ORCID Profile
0000-0002-4930-2638
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Water Resources Engineering | Surfacewater Hydrology | Civil Engineering |
Water Allocation and Quantification | Management of Water Consumption by Information and Communication Services | Water Services and Utilities
Publisher: American Geophysical Union (AGU)
Date: 10-2016
DOI: 10.1002/2016WR019193
Publisher: Copernicus GmbH
Date: 20-01-2016
Abstract: Abstract. This study develops a new error modelling method for short-term and real-time streamflow forecasting, called error reduction and representat ion in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias-correction, Stage 3 applies an autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. For a range of catchments, the forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. In particular, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. While the method produces ensemble forecasts, ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4846
Abstract: For streamflow forecasting, calibration of ensemble numerical weather prediction (NWP) models has long been considered a necessary evil. Necessary, because NWP forecasts are usually too biased to force calibrated hydrological models, they often produce unreliable ensembles and may produce forecasts that are less accurate than simple climatology at longer lead times. Evil, because calibration adds complexity to any forecasting system and the calibration process destroys spatial, temporal and inter-variable correlations in the ensemble, which then must be reconstructed in various and usually unsatisfying ways. As ensemble NWPs improve, the degree to which calibration is & #8216 necessary& #8217 declines.Here we investigate recent versions of two ensemble NWP models & #8211 the European Centre for Medium-range Weather Forecasts ensemble NWP (ECMWF-ens) and the Bureau of Meteorology& #8217 s Australian Community Climate and Earth-System Simulator Global Ensemble (ACCESS-GE) NWP. The models are tested over Tasmania, where CSIRO is working with Hydro Tasmania, Australia& #8217 s largest generator of hydropower, to establish new ensemble streamflow forecasting systems. Tasmania is mountainous and temperate and features strong rainfall gradients. We apply an existing calibration method & #8211 the Catchment-scale Hydrological Precipitation Processor (CHyPP) & #8211 which uses a Bayesian Joint Probability model to calibrate ensemble precipitation forecasts.We show that CHyPP improves reliability in both the ECMWF-ens and ACCESS-GE ensembles, but these improvements come at the cost of a slight reduction in skill at short lead times. Uncalibrated ACCESS-GE forecasts generally produce more biased and less reliable forecasts than ECMWF-ens, and we conclude that calibration is necessary for the ACCESS-GE model, both to reduce biases and improve reliability. However, the improvements in bias from calibrating the ECMWF-ens are negligible in some catchments, with the main benefit being improved reliability at longer lead times. This brings into question the need for calibration of the ECMWF-ens model with CHyPP. We note that these findings may not hold outside the Tasmanian catchments tested, where high resolution ensemble NWP forecasts generally perform well. We discuss the implications of these findings with respect to streamflow forecasts.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 07-2023
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/WRCR.20445
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 02-2016
Publisher: American Meteorological Society
Date: 05-2017
Abstract: GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called “coherence.” This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for postprocessing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative postprocessing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach.
Publisher: Copernicus GmbH
Date: 30-11-2017
DOI: 10.5194/HESS-21-6007-2017
Abstract: Abstract. Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple res led historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to res led historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.
Publisher: Copernicus GmbH
Date: 07-05-2012
DOI: 10.5194/HESS-16-1287-2012
Abstract: Abstract. Changes to streamflows caused by climate change may have major impacts on the management of water for hydro-electricity generation and agriculture in Tasmania, Australia. We describe changes to Tasmanian surface water availability from 1961–1990 to 2070–2099 using high-resolution simulations. Six fine-scale (∼10 km2) simulations of daily rainfall and potential evapotranspiration are generated with the CSIRO Conformal Cubic Atmospheric Model (CCAM), a variable-resolution regional climate model (RCM). These variables are bias-corrected with quantile mapping and used as direct inputs to the hydrological models AWBM, IHACRES, Sacramento, SIMHYD and SMAR-G to project streamflows. The performance of the hydrological models is assessed against 86 streamflow gauges across Tasmania. The SIMHYD model is the least biased (median bias = −3%) while IHACRES has the largest bias (median bias = −22%). We find the hydrological models that best simulate observed streamflows produce similar streamflow projections. There is much greater variation in projections between RCM simulations than between hydrological models. Marked decreases of up to 30% are projected for annual runoff in central Tasmania, while runoff is generally projected to increase in the east. Daily streamflow variability is projected to increase for most of Tasmania, consistent with increases in rainfall intensity. Inter-annual variability of streamflows is projected to increase across most of Tasmania. This is the first major Australian study to use high-resolution bias-corrected rainfall and potential evapotranspiration projections as direct inputs to hydrological models. Our study shows that these simulations are capable of producing realistic streamflows, allowing for increased confidence in assessing future changes to surface water variability.
