ALBERT

Description: There is a growing interest globally in the spatial distribution and temporal dynamics of intermittently flowing streams and rivers, and how this varies in relation to climatic and other environmental factors. However, biases in the distribution of stream gauges may give a misleading impression of spatial-temporal variations in streamflow intermittency within river networks. Here, we developed an approach to quantify catchment-wide streamflow intermittency over long time frames and in a spatially explicit manner, using readily accessible and spatially contiguous daily runoff data from a national-scale water balance model. We examined the ability of the water balance model to simulate streamflow in two hydro-climatically distinctive (subtropical and temperate) regions in Australia, with a particular focus on low-flow simulations. We also evaluated the effect of model time step (daily vs. monthly) on flow intermittency estimation to inform future model selection. The water balance model showed better performance in the temperate region characterised by steady baseflow than in the subtropical region with flashy hydrographs and frequent cease-to-flow periods. The model tended to overestimate low-flow magnitude mainly due to overestimation of gains (e.g. groundwater release to baseflow) during low-flow periods. Modelled patterns of flow intermittency revealed highly dynamic behaviour in space and time, with cease-to-flow events affecting between 29 and 80 % of the river network over the period of 1911–2016, using a daily streamflow model. The daily flow model did not perform better than the monthly flow model in quantifying flow intermittency at a monthly time step, and model selection should depend on the intended application of the model outputs. Our general approach to quantifying spatio-temporal patterns of flow intermittency is transferable to other parts of the world, and it can inform hydro-ecological understanding and management of intermittent streams where limited gauging data are available.