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To accurately project future water availability under a drying climate, it is important to understand how precipitation is partitioned into other terrestrial water balance components, such as fluxes (evaporation, transpiration, runoff) and changes in storage (soil moisture, groundwater). Many studies have reported unexpected large runoff reductions during drought, particularly for multi‐year events, and some studies report a persistent change in partitioning even after the meteorological drought has ended. This study focused on understanding how actual evapotranspiration (AET) and change in subsurface storage (ΔS) respond to climate variability and change, examining Australia's Millennium Drought (MD, 1997–2009). The study initially conducted a catchment‐scale water balance analysis to investigate interactions between ΔS and AET. Then the water balance analysis was extended to regional scale to investigate ΔS using interpolated rainfall and discharge with remotely sensed AET. Lastly, we evaluated conceptual rainfall‐runoff model performance of two commonly used models against these water balance estimates. The evaluation of water‐balance‐derived ΔS against Gravity Recovery and Climate Experiment (GRACE) estimates shows a significant multiyear storage decline; however, with different rates. In contrast, AET rates (annualized) remained approximately constant before and during the MD, contrasting with some reports of evapotranspiration enhancement elsewhere. Overall, given AET remained approximately constant, drought‐induced precipitation reductions were partitioned into ΔS and streamflow. The employed conceptual rainfall‐runoff models failed to realistically represent AET during the MD, suggesting the need for improved conceptualization of processes. This study provides useful implications for explaining future hydrological changes if similar AET behavior is observed under a drying climate. Key Points: For a multiyear drought, we investigated the impact of precipitation reductions on water balance fluxes and change in storageActual evapotranspiration was approximately unchanged even under prolonged droughts in a water‐limited regionBecause of this, precipitation reduction during the multiyear drought was partitioned into reductions in streamflow and subsurface storage [ABSTRACT FROM AUTHOR]

Hydrologic models are essential tools to understand and plan for the effect of changing climates; however, they underperform in transitory climate conditions. Existing research identifies models' inadequacy to perform during prolonged drought, but falls short on pinpointing which specific aspects of model performance are affected. We study five conceptual rainfall‐runoff models and their performance in 155 Australian catchments which recently experienced a 13‐year long drought. We use a wide range of performance metrics and a methodology based on ranked differences to a benchmark to fairly compare levels of degradation across metrics and periods. We show model performance degrading extensively during and after the drought, largely driven by overestimation of flow. Representation of shape and variability of hydrograph and flow‐duration curve are more resilient to the prolonged dry climate and rarely more degraded during the multi‐annual drought that on isolated dry years in the pre‐drought record. Conversely, volumetric error suffers from significant exacerbation over the multiple subsequent dry years. This indicates that catchment retention times and rates of storage depletion storage are significantly less affected by the drought than amounts of streamflow produced, pointing to a mismatch between reduction of influxes and out‐fluxes during the drought. We also identify a deficiency of models to delay and remove flow before it reaches the stream and keep track of moisture deficits over multiple dry seasons. By promoting rigorous investigation of models' shortcomings, we hope to foster the development of more robust model structures and/or calibration frameworks to improve applicability within climate change scenarios. Key Points: We compare aspects of model performance during and after multi‐annual drought against pre‐drought performancePerformance degradation is driven by bias in water balance estimates rather than errors in hydrograph shapeAccumulation and aggravation of errors over multiple dry years exacerbates performance degradation [ABSTRACT FROM AUTHOR]

