ORCID Profile
0000-0002-3811-5845
Current Organisations
University of Adelaide
,
Australian Bureau of Meteorology
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Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10624
Abstract: Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. & #8216 Seamless& #8217 probabilistic forecasts, i.e., forecasts that are reliable and sharp over a range of lead times (1-30 days) and aggregation time scales (e.g. daily to monthly) are of clear practical interest. However, existing forecast products are often & #8216 non-seamless& #8217 , i.e., developed and applied for a single time scale and lead time (e.g. 1 month ahead). If seamless forecasts are to be a viable replacement for existing & #8216 non-seamless& #8217 forecasts, it is important that they offer (at least) similar predictive performance at the time scale of the non-seamless forecast.This study compares forecasts from two probabilistic streamflow post-processing (QPP) models: the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model and the more traditional (non-seamless) monthly QPP model used in the Australian Bureau of Meteorology& #8217 s Dynamic Forecasting System. Streamflow forecasts from both post-processing models are generated for 11 Australian catchments, using the GR4J hydrological model and pre-processed rainfall forecasts from the ACCESS-S numerical weather prediction model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias and CRPS skill score), we find that the seamless MuTHRE model achieves essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). As such, MuTHRE provides the capability of & #8216 seamless& #8217 daily streamflow forecasts with no loss of performance at the monthly scale & #8211 the modeller can proverbially & #8216 have their cake and eat it too& #8217 . This finding demonstrates that seamless forecasting technologies, such as the MuTHRE post-processing model, are not only viable, but a preferred choice for future research development and practical adoption in streamflow forecasting.
Publisher: Copernicus GmbH
Date: 23-02-2023
Abstract: Abstract. Streamflow forecasts have the potential to improve water resource decision-making, but their economic value has not been widely evaluated, since current forecast value methods have critical limitations. The ubiquitous measure for forecast value, the relative economic value (REV) metric, is limited to binary decisions, the cost–loss economic model, and risk-neutral decision-makers (users). Expected utility theory can flexibly model more real-world decisions, but its application in forecasting has been limited and the findings are difficult to compare with those from REV. In this study, a new metric for evaluating forecast value, relative utility value (RUV), is developed using expected utility theory. RUV has the same interpretation as REV, which enables a systematic comparison of results, but RUV is more flexible and better represents real-world decisions because more aspects of the decision context are user-defined. In addition, when specific assumptions are imposed, it is shown that REV and RUV are equivalent, hence REV can be considered a special case of the more general RUV. The key differences and similarities between REV and RUV are highlighted, with a set of experiments performed to explore the sensitivity of RUV to different decision contexts, such as different decision types (binary, multi-categorical, and continuous-flow decisions), various levels of user risk aversion, and varying the relative expense of mitigation. These experiments use an illustrative case study of probabilistic subseasonal streamflow forecasts (with lead times up to 30 d) in a catchment in the southern Murray–Darling Basin of Australia. The key outcomes of the experiments are (i) choice of decision type has an impact on forecast value, hence it is critically important to match the decision type with the real-world decision (ii) forecasts are typically more valuable for risk averse users, but the impact varies depending on the decision context and (iii) risk aversion impact is mediated by how large the potential damages are for a given decision. All outcomes were found to critically depend on the relative expense of mitigation (i.e. the cost of action to mitigate damages relative to the magnitude of damages). In particular, for users with relatively high expense of mitigation, using an unrealistic binary decision to approximate a multi-categorical or continuous-flow decision gives a misleading measure of forecast value for forecasts longer than 1 week lead time. These findings highlight the importance of the flexibility of RUV, which enable evaluation of forecast value to be tailored to specific decisions/users and hence better capture real-world decision-making. RUV complements forecast verification and enables assessment of forecast systems through the lens of user impact.
