Articles | Volume 20, issue 10
https://doi.org/10.5194/hess-20-4117-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-20-4117-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Optimising seasonal streamflow forecast lead time for operational decision making in Australia
Andrew Schepen
CORRESPONDING AUTHOR
CSIRO Land and Water, 4102 Dutton Park, Australia
Tongtiegang Zhao
CSIRO Land and Water, 3168 Clayton, Australia
Q. J. Wang
CSIRO Land and Water, 3168 Clayton, Australia
Senlin Zhou
Bureau of Meteorology, 3001 Melbourne, Australia
Paul Feikema
Bureau of Meteorology, 3001 Melbourne, Australia
Related authors
Andrew Schepen, Andrew Bolt, Dorine Bruget, John Carter, Donald Gaydon, Mihir Gupta, Zvi Hochman, Neal Hughes, Chris Sharman, Peter Tan, and Peter Taylor
Nat. Hazards Earth Syst. Sci., 25, 4053–4070, https://doi.org/10.5194/nhess-25-4053-2025, https://doi.org/10.5194/nhess-25-4053-2025, 2025
Short summary
Short summary
The success of agricultural enterprises is affected by climate variability and other important factors like soil conditions and market prices. We have developed an agricultural drought forecasting system to help drought analysts and policymakers more accurately identify communities that are enduring financial stress. By coupling climate forecasts and agricultural models, we can skillfully predict crop yields and farm profits for the coming seasons, which will support proactive responses.
Neal Hughes, Donald Gaydon, Mihir Gupta, Andrew Schepen, Peter Tan, Geoffrey Brent, Andrew Turner, Sean Bellew, Wei Ying Soh, Christopher Sharman, Peter Taylor, John Carter, Dorine Bruget, Zvi Hochman, Ross Searle, Yong Song, Patrick Mitchell, Yacob Beletse, Dean Holzworth, Laura Guillory, Connor Brodie, Jonathon McComb, and Ramneek Singh
Nat. Hazards Earth Syst. Sci., 25, 3461–3482, https://doi.org/10.5194/nhess-25-3461-2025, https://doi.org/10.5194/nhess-25-3461-2025, 2025
Short summary
Short summary
Droughts can impact agriculture and regional economies, and their severity is rising with climate change. Our research introduces a new system, the Australian Agricultural Drought Indicators (AADI), which measures droughts based on their effects on crops, livestock and farm profits rather than on traditional weather metrics. Using climate data and modelling, AADI predicts drought impacts more accurately, helping policymakers prepare for and respond to financial and social impacts during droughts.
Qiang Li, Tongtiegang Zhao, Zexin Chen, and Zeqing Huang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3321, https://doi.org/10.5194/egusphere-2026-3321, 2026
Short summary
Short summary
This study explores how streamflow reanalysis responses to precipitation forcing errors across 671 catchments in the United States. For the GloFAS-ERA5 v4.0, every 1 mm increase in precipitation error causes an averaged increase of 0.51 mm streamflow error due to catchment’s buffering effects. This response varies by catchments where humid regions amplify the increased streamflow errors up to 2.5 mm due to saturated soils, but arid and snowy regions delay or dampen them through storage.
Yuhang Zhang, Quan J. Wang, Andrew J. Frost, Jayaram Pudashine, Blair Trewin, Carlos Velasco-Forero, Chun-Hsu Su, and Vincent Villani
EGUsphere, https://doi.org/10.5194/egusphere-2026-666, https://doi.org/10.5194/egusphere-2026-666, 2026
Short summary
Short summary
This study develops and tests a new system that combines satellite, radar, and rain gauge measurements to produce hourly rainfall maps at 2-kilometre resolution across Australia. The results show clearer and more reliable rainfall patterns than existing operational methods and products. The approach is efficient and supports both real-time monitoring and the reconstruction of historical rainfall. It also provides a practical reference for large-scale rainfall analysis in other regions.
