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

Bennett, James; Wang, QJ; Pokhrel, Prafulla; Robertson, David

doi:10.5194/nhess-14-219-2014

Cited by 22 publications

(15 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of data-driven modelling studies have demonstrated that monthly streamflow lagged by 1 month (or more) provided some useful information for forecasting at a 1-month lead time (e.g. Bennett et al, 2014;Humphrey et al, 2016;Yang et al, 2017). This study demonstrates that these benefits also hold when CRR models, rather than data-driven approaches, are used as the forecasting model.…”

Section: Beneficial Impact Of State Updating On Forecast Performancementioning

confidence: 67%

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Gibbs

McInerney

Humphrey

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

show abstract

Section: Beneficial Impact Of State Updating On Forecast Performancementioning

confidence: 67%

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Gibbs

McInerney

Humphrey

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…addressing issues such as model selection and calibration, observed catchment wetness, lagged and real-time forecasts, uncertainties around extremes, bias correction, requirements for high-resolution rainfall forecasts, integration with communication and response systems -is an active area of research in Australia (e.g. Cuo et al, 2011;Tuteja et al, 2011;Wang et al, 2012;Schepen et al, 2012b).…”

Section: Merging S2s Rainfall Forecasts and Hydrological Forecastsmentioning

confidence: 99%

“…Seasonal streamflow forecasts are provided operationally at around 70 sites across Australia, containing statements advising where the percentage chance of low, near-median or high streamflows may be expected at selected locations and where forecast skill is deemed to be acceptable through forecast verification. Bennett et al (2014) recently focused on forecasting high streamflows in southeast Australia for the next 1-3 months using the BJP modelling system, finding positive forecast skill in 1-month high streamflow forecasts. However, whilst longer-range streamflow forecasts are capable of providing additional information when there is not enough data to make specific flood predictions, they do not provide information about flood class, predicted timing or frequency, severity or extent of flood (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these have been used to develop skilful probabilistic forecasts of total seasonal streamflows (Wang et al, 2009;Piechota et al, 1998;Sharma, 2000) but there has, to date, been limited research on teleconnections to runoff on the extended-range timescales or to extreme rainfall on either the seasonal or S2S lead times in Australia. Bennett et al (2014), however, show that including lagged climate indices as predictors adds little skill to 1-month high streamflow forecasts based on the BJP modelling system, but that catchment wetness is by far the most important source of predictability where catchments have longer (i.e. month-to-month) flood memory.…”

Section: Identifying Sources Of Forecast Skillmentioning

confidence: 99%

See 1 more Smart Citation

Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

2015

View full text Add to dashboard Cite

Abstract. Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

show abstract

“…Innovative applications of Bayes's theorem to hydrological forecasting have recently successfully emerged [45][46][47][48]. These applications quantify the uncertainty in post-process deterministic streamflow forecasts [49].…”

Section: Introductionmentioning

confidence: 99%

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Molina

Zazo

Rodríguez‐Gonzálvez

et al. 2016

Water

View full text Add to dashboard Cite

Hydrological series are largely characterized by a strong random component in their behavior. More noticeable changes in the behavior patterns of rainfall/runoff temporal series are recently being observed. These modifications are not a trivial issue, especially in regards to peculiarities, non-linearities, diffused influences or higher time orders of dependence. This study mainly aimed to analyze the temporal dependence of an annual runoff series dynamically. This approach comprises a coupling between classic techniques (Autoregressive Moving Average Model, ARMA) and novel ones, based on Artificial Intelligent for hydrological research (Bayesian Networks, BNs). An ARMA model was built to provide reliable data to populate BNs. Then, causal reasoning, through Bayes's theorem, allows the identification of the logic structure of temporal dependences within time series. Furthermore, the resultant conditional probability permits the quantification of the relative percentage of annual runoff change, and provides the right time order of dependence. This research introduces an original methodology able to build a logic structure for a stochastic analysis of temporal behavior. This approach also aimed to provide a powerful and graphic modeling method for improving the understanding of the dynamic runoff series temporal behavior. This was successfully demonstrated in two unregulated river basin stretches, belonging to the Duero river basin which is the largest basin in Spain.

show abstract

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

Cited by 22 publications

References 47 publications

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Contact Info

Product

Resources

About