ORCID Profile
0000-0003-4230-8006
Current Organisation
CSIRO
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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 | Physical Geography and Environmental Geoscience | Environmental Engineering Modelling | Environmental Monitoring |
ANZSRC Socio-Economic Objective (SEO)
Natural Hazards in Fresh, Ground and Surface Water Environments | Water Allocation and Quantification | Weather | Climate Variability (excl. Social Impacts) | Management of Water Consumption by Information and Communication Services | Water Services and Utilities | Health Protection and/or Disaster Response
Related Links
Publications
Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting
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.
Using the Schaake shuffle when calibrating ensemble means can be problematic
Publisher: Elsevier BV
Date: 08-2020
Are ensemble NWP forecasts now so good that calibration is unnecessary?
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.
Forecasting monthly streamflow to long-lead times: a preliminary study
Publisher: CSIRO
Date: 2013
Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments
Publisher: Elsevier BV
Date: 02-2016
A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days
Publisher: Elsevier BV
Date: 11-2014
A dual-pass error-correction technique for forecasting streamflow
Publisher: Elsevier BV
Date: 08-2011
Preliminary analysis of rainfall forecast post-processor model parameters
Publisher: CSIRO
Date: 2016
The impacts of assimilating satellite soil moisture into a rainfall–runoff model in a semi-arid catchment
Publisher: Elsevier BV
Date: 11-2014
Service and Research on Seasonal Streamflow Forecasting in Australia
Publisher: World Scientific
Date: 21-12-2018
A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments
Publisher: Copernicus GmbH
Date: 03-2018
DOI: 10.5194/HESS-22-1615-2018
Abstract: Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.
Stochastic Generation of Plausible Hydroclimate Futures Using Climate Teleconnections for Southeastern Australia
Publisher: American Meteorological Society
Date: 10-2023
Abstract: Generating plausible future climate timeseries is needed for bottom-up climate impact modelling, as well as downscaling climate model output for hydrological applications. A novel method for generating multisite daily stochastic climate series is developed based on: 1) linear regression between climate teleconnection timeseries (e.g. IPO/SOI) and annual rainfall, 2) clustered method of fragments for subannual disaggregation, and 3) a regression-based approach to daily potential evapotranspiration (PET) for hydrological modelling. We demonstrate that bias (i.e. overs ling) occurs with the standard method of fragments disaggregation in the multisite context and show that selection of an analogue year from clustered rainfall amounts provides better s ling properties than the standard method of fragments. Using hydrological data for south-eastern Australia, we model runoff from observed and simulated rainfall and PET using the GR4J model. Simulated annual and daily rainfall and runoff characteristics from the new method are similar to existing methods, with improvements demonstrated in wet-wet transition probabilities and spatial (between-site) correlations.
A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts
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.
A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites
Publisher: American Geophysical Union (AGU)
Date: 05-2009
DOI: 10.1029/2008WR007355
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
Publisher: Informa UK Limited
Date: 02-07-2019
Further investigation and improvement of the FoGSS model
Publisher: CSIRO
Date: 2016
Value of seasonal streamflow forecasts in emergency response reservoir management
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.
An error model for long-range ensemble forecasts of ephemeral rivers
Publisher: Elsevier BV
Date: 05-2021
An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting
Publisher: Elsevier BV
Date: 11-2014
Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting
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.
Reliable hourly streamflow forecasting with emphasis on ephemeral rivers
Publisher: Elsevier BV
Date: 07-2021
The challenge of forecasting high streamflows in medium sized catchments 1–3 months in advance
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.
Performance of a new ensemble streamflow forecasting system for Australia
Publisher: CSIRO
Date: 2013
Better accounting of droughts in long-range inflows scenarios with TULIP
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.
Establishing the efficacy of continuous modelling for flood forecasting in Australia
Publisher: CSIRO
Date: 2014
How sensitive are catchment runoff estimates to on-farm storages under current and future climates?
