ORCID Profile
0000-0002-8787-2738
Current Organisation
University of Melbourne
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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 | Civil Engineering | Surfacewater Hydrology | Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.) | Physical Geography and Environmental Geoscience | Environmental Engineering Modelling | Environmental Monitoring | Meteorology |
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 | Farmland, Arable Cropland and Permanent Cropland Water Management | Health Protection and/or Disaster Response
Related Links
Publications
Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs
Publisher: Wiley
Date: 09-2020
DOI: 10.1002/JOC.6788
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.
Introducing long‐term trends into subseasonal temperature forecasts through trend‐aware postprocessing
Publisher: Wiley
Date: 06-01-2022
DOI: 10.1002/JOC.7515
Abstract: Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend‐aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30‐year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30‐year trends is to robustly represent long‐term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large s ling variability. Calibration is applied to 20‐year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day‐of‐year climatology, raw week‐1 forecasts reproduce temperature trends of the 20‐year observations in many regions while raw week‐4 forecasts do not exhibit the 20‐year observed trends. After trend‐aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week‐1 forecasts show apparent trends consistent with the 20‐year observations, as the calibration transfers forecast skill and embeds the 20‐year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week‐4 forecasts exhibit the 30‐year observed trends, as the calibration reverts the forecasts to the 30‐year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend‐aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend‐aware method can be applied to deliver high‐quality subseasonal forecasts and support decision‐making in a changing climate.
Assessing the Impact of Irrigation Efficiency Projects on Return Flows in the South-Eastern Murray–Darling Basin, Australia
Publisher: MDPI AG
Date: 14-05-2021
DOI: 10.3390/W13101366
Abstract: Improving irrigation efficiency (IE) is an approach used globally to help meet competing demands for water and facilitate reallocation of water between sectors. In the Murray–Darling Basin in Australia, the Australian government has invested heavily in IE projects to recover water for the environment. However, this approach has been seriously questioned, out of concerns that improved IE would reduce irrigation return flows to rivers and therefore offset water recovery. In this study, we use a water balance model to assess the impact of the IE projects on return flows and highlight sensitivities and uncertainties. The model enables the impact on return flows to be assessed on specific IE projects and regional characteristics. Overall, reductions in return flows are estimated to be less than 20% of the total proposed IE savings. The history of IE in the southern MDB has meant that most of the current reductions are in ground return flows. Our estimate is much lower than two previous studies, mainly due to different assumptions being used on groundwater connectivity between irrigation areas and major streams. While the IE projects significantly reduce seepage to groundwater (with off-farm and on-farm projects reducing seepage by 19% and 53% of total savings respectively), not all seepage reductions will translate to a reduction in ground return flows to rivers. A lower estimate is consistent with existing monitoring and groundwater modeling studies. In this paper, the study results are discussed in a broader context of impacts of IE projects on volumes and salinity of streams and groundwater resources.
An evaluation of daily precipitation from atmospheric reanalyses over Australia
Publisher: Copernicus GmbH
Date: 19-02-2019
Abstract: Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, modified Kling-Gupta efficiency), categorical metrics, and other statistics (wet day frequency, transition probabilities and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both point and grid scale of 5 km. BARRA also consistently reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias and modified Kling-Gupta efficiency (KGE') indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms AWAP in terms of KGE' score and the components of the KGE' score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Toward Accurate and Reliable Forecasts of Australian Seasonal Rainfall by Calibrating and Merging Multiple Coupled GCMs
Publisher: American Meteorological Society
Date: 25-11-2013
Abstract: The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the in idual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the in idually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.
A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts
Publisher: American Meteorological Society
Date: 18-12-2019
Abstract: Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.
