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Research into potential implications of climate change on flood hazard has made significant progress over the past decade, yet efforts to translate this research into practical guidance for flood estimation remain in their infancy. In this commentary, we address the question: how best can practical flood guidance be modified to incorporate the additional uncertainty due to climate change? We begin by summarizing the physical causes of changes in flooding and then discuss common methods of design flood estimation in the context of uncertainty. We find that although climate science operates across aleatory, epistemic and deep uncertainty, engineering practitioners generally only address aleatory uncertainty associated with natural variability through standards-based approaches. A review of existing literature and flood guidance reveals that although research efforts in hydrology do not always reflect the methods used in flood estimation, significant progress has been made with many jurisdictions around the world now incorporating climate change in their flood guidance. We conclude that the deep uncertainty that climate change brings signals a need to shift towards more flexible design and planning approaches, and future research effort should focus on providing information that supports the range of flood estimation methods used in practice. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'. [ABSTRACT FROM AUTHOR]

• An inverse approach is developed to generate perturbed multi-site stochastic rain. • Captures changes in spatial pattern of rain that can impact water resource systems. • Capability is shown to accurately perturb single and multi-site rainfall features. • Streamflow is stress tested for changes in rainfall spatial correlation. With emerging evidence that anthropogenic climate change is affecting the spatial pattern of rainfall, it is important to understand how such changes affect a range of climate-sensitive systems. A method for generating multi-site rainfall sequences is presented that enables the exploration of changes in spatial rainfall patterns separately from various other potential changes (e.g. to the averages, extremes, seasonality), thereby enabling the separation of key drivers of change as part of climate stress tests. The proposed method is based on the 'inverse approach' to stochastic weather generation, and is evaluated through a case study with rainfall at 12 sites in the Barossa Valley, South Australia, using a multi-site latent variable stochastic rainfall model. The new method accurately captures (1) observed marginal attributes at each site (i.e. annual totals, seasonality, extreme rainfall, number of dry days, and intermittency), (2) spatial correlation between sites, and (3) observed attributes for areal average rainfall. We then apply the new method to stress test the response of streamflow in the region to changes in spatial rainfall patterns, quantifying how reductions in spatial correlation (while holding all other attributes constant) will lead to reduced mean annual flow and extreme flows, and increased number of low-flow days. The results show that not only can a broad range of rainfall properties (including both marginal and spatial attributes) be simulated using the inverse approach, but also that changes to the spatial properties of rainfall can have large implications on water resources systems, with the potential to amplify or dampen other changes to rainfall attributes. [ABSTRACT FROM AUTHOR]

Summary Scenario-neutral approaches are being used increasingly for climate impact assessments, as they allow water resource system performance to be evaluated independently of climate change projections. An important element of these approaches is the generation of perturbed series of hydrometeorological variables that form the inputs to hydrologic and water resource assessment models, with most scenario-neutral studies to-date considering only shifts in the average and a limited number of other statistics of each climate variable. In this study, a stochastic generation approach is used to perturb not only the average of the relevant hydrometeorological variables, but also attributes such as the intermittency and extremes. An optimization-based inverse approach is developed to obtain hydrometeorological time series with uniform coverage across the possible ranges of rainfall attributes (referred to as the ‘exposure space’). The approach is demonstrated on a widely used rainfall generator, WGEN, for a case study at Adelaide, Australia, and is shown to be capable of producing evenly-distributed samples over the exposure space. The inverse approach expands the applicability of the scenario-neutral approach in evaluating a water resource system’s sensitivity to a wider range of plausible climate change scenarios. [ABSTRACT FROM AUTHOR]

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature ( T a ), relative humidity ( RH ) and wind speed ( u z ) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol’ sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems. [ABSTRACT FROM AUTHOR]

This study investigates the presence of trends in annual maximum daily streamflow data from the Global Runoff Data Centre database, which holds records of 9213 stations across the globe. The records were divided into three reference datasets representing different compromises between spatial coverage and minimum record length, followed by further filtering based on continent, Köppen-Weiger climate classification, presence of dams, forest cover changes and catchment size. Trends were evaluated using the Mann-Kendall nonparametric trend test at the 10% significance level, combined with a field significance test. The analysis found substantial differences between reference datasets in terms of the specific stations that exhibited significant increasing or decreasing trends, showing the need for careful construction of statistical methods. The results were more consistent at the continental scale, with decreasing trends for a large number of stations in western North America and the data-covered regions of Australia, and increasing trends in parts of Europe, eastern North America, parts of South America and southern Africa. Interestingly, neither the presence of dams nor changes in forest cover had a large effect on the trend results, but the catchment size was important, as catchments exhibiting increasing (decreasing) trends tended to be smaller (larger). Finally, there were more stations with significant decreasing trends than significant increasing trends across all the datasets analysed, indicating that limited evidence exists for the hypothesis that flood hazard is increasing when averaged across the data-covered regions of the globe. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including role of climatic changes on incidence of natural hazards of Australia; and climate-influenced natural hazard such as floods, drought and storms.

