ORCID Profile
0000-0003-4023-6061
Current Organisation
University of Adelaide
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Water Resources Engineering | Surfacewater Hydrology | Civil Engineering | Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.) | Physical Geography and Environmental Geoscience | Climate Change Processes |
ANZSRC Socio-Economic Objective (SEO)
Natural Hazards in Fresh, Ground and Surface Water Environments | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Water Allocation and Quantification | Urban and Industrial Water Management | Natural Hazards in Urban and Industrial Environments | Expanding Knowledge in Engineering | Farmland, Arable Cropland and Permanent Cropland Water Management
Related Links
Publications
Natural hazards in Australia: floods
Publisher: Springer Science and Business Media LLC
Date: 11-05-2016
A global analysis of the asymmetric effect of ENSO on extreme precipitation
Publisher: Elsevier BV
Date: 11-2015
Quantifying the dependence between extreme rainfall and storm surge in the coastal zone
Publisher: Elsevier BV
Date: 11-2013
Detection of continental-scale intensification of hourly rainfall extremes
Publisher: Springer Science and Business Media LLC
Date: 30-07-2018
A strategy for diagnosing and interpreting hydrological model nonstationarity
Publisher: American Geophysical Union (AGU)
Date: 06-2014
DOI: 10.1002/2013WR014719
Onkaparinga Catchment Climate Change Modelling
Publisher: Figshare
Date: 2017
Temperature‐extreme precipitation scaling: a two‐way causality?
Publisher: Wiley
Date: 05-12-2017
DOI: 10.1002/JOC.5370
Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017EF000649
Application of the design variable method to estimate coastal flood risk
Publisher: Wiley
Date: 07-2015
DOI: 10.1111/JFR3.12180
Impact of evapotranspiration process representation on runoff projections from conceptual rainfall‐runoff models
Publisher: American Geophysical Union (AGU)
Date: 2017
DOI: 10.1002/2016WR019627
Observed Trends in Global Indicators of Mean and Extreme Streamflow
Publisher: American Geophysical Union (AGU)
Date: 23-01-2019
DOI: 10.1029/2018GL079725
An empirical investigation into the effect of antecedent precipitation on flood volume
Publisher: Elsevier BV
Date: 12-2018
Compounding Heatwave‐Extreme Rainfall Events Driven by Fronts, High Moisture, and Atmospheric Instability
Publisher: American Geophysical Union (AGU)
Date: 28-10-2023
DOI: 10.1029/2023JD038761
Farmers' perceptions of and responses to environmental change in southwest coastal Bangladesh
Publisher: Wiley
Date: 28-08-2017
DOI: 10.1111/APV.12165
Temporally compounding heatwave–heavy rainfall events in Australia
Publisher: Wiley
Date: 30-09-2022
DOI: 10.1002/JOC.7872
Abstract: 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.
Generating realistic perturbed hydrometeorological time series to inform scenario-neutral climate impact assessments
Publisher: Elsevier BV
Date: 09-2019
Global Increasing Trends in Annual Maximum Daily Precipitation
Publisher: American Meteorological Society
Date: 31-05-2013
DOI: 10.1175/JCLI-D-12-00502.1
Abstract: 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.
Preliminary Analysis of Trends in Australian Flood Data
Publisher: American Society of Civil Engineers
Date: 14-05-2010
DOI: 10.1061/41114(371)14
Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations
Publisher: American Geophysical Union (AGU)
Date: 03-2018
DOI: 10.1002/2017WR022231
Natural hazards in Australia: sea level and coastal extremes
Publisher: Springer Science and Business Media LLC
Date: 24-03-2016
Efficient joint probability analysis of flood risk
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.
Addressing Climatic Non-Stationarity in the Assessment of Flood Risk
Publisher: Informa UK Limited
Date: 2010
An R package for modelling actual, potential and reference evapotranspiration
Publisher: Elsevier BV
Date: 04-2016
An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales
Publisher: American Meteorological Society
Date: 15-07-2011
Abstract: Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and litude 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 in idual 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.
A hybrid framework for quantifying the influence of data in hydrological model calibration
Publisher: Elsevier BV
Date: 06-2018
An inverse approach to perturb historical rainfall data for scenario-neutral climate impact studies
Publisher: Elsevier BV
Date: 2018
A programming tool to generate multi-site daily rainfall using a two-stage semi parametric model
Publisher: Elsevier BV
Date: 2015
Influential point detection diagnostics in the context of hydrological model calibration
Publisher: Elsevier BV
Date: 08-2015
Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach
Publisher: American Geophysical Union (AGU)
Date: 2012
DOI: 10.1029/2011WR010490
Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis
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.
The Global Streamflow Indices and Metadata Archive - Part 1: Station catalog and Catchment boundary
Publisher: No publisher found
Date: 2018
Investigating the mechanisms of diurnal rainfall variability using a regional climate model
Publisher: American Meteorological Society
Date: 24-04-2012
DOI: 10.1175/JCLI-D-11-00616.1
Abstract: 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.
