Gauged daily streamflow data were sourced from the BoM water data website (http://www.bom.gov.au/waterdata, last access: 10 November 2020) and were used to assess accuracy of AWRA-L-modelled streamflow (Sect. 3.3) and to estimate an appropriate zero-flow threshold of modelled streamflow data for quantifying patterns of streamflow intermittency (Sect. 3.4). A total of 25 gauges in SEQ and 15 gauges in Tamar were selected (Fig. 1b, c) to assess modelled streamflow accuracy. These gauges had less than 0.5 % missing values over the period from 1 January 2005 to 31 December 2017 and had minimal hydrologic modification due to human activities. A larger set of 43 gauges in SEQ (including 21 of the 25 gauges used by us for streamflow validation) was used to estimate the zero-flow threshold for this region (see Yu et al., 2018, for details of stream gauges). The gauges were widely dispersed throughout each study area and encompassed a range of stream sizes, catchment areas (22–3881 km2 in SEQ; 33–3294 km2 in Tamar), and flow regime types, ranging from highly intermittent to perennial streams (see results). However, the set of stream gauges used in our analyses under-represented the frequency of small low-order streams in both regions. Therefore, we regard the selected gauges to be representative of the range of environmental and hydrological conditions in the regions, except for extremely small catchments with an area < 22 km2 that likely have higher cease-to-flow occurrence.

AWRA-L is a daily 0.05∘ grid-based distributed water balance model that is conceptualised as a small catchment. It simulates the water flow through the landscape from the rainfall entering the grid cell through the vegetation and soil and then out of the grid cell through evapotranspiration, surface water flow, or lateral flow of groundwater to the neighbouring grid cells (Viney et al., 2015). AWRA-L was calibrated and validated at the national scale during its development by CSIRO and BoM, with 301 gauges used for calibration and a different set of 304 gauges used for validation (Zhang et al., 2013). Simulated daily runoff from the AWRA-L model (version 5) was downloaded from BoM (http://www.bom.gov.au/water/landscape, last access: 28 November 2018). These data are in gridded format and require conversion to streamflow for each sub-catchment by aggregating the gridded runoff data with a hierarchically nested catchment to simulate streamflow throughout river networks. The conversion process may or may not need to use a river routing model to propagate streamflow through river networks, partly depending on the size of the catchment of interest (Robinson et al., 1995). If streamflow simulated with a routing model shows little difference to that without a routing model, then the conversion process is more efficient without a routing model, and the readily available runoff data can be more accessible for potential applications, such as flow characterisation for ungauged stream segments. In addition, a conversion process involving a routing model can be computationally intensive and usually requires parallel computing to speed up the calculations (David et al., 2011b). Therefore, in this study, we applied two approaches to determine an effective and efficient runoff–streamflow conversion. The first approach coupled a river routing model to the water balance model, and its effects on flow simulations are compared to the model performance of a lumped model, which was operated without any river routing (Fig. 2). As the conversion process was achieved using the “catchstats” package (https://github.com/nickbond/catchstats, last access: 10 November 2020) in the R programming language (R Development Core Team, 2017), the second approach was to speed up the conversion process by incorporating a parallel algorithm to exiting functions of that package. The conversion process was run on a Griffith University high-performance computing node with 12 cores and 12 GB of RAM.

The hierarchically nested catchment dataset used in this study was sourced from the Geofabric dataset (Stein et al., 2014), which provides a fully connected and directed stream network derived from the national 9 arcsec DEM and flow direction grid (∼ 250 m resolution), and associated catchment hierarchy at the national scale. The routing model applied in this study was the Routing Application for Parallel computatIon of Discharge (RAPID) model (David et al., 2011b). RAPID solves the matrix-based Muskingum equation to route flow through each stream of the river network and performs streamflow computation for every stream segment of a river network, including ungauged streams. Various water balance models have been used in combination with RAPID (Follum et al., 2017; Lawrence et al., 2011; Lin et al., 2019).

To test the effects of river routing, we first calculated a series of flow metrics (Table 1) for flow simulations from both the lumped and coupled models. The calculated flow metrics are commonly used to describe the critical components of flow regimes across average-, high-, and low-flow conditions, including flow magnitude and variability; the timing, frequency, and duration of high and low flows; and rates of changes in flow events (Poff et al., 1997; Olden and Poff, 2003). Calculation of these streamflow characteristics allows a comprehensive assessment of the effects of river routing on streamflow simulations in the two regions. We then applied the Wilcoxon rank sum test for each flow metric to determine whether the inclusion of river routing can improve model accuracy based on a significance level of 5 %. We used the 10th and 90th percentiles of daily flows to respectively describe low-flow and high-flow thresholds (Leigh and Datry, 2016; Gudmundsson et al., 2019). The calculation process was conducted with the “hydrostats” package in the R language (Bond, 2019).