Using the Schaake shuffle when calibrating ensemble means can be problematic
Publisher: Elsevier BV
Date: 08-2020
Jammed Architectural Structures
Publisher: Routledge
Date: 29-09-2020
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
Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model
Publisher: Informa UK Limited
Date: 15-06-2023
A dual-pass error-correction technique for forecasting streamflow
Publisher: Elsevier BV
Date: 08-2011
Rock print Pavilion: robotically fabricating architecture from rock and string
Publisher: Springer Science and Business Media LLC
Date: 23-03-2020
The politicisation of science in the Murray-Darling Basin, Australia: discussion of ‘Scientific integrity, public policy and water governance’
Publisher: Informa UK Limited
Date: 03-07-2021
Quantifying parameter uncertainty in stochastic models using the Box–Cox transformation
Publisher: Elsevier BV
Date: 08-2002
Stable isotope composition of precipitation in the south and north slopes of Wushaoling Mountain, northwestern China
Publisher: Elsevier BV
Date: 12-2016
A Bayesian Joint Probability Approach for flood record augmentation
Publisher: American Geophysical Union (AGU)
Date: 06-2001
DOI: 10.1029/2000WR900401
Service and Research on Seasonal Streamflow Forecasting in Australia
Publisher: World Scientific
Date: 21-12-2018
Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach
Publisher: Elsevier BV
Date: 10-2016
Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation
Publisher: American Meteorological Society
Date: 24-01-2019
Abstract: Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for in idual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.
ESTIMATION OF THE GEV DISTRIBUTION FROM CENSORED SAMPLES BY METHOD OF PARTIAL PROBABILITY WEIGHTED MOMENTS
Publisher: Elsevier BV
Date: 12-1990
Relationship between hydraulic and basic properties for irrigated soils in southeast Australia
Publisher: Wiley
Date: 02-2011
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.
Evaluation and Statistical Post‐Processing of Two Precipitation Reforecast Products During Summer in the Mainland of China
Publisher: No publisher found
Date: 2022
DOI: 10.1029/2022JD036606
Temporally varied error modelling for improving simulations and quantifying uncertainty
Publisher: Elsevier BV
Date: 07-2020
Seasonal precipitation forecasts over China using monthly large-scale oceanic-atmospheric indices
Publisher: Elsevier BV
Date: 11-2014
Artificial neural network based hybrid modeling approach for flood inundation modeling
Publisher: Elsevier BV
Date: 2021
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.
Productivity and water use of grazed subsurface drip irrigated perennial pasture in Australia
Publisher: Springer Science and Business Media LLC
Date: 14-01-2015
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
The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Publisher: American Astronomical Society
Date: 2022
Abstract: The PHANGS program is building the first data set to enable the multiphase, multiscale study of star formation across the nearby spiral galaxy population. This effort is enabled by large survey programs with the Atacama Large Millimeter/submillimeter Array (ALMA), MUSE on the Very Large Telescope, and the Hubble Space Telescope (HST), with which we have obtained CO(2–1) imaging, optical spectroscopic mapping, and high-resolution UV–optical imaging, respectively. Here, we present PHANGS-HST, which has obtained NUV– U – B – V – I imaging of the disks of 38 spiral galaxies at distances of 4–23 Mpc, and parallel V - and I -band imaging of their halos, to provide a census of tens of thousands of compact star clusters and multiscale stellar associations. The combination of HST, ALMA, and VLT/MUSE observations will yield an unprecedented joint catalog of the observed and physical properties of ∼100,000 star clusters, associations, H ii regions, and molecular clouds. With these basic units of star formation, PHANGS will systematically chart the evolutionary cycling between gas and stars across a ersity of galactic environments found in nearby galaxies. We discuss the design of the PHANGS-HST survey and provide an overview of the HST data processing pipeline and first results. We highlight new methods for selecting star cluster candidates, morphological classification of candidates with convolutional neural networks, and identification of stellar associations over a range of physical scales with a watershed algorithm. We describe the cross-observatory imaging, catalogs, and software products to be released. The PHANGS high-level science products will seed a broad range of investigations, in particular, the study of embedded stellar populations and dust with the James Webb Space Telescope, for which a PHANGS Cycle 1 Treasury program to obtain eight-band 2–21 μ m imaging has been approved.