Publisher: Elsevier BV
Date: 11-2023
The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia
Publisher: American Geophysical Union (AGU)
Date: 10-2013
DOI: 10.1002/WRCR.20449
Use of data assimilation to improve rainfall-runoff model structure for climate change projections
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-365
Abstract: & & Data assimilation is a powerful tool that has been used to correct states and parameters of rainfall-runoff models based on recent streamflow, remotely sensed soil moisture or groundwater data. Data assimilation is now routinely applied by forecasting centres around the world to improve simulations and increase forecast skill. In this work, we are less concerned with the direct benefits of data assimilation on model outputs, but more on the nature of the corrections introduced and how they can be analysed to diagnose structural deficiencies in rainfall-runoff models.& & & & Rainfall-runoff models have been shown to lack extrapolation capacity in simulating dry and wet periods that are more extreme than calibration conditions. This is particularly concerning in the context of climate change studies where more climate extremes are generally predicted for expected. This is the case in South-Eastern Australia where annual rainfall is expected to decrease significantly under most climate scenarios. Consequently, the improvement of rainfall-runoff model structures to better simulate dry flow regimes is critical to obtain robust estimates of water resources availability.& & & & In this work, we assimilated streamflow data in the GR2M monthly rainfall-runoff models for 100 catchments in South-East Australia. The assimilation was conducted during a wet period between 1970 to 1995 and used to identify model structure deficiencies, particularly in the function computing water exchanges with nearby catchments. An attempt of correcting these deficiencies was undertaken using a simple regression approach. Finally, the correction was applied during a dry period (1995-2010) and performance was compared with the original (uncorrected) model. The results suggest that the corrected simulations better capture streamflow extremes, especially low flows. Further work is also discussed related to the use of additional data such as LAI and groundwater data to better constrain the correction regression.& &
Bayesian Approach to Predictor Selection for Seasonal Strearnflow Forecasting
Publisher: American Meteorological Society
Date: 02-2012
Abstract: Statistical methods commonly used for forecasting climate and streamflows require the selection of appropriate predictors. Poorly designed predictor selection procedures can result in poor forecasts for independent events. This paper introduces a predictor selection method for the Bayesian joint probability modeling approach to seasonal streamflow forecasting at multiple sites. The method compares forecasting models using a pseudo-Bayes factor (PsBF). A stepwise expansion of a base model is carried out by including the candidate predictor with the highest PsBF that exceeds a selection threshold. Predictors representing the initial catchment conditions are selected on their ability to forecast streamflows and predictors representing future climate influences are selected on their ability to forecast rainfall. The final forecasting model combines selected predictors representing both initial catchment conditions and future climate influences to jointly forecast seasonal streamflows and rainfall. Applications of the predictor selection method to two catchments in eastern Australia show that the best predictors representing initial catchment conditions and future climate influences vary with location and forecast date. Antecedent streamflows are the best indicator of the initial catchment conditions. Predictors representing future climate influences are only selected for forecasts made between July and January. Indicators of El Niño dominate the selected predictors representing future climate influences. The skill of streamflow forecasts varies considerably between locations and throughout the year. Skill scores for the perennial streams of the Goulburn River catchment exceed 40% for several seasons, while for the intermittent streams in the Burdekin River catchment, the skill scores are lower.
Post-processing rainfall forecasts from a numerical weather prediction model in Australia
Publisher: CSIRO
Date: 2013
Research to support water resources planning in the Murray-Darling Basin
Publisher: Copernicus GmbH
Date: 22-09-2022
DOI: 10.5194/IAHS2022-120
Abstract: & & Many hydrological models (GR4J, Sacramento and SIMHYD for ex le) currently exist to reproduce hydrological response at a catchment scale. Some models (IQQM, Source for ex le) also exist to assess the impacts of human interventions designed to in some way optimise the use of water in regulated river systems. There are however a much smaller number of models designed to assess the impacts of water resources management on socio-economics, the community and the environment more broadly.& & & & A current program of work known as MD-WERP & #8211 the Murray-Darling Water and Environment Research Program, seeks to improve the understanding and representation of key processes in hydrological models used to underpin basin analysis and planning. We are working with policy makers and water managers in State and Federal government to apply these models to assess the impacts of water resource management options on hydrological, ecological and socio-economic outcomes in the Murray-Darling Basin. This will allow planners to consider a wide range of management options in the review and revision of the Murray-Darling Basin Plan that is scheduled for the next few years.& & & & The vast majority of global and regional climate models, as well as understanding of changes in global and regional circulation patterns suggest a drier future for the Murray-Darling Basin with consequently more frequent and severe droughts. The management options to be assessed therefore are primarily those that minimise the impacts of drier conditions on the environment, irrigators and the Basin community, along with models that allow assessments of trade-offs between these disparate water users to be made.& & & & The models that are required to assess these adaptation options need to be erse, covering not only things such as changes in rainfall and hydrological response, but also climate adaptation options in river system operations, conjunctive use of groundwater and surface water, water trading and allocation, and consequent impacts on the environment, irrigators, basin communities and First Nations groups.& & & & This presentation will provide an overview of MD-WERP with a focus on the climate adaptation and hydrology themes, assessing how modelling tools can be used to better inform Basin-wide water resources policy and planning.& &
Propagating reliable estimates of hydrological forecast uncertainty to many lead times
Publisher: Elsevier BV
Date: 12-2021
Improving the merging of statistical and dynamic seasonal streamflow forecasts : testing at 40 locations
Publisher: CSIRO
Date: 2015
Forecasting Surface and Groundwater Resources
Publisher: CSIRO
Date: 2021
DOI: 10.25919/RER3-CH82
Assessment of an ensemble seasonal streamflow forecasting system for Australia
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.