To evaluate overall model performance in streamflow simulations, we calculated the modified Kling–Gupta efficiency (KGE; Kling et al., 2012) between the observed and modelled streamflow for all gauges in SEQ and Tamar (Eq. 1). KGE is an integrated skill metric which measures the Euclidean distance between a point and the optimal point that has the maximum correlation coefficient, zero variability error, and zero bias error between the simulated and observed streamflow (Kling et al., 2012; Gupta et al., 2009). KGE takes values from -1 to 1: KGE = 1 indicates perfect agreement between simulations and observations, and KGE < -0.41 indicates that the mean of observations provides better estimates than simulations (Knoben et al., 2019). To evaluate model performance in different components of flow regimes, we also calculated each summary flow metric (Table 1) for observed and modelled streamflow data at all gauges in SEQ and Tamar and visually compared their frequency distributions. The use of KGE provides an overall assessment of AWRA-L model performance, and the flow metrics in Table 1 are used to comprehensively evaluate the model accuracy for various components of flow regimes, including the flow metrics related to low flows. Only six of the 25 gauges in SEQ and three of the 15 gauges in Tamar were the same as those used to calibrate the AWRA-L water balance model. This small overlap between the AWRA-L calibration gauge set (n = 301) and the streamflow model validation gauge set (n = 25 in SEQ and 15 in Tamar) means that potential overestimation of streamflow model performance is likely to be minimal. 1KGE=1-(r-1)2+(β-1)2+(γ-1)2β=μsμo;γ=CVsCVo=σs/μsσo/μo, where KGE is the modified KGE statistic (dimensionless); r is the correlation coefficient between simulated and observed runoff (dimensionless); β is the bias ratio (dimensionless); γ is the variability ratio (dimensionless); μ is the mean runoff in cubic metres per second (m3s-1); CV is the coefficient of variation (dimensionless); σ is the standard deviation of runoff in cubic metres per second (m3 s-1); and subscripts s and o refer to simulated and observed runoff values, respectively.

Furthermore, considering that this study aims to apply flow simulations to quantify flow intermittency, the model accuracy of low-flow simulation is particularly important. The study period (1 January 2005–31 December 2017) was considered sufficient to assess low flows. The 13-year study period is close to a discharge record length of 15 years, which Kennard et al. (2010a) concluded is sufficient to enable accurate estimation of low-flow metrics. In addition, our study period begins in the middle of the Australian Millennium drought (2001–2009) and includes a significant low-flow period. A preliminary analysis showed that AWRA-L-modelled streamflow was sensitive to rainfall events, relative to the response of observed flow (Fig. 3). This finding indicates that over-responsiveness of AWRA-L to rainfall may potentially contribute to overestimation of low flow. We hypothesised that this over-responsiveness is partly due to overestimation of in situ gains to low-flow discharge (e.g. groundwater release to baseflow) as well as underestimation of transmission losses (e.g. depression filling and evapotranspiration) during water movement through various flow paths in the stream network (Davison and van der Kamp, 2008). Given that we do not have access to the AWRA-L model to directly adjust model parameters, we instead compared the observed and modelled low-flow magnitude at all gauges in the two study areas along the gradient of their catchment areas (22–3881 km2 in SEQ; 33–3294 km2 in Tamar) to test this hypothesis. We expect that (1) if the difference in low-flow magnitude occurs at all gauges, then low-flow overestimation can be at least attributed to the overestimation of gains to low-flow discharge. Alternatively, (2) if the difference in low-flow magnitude occurs towards the downstream of the catchment, then low-flow overestimation may be related to underestimation of transmission losses.