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Publisher: Copernicus GmbH
Date: 19-04-2017
DOI: 10.5194/HESS-21-2107-2017
Abstract: Abstract. Assessing the factors that have an impact on potential evapotranspiration (PET) sensitivity to changes in different climate variables is critical to understanding the possible implications of climatic changes on the catchment water balance. Using a global sensitivity analysis, this study assessed the implications of baseline climate conditions on the sensitivity of PET to a large range of plausible changes in temperature (T), relative humidity (RH), solar radiation (Rs) and wind speed (uz). The analysis was conducted at 30 Australian locations representing different climatic zones, using the Penman–Monteith and Priestley–Taylor PET models. Results from both models suggest that the baseline climate can have a substantial impact on overall PET sensitivity. In particular, approximately 2-fold greater changes in PET were observed in cool-climate energy-limited locations compared to other locations in Australia, indicating the potential for elevated water loss as a result of increasing actual evapotranspiration (AET) in these locations. The two PET models consistently indicated temperature to be the most important variable for PET, but showed large differences in the relative importance of the remaining climate variables. In particular for the Penman–Monteith model, wind and relative humidity were the second-most important variables for dry and humid catchments, respectively, whereas for the Priestley–Taylor model solar radiation was the second-most important variable, with the greatest influence in warmer catchments. This information can be useful to inform the selection of suitable PET models to estimate future PET for different climate conditions, providing evidence on both the structural plausibility and input uncertainty for the alternative models.
Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment
Publisher: Elsevier BV
Date: 11-2017
Interpreting variability in global SST data using independent component analysis and principal component analysis
Publisher: Wiley
Date: 2010
DOI: 10.1002/JOC.1888
The Global Streamflow Indices and Metadata Archive (GSIM) - Part 2: Time Series Indices and Homogeneity Assessment
Publisher: No publisher found
Date: 2018
A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate
Publisher: American Geophysical Union (AGU)
Date: 09-2016
DOI: 10.1002/2015WR018253
A Hidden Climate Indices Modeling Framework for Multivariable Space‐Time Data
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.
Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk
Publisher: American Geophysical Union (AGU)
Date: 03-2014
DOI: 10.1002/2013WR014616
Continuous rainfall simulation: 1. A regionalized subdaily disaggregation approach
Publisher: American Geophysical Union (AGU)
Date: 2012
DOI: 10.1029/2011WR010489
Evaluating regional climate models for simulating sub-daily rainfall extremes
Publisher: Springer Science and Business Media LLC
Date: 28-11-2015
Opposing local precipitation extremes
Publisher: Springer Science and Business Media LLC
Date: 23-04-2015
DOI: 10.1038/NCLIMATE2579
Dominant modes of interannual variability in Australian rainfall analyzed using wavelets
Publisher: American Geophysical Union (AGU)
Date: 02-03-2006
DOI: 10.1029/2005JD005996
A compound event framework for understanding extreme impacts
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
Impact of the El Niño–Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode on Daily to Subdaily Rainfall Characteristics in East Australia
Publisher: American Meteorological Society
Date: 05-2012
Abstract: 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 in idual 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 s le 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.
Hydroclimatology: perspectives and applications by M.L. Shelton
Publisher: SAGE Publications
Date: 04-2009
Impact of Scenario Selection on Robustness
Publisher: American Geophysical Union (AGU)
Date: 27-08-2020
DOI: 10.1029/2019WR026515
Abstract: Multiple plausible future scenarios are being used increasingly in preference to a single deterministic or probabilistic prediction of the future in the long‐term planning of water resources systems. These scenarios enable the determination of the robustness of a system—the consideration of performance across a range of plausible futures—and allow an assessment of which possible future system configurations result in a greater level of robustness. There are many approaches to selecting scenarios, and previous studies have observed that the choice of scenarios might affect the estimated robustness of the system. However, these observations have been anecdotal and qualitative. This paper develops a systematic, quantitative methodology for exploring the influence of scenario selection on the robustness and the ranking of decision alternatives. The methodology is illustrated on the Lake Problem. The quantitative results obtained confirm the qualitative observations of previous works, showing that the selection of scenarios is important, as it has a large influence on the robustness value calculated for each decision alternative. However, we show that it has a relatively small influence on how those decision alternatives are ranked. This implies that despite the difference in robustness values, similar decision outcomes will be reached in this case study, regardless of the basis on which the scenarios are obtained. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics.
What determines students’ expectations and preferences in university teaching and learning? An instrumental variable approach
Publisher: Elsevier BV
Date: 12-2017
The INTENSE project: using observations and models to understand the past, present and future of sub-daily rainfall extremes
Publisher: Copernicus GmbH
Date: 19-06-2018
Abstract: Abstract. 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.