Publisher: American Geophysical Union (AGU)
Date: 2020
DOI: 10.1029/2019WR026128
Abstract: Flow simulations of ephemeral rivers are often highly uncertain. Therefore, error models that can reliably quantify predictive uncertainty are particularly important. Existing error models are incapable of producing predictive distributions that contain % zeros, making them unsuitable for use in highly ephemeral rivers. We propose a new method to produce reliable predictions in highly ephemeral rivers. The method uses data censoring of observed and simulated flow to estimate model parameters by maximum likelihood. Predictive uncertainty is conditioned on the simulation in such a way that it can generate % zeros. Our method allows the setting of a censoring threshold above zero. Many conceptual hydrological models can only approach, but never equal, zero. For these hydrological models, we show that setting a censoring threshold slightly above zero is required to produce reliable predictive distributions in highly ephemeral catchments. Our new method allows reliable predictions to be generated even in highly ephemeral catchments.
Publisher: IOP Publishing
Date: 08-2010
Publisher: American Geophysical Union (AGU)
Date: 19-06-2014
DOI: 10.1002/2013JD021162
Publisher: World Scientific
Date: 21-12-2018
Publisher: Copernicus GmbH
Date: 28-09-2017
DOI: 10.5194/HESS-21-4841-2017
Abstract: Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap res ling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.
Publisher: Elsevier BV
Date: 11-2014
Publisher: Copernicus GmbH
Date: 29-09-2022
DOI: 10.5194/HESS-26-4801-2022
Abstract: Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7 d ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual rainfall–runoff model, GR4H (hourly), and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall forecasts, and the error reduction and representation in stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation. A bootstrapping block size of 1 month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.
Publisher: Copernicus GmbH
Date: 06-01-2015
Abstract: Abstract. For streamflow forecasting, rainfall–runoff models are often augmented with updating procedures that correct forecasts based on the latest available streamflow observations of streamflow. A popular approach for updating forecasts is autoregressive (AR) models, which exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of forecasts. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction usually occurs when streamflow is rapidly rising. In addition, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as forecasts should rely as much as possible on the base hydrological model, with updating only used to correct minor errors. To overcome the adverse effects of the conventional AR models, a restricted AR model applied to normalised errors is introduced. We show that the new model reduces over-correction and improves the performance of the base hydrological model considerably.
Publisher: Wiley
Date: 14-09-2013
DOI: 10.1002/JOC.3830
Publisher: American Meteorological Society
Date: 07-2020
Abstract: Calibrated high-temporal-resolution precipitation forecasts are desirable for a range of applications, for ex le, flood prediction in fast-rising rivers. However, high-temporal-resolution precipitation observations may not be available to support the establishment of calibration methods, particularly in regions with low population density or in developing countries. We present a new method to produce calibrated hourly precipitation ensemble forecasts from daily observations. Precipitation forecasts are taken from a high-resolution convective-scale numerical weather prediction (NWP) model run at the hourly time step. We conduct three experiments to develop the new calibration method: (i) calibrate daily precipitation totals and disaggregate daily forecasts to hourly (ii) generate pseudohourly observations from daily precipitation observations, and use these to calibrate hourly precipitation forecasts and (iii) combine aspects of (i) and (ii). In all experiments, we use the existing Bayesian joint probability model to calibrate the forecasts and the well-known Schaake shuffle technique to instill realistic spatial and temporal correlations in the ensembles. As hourly observations are not available, we use hourly patterns from the NWP as the template for the Schaake shuffle. The daily member matching method (DMM), method (iii), produces the best-performing ensemble precipitation forecasts over a range of metrics for forecast accuracy, bias, and reliability. The DMM method performs very similarly to the ideal case where hourly observations are available to calibrate forecasts. Overall, valuable spatial and temporal information from the forecast can be extracted for calibration with daily data, with a slight trade-off between forecast bias and reliability.