Assessment of catchment runoff and high flows in the Murray-Darling Basin under climate change
Publisher: CSIRO
Date: 2021
DOI: 10.25919/YVKF-4S63
A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers
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.
FoGSS - A model for generated forecast guided stochastic scenarios of monthly streamflows out to 12 months
Publisher: CSIRO Water for a Healthy Country Flagship
Date: 2014
Complex relationship between seasonal streamflow forecast skill and value in reservoir operations
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.
Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose
Publisher: Copernicus GmbH
Date: 21-05-2013
DOI: 10.5194/HESS-17-1913-2013
Abstract: Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant s ling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and s ling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
Development of a national 7-day ensemble streamflow forecasting service for Australia
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.
Calibrating Hourly Precipitation Forecasts with Daily Observations
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.
Estimating Lake Mulwala Diversions for Calibration of a Semi-Distributed Hydrologic Model of the Murray River
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 12-2019
Engendering stakeholder ownership in scenario planning
Publisher: Elsevier BV
Date: 02-2015
Improving error modelling and calibration of continuous hydrological models for streamflow and flood forecasting
Publisher: CSIRO
Date: 2014
Enhanced parameter inference method for rainfall forecast post processor (RPP)
Publisher: CSIRO
Date: 2016
Ensemble flood forecasting: Current status and future opportunities
Publisher: Wiley
Date: 29-03-2020
DOI: 10.1002/WAT2.1432
Abstract: Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk‐based decision‐making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for ex le, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real‐world flood management using probabilistic ensemble forecasts, especially through effective communication. This article is categorized under: Engineering Water Methods Science of Water Water Extremes
Dual assimilation of satellite soil moisture to improve streamflow prediction in data‐scarce catchments
Publisher: American Geophysical Union (AGU)
Date: 07-2016
DOI: 10.1002/2015WR018429
A Comparison of the Discrete Cosine and Wavelet Transforms for Hydrologic Model Input Data Reduction
Publisher: Copernicus GmbH
Date: 06-02-2017
DOI: 10.5194/HESS-2017-26
Abstract: Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Model input data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are used to reduce the dimensionality of rainfall time series observations from the 438 catchments in the MOdel Parameter Estimation eXperiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the measured hyetographs using standard simulation performance summary metrics and descriptive statistics as well as peak discharge errors. The results convincingly demonstrate that the DWT is superior to the DCT and best preserves and characterizes the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.
Bayesian Joint Probability modelling approach R packages
Publisher: CSIRO
Date: 2020
Assessing the performance of ensemble streamflow forecasts produced by the latest generation of rainfall forecasts
Publisher: CSIRO
Date: 2015
Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting
Publisher: Copernicus GmbH
Date: 27-09-2013
DOI: 10.5194/HESS-17-3587-2013
Abstract: Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for in idual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking s les from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for in idual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.
Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model
Publisher: American Geophysical Union (AGU)
Date: 10-2016
DOI: 10.1002/2016WR019193
Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences
Publisher: American Geophysical Union (AGU)
Date: 02-2011
DOI: 10.1029/2010WR009333
Insights from a new methodology to optimize rain gauge weighting for rainfall-runoff models
Publisher: Copernicus GmbH
Date: 05-09-2019
Abstract: Abstract. Floods continue to devastate societies and their economies. Resilient societies commonly incorporate flood forecasting into their strategy to mitigate the impact of floods. Hydrological models which simulate the rainfall-runoff process are at the core of flood forecasts. To date operational flood forecasting models use areal rainfall estimates that are based on geographical features. This paper introduces a new methodology to optimally blend the weighting of gauges for the purpose of obtaining superior flood forecasts. For a selection of 7 Australian catchments this methodology was able to yield improvements of 15.3 % and 7.1 % in optimization and evaluation periods respectively. Catchments with a low gauge density, or an overwhelming majority of gauges with a low proportion of observations available, are not well suited to this new methodology. Models which close the water balance and demonstrate internal model dynamics that are consistent with a conceptual understanding of the rainfall-runoff process yielded consistent improvement in streamflow simulation skill.
Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model
Publisher: Elsevier BV
Date: 09-2015
Post-processing of numerical weather predictions for multiple issue times
Publisher: CSIRO
Date: 2018
How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?
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.
Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting
Publisher: Copernicus GmbH
Date: 29-05-2013
DOI: 10.5194/HESSD-10-6765-2013
Abstract: Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for in idual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking s les from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for in idual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.
Seasonal Forecasts of Unregulated Inflows into the Murray River, Australia
Publisher: Springer Science and Business Media LLC
Date: 02-03-2013
Reducing bias in streamflow predictions derived from climate model output.
Publisher: Elsevier BV
Date: 04-2023
Assessing the performance of postprocessing method for ensemble rainfall forecasts
Publisher: CSIRO
Date: 2016
Evaluation of the Bayesian joint probability approach to seasonal streamflow forecasting for inflows into Melbourne Water and Hydro Tasmania storages
Publisher: CSIRO
Date: 2010
Performance of rainfall and streamflow forecasts for four catchments in the southern Murray-Darling basin
Publisher: CSIRO Land and Water
Date: 2017
Evaluating a prototype ensemble water quantity and quality forecasting system for the Fitzroy River Basin
Publisher: CSIRO
Date: 2014
Improving hydrological predictions for short-term forecasting
Publisher: CSIRO Land & Water
Date: 2014
Statistical analysis of attributions of climatic characteristics to nonstationary rainfall‐streamflow relationship
Publisher: Elsevier BV
Date: 12-2021
The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia
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.
Estimating hydraulic parameters for a surface irrigation model from field conditions
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA02191
Abstract: Border-check irrigation is the most common method of irrigating pastures in Northern Victoria. To make the best use of a border-check irrigation system, consideration needs to be given to the irrigation schedule and irrigation event management. Surface irrigation models can provide an inexpensive and rapid method for identifying optimal irrigation event performance. The most common difficulty encountered when using surface irrigation models is determining appropriate hydraulic parameters. Two experiments were conducted to investigate the relationship between hydraulic parameters of the Analytical Irrigation Model and easily observable field conditions. The field experiments were performed at Tatura, Victoria, on 12 irrigation bays characterised by a Lemnos loam, a red duplex soil, sown to perennial pasture. For each experiment, 3 replicates of 4 treatments were applied. The first experiment found a linear relationship between field soil water deficit, approximated by crop water use less effective rainfall, and the initial infiltration depth. The second experiment found no relationship between pasture height and the model surface roughness parameter. An alternative to estimate the surface roughness parameter is suggested, which involves making an early observation of irrigation advance and solving for the unknown roughness parameter. The parameter estimation method developed in this paper can assist in improving the management of border-check irrigation on Lemnos loam soil, which covers about 125 000 hectares in the Goulburn Valley. However, field-testing of the approach on commercial farms and other soil types is required.
Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging
Publisher: American Meteorological Society
Date: 15-08-2012
DOI: 10.1175/JCLI-D-11-00386.1
Abstract: Merging forecasts from multiple models has the potential to combine the strengths of in idual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant s ling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.