Evapotranspiration (ET) is a vital component of the hydrological cycle and there are a large number of alternative models for representing ET processes. However, implementing ET models in a consistent manner is difficult due to the significant diversity in process representations, assumptions, nomenclature, terminology, units and data requirements. An R package is therefore introduced to estimate actual, potential and reference ET using 17 well-known models. Data input is flexible, and customized data checking and pre-processing methods are provided. Results are presented as summary text and plots. Comparisons of alternative ET estimates can be visualized for multiple models, and alternative input data sets. The ET estimates also can be exported for further analysis, and used as input to rainfall-runoff models. [ABSTRACT FROM AUTHOR]

This study evaluates the role of the interdecadal Pacific oscillation (IPO) in modulating the El Niño-Southern Oscillation (ENSO)-precipitation relationship. The standard IPO index is described together with several alternatives that were derived using a low-frequency ENSO filter, demonstrating that an equivalent IPO index can be obtained as a low-frequency version of ENSO. Several statistical artifacts that arise from using a combination of raw and smoothed ENSO indices in modeling the ENSO-precipitation teleconnection are then described. These artifacts include the potentially spurious identification of low-frequency variability in a response variable resulting from the use of smoothed predictors and the potentially spurious modulation of a predictor-response relationship by the low-frequency version of the predictor under model misspecification. The role of the IPO index in modulating the ENSO-precipitation relationship is evaluated using a global gridded precipitation dataset, based on three alternative statistical models: stratified, linear, and piecewise linear. In general, the information brought by the IPO index, beyond that already contained in the Niño-3.4 index, is limited and not statistically significant. An exception is in northeastern Australia using annual precipitation data, and only for the linear model. Stratification by the IPO index induces a nonlinear ENSO-precipitation relationship, suggesting that the apparent modulation by the IPO is likely to be spurious and attributable to the combination of sample stratification and model misspecification. Caution is therefore required when using smoothed climate indices to model or explain low-frequency variability in precipitation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Dependence between rainfall and surge is statistically significant. [•] Majority of the tide gauges show asymptotic dependence. [•] Spatial and temporal variability of dependence was observed. [•] The obtained results have significant implications on flood risk estimation. [Copyright &y& Elsevier]

This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann-Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature, with the median intensity of extreme precipitation changing in proportion with changes in global mean temperature at a rate of between 5.9% and 7.7% K−1, depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 13°S and 11°N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation. [ABSTRACT FROM AUTHOR]

Summary: Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. [Copyright &y& Elsevier]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

This study investigates the ability of a regional climate model (RCM) to simulate the diurnal cycle of precipitation over southeast Australia, to provide a basis for understanding the mechanisms that drive diurnal variability. When compared with 195 observation gauges, the RCM tends to simulate too many occurrences and too little intensity for precipitation events at the 3-hourly time scale. However, the overall precipitation amounts are well simulated and the diurnal variability in occurrences and intensities are generally well reproduced, particularly in spring and summer. In terms of precipitation amounts, the RCM overestimated the diurnal cycle during the warmer months but was reasonably accurate during winter. The timing of the maxima and minima was found to match the observed timings well. The spatial pattern of diurnal variability in the Weather Research and Forecasting model outputs was remarkably similar to the observed record, capturing many features of regional variability. The RCM diurnal cycle was dominated by the convective (subgrid scale) precipitation. In the RCM the diurnal cycle of convective precipitation over land corresponds well to atmospheric instability and thermally triggered convection over large areas, and also to the large-scale moisture convergence at 700 hPa along the east coast, with the strongest diurnal cycles present where these three mechanisms are in phase. [ABSTRACT FROM AUTHOR]

Summary: Non-stationarity in extreme precipitation at sub-daily and daily timescales is assessed using a spatial extreme value model based on max-stable process theory. This approach, which was developed to simulate spatial fields comprising observations from multiple point locations, significantly increases the precision of a statistical inference compared to standard univariate methods. Applying the technique to a field of annual maxima derived from 30 sub-daily gauges in east Australia from 1965 to 2005, we find a statistically significant increase of 18% for 6-min rainfall over this period, with smaller increases for longer duration events. We also find an increase of 5.6% and 22.5% per degree of Australian land surface temperature and global sea surface temperature at 6-min durations, respectively, again with smaller scaling relationships for longer durations. In contrast, limited change could be observed in daily rainfall at most locations, with the exception of a statistically significant decline of 7.4% per degree land surface temperature in southwest Western Australia. These results suggest both the importance of better understanding changes to precipitation at the sub-daily timescale, as well as the need to more precisely simulate temporal variability by accounting for the spatial nature of precipitation in the statistical model. [ABSTRACT FROM AUTHOR]

Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and amplitude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for individual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L''Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4); when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables. [ABSTRACT FROM AUTHOR]

The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by low-frequency variability in the global sea surface temperature anomaly (SSTA) field and that can theoretically be predicted one or more seasons into the future, and a “weather noise” component that originates from nonlinear dynamical instabilities in the atmosphere and is not predictable beyond ~10 days. Asymptotic predictability of global precipitation was found to be 14.7% of total precipitation variance using 1900–2007 data, with only minor increases in predictability using shorter and presumably less error-prone records. This estimate was derived based on concurrent SSTA–precipitation relationships and therefore constitutes the maximum skill achievable assuming perfect forecasts of the evolution of the SSTA field. Imparting lags on the SSTA–precipitation relationship, the 3-, 6-, 9-, and 12-month predictability of global precipitation was estimated to be 7.3%, 5.4%, 4.2%, and 3.7%, respectively, demonstrating the comparative gains that can be achieved by developing improved SSTA forecasts compared to developing improved SSTA–precipitation relationships. Finally, the actual average cross-validated predictive skill was found to be 2.1% of the total precipitation variance using the full 1900–2007 dataset and was dominated by the El Niño–Southern Oscillation (ENSO) phenomenon. This indicates that there is still significant potential for increases in predictive skill through improved parameter estimates, the use of longer and/or more reliable datasets, and the use of larger spatial fields to substitute for limited temporal records. [ABSTRACT FROM AUTHOR]

The article presents an analysis and interpretation of global sea surface temperature (SST) data using independent component analysis (ICA) and principal component analysis (PCA). It states that PCA represents multivariates and expands the variance explicated by components. It compares the capacity of PCA, the Varimax rotation and ICA in demonstrating climate unpredictability in SST anomaly (SSTA) data distributed worldwide.