Analysis of flood and streamflow timing has recently gained prominence as a tool for attribution of climatic changes to flooding. Such studies generally apply circular statistics to the day of maximum flow in a calendar year and use nonparametric linear trend tests to investigate changes in flooding on a local or regional scale. Here we investigate both the center timing of streamflow and the day of maximum flow using a local water year. For each station, the start of the water year is defined as the month of lowest average monthly streamflow. This definition of water year prevents ambiguity in the direction of computed trends and enables flood and streamflow timing to be described by a normal distribution. Using the assumption of normality, we calculate the historical trend in both flood and streamflow timing using linear regression. While shifts in flood and streamflow timing are consistent with climate change and are shifting in a similar direction, shifts in the timing of the annual maxima flood are approximately three times that of streamflow timing. The results here have implications for water resources and environmental management where streamflow and flood timing are critical to planning. The applicability of the normal approximation to flood and streamflow timing will enable future analyses to use parametric statistics. Key Points: The water year definition can change the direction of trends in streamflow timingBy using a local water year, flood timing can often be approximated by a normal distributionShifts in flood timing are systematically greater than those in the center timing of streamflow [ABSTRACT FROM AUTHOR]

Due to difficulties in identifying a climate change signal in flood magnitude, it has been suggested that shifts in flood timing, that is, the day of annual streamflow maxima, may be detectable. Here, we use high-quality streamflow, largely free of snowmelt, from 221 catchments across Australia to investigate the influence of shifts in soil moisture and rainfall timing on annual streamflow maxima timing. In tropical areas we find that flood timing is strongly linked to the timing of both rainfall and soil moisture annual maxima. However, in southern Australia flood timing is more correlated with soil moisture maxima than rainfall maxima. The link between flood, soil moisture, and rainfall timing is confounded by event severity: For less extreme events flood timing is more likely to correspond to soil moisture timing, whereas rainfall timing becomes increasingly important as flood severity increases. Using circular regression to investigate nonstationarity, we find that flood timing is shifting to earlier in the year in the tropics and later in the year in the southwest of the continent, consistent with changes in mean and extreme rainfall and shifts in soil moisture timing due to tropical expansion. In southeast Australia, there is evidence that the mechanisms controlling flood seasonality are changing with a reversal of trends post Millennium Drought. Overall, changes in soil moisture timing, compared to changes in rainfall timing, are found to have a greater influence on changes in annual maxima streamflow flood timing. [ABSTRACT FROM AUTHOR]

Hydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade-offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results. [ABSTRACT FROM AUTHOR]

Changing hydrological conditions due to climate, land use and infrastructure pose significant ongoing challenges to the hydrological research and water management communities. While, traditionally, hydrological models have assumed stationary conditions, there has been much progress since 2005 on model parameter estimation under unknown or changed conditions and on techniques for modelling in those conditions. There is an analogy between extrapolation in space (termed Prediction in Ungauged Basins, PUB), and extrapolation in time (termed Prediction in Ungauged Climates, PUC) that can be exploited for estimating model parameters. Methods for modelling changing hydrological conditions need to progress beyond the current scenario approach, which is reliant upon precalibrated models. Top-down methods and analysis of spatial gradients of a variable of interest, instead of temporal gradients (a method termed ‘Trading space for time’) show much promise for validating more complex model projections. Understanding hydrological processes and how they respond to change, along with quantification of parameter estimation and modelling process uncertainty will continue to be active areas of research within hydrology. Contributions from these areas will not only help inform future climate change impact studies about what will change and by how much, but also provide insight into why any changes may occur, what changes we are able to predict in a realistic manner, and what changes are beyond the current predictability of hydrological systems. [ABSTRACT FROM PUBLISHER]

The interactions between catchment vegetation and runoff continue to be a staple area of hydrological research. Drawing mainly upon material published since 2002, this report briefly reviews progress in this area with specific reference to: (1) paired and single catchment studies; (2) top-down models; and (3) the likely impact of climate change. Results from a wider range of paired and single catchments studies are revealing the complex relationship between catchment vegetation and runoff and prompting a reassessment of the methodologies used to generalize this relationship. Vegetation appears to have a significant influence on runoff at small scales, which reduces to a second-order influence, relative to aridity, at larger scales. Top-down models of catchment behaviour generally reflect this second-order influence at the large scale. As vegetation responds to CO2 enrichment under climate change, the magnitude and direction of associated changes in runoff remains uncertain. A key element in quantifying the hydrological impact of climate change is the relationship between catchment vegetation and runoff, which continues to be a productive area of research within hydrology. [ABSTRACT FROM AUTHOR]

