Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5669-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5669-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Nov 2022
Research article |
| 10 Nov 2022
| 10 Nov 2022
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
David McInerney
CORRESPONDING AUTHOR
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Mark Thyer
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Dmitri Kavetski
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Richard Laugesen
Bureau of Meteorology, Canberra, ACT, Australia
Fitsum Woldemeskel
Bureau of Meteorology, Melbourne, VIC, Australia
Narendra Tuteja
Bureau of Meteorology, Canberra, ACT, Australia
George Kuczera
School of Engineering, University of Newcastle, Callaghan, NSW, Australia
Related authors
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 27, 873–893, https://doi.org/10.5194/hess-27-873-2023, https://doi.org/10.5194/hess-27-873-2023, 2023
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Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
Fitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018, https://doi.org/10.5194/hess-22-6257-2018, 2018
Short summary
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This paper evaluates several schemes for post-processing monthly and seasonal streamflow forecasts using the Australian Bureau of Meteorology's streamflow forecasting system. Through evaluation across 300 catchments, the best-performing scheme has been identified, which is found to substantially improve important aspects of the forecast quality. This finding is significant because the improved forecasts help water managers and users of the service to make better-informed decisions.
Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, https://doi.org/10.5194/hess-22-871-2018, 2018
Short summary
Short summary
This work developed models to predict how much water will be available in the next month to maximise environmental and social outcomes in southern Australia. Initialising the models with observed streamflow data, instead of warmed up by rainfall data, improved the results, even at a monthly lead time, making sure only data representative of the forecast period to develop the models were also important. If this step was ignored, and instead all data were used, poor predictions could be produced.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 27, 873–893, https://doi.org/10.5194/hess-27-873-2023, https://doi.org/10.5194/hess-27-873-2023, 2023
Short summary
Short summary
Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
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We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
Hapu Arachchige Prasantha Hapuarachchi, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, Xiaoyong Sophie Zhang, David Ewen Robertson, James Clement Bennett, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 26, 4801–4821, https://doi.org/10.5194/hess-26-4801-2022, https://doi.org/10.5194/hess-26-4801-2022, 2022
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Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Marco Dal Molin, Dmitri Kavetski, and Fabrizio Fenicia
Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, https://doi.org/10.5194/gmd-14-7047-2021, 2021
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This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
Bree Bennett, Mark Thyer, Michael Leonard, Martin Lambert, and Bryson Bates
Hydrol. Earth Syst. Sci., 23, 4783–4801, https://doi.org/10.5194/hess-23-4783-2019, https://doi.org/10.5194/hess-23-4783-2019, 2019
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A new stochastic rainfall model evaluation framework is introduced, with three key features: (1) streamflow-based, to directly evaluate modelled streamflow performance, (2) virtual, to avoid confounding errors in hydrological models or data, and (3) targeted, to isolate errors according to specific sites/months. The framework identified the importance of rainfall in the
wetting-upmonths for providing reliable predictions of streamflow over the entire year despite their low flow volumes.
Lanying Zhang, George Kuczera, Anthony S. Kiem, and Garry Willgoose
Hydrol. Earth Syst. Sci., 22, 6399–6414, https://doi.org/10.5194/hess-22-6399-2018, https://doi.org/10.5194/hess-22-6399-2018, 2018
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Analyses of run lengths of Pacific decadal variability (PDV) suggest that there is no significant difference between run lengths in positive and negative phases of PDV and that it is more likely than not that the PDV run length has been non-stationary in the past millennium. This raises concerns about whether variability seen in the instrumental record (the last ~100 years), or even in the shorter 300–400 year paleoclimate reconstructions, is representative of the full range of variability.
Fitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018, https://doi.org/10.5194/hess-22-6257-2018, 2018
Short summary
Short summary
This paper evaluates several schemes for post-processing monthly and seasonal streamflow forecasts using the Australian Bureau of Meteorology's streamflow forecasting system. Through evaluation across 300 catchments, the best-performing scheme has been identified, which is found to substantially improve important aspects of the forecast quality. This finding is significant because the improved forecasts help water managers and users of the service to make better-informed decisions.
Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, https://doi.org/10.5194/hess-22-871-2018, 2018
Short summary
Short summary
This work developed models to predict how much water will be available in the next month to maximise environmental and social outcomes in southern Australia. Initialising the models with observed streamflow data, instead of warmed up by rainfall data, improved the results, even at a monthly lead time, making sure only data representative of the forecast period to develop the models were also important. If this step was ignored, and instead all data were used, poor predictions could be produced.