A statistical estimation approach is presented and applied to multiple reservoir inflow series that form part of Sydney’s water supply system. The approach involves first identifying sources of interannual and interdecadal climate variability using a combination of correlation- and wavelet-based methods, then using this information to construct probabilistic, multivariate seasonal estimates using a method based on independent component analysis (ICA). The attraction of the ICA-based approach is that, by transforming the multivariate dataset into a set of independent time series, it is possible to maintain the parsimony of univariate statistical methods while ensuring that both the spatial and temporal dependencies are accurately captured. Based on a correlation analysis of the reservoir inflows with the original sea surface temperature anomaly data, the principal sources of variability in Sydney’s reservoir inflows appears to be a combination of the El Niño–Southern Oscillation (ENSO) phenomenon and the Pacific decadal oscillation (PDO). A multivariate ICA-based estimation model was then used to capture this variability, and it was shown that this approach performed well in maintaining the temporal dependence while also accurately maintaining the spatial dependencies that exist in the 11-dimensional historical reservoir inflow dataset. [ABSTRACT FROM AUTHOR]

Abstract Influence diagnostics are used to identify data points that have a disproportionate impact on model parameters, performance and/or predictions, providing valuable information for use in model calibration. Regression-theory influence diagnostics identify influential data by combining the leverage and the standardised residuals, and are computationally more efficient than case-deletion approaches. This study evaluates the performance of a range of regression-theory influence diagnostics on ten case studies with a variety of model structures and inference scenarios including: nonlinear model response, heteroscedastic residual errors, data uncertainty and Bayesian priors. A new technique is developed, generalised Cook's distance, that is able to accurately identify the same influential data as standard case deletion approaches (Spearman rank correlation: 0.93–1.00) at a fraction of the computational cost (<1%). This is because generalised Cook's distance uses a generalised leverage formulation which outperforms linear and nonlinear leverage formulations due to less restrictive assumptions. Generalised Cook's distance has the potential to enable influential data to be efficiently identified on a wide variety of hydrological and environmental modelling problems. Highlights • Influential data points have a disproportionate impact on model predictions. • A new generalised Cook's distance accurately identifies influential data points. • More efficient (<1% computational cost) than standard case-deletion approaches. • Applies to nonlinear regression and hydrological models with heteroscedastic errors. • Can be used in a Bayesian framework with priors or data uncertainty. [ABSTRACT FROM AUTHOR]

In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming. [ABSTRACT FROM AUTHOR]

Influence diagnostics aim to identify a small number of influential data points that have a disproportionate impact on the model parameters and/or predictions. The key issues with current influence diagnostic techniques are that the regression-theory approaches do not provide hydrologically relevant influence metrics, while the case-deletion approaches are computationally expensive to calculate. The main objective of this study is to introduce a new two-stage hybrid framework that overcomes these challenges, by delivering hydrologically relevant influence metrics in a computationally efficient manner. Stage one uses computationally efficient regression-theory influence diagnostics to identify the most influential points based on Cook’s distance. Stage two then uses case-deletion influence diagnostics to quantify the influence of points using hydrologically relevant metrics. To illustrate the application of the hybrid framework, we conducted three experiments on 11 hydro-climatologically diverse Australian catchments using the GR4J hydrological model. The first experiment investigated how many data points from stage one need to be retained in order to reliably identify those points that have the hightest influence on hydrologically relevant metrics. We found that a choice of 30–50 is suitable for hydrological applications similar to those explored in this study (30 points identified the most influential data 98% of the time and reduced the required recalibrations by 99% for a 10 year calibration period). The second experiment found little evidence of a change in the magnitude of influence with increasing calibration period length from 1, 2, 5 to 10 years. Even for 10 years the impact of influential points can still be high (>30% influence on maximum predicted flows). The third experiment compared the standard least squares (SLS) objective function with the weighted least squares (WLS) objective function on a 10 year calibration period. In two out of three flow metrics there was evidence that SLS, with the assumption of homoscedastic residual error, identified data points with higher influence (largest changes of 40%, 10%, and 44% for the maximum, mean, and low flows, respectively) than WLS, with the assumption of heteroscedastic residual errors (largest changes of 26%, 6%, and 6% for the maximum, mean, and low flows, respectively). The hybrid framework complements existing model diagnostic tools and can be applied to a wide range of hydrological modelling scenarios. [ABSTRACT FROM AUTHOR]

Natural hazards often occur in combination with other natural hazards rather than as isolated events. While some combinations of hazards are well studied and their physical connection is increasingly understood, other combinations have received considerably less attention. High temperatures are known to be an important component for conditions that lead to heavy rainfall; however, sequences of heatwaves followed by heavy rainfall are not well understood, especially in a compound event context. Here, we analyse heatwave–heavy rainfall events across Australia using rainfall observations at hourly resolution. Our results show that heavy rainfall is more likely to occur if preceded by a heatwave, demonstrating that heatwave–heavy rainfall sequences should be seen as temporally compounding events. In particular, many regions in Australia experience both more frequent and more extreme wet days immediately following a heatwave. This behaviour is strongest in coastal regions, especially on the Australian east coast. These findings highlight the need for heatwave–heavy rainfall sequences to be studied as compounding events, as future changes in either hazard is likely to have impacts on related risks such as flash flooding. [ABSTRACT FROM AUTHOR]

