Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

2016 ◽  
Vol 541 ◽  
pp. 839-849 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Andrew Schepen ◽  
Q.J. Wang
Keyword(s):  
Joint Probability ◽  
Ensemble Forecasting ◽  
Seasonal Streamflow ◽  
Probability Modelling ◽  
Modelling Approach

scholarly journals The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

2014 ◽  
Vol 14 (2) ◽  
pp. 219-233 ◽  
Author(s):  
J. C. Bennett ◽  
Q. J. Wang ◽  
P. Pokhrel ◽  
D. E. Robertson
Keyword(s):  
Large Scale ◽  
Bayesian Model Averaging ◽  
Emergency Services ◽  
Joint Probability ◽  
Model Averaging ◽  
Climate Indices ◽  
Warning Systems ◽  
Forecast Period ◽  
Considerable Benefit ◽  
Probability Modelling

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.

scholarly journals A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

2012 ◽  
Vol 9 (10) ◽  
pp. 11199-11225
Author(s):  
P. Pokhrel ◽  
D. E. Robertson ◽  
Q. J. Wang
Keyword(s):  
Time Scales ◽  
Process Model ◽  
Joint Probability ◽  
Prediction Errors ◽  
Streamflow Forecasting ◽  
Post Process ◽  
Eastern Australia ◽  
Monthly Streamflow ◽  
Model Predictions ◽  
Seasonal Streamflow

Abstract. Hydrological post-processors refer here to statistical models that are applied to hydrological model predictions to further reduce prediction errors and to quantify remaining uncertainty. For streamflow predictions, post-processors are generally applied to daily or sub-daily time scales. For many applications such as seasonal streamflow forecasting and water resources assessment, monthly volumes of streamflows are of primary interest. While it is possible to aggregate post-processed daily or sub-daily predictions to monthly time scales, the monthly volumes so produced may not have the least errors achievable and may not be reliable in uncertainty distributions. Post-processing directly at the monthly time scale is likely to be more effective. In this study, we investigate the use of a Bayesian joint probability modelling approach to directly post-process model predictions of monthly streamflow volumes. 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.

scholarly journals A new method for post-processing daily sub-seasonal to seasonal rainfall forecasts from GCMs and evaluation for 12 Australian catchments

2017 ◽  
Author(s):  
Andrew Schepen ◽  
Tongtiegang Zhao ◽  
Quan J. Wang ◽  
David E. Robertson
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Joint Probability ◽  
Global Climate Models ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Time Step ◽  
Post Process ◽  
Coherent Forecasts ◽  
Seasonal Streamflow

Abstract. Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal time scales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. However, for hydrological applications, GCM forecasts are often biased and unreliable in uncertainty spread, and therefore calibration is required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S) based on the Bayesian joint probability approach for calibrating daily forecasts and the Schaake Shuffle approach for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

scholarly journals A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

2018 ◽  
Vol 22 (2) ◽  
pp. 1615-1628 ◽  
Author(s):  
Andrew Schepen ◽  
Tongtiegang Zhao ◽  
Quan J. Wang ◽  
David E. Robertson
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Joint Probability ◽  
Global Climate Models ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Time Step ◽  
Post Process ◽  
Coherent Forecasts ◽  
Seasonal Streamflow

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.

scholarly journals Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

2017 ◽  
Author(s):  
Stephen P. Charles ◽  
Quan J. Wang ◽  
Mobin-ud-Din Ahmad ◽  
Danial Hashmi ◽  
Andrew Schepen ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Joint Probability ◽  
Indus Basin ◽  
Climate Index ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Upper Indus Basin ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

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 (ESP) 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 show that the BJP approach using two predictors (March flow and an 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.

Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

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