and correlate this with the inundation history experienced by the vegetation. Multiple randomly selected 6.25 ha stands of river red gum were assessed at 24 inundation thresholds to determine the appropriate level of variability in condition response to pre-determined inundation rules. By modelling the variability of condition responses between stands at a specific inundation threshold and between stands at multiple thresholds, allows a validation of previous rules and allows the incorporation of new inundation sequences to improve accuracy across scenarios of different inundation sequences.