Abstract Rivers in arid regions often rely on flow generated from wetter regions upstream, leading to high transmission losses of downstream flows. These transmission losses support a range of ecosystems, but partitioning the volume of the transmission losses across the floodplain, riparian zone and in‐channel is difficult. This study presents a methodology relying primarily on multi‐decade satellite remotely sensed actual evapotranspiration estimates to partition these losses. The method was applied to the ~40,000 km 2 floodplain of Cooper Creek in the central Australian arid zone, where first, the alluvial landscape was classified based on actual evapotranspiration rates, and second, both regional‐ (i.e., for the entire floodplain) and local‐scale (i.e., for each waterhole) water balances were calculated to partition these losses. Regional‐scale results estimated that 82% of transmission losses occurred on the floodplain, 13% in the riparian zone and 5% from open water in the river channel and waterholes. These results showed that a refinement of the conceptual model of recharge from the waterholes is necessary as vast areas of the riparian zone are likely to be accessing a shallow freshwater lens rather than a discrete freshwater lens below the permanent waterholes. This method can be used in other data‐poor arid river systems as it uses globally accessible data sources.
Environmental impact assessment (EIA) relies on rigorous scientific assessment of all potential causal pathways by which large-scale developments may impact on valued assets in a region. Despite their importance to informed decision-making, many EIAs are flawed by incomplete analysis of causal pathways, limited spatial assessment and a lack of transparency about how risks have been evaluated across the region. To address these, we describe an EIA methodology based on network analysis of potential causal pathways in a given region. This network approach is coupled with a systematic evaluation of the likelihood, consequence and mitigation options for each causal pathway from one or more human activities to multiple valued assets. The method includes analysis of the confidence in these evaluations, recognizing where knowledge gaps constrain assessments of risks to particular assets. The causal network approach is complemented by a spatially explicit analysis of the region that allows residual risk (i.e. risk remaining after all feasible mitigations) to be mapped for all valued assets. This identifies which activities could lead to potential impacts of varying concern (rated from 'very low' to 'very high'), their likely pathways, which valued assets are at risk and where these residual risks are greatest. The output maps reveal 'risk hotspots' where more detailed local-scale assessments and monitoring should focus. The method is demonstrated by application to potential impacts on 8 valued assets (aquifers, ecosystems and protected species) due to unconventional gas resource development in the Cooper Basin, central Australia. Results show which activities and causal pathways are of potential concern to different valued assets and where residual risk is greatest for particular species and ecosystems. This spatial causal network provides a systematic, consistent and transparent assessment of potential impacts, improving the quality of decision-making about planned developments and their environmental risks.
Abstract Land surface phenology (LSP) is useful to understand patterns of terrestrial ecosystems. Detecting LSP in drylands is more challenging when compared to agricultural and mesic environments due to vegetation heterogeneity, the presence of evergreen and seasonal species, and the dominant role of water (which is often received episodically with variable timing) in determining vegetation growth. In this study, LiDAR‐derived vegetation classes are defined to guide and improve the interpretation of LSP metrics extracted using temporally decomposed Landsat fPAR time series. This method was applied to waterholes within the Cooper Creek floodplain, in dryland Australia, which are important for ecological conservation. Results showed that phenology is mostly associated with the recurrent vegetation (approximately 80% of all identified phenological events) in all waterholes. However, during high streamflow periods, the number of phenological events associated with the persistent vegetation greatly increased (up to 40% of the identified events). Non‐annual phenology was also identified, with more than one phenological event occurring across a water year during high streamflow periods. The duration of the phenological events of the persistent vegetation exceeded one water year during periods of high streamflow. Phenological differences of the LiDAR‐derived vegetation classes occupying the riparian zone of the waterholes were also identified. Streamflow movement across the floodplain exerts an important influence on the vegetation phenology, as suggested by a lag in the phenology when comparing southern and northern waterholes. The method developed herein can be applied to other highly spatially heterogeneous ecosystems where vegetation species simultaneously present permanent and seasonal patterns.
Abstract. Describing and classifying a landscape for environmental impact and risk assessment purposes is a non-trivial challenge because this requires region-specific landscape classifications that cater for region-specific impacts. Assessing impacts on ecosystems from the extraction of water resources across large regions requires a causal link between landscape features and their water requirements. We present the rationale and implementation of an ecohydrological classification for regions where coal mine and coal seam gas developments may impact on water. Our classification provides the essential framework for modelling the potential impact of hydrological changes from future coal resource developments at the landscape level. We develop an attribute-based system that provides representations of the ecohydrological entities and their connection to landscape features and make use of existing broad-level classification schemes into an attribute-based system. We incorporate a rule set with prioritisation, which underpins risk modelling and makes the scheme resource efficient, where spatial landscape or ecosystem classification schemes, developed for other purposes, already exist. A consistent rule set and conceptualised landscape processes and functions allow for the combination of diverse data with existing classification schemes. This makes the classification transparent, repeatable and adjustable, should new data become available. We apply the approach in three geographically different regions, with widely disparate information sources, for the classification, and provide a detailed example of its application. We propose that it is widely applicable around the world for linking ecohydrology to environmental impacts.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)