A. F. M. Kamal Chowdhury, Natalie Lockart, Garry Willgoose, George Kuczera, Anthony S. Kiem, and Nadeeka Parana Manage
Hydrol. Earth Syst. Sci., 21, 6541–6558, https://doi.org/10.5194/hess-21-6541-2017, https://doi.org/10.5194/hess-21-6541-2017, 2017
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Stochastic rainfall models are required to be be able to assess the reliability of dams used for urban water supply. Traditional Markov chain stochastic models do well at reproducing the mean and variance of rainfall at daily to weekly resolution but fail to simultaneously reproduce the variability of monthly to decadal rainfall. This paper presents four new extensions to Markov chain models that address this decadal deficiency and compares their performance for two field sites.
Xiaoyong Sophie Zhang, Gnanathikkam E. Amirthanathan, Mohammed A. Bari, Richard M. Laugesen, Daehyok Shin, David M. Kent, Andrew M. MacDonald, Margot E. Turner, and Narendra K. Tuteja
Hydrol. Earth Syst. Sci., 20, 3947–3965, https://doi.org/10.5194/hess-20-3947-2016, https://doi.org/10.5194/hess-20-3947-2016, 2016
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The hydrologic reference stations website (www.bom.gov.au/water/hrs/), developed by the Australia Bureau of Meteorology, is a one-stop portal to access long-term and high-quality streamflow information for 222 stations across Australia. This study investigated the streamflow variability and inferred trends in water availability for those stations. The results present a systematic analysis of recent hydrological changes in Australian rivers, which will aid water management decision making.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Uncertainty analysis
Bayesian calibration of a flood simulator using binary flood extent observations
Intercomparison of global reanalysis precipitation for flood risk modelling
An uncertainty partition approach for inferring interactive hydrologic risks
Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models
Uncertainty quantification of floodplain friction in hydrodynamic models
Developing a drought-monitoring index for the contiguous US using SMAP
Technical note: Assessment of observation quality for data assimilation in flood models
Sedimentation monitoring including uncertainty analysis in complex floodplains: a case study in the Mekong Delta
Assessing the impact of uncertainty on flood risk estimates with reliability analysis using 1-D and 2-D hydraulic models
The transferability of hydrological models under nonstationary climatic conditions
Why hydrological predictions should be evaluated using information theory
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale
Confidence intervals for the coefficient of L-variation in hydrological applications
Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023, https://doi.org/10.5194/hess-27-1089-2023, 2023
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We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.
Fergus McClean, Richard Dawson, and Chris Kilsby
Hydrol. Earth Syst. Sci., 27, 331–347, https://doi.org/10.5194/hess-27-331-2023, https://doi.org/10.5194/hess-27-331-2023, 2023
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Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis. We investigate the impact of using four reanalysis products on simulations of past flood events. All reanalysis products underestimated the number of buildings inundated, compared to a benchmark national dataset. These findings show that while global reanalyses provide a useful resource for flood modelling where no other data are available, they may underestimate impact in some cases.
Yurui Fan, Kai Huang, Guohe Huang, Yongping Li, and Feng Wang
Hydrol. Earth Syst. Sci., 24, 4601–4624, https://doi.org/10.5194/hess-24-4601-2020, https://doi.org/10.5194/hess-24-4601-2020, 2020
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
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The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Guilherme Luiz Dalledonne, Rebekka Kopmann, and Thomas Brudy-Zippelius
Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019, https://doi.org/10.5194/hess-23-3373-2019, 2019
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This study presents how the concept of uncertainty quantification can be applied to river engineering problems and how important it is to understand the limitations of numerical models from a probabilistic point of view. We investigated floodplain friction formulations using different uncertainty quantification methods and estimated their contribution in the final results. The analysis of uncertainties is planned to be integrated in future projects and also extended to more complex scenarios.
Sara Sadri, Eric F. Wood, and Ming Pan
Hydrol. Earth Syst. Sci., 22, 6611–6626, https://doi.org/10.5194/hess-22-6611-2018, https://doi.org/10.5194/hess-22-6611-2018, 2018
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Of particular interest to NASA's SMAP-based agricultural applications is a monitoring product that assesses near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for the meaningful assessment of its indices. This study presents initial insights about using SMAP Level 3 and Level 4 for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS).
Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols
Hydrol. Earth Syst. Sci., 22, 3983–3992, https://doi.org/10.5194/hess-22-3983-2018, https://doi.org/10.5194/hess-22-3983-2018, 2018
N. V. Manh, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 17, 3039–3057, https://doi.org/10.5194/hess-17-3039-2013, https://doi.org/10.5194/hess-17-3039-2013, 2013
L. Altarejos-García, M. L. Martínez-Chenoll, I. Escuder-Bueno, and A. Serrano-Lombillo
Hydrol. Earth Syst. Sci., 16, 1895–1914, https://doi.org/10.5194/hess-16-1895-2012, https://doi.org/10.5194/hess-16-1895-2012, 2012
C. Z. Li, L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan
Hydrol. Earth Syst. Sci., 16, 1239–1254, https://doi.org/10.5194/hess-16-1239-2012, https://doi.org/10.5194/hess-16-1239-2012, 2012
S. V. Weijs, G. Schoups, and N. van de Giesen
Hydrol. Earth Syst. Sci., 14, 2545–2558, https://doi.org/10.5194/hess-14-2545-2010, https://doi.org/10.5194/hess-14-2545-2010, 2010
G. Buttafuoco, T. Caloiero, and R. Coscarelli
Hydrol. Earth Syst. Sci., 14, 2319–2327, https://doi.org/10.5194/hess-14-2319-2010, https://doi.org/10.5194/hess-14-2319-2010, 2010
A. Viglione
Hydrol. Earth Syst. Sci., 14, 2229–2242, https://doi.org/10.5194/hess-14-2229-2010, https://doi.org/10.5194/hess-14-2229-2010, 2010
A. P. Jacquin
Hydrol. Earth Syst. Sci., 14, 1681–1695, https://doi.org/10.5194/hess-14-1681-2010, https://doi.org/10.5194/hess-14-1681-2010, 2010
Cited articles
Bauer, D. F.: Constructing Confidence Sets Using Rank Statistics, J. Am. Stat. Assoc., 67, 687–690, https://doi.org/10.2307/2284469, 1972.
Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate – a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. B Met., 57, 289–300, 1995.
Boucher, M.-A. and Ramos, M.-H.: Ensemble Streamflow Forecasts for Hydropower Systems, in: Handbook of Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., Cloke, H. L., and Schaake, J. C., Springer Berlin Heidelberg, Berlin, Heidelberg, 1289–1306, ISBN 978-3-642-39924-4, 2019.
Box, G. E. P. and Cox, D. R.: An analysis of transformations, J. Roy. Stat. Soc. B, 26, 211–252, 1964.
Bureau of Meteorology: Long-range weather, climate and hydrology, http://www.bom.gov.au/climate/
Bureau of Meteorology: Water Data Online, http://www.bom.gov.au/waterdata/ (last access: 8 November 2022), 2022b.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50, 182–197, https://doi.org/10.1002/2012WR012757, 2014.
Evin, G., Thyer, M., Kavetski, D., McInerney, D., and Kuczera, G.: Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity, Water Resour. Res., 50, 2350–2375, https://doi.org/10.1002/2013WR014185, 2014.
Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., and Kavetski, D.: State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application, Hydrol. Earth Syst. Sci., 22, 871–887, https://doi.org/10.5194/hess-22-871-2018, 2018.
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems, Weather Forecast., 15, 559–570, 2000.
Hidalgo-Muñoz, J. M., Gámiz-Fortis, S. R., Castro-Díez, Y., Argüeso, D., and Esteban-Parra, M. J.: Long-range seasonal streamflow forecasting over the Iberian Peninsula using large-scale atmospheric and oceanic information, Water Resour. Res., 51, 3543–3567, https://doi.org/10.1002/2014WR016826, 2015.
Hudson, D., Alves, O., Hendon, H. H., Lim, E., Liu, G., Luo, J. J., MacLachlan, C., Marshall, A. G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M., and Zhou, X.: ACCESS-S1 The new Bureau of Meteorology multi-week to seasonal prediction system, Journal of Southern Hemisphere Earth System Sciences, 67, 132–159, https://doi.org/10.22499/3.6703.001, 2017.
Hunter, J., Thyer, M., McInerney, D., and Kavetski, D.: Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions, J. Hydrol., 603, 126578, https://doi.org/10.1016/j.jhydrol.2021.126578, 2021.
Kavetski, D. and Clark, M. P.: Ancient numerical daemons of conceptual hydrological modeling. Part 2: Impact of time stepping scheme on model analysis and prediction, Water Resour. Res., 46, W10511, https://doi.org/10.1029/2009WR008894, 2010.
Li, M., Wang, Q. J., Bennett, J. C., and Robertson, D. E.: Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting, Hydrol. Earth Syst. Sci., 20, 3561–3579, https://doi.org/10.5194/hess-20-3561-2016, 2016.
McInerney, D., Thyer, M., Kavetski, D., Lerat, J., and Kuczera, G.: Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors, Water Resour. Res., 53, 2199–2239, https://doi.org/10.1002/2016WR019168, 2017.
