Hydrological variables of a catchment and their corresponding extreme characteristics have a possibility of switching regimes, particularly when a catchment undergoes protracted dry periods. This can result in a catchment experiencing a flow anomaly that is even more extreme than what was historically considered an extreme low flow event for the catchment. Catchments in southeast Australia have been shown to exhibit multiple states of mean annual flows. Given this and studies that suggest that extreme events may be changing with time, it is important to understand whether extremes in flows also have the potential to exist in multiple states. To investigate this, we studied intensity, duration, and frequency (IDF) of low flows for 161 unregulated catchments in southeast Australia. A Hidden Markov Model-based approach was used to examine shifts in the low flow characteristics. We found very strong evidence of low flow intensity exhibiting two distinct states for at least 34 (21%) catchments in the region, providing convincing reasons to believe that extremes in low flows can and have undergone regime changes. The second state of these catchments is often associated with higher values of low flow intensities. Simulation of the duration and frequency of these events, however, needs improvement with the current approach and may be better studied by accounting for climate indicators that may more suitably explain them. Impacts from a changing climate may enhance the triggering of low flows into alternate states, which calls for water managers to plan for changing regimes of extremes.

To evaluate models as hypotheses, we developed the method of Flux Mapping to construct a hypothesis space based on dominant runoff generating mechanisms. Acceptable model runs, defined as total simulated flow with similar (and minimal) model error, are mapped to the hypothesis space given their simulated runoff components. In each modeling case, the hypothesis space is the result of an interplay of factors: model structure and parameterization, choice of error metric, and data information content. The aim of this study is to disentangle the role of each factor in model evaluation. We used two model structures (SACRAMENTO and SIMHYD), two parameter sampling approaches (Latin Hypercube Sampling of the parameter space and guided-search of the solution space), three widely used error metrics (Nash-Sutcliffe Efficiency – NSE, Kling-Gupta Efficiency skill score – KGEss, and Willmott’s refined Index of Agreement – WIA), and hydrological data from a large sample of Australian catchments. First, we characterized how the three error metrics behave under different error types and magnitudes independent of any modeling. We then conducted a series of controlled experiments to unpack the role of each factor in runoff generation hypotheses. We show that KGEss is a more reliable metric compared to NSE and WIA for model evaluation. We further demonstrate that only changing the error metric — while other factors remain constant — can change the model solution space and hence vary model performance, parameter sampling sufficiency, and/or the flux map. We show how unreliable error metrics and insufficient parameter sampling impair model-based inferences, particularly runoff generation hypotheses.

Recent shifts in the behaviour of natural watersheds suggest acute challenges for water planning under climate change. Shifts towards less annual streamflow for a given annual precipitation have now been reported on multiple continents, usually in response to a multi-year drought. Future drying under climate change may induce similar unexpected hydrological behaviour, and 15 this commentary discusses the implications for water planning and management. Commonly-used hydrological models poorly represent the shifting behaviour and cannot be relied upon to anticipate future shifts. Thus, their use may result in underestimation of hydroclimatic risk and exposure to “surprise” reductions in water supply, relative to projections. The onus is now on hydrologists to determine the underlying causes of shifting behaviour and incorporate more dynamic realism into 20 operational models. Main points 1. Drought-induced hydrological shifts towards less streamflow for a given precipitation have been reported across multiple continents. 2. Future drying under climate change may induce similar unexpected behaviour. 25 3. Such behaviour creates additional uncertainty in runoff projections, and may lead to ‘surprise’ reductions in future streamflow. Main text In a recent article, Peterson et al. (2021) reported shifts in hydrological behaviour induced by the “Millennium” drought (1997-2010) in Australia and persisting years after the drought ended. 30 Reductions in water resources during and after this drought were far more extreme than expected, even given low rainfall (Saft et al., 2015), because many watersheds shifted into a seemingly different state of streamflow behaviour. Concerningly, some watersheds remain in this state despite a return to near-average climate conditions, so that a year of average rainfall now produces less streamflow than it did before the drought (Peterson et al., 2021). With similar hydrological 35 shifts reported elsewhere in the world, including the USA (Avanzi et al., 2020), China (Tian et al.,