Given the fact that water balance models often over-predict the magnitude of very low flows due to the difficulties of quantifying hydrological processes influencing low-flow discharge (Ye et al., 1997; Smakhtin, 2001; Staudinger et al., 2011), we adopted the same method used in Yu et al. (2018) to estimate a threshold of zero flow from the model that related measured zero-flow duration at each gauge to catchment environment variables. We used linear regression to model the mean annual zero-flow duration (daily time step) at each gauge as a function of catchment environment variables. This regression analysis was only conducted in SEQ as most gauges in the Tamar catchment had perennial flow. The environmental variables were the same as those in Yu et al. (2018) and included variables related to climate (annual daily maximum temperature), catchment geology topography (catchment area, catchment average slope, and catchment average elevation), and catchment soil properties (catchment average saturated hydraulic conductivity). Regression models were developed using all possible predictor variable combinations, and we selected the “best” model for predicting zero-flow duration based on the corrected Akaike's information criterion (AICc) (Hurvich and Tsai, 1989). To estimate the prediction error of the selected model, we applied leave-one-out cross-validation on the selected 43 gauges and reported prediction error (R2) to estimate the model prediction performance. Regression model development and cross-validation were conducted with the MuMIn and boot packages in R (R Development Core Team, 2017). Regression analyses were performed on all combinations of predictor variables, and the best model with the lowest AICc (-54.2) retained five covariates, including annual daily maximum temperature, catchment area, slope, average elevation, and average saturated hydraulic conductivity. The developed predictive model showed a good model fit with an adjusted R2 of 0.71, and the leave-one-out cross-validation on the regression model showed relatively good model performance with an average R2 of 0.64. We checked for spatial autocorrelation of the regression model residuals (as recommended by Dormann et al., 2007) and found they were not significantly autocorrelated (Moran's I = -0.06, p = 0.69). Examination of spatial residual maps further supported this conclusion, with no spatial trends in model residuals apparent.

Next, we used the predictive models to extrapolate estimates of overall flow intermittency (in terms of the proportion of days with zero flow) to each segment throughout the river network. Finally, for each segment, the time series of daily runoff was truncated (flows below the threshold were set to “0”) by adopting an appropriate threshold of “zero flow” that preserved the proportion of days with flow as estimated in the previous step. The adopted thresholds ranged from 0 to 1.668 m3 s-1, with a median value of 0.002 m3 s-1. We recognise several sources of uncertainty in our approach to estimating the zero-flow thresholds. The unexplained variation in the predictive model may be due to the limited number of environmental attribute covariates used in the model and hence ability to adequately represent the range of environmental processes that influence streamflow intermittency. Additional uncertainty in model predictions may arise because the distribution of stream gauges used for model calibration under-represented the frequency of extremely small catchments that likely had higher cease-to-flow occurrence.

Based on the modelled daily streamflow from AWRA-L, we calculated annual flow intermittency as the number of zero-flow days per year over the period of 2005–2016. To evaluate the effect of time step (daily vs. monthly) on the relative performance of AWRA-L in replicating observed patterns of cease-to-flow periods, we compared model outputs with those derived from a monthly water balance model – the WaterDyn model (Fig. 2). Monthly flow intermittency estimated from WaterDyn was thus used to benchmark results from the monthly AWRA-L. To do this, we aggregated daily outputs to a monthly time step (termed “monthly AWRA-L” hereafter, Fig. 2). We tried two different aggregation methods. One considered that the flows for a month were zero when at least one day in that month had zero flow (termed “monthly AWRA-L_01” hereafter), and the other considered that all days in a month must have zero flow for that month to be zero (termed “monthly AWRA-L_30” hereafter). These two methods together should provide both upper and lower bounds of comparing daily and monthly models in estimating flow intermittency. The WaterDyn model was developed to provide monthly spatially contiguous water balance data at the Australian continental scale by CSIRO and BoM with a similar model structure to AWRA-L (Raupach et al., 2018), and it has been used to quantify the spatial and temporal patterns of flow intermittency in SEQ following similar methods to this study (Yu et al., 2018). Modelled flow intermittency from all three sources (i.e. daily and monthly AWRA-L, and monthly WaterDyn) was also tested against the measured flow intermittency derived respectively from daily and monthly observed streamflow data at gauged locations in SEQ.

Taking advantage of the modelled long-term runoff data from AWRA-L over the period of 1911–2016, we further quantified spatial and temporal dynamics of flow intermittency for every stream segment within SEQ, and compared the results with those from the WaterDyn model over the same period (Yu et al., 2018). The spatial pattern of flow intermittency was represented by the mean annual number of zero-flow days across the period of 1911–2016 for AWRA-L and by the mean annual number of calendar months for WaterDyn. The temporal pattern of flow intermittency was expressed as the proportion of streams with flow intermittency > 30 d and 1 month (termed “intermittent streams” hereafter) for AWRA-L and WaterDyn, respectively.