Droughts are a recurrent and natural part of the Australian hydroclimate, with evidence of drought dating back thousands of years. However, our ability to monitor, attribute, forecast and manage drought is exposed as insufficient whenever a drought occurs. This paper summarises what is known about drought hazard, as opposed to the impacts of drought, in Australia and finds that, unlike other hydroclimatic hazards, we currently have very limited ability to tell when a drought will begin or end. Understanding, defining, monitoring, forecasting and managing drought is also complex due to the variety of temporal and spatial scales at which drought occurs and the diverse direct and indirect causes and consequences of drought. We argue that to improve understanding and management of drought, three key research challenges should be targeted: (1) defining and monitoring drought characteristics (i.e. frequency, start, duration, magnitude, and spatial extent) to remove confusion between drought causes, impacts and risks and better distinguish between drought, aridity, and water scarcity due to over-extractions; (2) documenting historical (instrumental and pre-instrumental) variation in drought to better understand baseline drought characteristics, enable more rigorous identification and attribution of drought events or trends, inform/evaluate hydrological and climate modelling activities and give insights into possible future drought scenarios; (3) improving the prediction and projection of drought characteristics with seasonal to multidecadal lead times and including more realistic modelling of the multiple factors that cause (or contribute to) drought so that the impacts of natural variability and anthropogenic climate change are accounted for and the reliability of long-term drought projections increases. [ABSTRACT FROM AUTHOR]

Wildfires elicit a diversity of hydrological changes, impacting processes that drive both water quantity and quality. As wildfires increase in frequency and severity, there is a need to assess the implications for the hydrological response. Wildfire‐related hydrological changes operate at three distinct timescales: the immediate fire aftermath, the recovery phase, and long‐term across multiple cycles of wildfire and regrowth. Different dominant processes operate at each timescale. Consequentially, models used to predict wildfire impacts need an explicit representation of different processes, depending on modeling objectives and wildfire impact timescale. We summarize existing data‐driven, conceptual, and physically based models used to assess wildfire impacts on runoff, identifying the dominant assumptions, process representations, timescales, and key limitations of each model type. Given the substantial observed and projected changes to wildfire regimes and associated hydrological impacts, it is likely that physically based models will become increasingly important. This is due to their capacity both to simulate simultaneous changes to multiple processes, and their use of physical and biological principles to support extrapolation beyond the historical record. Yet benefits of physically based models are moderated by their higher data requirements and lower computational speed. We argue that advances in predicting hydrological impacts from wildfire will come through combining these physically based models with new computationally faster conceptual and reduced‐order models. The aim is to combine the strengths and overcome weaknesses of the different model types, enabling simulations of critical water resources scenarios representing wildfire‐induced changes to runoff. This article is categorized under:Water and Life > Conservation, Management, and AwarenessScience of Water > Hydrological ProcessesScience of Water > Water and Environmental Change [ABSTRACT FROM AUTHOR]

Summary Influential data are those that have a disproportionate impact on model performance, parameters and/or predictions. This paper evaluates two classes of diagnostics that identify influential data for hydrological model calibration: (1) numerical “case-deletion” diagnostics, which directly measure the influence of each data point on the calibrated model; and (2) analytical diagnostics based on Cook’s distance, which combine information on the model residuals with a measure of the distance of each input point from the centre of the range of the input data (i.e., the leverage). Case-deletion methods rank influence by changes in the model parameters (measured through the Mahalanobis distance), performance (using objective function displacement) and predictions (e.g. mean and maximum streamflow). For the analytical methods, both linear and nonlinear estimates of leverage are used to calculate Cook’s distance, which is used to rank influential data. We apply these diagnostics to three case studies and show that a single point could change mean/maximum streamflow predictions by 7%/9% for a rating curve model, and 13%/25%, for a hydrological model (GR4J) in an ephemeral catchment. In contrast, the influence was far less for GR4J in a humid catchment (0.2%/2.3%). Assuming the data are of high quality this indicates deficiencies in the ability of the GR4J model structure to reproduce the flow regime in the ephemeral catchment. The linear Cook’s distance-based metric produced reasonably similar rankings to the case-deletion metrics at a fraction of the computational cost (300–1000 times faster), but with less flexibility to rank influence using specific aspects of model behaviour. The nonlinear distance produced rankings that were virtually the same as the case-deletion metrics for all case studies – this highlights the importance of its use for nonlinear hydrological models. Visual assessment was not a reliable method of influence analysis as there was no direct relationship between the most influential data and the highest observed streamflows. The findings establish the feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hydrological model predictions are crucial for risk-based water management decisions. • Errors in hydrological predictions are typically neglected for risk-based decision-making. • New framework shows neglecting errors over-estimates yield, under-estimates risks. • Outcomes underscore the need to account for hydrological errors in decision-making. Risk-based decision making for water resource systems often relies on streamflow predictions from hydrological models. These predictions are integral for estimating the frequency of high consequence extreme events, such as floods and droughts. However, streamflow predictions are known to have errors due to various factors such as incomplete hydrological understanding, parameter misspecification, and uncertain data. Despite these errors being well known, they are frequently neglected when undertaking risk-based decision-making. This paper demonstrates that neglecting hydrological errors can impact on drought risk estimation for high stakes decisions with potentially severe consequences for water resource system performance. A generic framework is introduced to evaluate the impact of hydrological errors for a wide range of water resource system properties. This framework is applied in two Australian case study catchments, where we use a stochastic rainfall model, the GR4J hydrological model, a residual error model, and a simplified reservoir storage model to estimate water resource performance metrics (risk and yield). The results underscore the impact of neglecting hydrological errors on decision-making. In one case study catchment, the yield was over-estimated by ∼15%–55%, resulting in the (actual) risk of running out of water being ∼2–30 times larger than reservoir design. The magnitude of these errors in water resource performance metrics is striking, especially considering that the streamflow predictions appear reasonable based on typical performance metrics (e.g., NSE of ∼0.7). The errors in performance metrics stem from the complex propagation of hydrological errors through the water resource system modelling chain. By accounting for critically important hydrological errors we can mitigate highly erroneous risk estimates and improve decision-making related to water resource management. [ABSTRACT FROM AUTHOR]

Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations. Temporal and/or spatial coverage of rainfall data is often limited, so that stochastic models may be required to generate long synthetic rainfall records. This study describes a multi-site rainfall simulator (MRS) to stochastically generate daily rainfall at multiple locations. The MRS is available as an open-source software package in the R statistical computing environment. The software includes statistical analysis and graphics functions, and can display statistics and graphs at multiple time scales, including from individual sites and areal averages. The MRS thus provides a detailed set of modelling functions to simulate and analyse daily rainfall. The capabilities of the package are demonstrated using 30 gauges located in Sydney, Australia, and the results show that the model preserves observed year-to-year variability, interannual persistence and various daily distributional and space–time dependence attributes. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low, mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale. [ABSTRACT FROM AUTHOR]

This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records. [ABSTRACT FROM AUTHOR]

Six weather types (WTs) are computed for tropical Australia during the wet season (November–March 1979–2015) using cluster analysis of 6-hourly low-level winds at 850 hPa. The WTs may be interpreted as a varying combination of at least five distinct phenomena operating at different time scales: the diurnal cycle, fast and recurrent atmospheric phenomena such as transient low pressure, the intraseasonal Madden–Julian oscillation, the annual cycle, and interannual variations mostly associated with El Niño–Southern Oscillation. The WTs are also strongly phase-locked onto the break/active phases of the monsoon; two WTs characterize mostly the trade-wind regime prevalent either at the start and the end of the monsoon or during its breaks, while three monsoonal WTs occur mostly during its core and active phases. The WT influence is strongest for the frequency of wet spells, while the influence on intensity varies according to the temporal aggregation of the rainfall. At hourly time scale, the climatological mean wet intensity tends to be near-constant in space and not systematically larger for the monsoonal WTs compared to other WTs. Nevertheless, one transitional WT, most prevalent around late November and characterized by weak synoptic forcings and overall drier conditions than the monsoonal WTs, is associated with an increased number of high hourly rainfall intensities for some stations, including for the interior of the Cape York Peninsula. When the temporal aggregation exceeds 6–12 h, the mean intensity tends to be larger for some of the monsoonal WTs, in association with more frequent and also slightly longer wet spells. [ABSTRACT FROM AUTHOR]

• Guiding principles for crop model data assimilation in on-farm decision support context. • Real-world viticulture case study used to demonstrate guiding principles. • Situational awareness through mapping insights to end-of-season outcomes in real time. The ability of 'digital agriculture' to support on-farm decision making is predicated on the real-time combination of observations and prior knowledge into an integrated digital environment. The mathematical discipline that seeks to provide this integration is known as model data assimilation (DA), with demonstrated benefits including improved predictive reliability, and the capacity to identify unexpected changes in field conditions and potential measurement errors. Despite routine adoption in other fields, the delayed adoption of DA in agriculture is due to the need to express end-of-season outcomes such as yield, update forecasts of these outcomes throughout the growing season as data become available, and enhance forecast reliability. To overcome these challenges, three guiding principles are introduced, providing a means to operationalize crop model DA for robust on-farm decision support. We apply the guiding principles using a South Australian viticulture case study. Our case study involves application of an iterative form of a widely used DA algorithm (ensemble Kalman filter) to dynamically update both static parameters and states associated with a grapevine simulation model. Daily weather data as well as fortnightly ground-based leaf area index (LAI) data are used for assimilation. It is shown how crop model DA can lead to not only significant improvements in forecasts of LAI but also to forecasts of end-of-season yield. The guiding principles also enable observations of greatest value to be identified throughout the season. This study highlights the role that formal crop model DA can play in agricultural decision support through enhancing situational awareness in real time. [Display omitted] [ABSTRACT FROM AUTHOR]