Load-Bearing Capacity and Deformation of Jammed Architectural Structures
Publisher: Mary Ann Liebert Inc
Date: 12-2018
Unbiased estimation of probability weighted moments and partial probability weighted moments from systematic and historical flood information and their application to estimating the GEV distribution
Publisher: Elsevier BV
Date: 12-1990
Direct Sample Estimators of L Moments
Publisher: American Geophysical Union (AGU)
Date: 12-1996
DOI: 10.1029/96WR02675
Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths
Publisher: American Geophysical Union (AGU)
Date: 12-2022
DOI: 10.1029/2022WR033214
Abstract: Flood inundation emulation models based on deep neural networks have been developed to overcome the computational burden of two‐dimensional (2D) hydrodynamic models. Challenges remain for flat and complex floodplains where many anabranches form during flood events. In this study, we propose a new approach to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. A U‐Net‐based spatial reduction and reconstruction method (USRR) is used to find representative locations on the floodplain with complex flow paths. The water depths at these locations are simulated using one‐dimensional convolutional neural network (1D‐CNN) models, which are well‐suited to handling multivariate timeseries inputs. The flood surface is then reconstructed using the USRR method and the simulated flood depths at the representative locations. The combined 1D‐CNN and USRR method is compared with a previously developed approach based on the long short‐term memory recurrent neural network (LSTM) models and a 2D linear interpolation‐based SRR method. Compared to the LSTM model, the 1D‐CNN model is not only more accurate, but also takes less time to develop. Although both surface reconstruction methods take s to produce an inundation map for a specific point in time, the USRR method is more accurate than the SRR method, leading to an increase of 5.6% in the proportion of correctly detected inundation area. The combination of 1D‐CNN and USRR can detect over 95% of the inundated area simulated using a 2D hydrodynamic model but is 98 times faster.
An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting
Publisher: Elsevier BV
Date: 11-2014
Upskilling Low‐Fidelity Hydrodynamic Models of Flood Inundation Through Spatial Analysis and Gaussian Process Learning
Publisher: American Geophysical Union (AGU)
Date: 08-2022
DOI: 10.1029/2022WR032248
Abstract: Accurate flood inundation modeling using a complex high‐resolution hydrodynamic (high‐fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modeling the dynamical behavior of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial‐temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low‐resolution hydrodynamic (low‐fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low‐fidelity model is computationally efficient but has reduced accuracy compared to a high‐fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low‐fidelity modeling results. To address the challenges posed by the high dimensionality of the data from the low‐ and high‐fidelity models, Empirical Orthogonal Functions analysis is applied to reduce the spatial‐temporal data into a few key features. This enables training of the Sparse GP model to predict high‐fidelity flood data from low‐fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high‐fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low‐fidelity model and incurred only 39% of the computational cost of a high‐fidelity model.
A review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting
Publisher: American Meteorological Society
Date: 10-2011
Abstract: Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.
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.
Using genetic algorithms to optimise model parameters
Publisher: Elsevier BV
Date: 1997
Approaches for Quantifying and Managing Diffuse Phosphorus Exports at the Farm/Small Catchment Scale
Publisher: Wiley
Date: 09-2009
DOI: 10.2134/JEQ2007.0651
Abstract: Quantifying and managing diffuse P losses from small catchments or at the farm scale requires detailed knowledge of farming practices and their interaction with catchment processes. However, detailed knowledge may not be available and hence modeling is required. This paper demonstrates two approaches to developing tools that assist P losses from New Zealand or Australian dairy farms. The first is largely empirical and separates sources of P within a paddock into soil, fertilizer, dung, and treading impacts (including damage to grazed pasture). This information is combined with expert knowledge of hydrological processes and potential point sources (e.g., stream crossings) to create a deterministic model that can be used to evaluate the most cost and labor efficient method of mitigating P losses. For instance, in one ex le, 45% of annual P lost was attributed to the application of superphosphate just before a runoff event for which a mitigation strategy could be to use a less water soluble P fertilizer. The second approach uses a combination of interviews, expert knowledge and relationships to develop a Bayesian Network that describes P exports. The knowledge integration process helped stakeholders develop a comprehensive understanding of the problem. The Network, presented in the form of a "cause and effect", diagram provided a simple, visual representation of current knowledge that could be easily applied to in idual circumstances and isolate factors having the greatest influence on P loss. Both approaches demonstrate that modeling P losses and mitigation strategies does not have to cover every process or permutation and that a degree of uncertainty can be handled to create a working model of P losses at a farm or small catchment scale.