Ensemble forecasting of short-term system scale irrigation demands using real time flow data and numerical weather predictions
Publisher: American Geophysical Union (AGU)
Date: 06-2016
DOI: 10.1002/2015WR018532
A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions
Publisher: Copernicus GmbH
Date: 22-02-2013
Abstract: Abstract. Hydrologic model predictions are often biased and subject to heteroscedastic errors originating from various sources including data, model structure and parameter calibration. Statistical post-processors are applied to reduce such errors and quantify uncertainty in the predictions. In this study, we investigate the use of a statistical post-processor based on the Bayesian joint probability (BJP) modelling approach to reduce errors and quantify uncertainty in streamflow predictions generated from a monthly water balance model. The BJP post-processor reduces errors through elimination of systematic bias and through transient errors updating. It uses a parametric transformation to normalize data and stabilize variance and allows for parameter uncertainty in the post-processor. We apply the BJP post-processor to 18 catchments located in eastern Australia and demonstrate its effectiveness in reducing prediction errors and quantifying prediction uncertainty.
Improving the FoGSS system: generating streamflow forecasts beyond 3 months
Publisher: CSIRO Land & Water
Date: 2015
Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2023
Development of a national 7-day ensemble streamflow forecasting service for Australia
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.
Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods
Publisher: Copernicus GmbH
Date: 29-06-2018
DOI: 10.5194/HESS-22-3533-2018
Abstract: Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April–September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.
Improving statistical forecasts of seasonal streamflows using hydrological model output
Publisher: Copernicus GmbH
Date: 08-02-2013
Abstract: Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.
Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
Publisher: Copernicus GmbH
Date: 09-04-2015
DOI: 10.5194/HESS-19-1659-2015
Abstract: Abstract. Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large ( 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively the false alarm ratio was reduced by 9% in both cases the peak volume error was reduced by 58 and 1%, respectively the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively) and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation.
Effective use of general circulation model outputs for forecasting monthly rainfalls to long lead times
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/WRCR.20453
A strategy to overcome adverse effects of autoregressive updating of streamflow predictions
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.
Establishing the efficacy of continuous modelling for flood forecasting in Australia
Publisher: CSIRO Land & Water
Date: 2014
A Bayesian network approach to knowledge integration and representation of farm irrigation: 2. Model validation
Publisher: American Geophysical Union (AGU)
Date: 02-2009
DOI: 10.1029/2006WR005420
A Bayesian network approach to knowledge integration and representation of farm irrigation: 3. Spatial application
Publisher: American Geophysical Union (AGU)
Date: 02-2009
DOI: 10.1029/2006WR005421
A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction
Publisher: Copernicus GmbH
Date: 27-07-2017
DOI: 10.5194/HESS-21-3827-2017
Abstract: Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.
Estimating sub-catchment rainfall from rain gauge observations at daily time steps
Publisher: CSIRO
Date: 2013
A seasonally coherent calibration (SCC) model for postprocessing numerical weather predictions
Publisher: American Meteorological Society
Date: 24-09-2019
Abstract: Statistical calibration of forecasts from numerical weather prediction (NWP) models aims to produce forecasts that are unbiased, reliable in ensemble spread, and as skillful as possible. We suggest that the calibrated forecasts should also be coherent in climatology, including seasonality, consistent with observations. This is especially important when forecasts approach climatology as forecast skill becomes low, such as at long lead times. However, it is challenging to achieve these aims when data available to establish sophisticated calibration models are limited. Many NWP models have only a short period of archived data, typically one year or less, when they become officially operational. In this paper, we introduce a seasonally coherent calibration (SCC) model for working effectively with limited archived NWP data. Detailed rationale and mathematical formulations are presented. In the development of the model, three issues are resolved. These are 1) constructing a calibration model that is sophisticated enough to allow for seasonal variation in the statistical characteristics of raw forecasts and observations, 2) bringing climatology that is representative of long-term statistics into the calibration model, and 3) reducing the number of model parameters through sensible reparameterization to make the model workable with short NWP dataset. A case study is conducted to examine model assumptions and evaluate model performance. We find that the model assumptions are sound, and the developed SCC model produces well-calibrated forecasts.
A Bayesian hierarchical spatio-temporal rainfall model
Publisher: Informa UK Limited
Date: 15-05-2018
Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes
Publisher: Copernicus GmbH
Date: 23-09-2014
DOI: 10.5194/HESSD-11-10635-2014
Abstract: Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia ( 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively the NS of the ensemble mean increased by 7 and 38%, respectively the false alarm ratio was reduced by 15 and 25%, respectively and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.
