Groundwater thresholds for drought resilience in floodplain woodlands: a case study from the northern Murray-Darling Basin

Groundwater thresholds for drought resilience in floodplain woodlands: a case study from the northern Murray-Darling Basin

In ephemeral river systems, canopy condition in dominant
riparian and floodplain tree species may depend on access
to shallow groundwater resources, particularly during
drought. However, unsustainable groundwater extraction and
chronic groundwater decline, evident in many agricultural
landscapes worldwide, effectively decouples tree roots from
deep soil moisture resources, increasing the susceptibility
of trees to changes in precipitation.
In such regions, drought may trigger loss of canopy
condition and have long term consequences for the function
and survival of trees and the composition, structure and
function of ecosystems they dominate. However, critical groundwater depth thresholds have been difficult to identify.
This study used a novel approach including boosted regression trees, quantile regression and threshold
analysis to explore the relationship between groundwater
depth and tree condition for two dominant tree species,
Eucalyptus camaldulensis (river red gum) and E. populnea
(poplar box); both species occur on the Upper Condamine
floodplain, a region experiencing groundwater depth
declines of 25+m in the northern Murray-­‐Darling Basin,
southern Queensland.
Distinct non-­‐linear responses were apparent, with minimum
groundwater depth thresholds identified at 12.1m for E.
camaldulensis and 12.6m for E.populnea, beyond which canopy
condition declined significantly.
This approach represents a repeatable method of quantifying
ecological response thresholds along groundwater depth
gradients. Its application may enable safe operating limits for groundwater resource management to be identified, supporting improved decision making to support resilient floodplain ecosystems.
This will be particularly important in regions where
groundwater decline driven by increasing water demand and
drying climates is predicted.

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