Summary Future projections of water supply under climate change scenarios are fundamental for efficient water resource planning. However, runoff projections are affected by uncertainties in the modelling process that limit their utility to decision makers. The main source of uncertainty in runoff projections are the Global Climate Models (GCMs) used to produce future climate projections. The impact on projected runoff of this uncertainty has mainly been assessed through comparison of multi-model runs of future climate with little exploration of uncertainties inside the models due to different parameterisations. Here we investigate the uncertainty response of projected runoff due to perturbed physics parameter variations within a GCM using a novel 2500 member ensemble from the HadCM3L model. Our research evaluates the uncertainties in runoff modelling for southwest Western Australia, a Mediterranean climate region which has experienced reductions in precipitation during the last decades. Results for future projections in southwest Western Australian catchments indicate reductions in modelled precipitation between 0% and 40% and increases in temperature that fluctuate between 0.5 °C and 3 °C by 2050–2080 compared to 1970–2000, which lead to reductions in projected runoff of between 10% and 80%. This range of uncertainty for projected runoff is larger than that calculated for previous estimates of within-model uncertainties of runoff. The perturbed physics approach indicates that current water management assessments underestimate uncertainties in runoff projections. [ABSTRACT FROM AUTHOR]

Assessments of climate change impacts on freshwater ecosystems are generally based on global climate models (GCMs) and ecologically relevant “time-averaged” hydrological indicators derived from long-term records. Although uncertainties from GCMs have been recognized, the influence of downscaling methods remains unclear. This paper evaluates the influence of applying different downscaling methods of increasing complexity (annual scaling, monthly scaling, quantile scaling, and weather generator method) on the assessment of ecological outcomes. In addition to time-averaged indicators, “sequence-dependent” metrics which involve ecological dynamics by considering the impacts of flow sequencing are also adopted. In a case study in Australia, the condition of river red gum forest was assessed. Results show that the choice of downscaling methods can be of similar importance as that of GCMs in ecological impact studies. Where sequence-dependent metrics are adopted, more sophisticated downscaling techniques should be used to better represent changes in the frequency and sequence of flow events. [ABSTRACT FROM AUTHOR]

• Most conceptual bucket models have an upper limit on simulated soil moisture deficit. • Problems arise when the bucket "empties" because ET drops to unrealistic (low) levels. • Alternatives include bottomless buckets or deficit-based soil moisture accounting. • Here, we switch to a deficit-based scheme while keeping everything else constant. • Tested over historic drought, model performance and realism are enhanced. Rainfall-runoff models based on conceptual "buckets" are frequently used in climate change impact studies to provide runoff projections. When these buckets approach empty, the simulated evapotranspiration approaches zero, which places an implicit limit on the soil moisture deficit that can accrue within the model. Such models may cease to properly track the moisture deficit accumulating in reality as dry conditions continue, leading to overestimation of subsequent runoff and possible long-term bias under drying climate. Here, we suggest that model realism may be improved through alternatives which remove the upper limit on simulated soil moisture deficit, such as "bottomless" buckets or deficit-based soil moisture accounting. While some existing models incorporate such measures, no study until now has systematically assessed their impact on model realism under drying climate. Here, we alter a common bucket model by changing the soil moisture storage to a deficit accounting system in such a way as to remove the upper limit on simulated soil moisture deficit. Tested on 38 Australian catchments, the altered model is better able to track the decline in soil moisture at the end of seasonal dry periods, which leads to superior performance over varied historic climate, including the 13-year "Millennium" drought. However, groundwater and GRACE data reveal long-term trends that are not matched in simulations, indicating that further changes may be required. Nonetheless, the results suggest that a broader adoption of bottomless buckets and/or deficit accounting within conceptual rainfall runoff models may improve the realism of runoff projections under drying climate. [ABSTRACT FROM AUTHOR]