Modelling blackwater: predicting water quality during flooding of lowland river forests

The blackwater model was developed to predict adverse water quality associated with flooding of the Barmah-Millewa Forests on the River Murray. Specifically, the model examines the likelihood and severity of blackwater events—high dissolved organic carbon associated with low dissolved oxygen. The Barmah-Millewa Forests are dominated by an overstorey of River Red Gum (Eucalyptus camaldulensis) and the litter from these trees contributes a substantial proportion of the pulse of dissolved organic matter released from the floodplain during flooding. This model examines rates of litter accumulation and decay on the floodplain (prior to and during flooding), rates of carbon leaching, microbial degradation, oxygen consumption, reaeration processes and the effects of flow on the concentrations of dissolved organic carbon and dissolved oxygen in the water column (both on the floodplain and in the river channel downstream). The model has been calibrated with data from two blackwater events that have taken place in these forests within the last 5 years. Scenario testing with the model highlights the particularly important roles of flow and temperature in the development of anoxia. Pooled floods and those in the warmest months of the year are substantially more likely to result in blackwater events than floods in cooler times of the year and involving more water exchange between the river channel and the floodplain.

Predicting future ecological degradation based on modelled thresholds

Threshold models are becoming important in determining the ecological consequences of our actions within the environment and have a key role in setting bounds on targets used by natural resource managers. We have been using thresholds and related concepts adapted from the multiple stable-states literature to model ecosystem response in the Coorong, the estuary for Australia’s largest river. Our modelling approach is based upon developing a state-and-transition model, with the states defined by the biota and the transitions defined by a classification and regression tree (CART) analysis of the environmental data for the region. Here we explore the behaviour of thresholds within that model. Managers tend to plan for a set of often arbitrarily-derived thresholds in their natural resource management. We attempt to assess how the precision afforded by analyses such as CART translates into ecological outcomes, and explicitly trial several approaches to understanding thresholds and transitions in our model and how they might be relevant for management. We conclude that the most promising approach would be a mixture of further modelling (using past behaviour to predict future degradation) in conjunction with targeted experiments to confirm the results. Our case study of the Coorong is further developed, particularly for the modelling stages of the protocol, to provide recommendations to improve natural resource management strategies that are currently in use.