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• Highlights the limitation of existing NFFA approaches in differentiating the mechanism of frequent versus rare flood quantiles. • Proposes a novel NRFFA approach to capture the variation of flood quantiles with different return periods in a changing climate. • Use regional information to ascertain the design flood quantiles and provide plausible changes in design flood extremes in the year 2100. There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented. [ABSTRACT FROM AUTHOR]

• Highlights the limitation of existing NFFA approaches in differentiating the mechanism of frequent versus rare flood quantiles. • Proposes a novel NRFFA approach to capture the variation of flood quantiles with different return periods in a changing climate. • Use regional information to ascertain the design flood quantiles and provide plausible changes in design flood extremes in the year 2100. There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented. [ABSTRACT FROM AUTHOR]

Summary Bias correction is considered as a critical post-processing step to remove systematic errors and improve the quality of General Circulation Model (GCM) simulations before their use in climate change impact assessment applications. A majority of the bias correction approaches correct for biases either at a single time scale or at multiple pre-specified time scales. An inappropriate or insufficient selection of time scales may lead to improper or sub-optimal bias corrected outputs, especially when persistence attributes across a range of scales are of interest. In this paper, we present a new bias correction approach that works in the frequency space and is independent of specific time scales. The approach is named as frequency-based bias correction (FBC). The usefulness of the approach is demonstrated by applying it to the monthly rainfall simulations of MIROC5 GCM over Australia and comparing the results with two other approaches, namely, empirical quantile mapping and recursive nesting bias correction, in cross validation. The comparison is based on the reproduction of various observed distribution and persistence attributes. Cross-validation results indicate that the proposed approach shows similar performance in terms of reproducing the first- and second-order moments of observed precipitation time series, however, outperforms with regard to persistence attributes. The approach shows high potential for use in downscaling and other climate change impact assessment studies, especially for the planning and design of hydrologic systems that are sensitive to the characterisation of persistence in the hydrologic time series. [ABSTRACT FROM AUTHOR]

Summary Climate change has the potential to intensify the hydrological cycle, leading to more intense precipitation with associated changes in the intensity, frequency and severity of floods. Climate variability and change beyond a few years to a few decades ahead have significant social, economic, and environmental implications. It is believed that some aspects of this decadal variability could be predictable for a decade or longer in advance. Keeping this in mind, phase five of the Coupled Model Intercomparison Project (CMIP5), for the first time, provides 10–30 years predictions obtained from the General Circulation Models (GCMs). This study aims to analyse the CMIP5 decadal predictions for precipitation over five sub-basins of river Brahmaputra. Daily precipitation data of five GCMs, namely F-GOALS-g2, BCC-CSM1-1, IPSL-CM5A, CanCM4 and MRI-CGCM3 are used for this assessment. Empirical relationships between the basin averaged rainfall wet spell (storm) properties and the characteristics of the floods are formulated for storms which lead to significant short-term flood response. Following this, the changes in the flood behaviour in the future are derived on the basis of changes in the characteristics of wet rainfall spells in 2010–2020. The results suggest an increase in the number of spells with higher rainfall and longer duration which can lead to increase in peak flood and the total flood volume. [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]

Summary Many hydro-climatological applications require use of General Circulation Models (GCMs) outputs. However, the raw information as available from GCMs often contains significant systematic biases when compared with observations. This necessitates some kind of statistical adjustment to be carried out on the GCM fields before their use in any application. It is common to correct the GCM simulations by removing the systematic biases at the time scale of interest, usually individually for each GCM variable that is needed. The outcome is a set of bias corrected variables that are not assessed for bias in their joint dependence structure, nor biases in all attributes at other time scales (such as daily and monthly and annual) except the one under consideration. In this paper, we present a bias correction approach that simultaneously adjusts for the biases in multiple variables across multiple time scales. The proposed Multivariate Recursive Nesting Bias Correction (MRNBC) approach simultaneously corrects many GCM variables and repeats the procedure across different levels of temporal aggregation to impart observed distributional and persistence properties at multiple time scales. The bias corrected series exhibits improvements across all variables and over all the time scales considered. The use of the approach in hydrology and water resources related downscaling applications is expected to deliver better results. [ABSTRACT FROM AUTHOR]

Summary This study explores the potential skill of Coupled Model Intercomparison Project (CMIP5) decadal hindcasts from 9 GCMs and their ensembles over the period 1960–2010 from a hydrological perspective. This assessment is first conducted using GCM derived rainfall and subsequently extended to the other climate variables such as air temperature and geopotential height, with the intention of assessing the possible benefit of using these variables for formulation of appropriate precipitation modelling alternatives. A total of 78 ensemble members for rainfall, and 22 ensemble members for air temperature and geopotential height, are used in performing this decadal predictability assessment. The results suggest that while precipitation predictions show very limited skill when assessed at annual and multi-annual time-scales, air temperature and geopotential height exhibit greater predictive skill. The major part of the predictability of these variables comes from the long term trend. However, the predictability is not enough to drive impact models at decadal timescales and to influence the policy and decision making. The results indicate possible benefits of using a suitably structured modelling approach to derive precipitation using atmospheric predictors exhibiting better skills than the raw simulated rainfall. [ABSTRACT FROM AUTHOR]