Reliable hourly streamflow forecasting with emphasis on ephemeral rivers
Publisher: Elsevier BV
Date: 07-2021
On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs
Publisher: American Meteorological Society
Date: 2020
Abstract: Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle) and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-s le testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.
A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs
Publisher: Elsevier BV
Date: 05-2019
Using higher probability weighted moments for flood frequency analysis
Publisher: Elsevier BV
Date: 07-1997
Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data
Publisher: American Geophysical Union (AGU)
Date: 05-2011
DOI: 10.1029/2010WR009922
Using Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2023
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
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.
Accounting for seasonal dependence in hydrological model errors and prediction uncertainty
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/WRCR.20445
An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts
Publisher: Springer Science and Business Media LLC
Date: 14-07-2023
DOI: 10.1007/S00271-022-00807-W
Abstract: Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.
Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags
Publisher: American Geophysical Union (AGU)
Date: 04-2013
DOI: 10.1002/WRCR.20169
Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs
Publisher: Elsevier BV
Date: 2019
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.
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.
LH moments for statistical analysis of extreme events
Publisher: American Geophysical Union (AGU)
Date: 12-1997
DOI: 10.1029/97WR02134
Ensemble forecasts of monthly catchment rainfall out to long lead times by post-processing coupled general circulation model output
Publisher: Elsevier BV
Date: 11-2014
Assimilation of stream discharge for flood forecasting
Publisher: American Geophysical Union (AGU)
Date: 05-2015
DOI: 10.1002/2014WR016667
Limiting cases of water fluxes at the land surface
Publisher: Elsevier BV
Date: 03-1994
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.
Frequency analysis of water consumption for metropolitan area of Melbourne
Publisher: Elsevier BV
Date: 06-2001
Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts
Publisher: American Meteorological Society
Date: 12-2019
Abstract: Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants of a censored regression model with conditional heteroscedasticity (CRCH). For the fourth factor, we compared CRCH as an ex le of the distributional regression with a joint probability model. The results show that the CRCH with normal quantile transformation (NQT) or power transformation performs better than the CRCH with log–sinh transformation for most of the subbasins in Huai River basin with a subhumid climate. The incorporation of ensemble spread as a predictor in CRCH models can improve forecast skill in our research region at short lead times. The influence of different objective functions (minimum continuous ranked probability score or maximum likelihood) on postprocessed results is limited to a few relatively dry subbasins in the research region. Both the distributional regression and the joint probability models have their advantages, and they are both able to achieve reliable and skillful forecasts.
