Mapping resilience: A framework for analysing surface-water dynamics and persistent pools in non-perennial rivers using remote sensing, rainfall and river discharge data

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Persistent pools in non-perennial rivers (NPRs) are critical ecological refuges that sustain biodiversity and ecosystem function during dry periods. However, analysing their long-term dynamics is challenging; existing methods are often field-intensive and unscalable, especially in remote, data-scarce regions. To fill this gap, this study introduces a scalable, data-driven framework that integrates freely available remote sensing, rainfall, and discharge data to investigate multi-decadal surface water and persistent pool dynamics across large scales. The four-step framework (scope definition, data collection, processing, and analysis) is used to derive ecohydrological metrics, identify long-term trends, and determine the key environmental factors influencing pool dynamics. We demonstrate its utility through a case study on a 100 km section of the Gilbert River, Australia (1986–2023). The analysis revealed a dynamic mosaic of 29 persistent pools, with no single pool remaining permanent throughout the study period. Long-term trends showed statistically significant increases in rainfall and discharge, resulting in more numerous, larger, and less fragmented pools. The study confirmed strong correlations between hydrometeorological factors, such as wet-season rainfall and zero-flow duration, and pool morphology and persistence, highlighting the sensitivity of these refuges to seasonal variability and climatic shifts. Spatially, pools were clustered, with off-channel pools being larger and more persistent than their in-channel counterparts, suggesting local hydrogeomorphic controls. By combining standardised ecohydrological metrics with dynamic hydrological years, the approach enables reproducible, cross-system comparison while remaining sensor-agnostic and cost-efficient. This framework provides a transferable pathway for researchers and agencies to support targeted conservation and water-allocation decisions using open data and transparent analytics.