• A new basis for presenting illustrations of spatial dependence related to extreme rain. • Extreme rainfall spatial connectivity increases under ENSO phases. • RC weakens at warmer temperatures, with localized lower relative connectivity. The frequency and severity of extreme weather events have been noted to be changing in both time and space as a result of rising global temperatures. In this regard, the analysis of joint occurrences of extreme climate events has gained considerable importance for planning and emergency management as such events often result in system failures that extend beyond a single location in the region impacted. By their very definition, extremes cannot be sampled frequently, necessitating approaches that are robust and efficient and can take advantage of the Big Data movement we are now a part of. This study introduces a new concept for characterizing such dependence, termed relative connectivity, formulated using the theory of empirical copulas which represent a non-parametric method to describe the joint dependence of random variables. The spatial relative connectivity of extreme rainfall events across 2,708 rainfall stations in Australia is ascertained and conclusions drawn. The extreme rainfall events in Western Australia, southeast South Australia, and southwest Victoria exhibit higher relative connectivity than other regions, suggesting a stronger spatial dependence and higher possibility of concurrence when compared to other regions. The overall variation of relative connectivity is also examined under different low-frequency climatic anomalies (i.e. El Nino and La Nina events) and temperature states. We observe that spatial dependence across raingauges increases under both El Nino and La Nina periods, while it weakens at warmer temperatures, with localized lower relative connectivity at target raingauges. [ABSTRACT FROM AUTHOR]

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]

Summary: This paper evaluates three distinct approaches for disaggregating daily rainfall to sub-daily sequences: (1) random multiplicative cascades (microcanonical and canonical versions), (2) point process (randomized Bartlett–Lewis model – RBLM), and (3) resampling (method of fragments). These methods are used to perform disaggregation of daily rainfall to hourly rainfall at four point locations across Australia (Sydney, Perth, Cairns, and Hobart), which are associated with different climatic regimes. The methods are evaluated based on parameter estimation procedures applied (including introduction of the sequential Monte Carlo sampler in RBLM), the capability of the resulting sequences to reproduce standard validation statistics, and the representation of observed rainfall variability and intermittency, within-day wet spells, and extreme rainfall percentiles. The results generally indicate that the method of fragments outperforms the other models. While all the models are found to simulate reasonably well the commonly used statistical measures (e.g. mean and dry proportions) of rainfall at the hourly timestep, the microcanonical model is found to significantly overestimate the hourly rainfall variance. With respect to extreme value characteristics, the resampling approach is found to match well the observed intensity–frequency relationship at an hourly scale, with the cascade models underestimating (canonical) and overestimating (microcanonical) extreme rainfall. The point process model’s performance is poor in Cairns but reasonably good at other locations. An analysis of the empirical within-day wet- and dry-spell distributions further reveals that the cascade-based models are not robust for observed wet and dry spells. [Copyright &y& Elsevier]

The occurrence of algal bloom results in deterioration of water quality, undesirable sights, tastes and odors, and the possibility of infections to humans and fatalities to livestock, wildlife and pets. Earlier studies have identified a range of factors including water temperature, flow, and nutrient concentrations that could affect cyanobacterial proliferation. Lack of enough data, independence in data across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized prediction frameworks for cyanobacteria. In this study, a prediction model for cyanobacteria occurrences was developed using only three dominant environmental variables; water temperature, velocity and phosphorus concentration. These environmental variables were selected due to not only direct or joint contribution to algal bloom but also the ease of their availability either through direct measurements or as modelled responses in the river location of interest. In order to apply bacterial growth dynamic to the model, weight functions which quantify the importance assigned to the three variables depending on the cell number at the preceding time, were formulated. An extensive dataset spanning from 2013 to 2018 at 16 representative locations across the four major rivers in South Korea was used to develop and validate the model. Through cross-validation, this model was shown to have more than 75% forecasting accuracy despite the use of a relatively simple predictive algorithm. As the developed model makes use of commonly available environmental variables, it can easily be extended to locations across the country where very limited or no prior information about cyanobacteria bloom is available. • A model developed to predict binary occurrences of cyanobacteria bloom in rivers. • The model uses only water temperature (T), velocity (V) and phosphorus (P). • The model weights T , V and P by the cyanobacteria cell number at the preceding time. • Through cross-validation over South Korea, the model showed encouraging results. [ABSTRACT FROM AUTHOR]