Jiaquan Wan, Xing Wang, Yannian Cheng, Cuiyan Zhang, Fengchang Xue, Tao Yang, Fei Tong, and Quan J. Wang
Nat. Hazards Earth Syst. Sci., 25, 4361–4373, https://doi.org/10.5194/nhess-25-4361-2025, https://doi.org/10.5194/nhess-25-4361-2025, 2025
Short summary
Short summary
Urban flooding is a growing issue in cities, often disrupting daily life, especially at night, when the extent of flooding is harder to identify. This study introduces NWseg, a new deep learning model designed to identify the extent of urban flooding at night. Using a dataset of 4000 nighttime images, we found that NWseg outperforms existing models in accuracy. This research offers a practical solution for real-time flood monitoring, helping improve urban disaster response and management.
Andrew Schepen, Andrew Bolt, Dorine Bruget, John Carter, Donald Gaydon, Mihir Gupta, Zvi Hochman, Neal Hughes, Chris Sharman, Peter Tan, and Peter Taylor
Nat. Hazards Earth Syst. Sci., 25, 4053–4070, https://doi.org/10.5194/nhess-25-4053-2025, https://doi.org/10.5194/nhess-25-4053-2025, 2025
Short summary
Short summary
The success of agricultural enterprises is affected by climate variability and other important factors like soil conditions and market prices. We have developed an agricultural drought forecasting system to help drought analysts and policymakers more accurately identify communities that are enduring financial stress. By coupling climate forecasts and agricultural models, we can skillfully predict crop yields and farm profits for the coming seasons, which will support proactive responses.
Neal Hughes, Donald Gaydon, Mihir Gupta, Andrew Schepen, Peter Tan, Geoffrey Brent, Andrew Turner, Sean Bellew, Wei Ying Soh, Christopher Sharman, Peter Taylor, John Carter, Dorine Bruget, Zvi Hochman, Ross Searle, Yong Song, Patrick Mitchell, Yacob Beletse, Dean Holzworth, Laura Guillory, Connor Brodie, Jonathon McComb, and Ramneek Singh
Nat. Hazards Earth Syst. Sci., 25, 3461–3482, https://doi.org/10.5194/nhess-25-3461-2025, https://doi.org/10.5194/nhess-25-3461-2025, 2025
Short summary
Short summary
Droughts can impact agriculture and regional economies, and their severity is rising with climate change. Our research introduces a new system, the Australian Agricultural Drought Indicators (AADI), which measures droughts based on their effects on crops, livestock and farm profits rather than on traditional weather metrics. Using climate data and modelling, AADI predicts drought impacts more accurately, helping policymakers prepare for and respond to financial and social impacts during droughts.
Tongtiegang Zhao, Qiang Li, Tongbi Tu, and Xiaohong Chen
Geosci. Model Dev., 18, 5781–5799, https://doi.org/10.5194/gmd-18-5781-2025, https://doi.org/10.5194/gmd-18-5781-2025, 2025
Short summary
Short summary
The recent WeatherBench 2 provides a versatile framework for the verification of deterministic and ensemble forecasts. In this paper, we present an explicit extension to binary forecasts of hydroclimatic extremes. Seventeen verification metrics for binary forecasts are employed, and scorecards are generated to showcase the predictive performance. The extension facilitates more comprehensive comparisons of hydroclimatic forecasts and provides useful information for forecast applications.
Tongtiegang Zhao, Zecong Chen, Yongyong Zhang, Bingyao Zhang, and Yu Li
Hydrol. Earth Syst. Sci., 29, 2429–2443, https://doi.org/10.5194/hess-29-2429-2025, https://doi.org/10.5194/hess-29-2429-2025, 2025
Short summary
Short summary
The classic logistic function characterizes the stationary relationship between drought loss and intensity. This paper accounts for time in the magnitude, shape and location parameters of the logistic function and derives nonstationary intensity loss functions. A case study is designed to test the functions for drought-affected populations by province in mainland China from 2006 to 2023. Overall, the nonstationary intensity loss functions are shown to be a useful tool for drought management.