Water policy, planning and design problems are often nested across scales and can require detailed understanding of upstream processes (e.g. distributed agricultural dams, upstream land use change and flow intermittency). Traditional approaches to streamflow monitoring are limited by sparse geographical coverage and relatively high costs of gathering observations with streamflow measurements typically collected at a catchment outlet. This can be problematic when attempting to understand the non-linear and complex runoff behaviour present on hillslopes and reaches throughout an entire river network. Differences in catchment characteristics result in highly variable drainage patterns. This presents challenges for the calibration of hydrological models regarding their ability to represent inter-catchment runoff behaviour. Low cost and innovative data collection approaches provide opportunities for water management questions which span multiple scales by complementing existing observations and addressing local data deficiencies.This study combines process modelling and complementary data (e.g. from distributed sensors) to improve the representation of local scale flow processes. Paired (in-stream and on-bank) temperature sensors are implemented across nine headwater sites within a small catchment located in South Australia. A classification scheme was developed to infer whether the stream was 'wet' (flowing) or 'dry' (not flowing) and low flow metrics were determined at each site (e.g. number of zero flow days, number of zero flow periods and average duration of zero flow periods). The results demonstrated a high degree of heterogeneity of flow permanence within the small catchment area (10 km2). A physically based hydrological model, HydroGeoSphere, was calibrated to the outlet and validated with the classified 'wet'-'dry' binary data showing reduced performance at three out of four upstream sites. These results highlight the value of inexpensive data collection methodologies by providing opportunities to improve the calibration and validation of hydrological models. [ABSTRACT FROM AUTHOR]

Simulated and observed trends in flood hazard are investigated across more than 4,000 locations globally using data from the Global Streamflow Indices and Metadata (GSIM) archive. Mann-Kendall test and Poisson regression are used respectively to evaluate the significance of changes in flood magnitude (i.e. annual maxima streamflow) and frequency (i.e. the number of streamflow events above a threshold) at each streamflow gauge. These trends were compared with trends of simulated flood hazard from six global hydrological models (GHMs; available through the Inter-Sectoral Impact Model Intercomparison Project), and the fraction of stations with significant trends are found to be similar between observed and simulated trends. A bootstrapping procedure is adopted to assess the global spatial patterns of observed and simulated trends, considering two similarity measures (correlation and amplitude). The findings highlight statistically significant similarity in trend patterns exhibited in the GHMs and GSIM, indicating positive skill of GHMs in reproducing spatial pattern of trends at the global scale. Most GHMs, however, tend to underestimate the spread (variance) of trend magnitudes, especially over the data-covered regions in Australia and Asia. This assessment provides a first-hand comparison of flood trends at the station-level using large datasets of streamflow discharge. [ABSTRACT FROM AUTHOR]

Highlights • Antecedent moisture conditions are shown to significantly influence flood volume. • Flood to antecedent precipitation elasticity is less than for event precipitation. • Flood to antecedent precipitation elasticity decreases as flood extremity increases. Abstract The magnitude of floods depends not only on the intensity and pattern of precipitation during the flood event (the 'flood-producing' precipitation), but also on the moisture stored in the catchment, which arises from antecedent hydrological processes over many preceding timescales. To characterise this effect, an empirical study is conducted on the influence of antecedent precipitation on significant flood events across multiple climate zones and catchment conditions, using 100 Australian catchments with hourly streamflow and precipitation. Antecedent conditions are shown to have a significant influence on flood volume, with three quarters of catchments having at least a 50% difference in flood volume depending on whether the catchment is wet or dry before the flood-producing precipitation event. The study considers the sensitivity of flow to antecedent precipitation by means of an 'elasticity' metric, which indicates the proportional change in flow for a change in antecedent precipitation or flood-producing precipitation. Flood-producing precipitation nevertheless remains the dominant flood driver across most catchments, with the elasticity of flow to antecedent precipitation typically being between 28% and 37% of the elasticity to flood-producing precipitation. Importantly, the elasticity of flow to antecedent precipitation relative to flood-producing precipitation decreases with increasing event magnitude, highlighting that conclusions of future change based on annual maximum streamflow may not be reflective of the processes that operate for more extreme floods that have the greatest impact on society. [ABSTRACT FROM AUTHOR]

Highlights • Areal reduction factors (ARFs) are needed to estimate flood risk for rare events. • Behaviour of ARFs for long return periods depends on asymptotic properties. • Spatial rainfall is asymptotically independent. • Areal reduction factors decrease with increasing return period. Abstract Areal reduction factors (ARFs) transform an estimate of extreme rainfall at a point to an estimate of extreme rainfall over a spatial domain, and are commonly used in flood risk estimation. For applications such as the design of large infrastructure, dam safety and land use planning, ARFs are needed to estimate flood risk for very rare events that are often larger than the biggest historical events. The nature of the relationship between ARFs and frequency for long return periods is unclear as it depends on the asymptotic dependence structure of rainfall over a region, i.e., the extent to which rainfall from a surrounding region is extreme as rainfall at a point becomes more extreme. Miscalculating this for very rare events could lead to poor design of infrastructure. To investigate this, spatial rainfall processes are simulated using asymptotically dependent and independent models, and the implications for ARFs of the asymptotic assumptions are explored in a synthetic study. The models are then applied to a case study in Victoria, Australia, using 88 daily rainfall gauges with 50 years of data. The analysis shows that the observed data follow the behaviour of an asymptotically independent process, leading to ARFs that decrease with increasing return period. The study demonstrates that the use of inverted max-stable process models to simulate ARFs can provide a rigorous alternative to empirical approaches, particularly for long return periods requiring significant extrapolation from the data. [ABSTRACT FROM AUTHOR]