The lumped and coupled (i.e. with routing) models using AWRA-L-simulated runoff were run in both SEQ and Tamar, and produced similar values for various flow metrics between the lumped and coupled models in both regions (Fig. 4; p values were greater than 0.50 for most flow metrics based on the Wilcoxon test results). There were noticeable but not statistically significant differences for two flow metrics related to low flows (the variability in timing and the frequency of low-flow spells), and only the duration of low-flow spells was statistically significant (p = 0.03). These results suggested that the routing algorithm has nearly negligible effects on flow simulations in our study areas, which is reasonable because of the small size of the two watersheds. Therefore, in the subsequent analysis, we only used the results from the AWRA-L lumped model as it is relatively less computationally intensive and was able to maintain a comparable model performance to that of the coupled model taking into account the routing effect.

The overall accuracy of streamflow estimated by the AWRA-L lumped model (referred to as “modelled streamflow” in this section) was evaluated for 25 gauges in SEQ and 15 gauges in Tamar. Results suggested a fair to good explanatory value across all gauges (Fig. 5). The KGE values varied across the 25 gauges in SEQ, ranging from -0.19 (gauge no. 145103) to 0.76 (gauge no. 143901), with a median value of 0.42, while the model generally performed better in Tamar and the KGE values ranged from 0.11 (gauge no. 18219.1) to 0.71 (gauge no. 852.1) across 15 gauges, with a median value of 0.47 (Fig. 5). However, no significant difference was found in the overall model performance between the two hydro-climatically distinctive regions, according to the Wilcoxon test (w = 247, p = 0.10).

Concerning model performance in simulating different components of flow regimes, the modelled streamflow in SEQ revealed a generally good match with the observed streamflow across all high-flow metrics and the magnitude of average flow, but the model tended to overestimate the variation in the magnitude of average flow (almost 2 times higher on average), report earlier timing of low flows, overestimate the frequency (48 % higher), and underestimate the duration (74 % lower) of low flows (Fig. 6). Compared to the model performance in SEQ, the flow simulations in Tamar showed slightly better performance, predicting well not only for the high-flow metrics but also for the metrics related to average flows (Fig. 6). However, flow simulations in Tamar also exhibited slightly earlier estimations for the timing of low-flow spells (13 % earlier), overestimations for low-flow spell frequency (92 % lower on average), and underestimation for low-flow spell duration (58 % lower) (Fig. 6).

Varying degrees of difference in the magnitude of low flow between the observed and simulation were found among the gauges. There appeared to be a tendency toward larger differences with increasing catchment area in SEQ but not in Tamar (Fig. 7). The models appeared to overestimate in situ gains to low flow in some reaches in both regions, while underestimating transmission losses in SEQ, suggesting that overestimation of in situ gains in AWRA-L likely contributes to the overall overestimation of low flow in downstream catchments.

We calculated annual flow intermittency at gauged locations in SEQ using three sources of modelled flow (daily and monthly AWRA-L, and monthly WaterDyn). Annual flow intermittency calculated using daily AWRA-L flow (i.e. the average number of cease-to-flow days per year) was tested against annual flow intermittency estimated using observed data (Fig. 8a). The AWRA-L model displayed the potential to be used to estimate flow intermittency at a daily time step, with a fair match with the observed flow intermittency (R2 = 0.56) in SEQ. Nonetheless, the model tended to overestimate flow intermittency for gauges located in relatively wet areas (e.g. ≤ 40 d of flow intermittency per year) while underestimating it for gauges located in relatively dry areas (e.g. ≥ 40 d of flow intermittency per year).

Figure 8b shows annual flow intermittency calculated using monthly AWRA-L flow and monthly WaterDyn flow. In this case, annual flow intermittency was defined as the average number of months characterised with zero flow. The WaterDyn model showed much more accuracy than the two aggregation methods based on the monthly AWRA-L model in estimating flow intermittency (R2 = 0.53, 0.43, and 0.32 respectively for monthly WaterDyn, monthly AWRA-L_01, and monthly AWRA-L_30). More specifically, the WaterDyn model displayed a similar estimation pattern to the daily AWRA-L model: overestimation in relatively wet areas and underestimation in relatively dry areas. By contrast, not surprisingly, the two aggregation methods showed the upper and lower bounds of flow intermittency estimates from the monthly AWRA-L model: monthly AWRA-L_01 overestimated flow intermittency and monthly AWRA-L_30 underestimated flow intermittency at nearly all gauges (Fig. 8b).

