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

2013 ◽  
Vol 17 (2) ◽  
pp. 795-804 ◽  
Author(s):  
P. Pokhrel ◽  
D. E. Robertson ◽  
Q. J. Wang
Keyword(s):  
Joint Probability ◽  
Hydrologic Model ◽  
Balance Model ◽  
Systematic Bias ◽  
Prediction Errors ◽  
Eastern Australia ◽  
Monthly Streamflow ◽  
Heteroscedastic Errors ◽  
Monthly Water Balance ◽  
Quantifying Uncertainty

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.

scholarly journals Improving statistical forecasts of seasonal streamflows using hydrological model output

2012 ◽  
Vol 9 (7) ◽  
pp. 8701-8736 ◽  
Author(s):  
D. E. Robertson ◽  
P. Pokhrel ◽  
Q. J. Wang
Keyword(s):  
Hydrological Model ◽  
Historical Record ◽  
Base Flow ◽  
Seasonal Forecasting ◽  
Streamflow Forecasting ◽  
Eastern Australia ◽  
True Source ◽  
Hybrid Forecasting ◽  
Forecasting System ◽  
Seasonal Streamflow

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.

Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime

2014 ◽  
Vol 15 (6) ◽  
pp. 2470-2483 ◽  
Author(s):  
Tushar Sinha ◽  
A. Sankarasubramanian ◽  
Amirhossein Mazrooei
Keyword(s):  
Real Time ◽  
Lead Time ◽  
General Circulation ◽  
Streamflow Forecasting ◽  
Climate Forecasts ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Temporal Disaggregation ◽  
Seasonal Streamflow ◽  
Spatial Downscaling

Abstract Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

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 Improving statistical forecasts of seasonal streamflows using hydrological model output

2013 ◽  
Vol 17 (2) ◽  
pp. 579-593 ◽  
Author(s):  
D. E. Robertson ◽  
P. Pokhrel ◽  
Q. J. Wang
Keyword(s):  
Hydrological Model ◽  
Historical Record ◽  
Base Flow ◽  
Seasonal Forecasting ◽  
Streamflow Forecasting ◽  
Eastern Australia ◽  
True Source ◽  
Hybrid Forecasting ◽  
Forecasting System ◽  
Seasonal Streamflow

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.

Seasonal streamflow forecasting in eastern Australia and the El Niño-Southern Oscillation

1998 ◽  
Vol 34 (11) ◽  
pp. 3035-3044 ◽  
Author(s):  
Thomas C. Piechota ◽  
Francis H. S. Chiew ◽  
John A. Dracup ◽  
Thomas A. McMahon
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Streamflow Forecasting ◽  
Eastern Australia ◽  
Seasonal Streamflow

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.

scholarly journals A Bayesian Approach to Predictor Selection for Seasonal Streamflow Forecasting

2012 ◽  
Vol 13 (1) ◽  
pp. 155-171 ◽  
Author(s):  
David E. Robertson ◽  
Q. J. Wang
Keyword(s):  
Bayes Factor ◽  
Selection Method ◽  
Future Climate ◽  
Streamflow Forecasting ◽  
River Catchment ◽  
Independent Events ◽  
Skill Scores ◽  
Eastern Australia ◽  
Climate Influences ◽  
Seasonal Streamflow

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.

scholarly journals Role of Retrospective Forecasts of GCMs Forced with Persisted SST Anomalies in Operational Streamflow Forecasts Development

2008 ◽  
Vol 9 (2) ◽  
pp. 212-227 ◽  
Author(s):  
A. Sankarasubramanian ◽  
Upmanu Lall ◽  
Susan Espinueva
Keyword(s):  
The Philippines ◽  
Monthly Precipitation ◽  
Streamflow Forecasting ◽  
Spatially Correlated ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Streamflow Variability ◽  
Tropical Ssts ◽  
Seasonal Streamflow ◽  
Sst Anomalies

Abstract Seasonal streamflow forecasts contingent on climate information are essential for water resources planning and management as well as for setting up contingency measures during extreme years. In this study, operational streamflow forecasts are developed for a reservoir system in the Philippines using ECHAM4.5 precipitation forecasts (EPF) obtained using persisted sea surface temperature (SST) scenarios. Diagnostic analyses on SST conditions show that the tropical SSTs influence the streamflow during extreme years, whereas the local SSTs (0°–25°N, 115°–130°E) account for streamflow variability during normal years. Given that the EPF, local, and tropical SST conditions are spatially correlated, principal components regression (PCR) is employed to downscale the GCM-predicted precipitation fields and SST anomalies to monthly streamflow forecasts and to update them every month within the season using the updated EPF and SST conditions. These updated forecasts improve the prediction of monthly streamflows within the season in comparison to the skill of the monthly streamflow forecasts issued at the beginning of the season. It is also shown that the streamflow forecasting model developed using EPF under persisted SST conditions performs well upon employing EPF obtained under predicted SSTs as predictor. This has potential implications in the development of operational streamflow forecasts and statistical downscaling, which requires adequate years of retrospective GCM forecasts for recalibration. Finally, the study also shows that predicting the seasonal streamflow using the monthly precipitation forecasts reproduces the observed seasonal total better than the conventional approach of using seasonal precipitation forecasts to predict the seasonal streamflow.

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