Influence of hydrologic alteration on floodplain vegetation distribution and condition across the upper Darling River landscape
Semi-arid floodplain vegetation is an essential component of floodplain and terrestrial ecosystems, providing many ecological, aesthetic and economic benefits. Since overbank flooding has been considered the driving factor of vegetation dynamics and composition on floodplains, sustainable water resource management requires a better understanding of the temporal and spatial variability of inundation patterns and its influence on vegetation communities. Nevertheless, quantifying inundation at the fine resolution required for ecological modelling is an immense challenge in these environments.
In this thesis, machine learning techniques were implemented to develop a spatially explicit fine-scale inundation model as a function of hydrologic, climatic and topographic parameters across the Darling River floodplain. The results demonstrate the computing efficiency of machine learning algorithms to reproduce long‐term daily inundation extent to characterise the flooding regime at a floodplain scale, which is critical to estimate the water requirements of various floodplain ecological assets.
Subsequently, the model was utilised to compare floodplain inundation regimes associated with current hydrological development and pre-development (natural regime) scenarios. The results suggest that the Darling River catchment's hydrological developments have substantially increased inter-flood period, an effect particularly pronounced during dry climatic phases. The results also explain the decline in waterbird utilisation in this section of the Darling River, the low resilience of outer floodplain vegetation to the recent drought, and the reduction in grazing capacity. Water resource development had less impact on large inundation events.
The relationship between the distribution of four flood-dependant vegetation communities and long-term inundation metrics on the upper Darling River floodplain was examined using electivity analysis and generalised additive models (GAMs) by generating a total of 10,478 individual inundation maps with a high spatial resolution over a period of 29 years (1988-2016). GAM results confirmed that, although the hydrological regime is reported to be a critical factor influencing vegetation distribution, other environmental factors should be considered while studying vegetation distribution, especially for Black Box, Coolabah and Lignum. The overall results indicated that the floodplain forests and woodlands in the upper Darling could survive long periods of drought and shorter inundation duration than previously reported.
In the final part of this thesis, an assessment of the spatiotemporal variation in vegetation dynamics in response to inundation and climate variables across three decades was addressed. Vegetation dynamics showed high variability between years, associated with irregular patterns of rainfall and overbank flooding. The results suggest that rainfall may be a more significant driver of vegetation productivity, meaning that regional floodplain vegetation responses are dependent on the uncertain trajectory of rainfall trends under climate change. Findings also highlight the importance of lag times in explaining vegetation productivity in semi-arid floodplains and should be considered in the design of environmental water monitoring programs. It was also noted that the lag associated with flooding appears longer than that of rainfall, with flooding characteristics two seasons prior still exerting an influence on productivity.
In conclusion, the thesis provides an integrated approach to overcome the technical challenge of flow-ecology modelling in floodplains, which is critical for sustainable water resource management. Furthermore, the integrated approach performed in this thesis is generic and can be applied to other floodplains across the world, although some adaptation would be necessary depending on available data.