Publisher: Springer Science and Business Media LLC
Date: 09-06-2012
Publisher: American Meteorological Society
Date: 31-08-2015
Abstract: This paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach. Calibrated QPFs are produced for 10 Australian catchments that cover a wide range of climatic conditions and hydrological characteristics. The calibrated QPFs are bias free, contain smaller forecast errors than that of the raw QPFs, reliably quantify the forecast uncertainty at a range of lead times, and successfully discriminate common and rare events of precipitation occurrences at shorter lead times. The postprocessing method is able to instill realistic within-catchment spatial variability in the QPFs, which is crucial for accurate and reliable streamflow forecasting.
Publisher: Copernicus GmbH
Date: 04-01-2017
Abstract: Abstract. Considerable research effort has recently been directed at improving ensemble seasonal streamflow forecasts, and transferring these methods into operational services. This paper examines the value of forecasts when applied to a range hypothetical reservoirs. We compare forecast-informed reservoir operations with operations based on more traditional control rules established from historical records. Using synthetic forecasts, we show that forecast-informed operations can improve reservoir operations where forecasts are accurate, but that this benefit is far more likely to occur in reservoirs operated for continually adjusted objectives (e.g., for hydropower generation) than compared with those operated for emergency response objectives (e.g., urban water supply, for which water use restrictions are seldom imposed). We then test whether a modern experimental forecasting system — called Forecast Guided Stochastic Scenarios (FoGSS) — can benefit a wide range of reservoirs operated for emergency response objectives. FoGSS-informed operations improved reservoir operations in a large majority of the reservoirs tested. In the catchments where FoGSS forecasts sometimes failed to improve operations over conventional control rules, we show that this is partly due to less consistently skilful forecasts at the timing during critical decisions are made.
Publisher: Copernicus GmbH
Date: 13-02-2014
DOI: 10.5194/NHESS-14-219-2014
Abstract: Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments ( 2000 km2) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.
Publisher: Elsevier BV
Date: 12-2017
Publisher: CSIRO Publishing
Date: 2011
DOI: 10.1071/MF10308
Abstract: Freshwater environments and their fishes are particularly vulnerable to climate change because the persistence and quality of aquatic habitat depend heavily on climatic and hydrologic regimes. In Australia, projections indicate that the rate and magnitude of climate change will vary across the continent. We review the likely effects of these changes on Australian freshwater fishes across geographic regions encompassing a ersity of habitats and climatic variability. Commonalities in the predicted implications of climate change on fish included habitat loss and fragmentation, surpassing of physiological tolerances and spread of alien species. Existing anthropogenic stressors in more developed regions are likely to compound these impacts because of the already reduced resilience of fish assemblages. Many Australian freshwater fish species are adapted to variable or unpredictable flow conditions and, in some cases, this evolutionary history may confer resistance or resilience to the impacts of climate change. However, the rate and magnitude of projected change will outpace the adaptive capacities of many species. Climate change therefore seriously threatens the persistence of many of Australia’s freshwater fish species, especially of those with limited ranges or specific habitat requirements, or of those that are already occurring close to physiological tolerance limits. Human responses to climate change should be proactive and focus on maintaining population resilience through the protection of habitat, mitigation of current anthropogenic stressors, adequate planning and provisioning of environmental flows and the consideration of more interventionist options such as managed translocations.