Engendering stakeholder ownership in scenario planning
Publisher: Elsevier BV
Date: 02-2015
Contribution from frozen soil meltwater to runoff in an in-land river basin under water scarcity by isotopic tracing in northwestern China
Publisher: Elsevier BV
Date: 2016
Direct Deposition of Jammed Architectural Structures
Publisher: Springer International Publishing
Date: 26-08-2018
Upskilling low-fidelity hydrodynamic models of flood inundation through spatial analysis and Gaussian Process learning
Publisher: Wiley
Date: 17-07-2022
Water balance comparison between a dry and a wet landfill — a full-scale experiment
Publisher: Elsevier BV
Date: 09-2001
Evaluation of Downscaled POAMA M24 for Monthly and 3-Monthly Streamflow Forecasts
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Upskilling low-fidelity hydrodynamic models of flood inundation through spatial analysis and Gaussian Process learning
Publisher: Wiley
Date: 11-04-2022
Minimisation or remediation: A cost benefit comparison of two approaches for managing irrigation-induced deep percolation
Publisher: Elsevier BV
Date: 02-2008
An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm
Publisher: Elsevier BV
Date: 12-2019
Supercharging hydrodynamic inundation models for instant flood insight
Publisher: Springer Science and Business Media LLC
Date: 11-09-2023
The POT model described by the generalized Pareto distribution with Poisson arrival rate
Publisher: Elsevier BV
Date: 12-1991
An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia
Publisher: Copernicus GmbH
Date: 19-08-2019
DOI: 10.5194/HESS-23-3387-2019
Abstract: Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE′ score and the components of the KGE′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Spatial Mode-based Calibration (SMoC) of Forecast Precipitation Fields from Numerical Weather Prediction Models
Publisher: Wiley
Date: 31-03-2022
Generalized extreme value distribution fitted by LH moments for low-flow frequency analysis
Publisher: American Geophysical Union (AGU)
Date: 06-2007
DOI: 10.1029/2006WR004913
Simulating the water balance of border-check irrigated pasture on a cracking soil
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA02125
Abstract: The irrigated dairy industry relies on perennial pasture and is a major user of water in the Murray–Darling Basin of Australia. The sustainability of the irrigated dairy industry is threatened by high watertables and land salinisation. Options to alleviate these problems by reducing deep drainage are required. This paper assesses the potential to use the simulation model 'SWAP' to appraise options for reducing deep drainage. Minor modifications were made to SWAP so that it could simulate border-check irrigated pasture on a cracking soil. The model was tested against lysimeter data describing the water balance of irrigated pasture. Evapotranspiration, runoff, infiltration, soil water storage and deep drainage were well simulated when infiltration through soil cracks was modelled using the physically based approached in SWAP. Large errors in evapotranspiration, infiltration, runoff, soil water storage and deep drainage occurred when the process of infiltration through cracks was not simulated. Slight improvements in model predictions were achieved by specifying monthly crop factors, as opposed to a constant annual crop factor. However, a constant annual crop factor should be sufficiently accurate for most studies of deep drainage under border-check irrigated pastures.
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
An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions
Publisher: Elsevier BV
Date: 02-2020
Contributions of local terrestrial evaporation and transpiration to precipitation using δ 18 O and D-excess as a proxy in Shiyang inland river basin in China
Publisher: Elsevier BV
Date: 11-2016
Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems
Publisher: MDPI AG
Date: 05-03-2022
DOI: 10.3390/RS14051286
Abstract: Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model
Publisher: Elsevier BV
Date: 09-2015
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.
Comparison of weather radar, numerical weather prediction and gauge-based rainfall estimates
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-12-2011
Material- and Fabrication-informed Design of Structurally-sound Jammed Architectural Structures
Publisher: ETH Zurich
Date: 2019
Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China
Publisher: American Geophysical Union (AGU)
Date: 19-06-2014
DOI: 10.1002/2013JD021162
Seasonal Forecasts of Unregulated Inflows into the Murray River, Australia
Publisher: Springer Science and Business Media LLC
Date: 02-03-2013
Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia
Publisher: American Geophysical Union (AGU)
Date: 03-2015
DOI: 10.1002/2014WR016163
Calibration, Bridging, and Merging to Improve GCM Seasonal Temperature Forecasts in Australia
Publisher: American Meteorological Society
Date: 06-2016
Abstract: There are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical model. Bridging is producing additional forecasts using statistical models that have other GCM output variables (e.g., SST) as predictors. It is demonstrated that merging calibration and bridging forecasts through CBaM effectively improves the skill of POAMA seasonal minimum and maximum temperature forecasts for Australia. It is demonstrated that CBaM produces bias-corrected forecasts that are reliable in ensemble spread and reduces forecasts to climatology when there is no evidence of forecasting skill. This work will help enable the adoption of GCM forecasts by climate-sensitive industries for quantitative modeling and decision-making.
