ORCID Profile
0000-0002-9519-3188
Current Organisation
University of Adelaide
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Water Resources Engineering | Surfacewater Hydrology | Civil Engineering |
Natural Hazards in Fresh, Ground and Surface Water Environments | Natural Hazards in Urban and Industrial Environments
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2010
Publisher: American Association for the Advancement of Science (AAAS)
Date: 12-03-2021
Abstract: Anthropogenic influence on climate has changed temperatures, precipitation, atmospheric circulation, and many other related physical processes, but has it changed river flow as well? Gudmundsson et al. analyzed thousands of time series of river flows and hydrological extremes across the globe and compared them with model simulations of the terrestrial water cycle (see the Perspective by Hall and Perdigão). They found that the observed trends can only be explained if the effects of climate change are included. Their analysis shows that human influence on climate has affected the magnitude of low, mean, and high river flows on a global scale. Science , this issue p. 1159 see also p. 1096
Publisher: American Geophysical Union (AGU)
Date: 03-2014
DOI: 10.1002/2013WR014616
Publisher: American Geophysical Union (AGU)
Date: 11-2006
DOI: 10.1029/2006WR004986
Publisher: American Geophysical Union (AGU)
Date: 06-2014
DOI: 10.1002/2013WR014719
Publisher: Springer Science and Business Media LLC
Date: 23-04-2015
DOI: 10.1038/NCLIMATE2579
Publisher: IWA Publishing
Date: 02-2007
DOI: 10.2166/WST.2007.093
Abstract: This paper investigates a Spatial Neyman–Scott Rectangular Pulse (SNSRP) model, which is one of only a few models capable of continuous simulation of rainfall in both space and time. The SNSRP is a spatial extension of the Neyman–Scott Rectangular Pulse model at a single point. The model is highly idealized having six parameters: storm arrival, cell arrival, cell radius, cell lifetime and two cell intensity parameters. A spatial interpolation of the scale parameter is used so that the model can be simulated continuously in space, rather than as a multi-site model. The parameters are calibrated using least-squares fits to statistical moments based on data aggregated to hourly and daily totals. The SNSRP model is calibrated to a very large network of 85 gauges over metropolitan Sydney and shows a good agreement to calibrated statistics. A simulation of 50 replicates over the region compares favourably to several observed temporal statistics, with an ex le given for one site. A qualitative discussion of the simulated spatial images demonstrates the underlying structure of non-advecting cylindrical cells.
Publisher: IWA Publishing
Date: 09-02-2015
Abstract: Flood attributes such as the water level may depend on multiple forcing variables that arise from common meteorological conditions. To correctly estimate flood risk in these situations, it is necessary to account for the joint probability distribution of all the relevant forcing variables. An ex le of a joint probability approach is the design variable method, which focuses on the extremes of the forcing variables, and approximates the hydraulic response to forcing variables with a water level table. In practice, however, application of the design variable method is limited, even for the bivariate case, partly because of the high computational cost of the hydrologic/hydraulic simulations. We develop methods to minimise the computational cost and assess the appropriate extent and resolution of the water level table in a bivariate context. Flood risk is then evaluated as a bivariate integral, which we implement as an equivalent line integral. The line integral is two orders of magnitude quicker and therefore beneficial to settings that require multiple evaluations of the flood risk (e.g., optimisation studies or uncertainty analyses). The proposed method is illustrated using a coastal case study in which floods are caused by extreme rainfall and storm tide. An open-source R package has been developed to facilitate the uptake of joint probability methods among researchers and practitioners.