The spatial patterns of flow intermittency derived from the daily AWRA-L and monthly WaterDyn flow simulations aligned well for the main stems and some coastal streams, which were predicted to flow for most of the time (Fig. 9a, b). There were noticeable spatial differences between model predictions of streamflow intermittency for low-order inland streams. For example, in the western Brisbane River catchment and the South Coast River catchment, most inland streams were predicted by the daily model to flow for a longer period than by the monthly model; while in the Pine River catchment and the Logan–Albert River catchment, many inland streams were predicted by the daily model to flow for a shorter period (Fig. 9a). Compared to the monthly WaterDyn model, fewer streams were predicted by the daily AWRA-L to experience extremely long dry events as well as less than 1 month of zero flows (Fig. 9c, d). However, more streams on average (60 and 49 % for the AWRA-L and WaterDyn model, respectively) were predicted to flow intermittently (> 30 d or > 1 month) to varying degrees in SEQ, which suggests that flow intermittency was prevalent in SEQ, irrespective of the water balance model used.

Temporally, the daily model estimated that the proportion of intermittent streams in SEQ varied from 29 to 80 % over the study period (1911–2016), while the monthly model estimated the range to be from 3 to 80 % estimated during the same time span (Fig. 10). The two temporal patterns were temporally correlated (r = 0.71), and similar predictions with higher proportions of intermittent streams were estimated for the dry years by both models. Compared to dry years, the two models exhibited greater differences in predictions for the wet years, where the daily model tended to predict a higher proportion of intermittent streams. Overall, the daily model suggested a drier history in SEQ in terms of flow intermittency than the monthly model. The models successfully identified the extensive drying associated with severe drought periods. Notably, the Widespread drought (1914–1920), WWII drought (1939–1946), and Millennium drought (2001–2009) were all visible in both two sets of model predictions.

Effects of river routing on daily flow simulations were found to be negligible in SEQ and Tamar, most probably due to the relatively small size of the two catchments and the relatively short length of even the longest streams (Cunha et al., 2012). This can be verified with the concept of time of concentration, which is commonly used to measure the time needed for water to flow from the most remote point in a catchment to the catchment outlet. By following the formula for calculating the time of concentration proposed by Pilgrim and McDermott (1982) that has been widely used in Australia, we found the time of concentration in SEQ is around 33 h, only slightly more than a daily time step (24 h). This illustrates why it is difficult for a daily time-step routing model to effectively capture routing lags in our study domain. A negligible effect of river routing on flow simulations was also observed in previous studies (David et al., 2011a). Robinson et al. (1995) found that catchment size is a primary factor in determining which process, the hillslope or the channel network transport component, characterises lags in catchment runoff down the river network. In areas such as SEQ and Tamar that have a relatively small catchment size, the inclusion of channel network transport contributes little to the improvement of flow simulations. The negligible effect of river routing in SEQ and Tamar allowed us to simplify the simulation of daily flows without coupling with a river routing model. Hence we were able to use existing runoff outputs from the daily AWRA-L model. Arguably, similar opportunities exist in other small catchments.

Daily streamflow estimates showed a fair to good overall alignment with the observed flows in both SEQ and Tamar, with all gauges showing that flow simulations were better estimates than the mean of observations (KGE ≥ -0.41 at all gauges). Interestingly, although streamflow was more accurately simulated in the Tamar than in SEQ (the median values of KGE were 0.47 and 0.42, respectively), the differences between the two hydro-climatically distinctive regions were relatively small. Despite ongoing efforts to calibrate AWRA-L against a set of reference scales distributed across the continent (Viney et al., 2015), this finding was reassuring given the much higher variability in rainfall and soil moisture in SEQ, factors that typically can lead to a more non-linear streamflow response to rainfall (Poncelet et al., 2017), which possibly undermines the ability of water balance models to reliably predict runoff (Sheng et al., 2017). These results hence bode well for the application of AWRA-L outputs across diverse hydroclimatic regions.