An economic analysis of conversion from border-check to centre pivot irrigation on dairy farms in the Murray Dairy Region, Australia
Publisher: Springer Science and Business Media LLC
Date: 02-03-2007
Does improved SSTA prediction ensure better seasonal rainfall forecasts?
Publisher: American Geophysical Union (AGU)
Date: 05-2015
DOI: 10.1002/2014WR015997
Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites
Publisher: Wiley
Date: 19-11-2019
DOI: 10.1002/JOC.6346
A review of advances in flash flood forecasting
Publisher: Wiley
Date: 15-03-2011
DOI: 10.1002/HYP.8040
A lysimeter study of the water balance of border-check irrigated perennial pasture
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA03049
Abstract: The dairy industry is a major user of water in northern Victoria and southern New South Wales. Water is typically applied to pasture using the border-check irrigation system. The border-check system is largely gravity driven and thus energy efficient. However, deep drainage can potentially be high because the system allows only limited control over the depth of water applied in each irrigation event. For this reason, heavy soils are regarded as the most suitable for border-check irrigation. This study quantified net deep drainage (deep drainage less capillary rise) under border-check irrigated pasture on a Goulburn clay loam soil. Additionally, the study investigated the extent to which irrigation frequency and watertable conditions influence water use, dry matter production and deep drainage. The water balance and dry matter production were monitored over 2.5 years in a lysimeter facility in northern Victoria. The Goulburn clay loam is representative of the heavier textured soils used for border-check irrigation of pasture in northern Victoria. The average measured net deep drainage was 4 mm/year. This indicates that relatively small levels of net deep drainage can be achieved under well-managed border-check irrigation on a Goulburn clay loam soil. Net deep drainage losses were greatest following winter, when rainfall exceeded pasture water use for an extended period. Increasing the interval between irrigation events resulted in reduced plant water use, infiltration of irrigation water, rainfall runoff and pasture production. However, increasing the interval did not impact on net deep drainage or water use efficiency. Depth of watertable had a relatively minor impact on the water balance.
Potential cumulative impacts on river flow volume from increased groundwater extraction under the Murray-Darling Basin Plan
Publisher: Informa UK Limited
Date: 02-07-2020
Calibrating anomalies improves forecasting of daily reference crop evapotranspiration
Publisher: Elsevier BV
Date: 07-2022
Step-function response of muskingum reach
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-1993
The influence from the shrinking cryosphere and strengthening evopotranspiration on hydrologic process in a cold basin, Qilian Mountains
Publisher: Elsevier BV
Date: 09-2016
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.
A noise adaptive approach for nodal water demand estimation in water distribution systems
Publisher: Elsevier BV
Date: 03-2021
Selecting reference streamflow forecasts to demonstrate the performance of NWP-forced streamflow forecasts
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
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.
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.
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.
Approximate Goodness-of-Fit Tests of fitted generalized extreme value distributions using LH moments
Publisher: American Geophysical Union (AGU)
Date: 12-1998
DOI: 10.1029/98WR02364
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.
Optimising seasonal streamflow forecast lead time for operational decision making in Australia
Publisher: Copernicus GmbH
Date: 10-10-2016
DOI: 10.5194/HESS-20-4117-2016
Abstract: Abstract. Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0–14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to −15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.
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 method for coupling daily and monthly time scales in stochastic generation of rainfall series
Publisher: Elsevier BV
Date: 11-2007
Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon
Publisher: Elsevier BV
Date: 07-2021
Achieving effective calibration of precipitation forecasts over a continental scale
Publisher: Elsevier BV
Date: 06-2021
Which precipitation forecasts to use? Deterministic versus coarser‐resolution ensemble NWP models
Publisher: Wiley
Date: 28-12-2021
DOI: 10.1002/QJ.3952
A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations
Publisher: Springer Science and Business Media LLC
Date: 20-11-2016
An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications – A case study on prediction of sugarcane yield in Australia
Publisher: Elsevier BV
Date: 09-2020
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
Ability of an Australian reanalysis dataset to characterise sub-daily precipitation
Publisher: Copernicus GmbH
Date: 04-06-2020
DOI: 10.5194/HESS-24-2951-2020
Abstract: Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.