Publisher: Copernicus GmbH
Date: 21-03-2022
DOI: 10.5194/HESS-2022-65
Abstract: Abstract. Forecasts have the potential to improve decision-making but have not been widely evaluated because current forecast value methods have critical limitations. The ubiquitous Relative Economic Value (REV) metric is limited to binary decisions, cost-loss economic model, and risk neutral decision-makers. Expected Utility Theory can flexibly model more real-world decisions, but its application in forecasting has been limited and the findings are difficult to compare with those from REV. A new metric, Relative Utility Value (RUV), is developed using Expected Utility Theory. RUV has the same interpretation as REV which enables a systematic comparison of results, but RUV is more flexible and able to handle a wider range of real-world decisions because all aspects of the decision-context are user-defined. In addition, when specific assumptions are imposed it is shown that REV and RUV are equivalent. We demonstrate the key differences and similarities between the methods with a case study using probabilistic subseasonal streamflow forecasts in a catchment in the southern Murray-Darling Basin of Australia. The ensemble forecasts were more valuable than a reference climatology for all lead-times (max 30 days), decision types (binary, multi-categorical, and continuous-flow), and levels of risk aversion for most decision-makers. Beyond the second week however, decision-makers who were highly exposed to damages should use the reference climatology for the binary decision, and forecasts for the multi-categorical and continuous-flow decision. Risk aversion impact was governed by the relationship between the decision thresholds and the damage function, leading to a mixed impact across the different decision-types. The generality of RUV makes it applicable to any domain where forecast information is used for making decisions, and the flexibility enables forecast assessment tailored to specific decisions and decision-makers. It complements forecast verification and enables assessment of forecast systems through the lens of customer impact.
Publisher: American Geophysical Union (AGU)
Date: 11-2020
DOI: 10.1029/2019WR026979
Publisher: Microbiology Society
Date: 11-2014
Abstract: Fungaemia caused by Malassezia spp. in hospitalized patients requires prompt and appropriate therapy, but standard methods for the definition of the in vitro antifungal susceptibility have not been established yet. In this study, the in vitro susceptibility of Malassezia furfur from bloodstream infections (BSIs) to hotericin B (AMB), fluconazole (FLC), itraconazole (ITC), posaconazole (POS) and voriconazole (VRC) was assessed using the broth microdilution (BMD) method of the Clinical and Laboratory Standards Institute (CLSI) with different media such as modified Sabouraud dextrose broth (SDB), RPMI and Christensen’s urea broth (CUB). Optimal broth media that allow sufficient growth of M. furfur , and produce reliable and reproducible MICs using the CLSI BMD protocol were assessed. Thirty-six M. furfur isolates collected from BSIs of patients before and during AMB therapy, and receiving FLC prophylaxis, were tested. A good growth of M. furfur was observed in RPMI, CUB and SDB at 32 °C for 48 and 72 h. No statistically significant differences were detected between the MIC values registered after 48 and 72 h incubation. ITC, POS and VRC displayed lower MICs than FLC and AMB. These last two antifungal drugs showed higher and lower MICs, respectively, when the isolates were tested in SDB. SDB is the only medium in which it is possible to detect isolates with high FLC MICs in patients receiving FLC prophylaxis. A large number of isolates showed high AMB MIC values regardless of the media used. In conclusion, SDB might be suitable to determine triazole susceptibility. However, the media, the drug formulation or the breakpoints herein applied might not be useful for assessing the AMB susceptibility of M. furfur from BSIs.
Publisher: American Geophysical Union (AGU)
Date: 11-2021
DOI: 10.1029/2020WR029317
Abstract: Sub‐seasonal streamflow forecasts are important for a range of water resource management applications, with a distinct practical interest in forecasts of high flows (e.g., for managing flood events) and low flows (e.g., for managing environmental flows). Despite this interest, differences in forecast performance for high and low flow events are not routinely investigated. Our study reveals that while forecasts evaluated over the full flow range can appear reliable, stratification into high/low flow ranges highlights significant under/over‐estimation of forecast uncertainty, respectively. We overcome this challenge by introducing a flow‐dependent (FD) nonparametric component into a post‐processing model of hydrological forecasting errors, the Multi‐Temporal Hydrological Residual Error (MuTHRE) model, yielding the MuTHRE‐FD model. The MuTHRE and MuTHRE‐FD models are compared in a case study with 11 Australian catchments, the GR4J rainfall‐runoff model and post‐processed rainfall forecasts from ACCESS‐S. Through its improved treatment of flow‐dependence, the MuTHRE‐FD model achieves practically significant improvements over the original MuTHRE model in the reliability of forecasted cumulative volumes for: (a) high flows out to 7 days (b) low flows out to 2 days and (c) mid flows for majority of lead times. The new MuTHRE‐FD model provides seamless sub‐seasonal forecasts with high quality performance for both high and low flows over a range of lead times. This improvement provides forecast users with increased confidence in using sub‐seasonal forecasts across a wide range of applications.