Publisher: Elsevier BV
Date: 09-2006
Publisher: Wiley
Date: 30-09-2013
DOI: 10.1002/WCC.252
Abstract: Climate and weather variables such as rainfall, temperature, and pressure are indicators for hazards such as tropical cyclones, floods, and fires. The impact of these events can be due to a single variable being in an extreme state, but more often it is the result of a combination of variables not all of which are necessarily extreme. Here, the combination of variables or events that lead to an extreme impact is referred to as a compound event. Any given compound event will depend upon the nature and number of physical variables, the range of spatial and temporal scales, the strength of dependence between processes, and the perspective of the stakeholder who defines the impact. Modeling compound events is a large, complex, and interdisciplinary undertaking. To facilitate this task we propose the use of influence diagrams for defining, mapping, analyzing, modeling, and communicating the risk of the compound event. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate‐related hazards. WIREs Clim Change 2014, 5:113–128. doi: 10.1002/wcc.252 This article is categorized under: Climate Models and Modeling Knowledge Generation with Models Assessing Impacts of Climate Change Representing Uncertainty
Publisher: Wiley
Date: 07-2015
DOI: 10.1111/JFR3.12180
Publisher: Elsevier BV
Date: 02-2011
Publisher: American Geophysical Union (AGU)
Date: 2020
DOI: 10.1029/2019WR024945
Abstract: The annual timing of flood events is a useful indicator to study the interaction between atmospheric and catchment processes in generating floods. This paper presents an assessment of the seasonal timing of floods for 7,894 gauging locations across the globe over a common period from 1981 to 2010. The averaged ordinal date of annual maximum streamflow is then estimated for ungauged locations following a two‐stage prediction scheme. The first stage identifies regions that share a common climatic predictor of flood timing by analyzing the similarity of flood timing with seven climate variables. These variables represent precipitation timing and snowmelt dynamics and are derived from a global climate reanalysis data set. Homogeneous regions in terms of the dominant predictor are generalized in the second stage through a rule‐based classification. The classification partitions the world into five hydroclimate classes, where each class has flood timing predicted using the most relevant climate predictor. Using this relatively simple and interpretable model structure, flood timing could be predicted with a global mean absolute error of approximately 31 days while maintaining consistency across large regions. Potential applications of the developed map include better understanding of climatic drivers of flooding and benchmarking the performance of global hydrological models in simulating the processes relevant to flooding.
Publisher: American Geophysical Union (AGU)
Date: 09-2008
DOI: 10.1029/2007WR006110
Publisher: Elsevier BV
Date: 2008
Publisher: American Geophysical Union (AGU)
Date: 28-12-2014
DOI: 10.1002/2014GL062156
Publisher: American Geophysical Union (AGU)
Date: 04-05-2023
DOI: 10.1029/2022JD037908
Abstract: Floods and heavy precipitation have disruptive impacts worldwide, but their historical variability remains only partially understood at the global scale. This article aims at reducing this knowledge gap by jointly analyzing seasonal maxima of streamflow and precipitation at more than 3,000 stations over a 100‐year period. The analysis is based on Hidden Climate Indices (HCIs). Like standard climate indices (e.g., Nino 3.4, NAO), HCIs are used as covariates explaining the temporal variability of data, but unlike them, HCIs are estimated from the data. In this work, a distinction is made between common HCIs, that affect both heavy precipitation and floods, and specific HCIs, that exclusively affect one or the other. Overall, HCIs do not show noticeable autocorrelation, but some are affected by noticeable trends. In particular, strong and wide‐ranging trends are identified in precipitation‐specific HCIs, while trends affecting flood‐specific HCIs are weaker and have more localized effects. A probabilistic model is then derived to link HCIs and large‐scale atmospheric variables (pressure, wind, temperature) and to reconstruct HCIs since 1836 using the 20CRv3 reanalysis. In turn this allows estimating the probability of occurrence of floods and heavy precipitation at the global scale. This 180‐year reconstruction highlights flood hot‐spots and hot‐moments in the distant past, well before the establishment of perennial monitoring networks. The approach presented in this study is generic and paves the way for an improved characterization of historical variability by making a better use of long but highly irregular station data sets.
Publisher: American Society of Civil Engineers
Date: 17-05-2012
Publisher: American Geophysical Union (AGU)
Date: 28-12-2021
DOI: 10.1029/2021WR030007
Abstract: Risk assessment for climate‐sensitive systems often relies on the analysis of several variables measured at many sites. In probabilistic terms, the task is to model the joint distribution of several spatially distributed variables, and how it varies in time. This paper describes a Bayesian hierarchical framework for this purpose. Each variable follows a distribution with parameters varying in both space and time. Temporal variability is modeled by means of hidden climate indices (HCIs) that are extracted from observed variables. This is to be contrasted with the usual approach using predefined standard climate indices (SCIs) for this purpose. In the second level of the model, the HCIs and their effects are assumed to follow temporal and spatial Gaussian processes, respectively. Both intervariable and intersite dependencies are induced by the strong effect of common HCIs. The flexibility of the framework is illustrated with a case study in Southeast Australia aimed at modeling “hot‐and‐dry” summer conditions. It involves three physical variables (streamflow, precipitation, and temperature) measured on three distinct station networks, with varying data availability and representing hundreds of sites in total. The HCI model delivers reliable and sharp time‐varying distributions for in idual variables and sites. In addition, it adequately reproduces intervariable and intersite dependencies, whereas a corresponding SCI model (where hidden climate indices are replaced with standard ones) strongly underestimates them. It is finally suggested that HCI models may be used as downscaling tools to estimate the joint distribution of several variables at many stations from climate models or reanalyzes.
Publisher: American Society of Civil Engineers (ASCE)
Date: 2007
Start Date: 02-2016
End Date: 06-2020
Amount: $156,905.00
Funder: Australian Research Council
View Funded Activity