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to evaluating streamflow analysis at different timescales. The reanalysis is observed to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. Also, the local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1449, https://doi.org/10.5194/egusphere-2024-1449, 2024
Preprint withdrawn
Short summary
Short summary
This paper focuses on the effect of the water balance constraint on the robustness of the long short-term memory (LSTM) network in learning rainfall-runoff relationships. Through large-sample tests, it is found that incorporating this constraint into the LSTM improves the robustness, while the improvement tends to decrease as the amount of training data increases. The results point to the compensation effects between training data and process knowledge on the LSTM’s performance.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2023-2841, https://doi.org/10.5194/egusphere-2023-2841, 2024
Preprint archived
Short summary
Short summary
The lack of physical mechanism is a critical issue for the use of popular deep learning models. This paper presents an in-depth investigation of the fundamental mass balance constraint for deep learning-based rainfall-runoff prediction. The robustness against data sparsity, random parameters initialization and contrasting climate conditions are detailed. The results highlight that the water balance constraint evidently improves the robustness in particular when there is limited training data.
Yuan Li, Kangning Xü, Zhiyong Wu, Zhiwei Zhu, and Quan J. Wang
Hydrol. Earth Syst. Sci., 27, 4187–4203, https://doi.org/10.5194/hess-27-4187-2023, https://doi.org/10.5194/hess-27-4187-2023, 2023
Short summary
Short summary
A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.
Huayang Cai, Bo Li, Junhao Gu, Tongtiegang Zhao, and Erwan Garel
Ocean Sci., 19, 603–614, https://doi.org/10.5194/os-19-603-2023, https://doi.org/10.5194/os-19-603-2023, 2023
Short summary
Short summary
For many problems concerning water resource utilization in estuaries, it is essential to be able to express observed salinity distributions based on simple theoretical models. In this study, we propose an analytical salt intrusion model inspired from a theory for predictions of flood hydrographs in watersheds. The newly developed model can be well calibrated using a minimum of three salinity measurements along the estuary and has been successfully applied in 21 estuaries worldwide.
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
Short summary
Short summary
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.
Huayang Cai, Hao Yang, Pascal Matte, Haidong Pan, Zhan Hu, Tongtiegang Zhao, and Guangliang Liu
Ocean Sci., 18, 1691–1702, https://doi.org/10.5194/os-18-1691-2022, https://doi.org/10.5194/os-18-1691-2022, 2022
Short summary
Short summary
Quantifying spatial–temporal water level dynamics is essential for water resources management in estuaries. In this study, we propose a simple yet powerful regression model to examine the influence of the world’s largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the Yangtze River estuary. The presented method is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide.
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
Short summary
Short summary
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
Tongtiegang Zhao, Haoling Chen, Yu Tian, Denghua Yan, Weixin Xu, Huayang Cai, Jiabiao Wang, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 26, 4233–4249, https://doi.org/10.5194/hess-26-4233-2022, https://doi.org/10.5194/hess-26-4233-2022, 2022
Short summary
Short summary
This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts).
Qichun Yang, Quan J. Wang, Andrew W. Western, Wenyan Wu, Yawen Shao, and Kirsti Hakala
Hydrol. Earth Syst. Sci., 26, 941–954, https://doi.org/10.5194/hess-26-941-2022, https://doi.org/10.5194/hess-26-941-2022, 2022
Short summary
Short summary
Forecasts of evaporative water loss in the future are highly valuable for water resource management. These forecasts are often produced using the outputs of climate models. We developed an innovative method to correct errors in these forecasts, particularly the errors caused by deficiencies of climate models in modeling the changing climate. We apply this method to seasonal forecasts of evaporative water loss across Australia and achieve significant improvements in the forecast quality.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Cited articles
Ashok, K., Behera, S. K., Rao, S. A., Weng, H., and Yamagata, T.: El Niño Modoki and its possible teleconnection, J. Geophys. Res.-Oceans, 112, C11007, https://doi.org/10.1029/2006JC003798, 2007.
Chiew, F. H. and Siriwardena, L. W.: Probabilistic seasonal streamflow forecasting methods, 29th Hydrology and Water Resources Symposium: Water Capital, 20–23 February 2005, Rydges Lakeside, Canberra, 208 pp., 2005.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts, calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268, 2007.
Huang, B., Stone, P., Sokolov, A., and Kamenkovich, I.: Extended reconstructed Sea surface temperature Version 4 (ERSSTv4). Part I: upgrades and intercomparisons, J. Climate, 28, 911–930, 2015.