WIRADA 4.1 flood and short-term streamflow forecasting: Synthesis of 2012/13 results and recommendations for ensemble short-term streamflow forecasting
Publisher: CSIRO Water for a Healthy Country Flagship
Date: 2013
Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall
Publisher: American Geophysical Union (AGU)
Date: 20-10-2012
DOI: 10.1029/2012JD018011
Coherent intra-annual ensemble forecasts of surface and groundwater availability
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-13483
Abstract: & & In many parts of the world, surface water and groundwater are used complementarily to supply agricultural production and to meet urban water demands. Conjunctive management of these water resources requires balancing of the different characteristics of surface water and groundwater with respect to availability, quality and cost of supply. Ensemble forecasts of surface water and groundwater availability can inform management decisions but require explicit representation of the complex processes controlling surface and groundwater interactions. While many methods and operational services exist that provide independent forecasts for surface and groundwater availability, to our knowledge no approaches for coupled forecasting have been developed yet.& & & & In this presentation we introduce an approach that generates coupled forecasts of surface water and groundwater availability. It extends the Forecast Guided Stochastic Scenarios (FoGSS) (Bennett et al., 2016) approach to forecast groundwater level at specified locations, in addition to streamflow totals, to lead times of 12 months at monthly time steps. We adapt a conceptual hydrological model to improve predictions of streamflow and, as a by-product, groundwater level. We then apply independent error models to streamflow and groundwater level to reduce bias, update predictions using recent observations and quantify residual uncertainty. Ensemble streamflow and groundwater forecasts are generated by forcing the hydrological and error models with ensemble rainfall forecasts generated by post-processing ECMWF System 5 outputs. The skill, bias and reliability of the rainfall, streamflow and groundwater level forecasts were assessed for a case-study catchment in South-East Queensland, Australia. We find that skill of forecasts is dependent on the forecast issue month and lead time, with groundwater level forecasts displaying significant skill to lead times of 12 months, while streamflow forecast skill rarely persists beyond 3 months.& We conclude by describing opportunities to improve forecast skill and some of the challenges that may be faced in the operational delivery of water resource forecasts in real-time.& & & & Reference& & & & Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.: Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model, Water Resources Research, 52, 8238-8259, 10.1002/2016WR019193, 2016.& &
A new method for post-processing daily sub-seasonal to seasonal rainfall forecasts from GCMs and evaluation for 12 Australian catchments
Publisher: Copernicus GmbH
Date: 06-07-2017
Abstract: Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal time scales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. However, for hydrological applications, GCM forecasts are often biased and unreliable in uncertainty spread, and therefore calibration is required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S) based on the Bayesian joint probability approach for calibrating daily forecasts and the Schaake Shuffle approach for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.
A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development
Publisher: American Geophysical Union (AGU)
Date: 02-2009
DOI: 10.1029/2006WR005419
Verification of rainfall forecast products for use in short-term streamflow forecasting
Publisher: CSIRO
Date: 2014
Bayesian networks for decision analyses — an application to irrigation system selection
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA02178
Abstract: Farmers are under continual pressure from Government and industry to change farm practices to meet productivity and environmental targets. In response to these pressures, farmers will make decisions to adopt practices that reflect their motivations and priorities. However, where the changes of practice are major, there may be considerable uncertainty associated with the decision-making process. Decision support tools are one method that may assist in reducing the uncertainty associated with decisions about changes in farm practices.Bayesian networks provide a useful tool to assist in the structuring and analysis of decision problems. A Bayesian network is a decision analysis framework, based on Bayesian probability theory, which allows the integration of scientific and experiential knowledge, and the uncertainty associated with this knowledge. The approach involves describing a system in terms of variables and linkages, or relationships between variables, at a level appropriate to the decision making. This is achieved through representing linkages as conditional probability tables and propagating probabilities through the network to give the likelihood of variable outcomes. Therefore, the approach ensures that treatment of risks and uncertainties is an intrinsic part of the decision-making processes. The Bayesian network is dynamic and interactive, and hence if a network previously developed does not fit a user's conceptual understanding of the system, it can be adapted quickly and simply to the cognitive understanding of the user.A case study Bayesian network has been developed for decisions associated with the selection of irrigation systems for irrigated dairy farms in Northern Victoria. This case study demonstrates that the most appropriate irrigation system for a dairy farm is dependent on factors including the amount of irrigation water available and soil types. Analysis of the Bayesian network indicates that the appropriate irrigation system is more sensitive to the income generated from pasture than to the price of water. The Bayesian network can demonstrate the impacts of decisions on the farmer's system and can allow the farmer to evaluate these impacts according to their own priorities and criteria. This information can then be used by the natural resource manager to assess the appropriate level of incentive or penalty required if the farmer is to adopt the preferred option that will also achieve preferable outcomes from a natural resource management perspective.