Changes to the temporal distribution of daily precipitation
Publisher: American Geophysical Union (AGU)
Date: 28-12-2014
DOI: 10.1002/2014GL062156
Weather Types and Hourly to Multiday Rainfall Characteristics in Tropical Australia
Publisher: American Meteorological Society
Date: 14-06-2019
Abstract: 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.
Multivariate streamflow forecasting using independent component analysis
Publisher: American Geophysical Union (AGU)
Date: 02-2008
DOI: 10.1029/2007WR006104
Future changes to the intensity and frequency of short-duration extreme rainfall
Publisher: American Geophysical Union (AGU)
Date: 27-08-2014
DOI: 10.1002/2014RG000464
Mapping Dependence Between Extreme Rainfall and Storm Surge
Publisher: American Geophysical Union (AGU)
Date: 04-2018
DOI: 10.1002/2017JC013472
An Upper Limit to Seasonal Rainfall Predictability?
Publisher: American Meteorological Society
Date: 15-06-2010
Abstract: 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 ided 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.
Modeling multivariable hydrological series: Principal component analysis or independent component analysis?
Publisher: American Geophysical Union (AGU)
Date: 06-2007
DOI: 10.1029/2006WR005617
Reply to comments on “Temperature‐extreme precipitation scaling: a two‐way causality?”
Publisher: Wiley
Date: 17-08-2018
DOI: 10.1002/JOC.5799
Author Correction: Future climate risk from compound events
Publisher: Springer Science and Business Media LLC
Date: 20-06-2018
What determines students' study practices in higher education? An instrumental variable approach
Publisher: Elsevier BV
Date: 09-2016
A global-scale investigation of trends in annual maximum streamflow
Publisher: Elsevier BV
Date: 09-2017
Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches
Publisher: Wiley
Date: 16-05-2022
DOI: 10.1002/WAT2.1599
Abstract: Wildfires elicit a ersity 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 Awareness Science of Water Hydrological Processes Science of Water Water and Environmental Change
Evaluating the non-stationarity of Australian annual maximum flood
Publisher: Elsevier BV
Date: 06-2013
Why continuous simulation? The role of antecedent moisture in design flood estimation
Publisher: American Geophysical Union (AGU)
Date: 06-2012
DOI: 10.1029/2011WR010997
Probabilistic Estimation of Multivariate Streamflow Using Independent Component Analysis and Climate Information
Publisher: American Meteorological Society
Date: 12-2009
Abstract: 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.
Future climate risk from compound events
Publisher: Springer Science and Business Media LLC
Date: 14-05-2018
Dependence properties of spatial rainfall extremes and areal reduction factors
Publisher: Elsevier BV
Date: 10-2018
Spatial and temporal variability in seasonal snow density
Publisher: Elsevier BV
Date: 03-2013
Constraining continuous rainfall simulations for derived design flood estimation
Publisher: Elsevier BV
Date: 11-2016
Reduced spatial extent of extreme storms at higher temperatures
Publisher: American Geophysical Union (AGU)
Date: 25-04-2016
DOI: 10.1002/2016GL068509
The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment
Publisher: Copernicus GmbH
Date: 17-04-2018
Abstract: Abstract. 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 in idual 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 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.
A conditional disaggregation algorithm for generating fine time-scale rainfall data in a warmer climate
Publisher: Elsevier BV
Date: 02-2013
Natural hazards in Australia: droughts
Publisher: Springer Science and Business Media LLC
Date: 19-09-2016
The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata
Publisher: Copernicus GmbH
Date: 17-04-2018
Abstract: Abstract. 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-to-access streamflow databases (seven national databases and five international collections). It also describes the development of three metadata products (freely available at 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.
Introduction to the special issue: historical and projected climatic changes to Australian natural hazards
Publisher: Springer Science and Business Media LLC
Date: 21-10-2016
The ENSO–Precipitation Teleconnection and Its Modulation by the Interdecadal Pacific Oscillation
Publisher: American Meteorological Society
Date: 11-06-2015
DOI: 10.1175/JCLI-D-14-00722.1
Abstract: 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 s le stratification and model misspecification. Caution is therefore required when using smoothed climate indices to model or explain low-frequency variability in precipitation.
Observed relationships between extreme sub‐daily precipitation, surface temperature, and relative humidity
Publisher: American Geophysical Union (AGU)
Date: 11-2010
DOI: 10.1029/2010GL045081
Detection of non-stationarity in precipitation extremes using a max-stable process model
Publisher: Elsevier BV
Date: 08-2011
Floods and Heavy Precipitation at the Global Scale: 100‐Year Analysis and 180‐Year Reconstruction
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.
A generalised approach for identifying influential data in hydrological modelling
Publisher: Elsevier BV
Date: 2019
A basis function approach for exploring the seasonal and spatial features of storm surge events
Publisher: American Geophysical Union (AGU)
Date: 18-07-2017
DOI: 10.1002/2017GL074357
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