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 Bayesian hierarchical spatio-temporal rainfall model
Publisher: Informa UK Limited
Date: 15-05-2018
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.
Understanding and predicting deep percolation under surface irrigation
Publisher: American Geophysical Union (AGU)
Date: 12-2008
DOI: 10.1029/2007WR006380
Comment on “An investigation of the relationship between ponded and constant flux rainfall infiltration” by A. Poulovassilis et al.
Publisher: American Geophysical Union (AGU)
Date: 04-2010
DOI: 10.1029/92WR02648
Quantitative evaluation on the influence from cryosphere meltwater on runoff in an inland river basin of China
Publisher: Elsevier BV
Date: 08-2016
A strategy for quality controlling hourly rainfall observations and its impact on hourly streamflow simulations
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 29-11-2015
A method to extend temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates
Publisher: Elsevier BV
Date: 02-2020
Unbiased plotting positions for historical flood information
Publisher: Elsevier BV
Date: 05-1991
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
A Bayesian method for multi-site stochastic data generation: Dealing with non-concurrent and missing data, variable transformation and parameter uncertainty
Publisher: Elsevier BV
Date: 04-2008
Using partial probability weighted moments to fit the extreme value distributions to censored samples
Publisher: American Geophysical Union (AGU)
Date: 06-1996
DOI: 10.1029/96WR00352
Efficient River Management using Stochastic MPC and Ensemble Forecast of Uncertain In-flows ⁎ ⁎The first and the third authors acknowledge the financial support from the Australian Research Council L
Publisher: Elsevier BV
Date: 2018
STEP-FUNCTION RESPONSE OF MUSKINGUM REACH - CLOSURE
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-1994
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
Options for management of saline groundwater in an irrigated farming system
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/EA02179
Abstract: Groundwater pumping is used to control salinity problems in many irrigation regions of Australia. Options for managing the pumped groundwater are required to be consistent with achieving high farm production levels and minimising salt export from irrigation regions. In this study, pasture production and economic aspects of 6 options for managing pumped groundwater are compared. The 6 options include (i) complete farm reuse of pumped groundwater for irrigation (ii) complete export to river system (iii) complete disposal to evaporation basin (iv) partial farm reuse with reduced salt export (v) partial farm reuse with reduced disposal to evaporation basin and (vi) partial farm reuse with disposal to a salt tolerant forage crop. The comparison between the 6 options is made for a hypothetical 100 ha dairy farm that has a perennial pasture based production system. Complete farm reuse was the most economic option in areas where groundwater salinity is low ( dS/m). Partial farm reuse with disposal of surplus groundwater to a salt tolerant forage species was the most economical option for managing higher salinity groundwater.
Challenges for including error updating in real-time hydrological error models
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
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.
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
The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall‐Runoff Models
Publisher: American Geophysical Union (AGU)
Date: 09-1991
DOI: 10.1029/91WR01305
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
A stochastic weather generation method for temporal precipitation simulation
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 12-2013
Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations
Publisher: American Geophysical Union (AGU)
Date: 06-2023
DOI: 10.1029/2022WR033836
Abstract: High computational cost is often the most limiting factor when running high‐resolution hydrodynamic models to simulate spatial‐temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low‐fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low‐resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high‐resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high‐resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent‐based LSG model rather than deriving it from water depth predictions.
An evaluation of numerical weather prediction based rainfall forecasts
Publisher: Informa UK Limited
Date: 05-08-2016
The utility of L-moment ratio diagrams for selecting a regional probability distribution
Publisher: Informa UK Limited
Date: 02-2001
Monthly versus daily water balance models in simulating monthly runoff
Publisher: Elsevier BV
Date: 07-2011
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