A qualitative approach to improve the effectiveness of products of research findings for users: a case study of the Analytical Irrigation Model
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA02177
Abstract: The widespread adoption of research findings by the farming community has traditionally been challenging. Addressing this challenge is a priority as the products of research often aid and underpin the implementation of environmental objectives to ensure that natural resources are used in a sustainable manner. One approach to tackling this challenge is to develop products that are tailored to meet the needs of the users. The Analytical Irrigation Model (AIM, a software tool) was developed with the intention of creating a field-tool to assist farmers to improve their management of border-check irrigation. Using AIM as a case study, this paper demonstrates the value of using a qualitative approach in assessing potential users of research findings, and understanding their requirements. While developing AIM, anecdotal feedback suggested that widespread adoption of the envisaged research products, namely a field-tool, was unlikely. The qualitative study found that service providers to the dairy industry were likely to be the primary users of products of the AIM research. From conducting this qualitative study, service providers identified 4 types of research products that would suit their needs. Incorporating their perspectives enabled the development of products that were more likely to be adopted and consequently increased the effective targeting of the AIM research findings.
Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs
Publisher: American Meteorological Society
Date: 30-04-2014
Abstract: Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts in idually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.
Improving Precipitation Forecasts by Generating Ensembles through Postprocessing
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.
Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs
Publisher: Elsevier BV
Date: 12-2017
Evidence for Using Lagged Climate Indices to Forecast Australian Seasonal Rainfall
Publisher: American Meteorological Society
Date: 08-02-2012
DOI: 10.1175/JCLI-D-11-00156.1
Abstract: Lagged oceanic and atmospheric climate indices are potentially useful predictors of seasonal rainfall totals. A rigorous Bayesian joint probability modeling approach is applied to find the cross-validation predictive densities of gridded Australian seasonal rainfall totals using lagged climate indices as predictors over the period of 1950–2009. The evidence supporting the use of each climate index as a predictor of seasonal rainfall is quantified by the pseudo-Bayes factor based on cross-validation predictive densities. The evidence strongly supports the use of climate indices from the Pacific region with weaker, but positive, evidence for the use of climate indices from the Indian region and the extratropical region. The spatial structure and seasonal variation of the evidence for each climate index is mapped and compared. Spatially, the strongest supporting evidence is found for forecasting in northern and eastern Australia. Seasonally, the strongest evidence is found from August–October to November–January and the weakest evidence is found from March–May to May–July. In some regions and seasons, there is little evidence supporting the use of climate indices for forecasting seasonal rainfall. Climate indices derived from sea surface temperature anomalies in the Pacific region show stronger persistence in the relationship with Australian seasonal rainfall totals than climate indices derived from sea surface temperature anomalies in the Indian region. Climate indices derived from atmospheric variables are also strongly supported, provided they represent the large-scale circulation. Many climate indices are found to show similar supporting evidence for forecasting Australian seasonal rainfall, leading to the prospect of combining climate indices in multiple predictor models and/or model averaging.
On the importance of soil moisture in calibration of rainfall–runoff models: two case studies
Publisher: Informa UK Limited
Date: 04-07-2018
Assessment of an ensemble seasonal streamflow forecasting system for Australia
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 (
Monthly versus daily water balance models in simulating monthly runoff
Publisher: Elsevier BV
Date: 07-2011
Related Organisations
Commonwealth Scientific And Industrial Research Organisation
Location: Australia
Related Funding Activities
Linkage Projects - Grant ID: LP110200520
Start Date: 01-2012
End Date: 01-2015
Amount: $174,003.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140102394
Start Date: 03-2014
End Date: 08-2017
Amount: $550,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP170100922
Start Date: 05-2019
End Date: 12-2023
Amount: $771,000.00
Funder: Australian Research Council
View Funded Activity