Publisher: Copernicus GmbH
Date: 04-03-2022
DOI: 10.5194/HESS-2022-72
Abstract: Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7-day ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual hourly rainfall-runoff model, GR4H (hourly) and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical Catchment Hydrologic Pre-Processor (CHyPP) is used for calibrating rainfall forecasts, and the Error Reduction and Representation In Stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation a bootstrapping block size of one month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 281 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days with daily updates.
Publisher: Copernicus GmbH
Date: 06-09-2016
DOI: 10.5194/HESS-20-3561-2016
Abstract: Abstract. This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2023
Publisher: Copernicus GmbH
Date: 08-07-2013
DOI: 10.5194/NHESSD-1-3129-2013
Abstract: Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for small-medium sized catchments ( 2000km2), where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1 month and coming 3 month periods using large-scale ocean/atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modeling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5 day streamflows that occur during each 1 month or 3 month forecast period. Skill is clearly evident in the 1 month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3 month period. We show that including climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.
Publisher: Copernicus GmbH
Date: 06-09-2023
DOI: 10.5194/EMS2023-400
Abstract: Many water management agencies rely on stochastic inflow scenarios to plan water operations. For ex le, Hydro Tasmania, Australia& #8217 s largest hydropower generator and water manager, relies on 20+ year inflow scenarios to assess the long-range sustainability of their power generation system. A variety of methods are available for stochastic data generation, but many assume a stationary climate. In locations where inflow has long-term trends, assuming a stationary climate in stochastic data generation is likely to underestimate future wet or dry extremes, in particular for sequences of dry or wet months or years. To address this issue, we have developed the Trend and Uncertainty in Long Inflow Predictions (TULIP) model. TULIP is a Bayesian model that generates long-range predictions of inflows at the monthly time step. TULIP accounts for: Heteroscedasticity and skew in inflow data by using data transformation with the sinh-arcsinh transformation, and zero values with censoring Spatial correlation between inflow sites Autocorrelation using a first-order autoregressive model Linear trend in inflow Seasonal variation in properties (1)-(4), using Fourier series to control the parameters TULIP is being implemented operationally by Hydro Tasmania to replace its existing method of generating stochastic scenarios, which assumes a stationary climate. At sites with long-term trends in historical inflow, we show that TULIP produces more reliable long-range predictions than is possible if a stationary climate is assumed. This allows TULIP to produce sharper ensembles and more realistic projections of future drought, allowing Hydro Tasmania to better plan for the long-range sustainability of its system. In this presentation we describe the TULIP model and its performance. We also discuss future plans to incorporate information on inflow trends from global and regional climate models into TULIP.
Publisher: American Geophysical Union (AGU)
Date: 08-11-2013
DOI: 10.1002/2013JD020087
Publisher: Copernicus GmbH
Date: 07-07-2017
Abstract: Abstract. Despite an increasing availability of skillful long-range streamflow forecasts, many water agencies still rely on simple res led historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skillful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to res led historical inflow sequences. FoGSS generally produces skillful forecasts at shorter lead times (
Publisher: Copernicus GmbH
Date: 10-06-2014
DOI: 10.5194/HESSD-11-6035-2014
Abstract: Abstract. For streamflow forecasting applications, rainfall–runoff hydrological models are often augmented with updating procedures that correct streamflow predictions based on the latest available observations of streamflow and their departures from model simulations. The most popular approach uses autoregressive (AR) models that exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of predictions. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction usually occurs when streamflow is rapidly rising. Furthermore, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as predictions should rely as much as possible on the base hydrological model, and updating should be applied only to correct minor errors. To overcome the adverse effects of the ordinary AR models, a restricted AR model applied to normalised errors is introduced. The new model is evaluated on a number of catchments and is shown to reduce over-correction and to improve the performance of the base hydrological model considerably.
No related organisations have been discovered for James Bennett.
Start Date: 05-2019
End Date: 12-2023
Amount: $771,000.00
Funder: Australian Research Council
View Funded Activity