When looking into the model performance for specific components of the flow regime, average- and high-flow metrics were both modelled well in Tamar, while only high-flow metrics were modelled well in SEQ. However, in both regions, the AWRA-L model showed poor performance in low-flow metrics: overestimating the frequency and underestimating the duration of low flows, consistent with previous studies (Costelloe et al., 2005; Ivkovic et al., 2014; Ye et al., 1997; Staudinger et al., 2011). This suggests that the AWRA-L model is a generally robust model in predicting average and high flows but still needs some improvement to better simulate low flows. Runoff generation processes can vary substantially through space and time due to such factors as variations in soil depth, antecedent soil moisture, and groundwater connectivity, and this can influence spatio-temporal variations in low-flow characteristics, including streamflow intermittency (Zimmer and McGlynn, 2017). However, it is unknown to what extent this contributed to uncertainty in the simulation of low flows and estimation in streamflow intermittency in this study. The uncertainty of AWRA-L in low-flow simulations can be linked to its over-responsiveness to rainfall, partly caused by overestimation of in situ gains and underestimation of transmission losses to low-flow discharge, as shown in SEQ. Previous studies found that lateral flow exchange between grid cells of land surface models (e.g. AWRA-L) plays a significant role in redistributing soil water (Kim and Mohanty, 2016), and thus may improve in situ surface/subsurface runoff simulations (Lee and Choi, 2017). On the other hand, hydrological processes involved in transmission losses have been extensively discussed (Jarihani et al., 2015; Konrad, 2006), and studies have developed methods to calculate transmission losses for better flow simulations (Lange, 2005; Costa et al., 2012). Therefore, low-flow simulations by AWRA-L can possibly be improved by incorporating lateral flow exchange algorithms and better accounting for hydrological process such as evapotranspiration from riparian vegetation and infiltration into channel beds. This improvement is made more likely as AWRA-L has been released as a community modelling system (https://github.com/awracms/awra_cms, last access: 10 November 2020), which allows co-development by the research community.

Our results suggest that the temporal resolution of analysis should be dictated by the resolution of input streamflow data. More specifically, the daily AWRA-L flow showed promise for estimating flow intermittency at a daily time step, while the monthly WaterDyn model was better than the monthly AWRA-L model in flow intermittency estimation at a monthly time step. This suggests that monthly flow models can sometimes outperform daily flow models in quantifying flow intermittency, depending on the intended temporal resolution of the analysis. For example, daily flow models may be appropriate for studies aimed at quantifying ecological responses to short-term flow events, while monthly flow models are more suitable for research requiring the average degree of flow intermittency at a large spatial or temporal scale, such as examining the effect of flow intermittency on aquatic/streamside vegetation or species distributions (Stromberg et al., 2005). In addition, our study also suggested that the suitability of a monthly model (WaterDyn) for monthly resolution of analysis was not challenged by a daily model (AWRA-L) simply through aggregating daily streamflow simulations to a monthly time step. The aggregation methods used here applied 1 or 30 d as a threshold and, respectively, either substantially overestimated or underestimated flow intermittency.

Spatially contiguous runoff data were used in this study to quantify spatial and temporal dynamics of flow intermittency, shedding light on the temporal aspect of flow intermittency that has been often overlooked in previous studies. Annual flow intermittency in SEQ was shown to vary significantly from year to year, ranging from 29 to 80 % of total stream length for the AWRA-L model. However, given the limited spatial resolution of the Geofabric stream network data (9 arcsec longitude–latitude resolution, with a minimum upstream drainage area of 1.5 km2) and hence ability to resolve the smallest streams, and that small streams are more likely to be intermittent, the proportion of predicted intermittent streams in SEQ may be underestimated in our study. Although there are differences in the temporal patterns of estimated flow intermittency between the AWRA-L and WaterDyn models, neither model estimated intermittency to have a clear trend over the past century. However, there is still the concern about the potential shift of some perennial streams to intermittent streams due to climate change and intense human activities, as it has been evident in several regions where the number of low-flow and/or no-flow days is increasing (King et al., 2015; Ruhí et al., 2016; Sabo, 2014). Jaeger et al. (2014) investigated the effect of climate change on flow intermittency patterns and found that annual zero-flow days frequency were projected to increase by 27 % by mid-century in the Lower Colorado River basin of the United States. Research looking into projected changes in regional climate regimes can provide insights into future scenarios people may face, but such research is still scarce.

The approach developed here to generate spatially continuous estimates of streamflow characteristics (including flow intermittency) throughout stream networks has potential applicability to other regions of Australia and globally. All the data used in this study are available for the Australian national scale, and similar datasets also exist in other countries. For example, similar to the Geofabric data (Stein et al., 2014) used here, the National Hydrography Dataset Plus (NHDPlus) and HydroATLAS (Linke et al., 2019) provide hydrographic datasets and hydro-environmental attributes for national (USA) and global scales, respectively. In addition, similar to the daily flow model AWRA-L used in this study, other global- and national-scale hydrologic models are also available, such as the global WaterGAP model (Döll et al., 2003), the community Noah land surface model (Noah-MP) (Niu et al., 2011) in the USA, and the HYPE model (Lindström et al., 2010) in Sweden.