Publisher: Copernicus GmbH
Date: 21-03-2022
Publisher: Copernicus GmbH
Date: 18-01-2016
Abstract: Abstract. Streamflow variability and trends in Australia were investigated for 222 high quality stream gauging stations having 30 years or more continuous unregulated streamflow records. Trend analysis identified seasonal, inter-annual and decadal variability, long-term monotonic trends, and step changes in streamflow. Trends were determined for annual total flow, baseflow, seasonal flows, daily maximum flow, and three quantiles of daily flow. A distinct pattern of spatial and temporal variation in streamflow was evident across different hydroclimatic regions in Australia. Most of the stations in south-eastern Australia spread across New South Wales and Victoria showed a significant decreasing trend in annual streamflow, while increasing trends were observed in the Northern Territory and the north-west of Western Australia. No trend was observed for stations in the central region of Australia. The findings from step change analysis demonstrated evidence of changes in hydrologic responses consistent with observed changes in climate over the past decades. For ex le, in the Murray-Darling Basin 51 out of 75 stations were identified with step changes of significant reduction in annual streamflow during the middle to late 1990s, when relatively dry years were recorded across the area. Overall, the Hydrologic Reference Stations (HRS) serve as "living gauges" for streamflow monitoring and changes in long-term water availability inferred from observed datasets. A wealth of freely downloadable hydrologic data is provided at the HRS web portal including annual, seasonal, monthly and daily streamflow data, as well as trend analysis products, and relevant site information.
Publisher: Copernicus GmbH
Date: 26-09-2016
DOI: 10.5194/HESS-20-3947-2016
Abstract: Abstract. Streamflow variability and trends in Australia were investigated for 222 high-quality stream gauging stations having 30 years or more continuous unregulated streamflow records. Trend analysis identified seasonal, inter-annual and decadal variability, long-term monotonic trends and step changes in streamflow. Trends were determined for annual total flow, baseflow, seasonal flows, daily maximum flow and three quantiles of daily flow. A distinct pattern of spatial and temporal variation in streamflow was evident across different hydroclimatic regions in Australia. Most of the stations in southeastern Australia spread across New South Wales and Victoria showed a significant decreasing trend in annual streamflow, while increasing trends were retained within the northern part of the continent. No strong evidence of significant trend was observed for stations in the central region of Australia and northern Queensland. The findings from step change analysis demonstrated evidence of changes in hydrologic responses consistent with observed changes in climate over the past decades. For ex le, in the Murray–Darling Basin, 51 out of 75 stations were identified with step changes of significant reduction in annual streamflow during the middle to late 1990s, when relatively dry years were recorded across the area. Overall, the hydrologic reference stations (HRSs) serve as critically important gauges for streamflow monitoring and changes in long-term water availability inferred from observed datasets. A wealth of freely downloadable hydrologic data is provided at the HRS web portal including annual, seasonal, monthly and daily streamflow data, as well as trend analysis products and relevant site information.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13084
Abstract: & & Forecasts have the potential to improve decision-making but have not been widely evaluated because current forecast value methods have critical limitations. The ubiquitous Relative Economic Value (REV) is limited to binary decisions, cost-loss economic model, and risk neutral decision-makers. Expected Utility Theory can flexibly model more real-world decisions, but its application in forecasting has been limited and the findings are difficult to compare. To enable a systematic comparison of these methods a new metric, Relative Utility Value (RUV), is developed based on Expected Utility Theory. It has the same interpretation as REV but is more flexible and able to handle a wider range of real-world decisions because all aspects of the decision-context are user-defined. Also, when specific assumptions are imposed it is shown that REV and RUV are equivalent. We demonstrate the key differences and similarities between the methods with a case study using probabilistic subseasonal streamflow forecasts in a catchment in the Southern Murray-Darling Basin of Australia. This showed that for most decision-makers the ensemble forecasts were more valuable than a reference climatology for all lead-times (max 30 days), decision types (binary, multi-categorical, and continuous-flow), and levels of risk aversion. Risk aversion had a mixed impact across the different decision-types and the key driver was found to be the specific decision thresholds relative to the damage function. The generality of RUV makes it applicable to any domain where forecast information is used for making decisions, and the flexibility enables forecast assessment tailored to specific decisions and decision-makers. It complements forecast verification and enables assessment of forecast systems through the lens of customer impact.& &
No related grants have been discovered for Richard Laugesen.