Floodplains and their associated wetlands are important features of semiarid and arid landscapes, providing habitat and refugia for native species as well as contributing to human needs for freshwater. Globally, floodplain habitats are some of the most modified ecological communities because of water resource development and land-use changes. However, the hydrological changes that have occurred in highly variable semiarid and arid systems are rarely quantified in a way that helps us understand the consequences for different floodplain habitat types. This study investigated changes in floodplain-river connectivity that have occurred because of water resource development on four floodplain habitat types in the Lachlan River Catchment, Australia: (a) temporary floodplain lakes, (b) intermittent river red gum (Eucalyptus camaldulensis) swamps, (c) intermittent black box (Eucalyptus largiflorens) swamps, and (d) terminal wetlands (wetlands along distributary creeks). Changes to floodplain-river connectivity characteristics were calculated using their commence to fill thresholds and flow scenarios derived from a river hydrology model, enabling comparison of long-term data sets (120 years) encompassing a range of climate conditions. Connection regime metrics have changed significantly in all floodplain habitats except intermittent black box swamps. Temporary floodplain lakes have experienced the greatest reduction in number of connection events (60% reduction), followed by intermittent river red gum swamps (55% reduction). Intermittent black box swamps and terminal wetlands have experienced the least change in number of connection events (35% reduction). The nature of the change in connection suggests a change in vegetation communities will occur in response to long-term hydrological change.

Detailed vegetation maps are needed for wetland conservation and restoration as different vegetation communities have distinct water requirements. It is a continuous challenge to map the distribution of different wetland types on a regional scale, and a trade-off between the categorical details and availability of resources to ensure broad applications is often necessary for operational mapping. Here, we evaluated the capacity and performance of statistical learning in discriminating wetland types using Landsat time series and geomorphological variables computed from Light Detection and Ranging (LiDAR) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). Our study showed that there was a discrimination limit of statistical learning in wetland mapping. The approach was clearly inadequate in distinguishing certain wetland types. In semiarid Australia, our results suggested that the appropriate level for floodplain wetland mapping included four classes: tree-dominated woodlands, shrublands, vegetated swamps, and non-flood-dependent terrestrial communities. Our results also demonstrated that the geomorphological metrics significantly improved the accuracy of wetland classification. Furthermore, geomorphological metrics derived from the freely available coarser resolution SRTM DEM were as beneficial for wetland mapping as those extracted from finer scale commercially-based LiDAR DEM. The finding enables the widespread applications of our approach, as both data sources are freely available globally.

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