This is Part 2 of a two-paper series presenting the Global Streamflow Indices and Metadata Archive (GSIM), which is a collection of daily streamflow observations at more than 30 000 stations around the world. While Part 1 (Do et al., 2018a) describes the data collection process as well as the generation of auxiliary catchment data (e.g. catchment boundary, land cover, mean climate), Part 2 introduces a set of quality controlled time-series indices representing (i) the water balance, (ii) the seasonal cycle, (iii) low flows and (iv) floods. To this end we first consider the quality of individual daily records using a combination of quality flags from data providers and automated screening methods. Subsequently, streamflow time-series indices are computed for yearly, seasonal and monthly resolution. The paper provides a generalized assessment of the homogeneity of all generated streamflow time-series indices, which can be used to select time series that are suitable for a specific task. The newly generated global set of streamflow time-series indices is made freely available with an digital object identifier at https://doi.pangaea.de/10.1594/PANGAEA.887470 and is expected to foster global freshwater research, by acting as a ground truth for model validation or as a basis for assessing the role of human impacts on the terrestrial water cycle. It is hoped that a renewed interest in streamflow data at the global scale will foster efforts in the systematic assessment of data quality and provide momentum to overcome administrative barriers that lead to inconsistencies in global collections of relevant hydrological observations. [ABSTRACT FROM AUTHOR]

This is the first part of a two-paper series presenting the Global Streamflow Indices and Metadata archive (GSIM), a worldwide collection of metadata and indices derived from more than 35 000 daily streamflow time series. This paper focuses on the compilation of the daily streamflow time series based on 12 free-toaccess streamflow databases (seven national databases and five international collections). It also describes the development of three metadata products (freely available at https://doi.pangaea.de/10.1594/PANGAEA.887477): (1) a GSIM catalogue collating basic metadata associated with each time series, (2) catchment boundaries for the contributing area of each gauge, and (3) catchment metadata extracted from 12 gridded global data products representing essential properties such as land cover type, soil type, and climate and topographic characteristics. The quality of the delineated catchment boundary is also made available and should be consulted in GSIM application. The second paper in the series then explores production and analysis of streamflow indices. Having collated an unprecedented number of stations and associated metadata, GSIM can be used to advance large-scale hydrological research and improve understanding of the global water cycle. [ABSTRACT FROM AUTHOR]

The potential of digital agriculture to support on-farm decision making is predicated on the assumption that 'cause-and-effect' relationships can be encoded in a mathematical form. One particularly important application area is irrigation decision making, which is informed by the relationship between applied water and end-of-season crop yield ('water production relations'). Yet this relationship is often partial, owing to its many determining factors, especially for woody perennial crops such as grapevines. Process-based models are a way in which to represent these relationships in a manner that is both interpretable and generalizable. Here we conduct numerical experiments using a process-based crop model to evaluate water production relations for grapevines and how these relations are influenced by genetic and environmental factors as well as irrigation timing decisions. A real-world case study representing a Shiraz vineyard in South Australia is considered. Results show a largely linear relation between total irrigation applied and yield across all numerical experiments, notwithstanding significant uncertainty due to genetic and environmental factors. However, when considering water production relations in relative terms (e.g., change in tonnes per megalitre), the influence of these factors between seasons is reduced, allowing for more robust insights. Exploration of water productivity as a function of phenological stage shows that the average production sensitivity is greatest during veraison (3.5 tonnes per megalitre) and least between bud burst and flowering (2.3 tonnes per megalitre), despite considerable overlap in productivity range between stages. By putting meaningful bounds on water production relations through process-based modeling, growers and their advisors can achieve improved farm outcomes by better informed water application decisions. • Numerical experiments with a crop model used to evaluate water production relations. • Accounted for influence of water application decisions as well as genetic and environmental factors. • A viticulture case study is considered. [ABSTRACT FROM AUTHOR]

Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future. [ABSTRACT FROM AUTHOR]

This is Part 2 of a two-paper series presenting the Global Streamflow Indices and Metadata Archive (GSIM), which is a collection of daily streamflow observations at more than 30000 stations around the world. While Part 1 describes the data collection process as well as the generation of auxiliary catchment data (e.g. catchment boundary, land-cover, mean climate), Part 2 introduces a set of quality controlled time series indices representing (i) the water balance, (ii) the seasonal cycle, (iii) low-flows and (iv) floods. To this end we first consider the quality of individual daily records using a combination of quality flags of the data providers and automated screening methods. Subsequently streamflow time series indices are computed for yearly, seasonal and monthly resolution. The paper closes with a generalized assessment of the homogeneity of all generated streamflow time series indices, which can be used to select time series that are suitable for a specific task. The presented global set of streamflow time series indices is made freely available at https://iacweb.ethz.ch/staff/lukasgu/GSIM/GSIM_indices.zip and is expected to foster global freshwater research, by acting as a ground-truth for model validation or as a basis for assessing the role of human impacts on the terrestrial water cycle. [ABSTRACT FROM AUTHOR]

This is the first part of a two paper series presenting the Global Streamflow Indices and Metadata archive (GSIM), a worldwide collection of metadata and indices derived from more than 35,002 daily streamflow timeseries. This paper focuses on the compilation of the daily streamflow timeseries based on 12 free-to-access streamflow databases (seven national databases and five international collections). It also describes the development of three metadata products (freely available at https://iacweb.ethz.ch/staff/lukasgu/GSIM/GSIM_metadata.zip): (1) a GSIM catalogue collating basic metadata associated with each timeseries, (2) catchment boundaries for the contributing area of each gauge, and (3) catchment metadata extracted from 12 gridded global data products representing essential properties such as land cover type, soil type, climate and topographic characteristics. The second paper in the series then explores production and analysis of streamflow indices. Having collated an unprecedented number of stations and associated metadata, GSIM can be used to advance large-scale hydrological research and improve understanding of the global water cycle. [ABSTRACT FROM AUTHOR]