Kirono, D. G., Chiew, F. H., and Kent, D. M.: Identification of best predictors for forecasting seasonal rainfall and runoff in Australia, Hydrol. Process., 24, 1237–1247, 2010.
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Manage. Sci., 22, 1087–1096, 1976.
Mo, K. C.: Relationships between Low-Frequency Variability in the Southern Hemisphere and Sea Surface Temperature Anomalies, J. Climate, 13, 3599–3610, https://doi.org/10.1175/1520-0442(2000)013<3599:RBLFVI>2.0.CO;2, 2000.
Pagano, T., Garen, D., and Sorooshian, S.: Evaluation of Official Western U.S. Seasonal Water Supply Outlooks, 1922–2002, J. Hydrometeorol., 5, 896–909, https://doi.org/10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2, 2004.
Piechota, T. C., Chiew, F. H., Dracup, J. A., and McMahon, T. A.: Seasonal streamflow forecasting in eastern Australia and the El Niño–Southern Oscillation, Water Resour. Res., 34, 3035–3044, 1998.
Piechota, T. C., Chiew, F. H., Dracup, J. A., and McMahon, T. A.: Development of exceedance probability streamflow forecast, J. Hydrol. Eng., 6, 20–28, 2001.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily high-resolution-blended analyses for sea surface temperature, J. Climate, 20, 5473–5496, 2007.
Robertson, D. E. and Wang, Q.: A Bayesian approach to predictor selection for seasonal streamflow forecasting, J. Hydrometeorol., 13, 155–171, 2012.
Saji, N., Goswami, B. N., Vinayachandran, P., and Yamagata, T.: A dipole mode in the tropical Indian Ocean, Nature, 401, 360–363, 1999.
Schepen, A., Wang, Q. J., and Robertson, D.: Evidence for Using Lagged Climate Indices to Forecast Australian Seasonal Rainfall, J. Climate, 25, 1230–1246, https://doi.org/10.1175/JCLI-D-11-00156.1, 2012.
Verdon, D. C. and Franks, S. W.: Indian Ocean sea surface temperature variability and winter rainfall: Eastern Australia, Water Resour. Res., 41, W09413, https://doi.org/10.1029/2004WR003845, 2005.
Wang, Q. and Robertson, D.: Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences, Water Resour. Res., 47, W02546, https://doi.org/10.1029/2010WR009333, 2011.
Wang, Q., Robertson, D., and Chiew, F.: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites, Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008WR007355, 2009.
Wang, Q. J., Shrestha, D. L., Robertson, D. E., and Pokhrel, P.: A log-sinh transformation for data normalization and variance stabilization, Water Resour. Res., 48, W05514, https://doi.org/10.1029/2011WR010973, 2012.
Westra, S., Sharma, A., Brown, C., and Lall, U.: Multivariate streamflow forecasting using independent component analysis, Water Resour. Res., 44, W02437, https://doi.org/10.1029/2007WR006104, 2008.
Xu, K., Brown, C., Kwon, H.-H., Lall, U., Zhang, J., Hayashi, S., and Chen, Z.: Climate teleconnections to Yangtze river seasonal streamflow at the Three Gorges Dam, China, Int. J. Climatol., 27, 771–780, https://doi.org/10.1002/joc.1437, 2007.
Yeo, I. K. and Johnson, R. A.: A new family of power transformations to improve normality or symmetry, Biometrika, 87, 954–959, 2000.
Zhao, T., Schepen, A., and Wang, Q.: Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach, J. Hydrol., in press, 2016.
Short summary
Australian seasonal streamflow forecasts are issued by the Bureau of Meteorology with up to two weeks' delay. Timelier forecast release will enhance forecast value and enable sub-seasonal forecasting. The bureau's forecasting approach is modified to allow timelier forecast release, and changes in reliability and skill are quantified. The results are combined with insights into the forecast production process to recommend a more flexible forecasting system to better meet the needs of users.
Australian seasonal streamflow forecasts are issued by the Bureau of Meteorology with up to two...
Special issue