McInerney, D., Kavetski, D., Thyer, M., Lerat, J., and Kuczera, G.: Benefits of explicit treatment of zero flows in probabilistic hydrological modelling of ephemeral catchments, Water Resour. Res., 55, 11035–11060, https://doi.org/10.1029/2018wr024148, 2019.
McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N., and Kuczera, G.: Multi-temporal hydrological residual error modelling for seamless sub-seasonal streamflow forecasting, Water Resour. Res., 56, e2019WR026979, https://doi.org/10.1029/2019wr026979, 2020.
McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., and Kuczera, G.: Improving the Reliability of Sub-Seasonal Forecasts of High and Low Flows by Using a Flow-Dependent Nonparametric Model, Water Resour. Res., 57, e2020WR029317, https://doi.org/10.1029/2020WR029317, 2021a.
McInerney, D., Thyer, M., and Kavetski, D.: Supporting data for “Improving the reliability of sub-seasonal forecasts of high and low flows by using a flow-dependent non-parametric model” by McInerney et al. (2021), The University of Adelaide [data set], https://doi.org/10.25909/14604180.v1, 2021b.
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D., and Arnold, J. R.: An intercomparison of approaches for improving operational seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, 2017.
Murray-Darling Basin Authority: Basin Environmental Watering Outlook for 2019–20, Murray-Darling Basin Authority, ISBN 978-1-925762-20-4,
https://www.mdba.gov.au/sites/default/files/pubs/Basin-environmental-watering-outlook-2019-2020.pdf (last access: 8 November 2022), 2019.
Pagano, T. C., Shrestha, D. L., Wang, Q. J., Robertson, D., and Hapuarachchi, P.: Ensemble dressing for hydrological applications, Hydrol. Process., 27, 106–116, https://doi.org/10.1002/hyp.9313, 2013.
Pal, I., Lall, U., Robertson, A. W., Cane, M. A., and Bansal, R.: Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India, Hydrol. Earth Syst. Sci., 17, 2131–2146, https://doi.org/10.5194/hess-17-2131-2013, 2013.
Perrin, C., Michel, C., and Andreassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Pokhrel, P., Wang, Q. J., and Robertson, D. E.: The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia, Water Resour. Res., 49, 6671–6687, https://doi.org/10.1002/wrcr.20449, 2013.
Schepen, A., Zhao, T., Wang, Q. J., and Robertson, D. E.: A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments, Hydrol. Earth Syst. Sci., 22, 1615–1628, https://doi.org/10.5194/hess-22-1615-2018, 2018.
Souza Filho, F. A. and Lall, U.: Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: Applications of a multivariate, semiparametric algorithm, Water Resour. Res., 39, 1307, https://doi.org/10.1029/2002WR001373, 2003.
Verkade, J. S., Brown, J. D., Davids, F., Reggiani, P., and Weerts, A. H.: Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine, J. Hydrol., 555, 257–277, https://doi.org/10.1016/j.jhydrol.2017.10.024, 2017.
Wang, Q. J., Pagano, T. C., Zhou, S. L., Hapuarachchi, H. A. P., Zhang, L., and Robertson, D. E.: Monthly versus daily water balance models in simulating monthly runoff, J. Hydrol., 404, 166–175, https://doi.org/10.1016/j.jhydrol.2011.04.027, 2011.
Wang, Q. J., Bennett, J. C., Robertson, D. E., and Li, M.: A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers, Water Resour. Res., 56, e2019WR026128, https://doi.org/10.1029/2019WR026128, 2020.
Welsh, W. D., Vaze, J., Dutta, D., Rassam, D., Rahman, J. M., Jolly, I. D., Wallbrink, P., Podger, G. M., Bethune, M., Hardy, M. J., Teng, J., and Lerat, J.: An integrated modelling framework for regulated river systems, Environ. Modell. Softw., 39, 81–102, https://doi.org/10.1016/j.envsoft.2012.02.022, 2013.
Wilks, D. S.: On “field significance” and the false discovery rate, J. Appl. Meteorol. Clim., 45, 1181–1189, 2006.
Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., Tuteja, N., and Kuczera, G.: Evaluating post-processing approaches for monthly and seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018, 2018.
Yang, X., Liu, Q., He, Y., Luo, X., and Zhang, X.: Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China, Stoch. Env. Res. Risk A., 30, 959–972, https://doi.org/10.1007/s00477-015-1099-0, 2016.
Zhao, T. and Zhao, J.: Joint and respective effects of long- and short-term forecast uncertainties on reservoir operations, J. Hydrol., 517, 83–94, https://doi.org/10.1016/j.jhydrol.2014.04.063, 2014.
Short summary
Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
Streamflow forecasts a day to a month ahead are highly valuable for water resources management....