Floods are caused by a number of interacting factors, making it remarkably difficult to explain changes in flood hazard. This paper reviews the current understanding of historical trends and variability in flood hazard across Australia. Links between flood and rainfall trends cannot be made due to the influence of climate processes over a number of spatial and temporal scales as well as landscape changes that affect the catchment response. There are also still considerable uncertainties in future rainfall projections, particularly for sub-daily extreme rainfall events. This is in addition to the inherent uncertainty in hydrological modelling such as antecedent conditions and feedback mechanisms. Research questions are posed based on the current state of knowledge. These include a need for high-resolution climate modelling studies and efforts in compiling and analysing databases of sub-daily rainfall and flood records. Finally there is a need to develop modelling frameworks that can deal with the interaction between climate processes at different spatio-temporal scales, so that historical flood trends can be better explained and future flood behaviour understood. [ABSTRACT FROM AUTHOR]

The Australian coastal zone encompasses tropical, sub- and extra-tropical climates and accommodates about 80 % of Australia's population. Sea level extremes and their physical impacts in the coastal zone arise from a complex set of atmospheric, oceanic and terrestrial processes that interact on a range of spatial and temporal scales and will be modified by a changing climate, including sea level rise. This review details significant progress over recent years in understanding the causes of past and projections of future changes in sea level and coastal extremes, yet a number of research questions, knowledge gaps and challenges remain. These include efforts to improve knowledge on past sea level extremes, integrate a wider range of processes in projections of future changes to sea level extremes, and focus efforts on understanding long-term coastline response from the combination of contributing factors. [ABSTRACT FROM AUTHOR]

Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model 'gets the right answers for the right reasons'. This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM-the Weather Research Forecasting model-over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement. [ABSTRACT FROM AUTHOR]

Summary The global and regional influence of the El Niño-Southern Oscillation (ENSO) phenomenon on extreme precipitation was analyzed using a global database comprising over 7000 high quality observation sites. To better quantify possible changes in relatively rare design-relevant precipitation quantiles (e.g. the 1 in 10 year event), a Bayesian regional extreme value model was used, which employed the Southern Oscillation Index (SOI) – a measure of ENSO – as a covariate. Regions found to be influenced by ENSO include parts of North and South America, southern and eastern Asia, South Africa, Australia and Europe. The season experiencing the greatest ENSO effect varies regionally, but in most of the ENSO-affected regions the strongest effect happens in boreal winter, during which time the 10-year precipitation for |SOI| = 20 (corresponding to either a strong El Niño or La Niña episode) can be up to 50% higher or lower than for SOI = 0 (a neutral phase). Importantly, the effect of ENSO on extreme precipitation is asymmetric, with most parts of the world experiencing a significant effect only for a single ENSO phase. This finding has important implications on the current understanding of how ENSO influences extreme precipitation, and will enable a more rigorous theoretical foundation for providing quantitative extreme precipitation intensity predictions at seasonal timescales. We anticipate that incorporating asymmetric impacts of ENSO on extreme precipitation will help lead to better-informed climate-adaptive design of flood-sensitive infrastructure. [ABSTRACT FROM AUTHOR]

Context: Effective vineyard decision making at strategic and operational timescales requires consideration of many factors, including vineyard design and management options, exogenous factors that are outside of the decision maker's control such as climate/weather, and desired outcomes such as yield and grape composition attributes. Process-based models enable prediction of vineyard outcomes in response to different decisions and with respect to exogenous factors. Objective: We present a review of process-based models that can simulate both key vineyard outcomes and their sensitivity to decisions and exogenous factors, and highlight how these models can 'bridge the gap' between data and decisions. Methods: We assess the suitability of process-based models to water, canopy and nutrient management contexts using a conceptual systems framework. Results and Conclusions: Currently available process-based models are most advanced in terms of supporting decisions regarding water management, and least advanced regarding nutrient management. They are also better suited to support decisions with respect to yield, compared to grape composition attributes. Significance: This paper provides guidance to modelling practitioners regarding appropriate process-based models for a range of vineyard decision contexts and assists viticulturists and decision makers in understanding how these models can support decisions for improved outcomes. [Display omitted] • A review of process-based models for decision support in viticulture. • Roles and requirements of process-based models for decision support identified using a conceptual systems framework. • Assessment of the suitability of process-based models for water, canopy and nutrient management. • Address the growing 'data to decisions' gap in viticulture due to under-utilization of process-based models. [ABSTRACT FROM AUTHOR]

The relationship between seasonal aggregate rainfall and large-scale climate modes, particularly the El Niño-Southern Oscillation (ENSO), has been the subject of a significant and ongoing research effort. However, relatively little is known about how the character of individual rainfall events varies as a function of each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm interevent time at both daily and subdaily time scales in east Australia, as a function of indices for ENSO, the Indian Ocean dipole (IOD), and the southern annular mode (SAM), with a focus on the cool season months. Long-record datasets have been used to sample a large variety of climate events for better statistical significance. Results using both the daily and subdaily rainfall datasets consistently show that it is the occurrence of rainfall events, rather than the average intensity of rainfall during the events, which is most strongly influenced by each of the climate modes. This is shown to be most likely associated with changes to the time between wet spells. Furthermore, it is found that despite the recent attention in the research literature on other climate modes, ENSO remains the leading driver of rainfall variability over east Australia, particularly farther inland during the winter and spring seasons. These results have important implications for how water resources are managed, as well as how the implications of large-scale climate modes are included in rainfall models to best capture interannual and longer-scale variability. [ABSTRACT FROM AUTHOR]