‘Where the Creeks Run Dry or Ten Feet High’: A Probabilistic Approach to Stage Height Forecasting in Australia

Athukorala, R; Mohasel Afshar, H; Cripps, S; Vervoort, RW

‘Where the Creeks Run Dry or Ten Feet High’: A Probabilistic Approach to Stage Height Forecasting in Australia

Athukorala, R Mohasel Afshar, H

Cripps, S

Vervoort, RW

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Publisher:: AMER GEOPHYSICAL UNION
Publication Type:: Journal Article
Citation:: Water Resources Research, 2025, 61, (9)
Issue Date:: 2025-09-01

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Field	Value	Language
dc.contributor.author	Athukorala, R
dc.contributor.author	Mohasel Afshar, H https://orcid.org/0009-0009-9748-9116
dc.contributor.author	Cripps, S https://orcid.org/0000-0003-3207-172X
dc.contributor.author	Vervoort, RW
dc.date.accessioned	2026-03-09T04:29:30Z
dc.date.available	2026-03-09T04:29:30Z
dc.date.issued	2025-09-01
dc.identifier.citation	Water Resources Research, 2025, 61, (9)
dc.identifier.issn	0043-1397
dc.identifier.issn	1944-7973
dc.identifier.uri	http://hdl.handle.net/10453/193800
dc.description.abstract	Streamflow prediction is a challenging but important task as it affects many processes in the environment. In Australia, the task is particularly complex because the transition from low to high flows can be rapid, and the overall variability is high. Accurate water resource management in Australia requires models that can accommodate these unique characteristics. Complicating matters further, streamflow is not directly measured but inferred from stage height measurements using rating curves. To address the challenges posed by the unique Australian hydrological features mentioned above, the present work proposes a model that: (a) directly predicts stage height rather than streamflow, (b) is sufficiently complex to learn the high variability of stage heights and (c) quantifies epistemic and aleatoric uncertainty by using a Bayesian framework. Our model is a Bayesian Hierarchical Mixture of Experts (BHME) model, with two mixture components. The two components are gamma densities in which the mean and variance are parameterized to depend on upstream stage height and rainfall from the previous day. The mixture weights are also parameterized to depend on the previous day's upstream stage height and rainfall through a logit link function. The method was tested on three gauging stations in the Orara and Richmond rivers. The BHME model provides better fit to the data and better predictive densities compared to a single component model. Notably, the BHME model excels in forecasting less frequent, high stage heights, crucial for managing high flow events. Additionally, transitions between mixture components offer insights into sudden stage height regime changes.
dc.language	English
dc.publisher	AMER GEOPHYSICAL UNION
dc.relation.ispartof	Water Resources Research
dc.relation.isbasedon	10.1029/2025WR040087
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0905 Civil Engineering, 0907 Environmental Engineering
dc.subject.classification	Environmental Engineering
dc.subject.classification	3707 Hydrology
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4011 Environmental engineering
dc.title	‘Where the Creeks Run Dry or Ten Feet High’: A Probabilistic Approach to Stage Height Forecasting in Australia
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0905 Civil Engineering
utslib.for	0907 Environmental Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Education and Students)
pubs.organisational-group	University of Technology Sydney/DVC (Education and Students)/Human Technology Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/
dc.date.updated	2026-03-09T04:29:23Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	61
utslib.citation.issue	9

Abstract:

Streamflow prediction is a challenging but important task as it affects many processes in the environment. In Australia, the task is particularly complex because the transition from low to high flows can be rapid, and the overall variability is high. Accurate water resource management in Australia requires models that can accommodate these unique characteristics. Complicating matters further, streamflow is not directly measured but inferred from stage height measurements using rating curves. To address the challenges posed by the unique Australian hydrological features mentioned above, the present work proposes a model that: (a) directly predicts stage height rather than streamflow, (b) is sufficiently complex to learn the high variability of stage heights and (c) quantifies epistemic and aleatoric uncertainty by using a Bayesian framework. Our model is a Bayesian Hierarchical Mixture of Experts (BHME) model, with two mixture components. The two components are gamma densities in which the mean and variance are parameterized to depend on upstream stage height and rainfall from the previous day. The mixture weights are also parameterized to depend on the previous day's upstream stage height and rainfall through a logit link function. The method was tested on three gauging stations in the Orara and Richmond rivers. The BHME model provides better fit to the data and better predictive densities compared to a single component model. Notably, the BHME model excels in forecasting less frequent, high stage heights, crucial for managing high flow events. Additionally, transitions between mixture components offer insights into sudden stage height regime changes.

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http://hdl.handle.net/10453/193800