the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved seasonal hydrological forecasting for Great Britain
Abstract. Great Britain’s variable maritime climate has until relatively recently limited the utility of seasonal hydrological forecasts. The latest generations of seasonal atmospheric forecasting systems have created new opportunities to improve flow forecasting across Great Britain, such as for the UK Hydrological Outlook. Here, newly-developed high-resolution rainfall forecasts derived from historical weather analogues (HWA) conditioned on large-scale circulation patterns are used to drive a monthly-resolution national-scale hydrological model. We use rainfall hindcasts from 1993–2016 to evaluate the performance of these flow forecasts and demonstrate their skill, particularly for the UK winter. We show that the high performance of the rainfall forecasts is spatially complementary to the skill provided by hydrological memory in groundwater-dominated catchments. Our analyses pinpoint the regions which would benefit most from future improvements in the rainfall forecasting or hydrological modelling systems. The introduction of these rainfall forecasts now enables hydrological forecasting at unprecedented levels of detail across Great Britain and is a model that may be similarly beneficial elsewhere in the world.
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Status: open (until 07 Oct 2025)
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RC1: 'Comment on egusphere-2025-2506', Anonymous Referee #1, 16 Aug 2025
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Streamflow forecasting plays a critical part in water resources management. In this paper, streamflow forecasting models are developed for Great Britain. The usefulness of the models are demonstrated through the Hydrological Outlook Portal (https://ukho.ceh.ac.uk/).
There are three comments on the paper.
First of all, the paper is more like a report, rather than a research article. It is mainly due to that the methods presented along with the results. For example, in “4.2 Performance of the forecast distribution”, the equation of CRPS is illustrated and then the detailed results are presented. It is pointed out that for research articles, methods and results are mostly presented in different sections. In this way, all the methods are illustrated in the same section, so that people can better understand the framework of the proposed methods.
Secondly, the rainfall forecasts that drive the hydrological models can be improved. In “4.1 Performance of the ensemble mean”, the performance of raw rainfall forecasts is examined. It is not surprising to see that the performance of raw forecasts is not satisfactory. It is noted that peer studies have developed post-processing methods to exploit the skill of raw rainfall forecasts. The authors are suggested to considered forecast post-processing.
Thirdly, there exist some forecasting systems in Europe. Is it possible to conduct some comparisons?
References:
Alfieri, Lorenzo, Peter Burek, Emanuel Dutra, Blazej Krzeminski, David Muraro, Jutta Thielen, and Florian Pappenberger. "GloFAS–global ensemble streamflow forecasting and flood early warning." Hydrology and Earth System Sciences 17, no. 3 (2013): 1161-1175.
Li, Wentao, Quan J. Wang, and Qingyun Duan. "A variable-correlation model to characterize asymmetric dependence for postprocessing short-term precipitation forecasts." Monthly Weather Review 148, no. 1 (2020): 241-257.
Zhao, T., Bennett, J.C., Wang, Q.J., Schepen, A., Wood, A.W., Robertson, D.E. and Ramos, M.H., 2017. How suitable is quantile mapping for postprocessing GCM precipitation forecasts?. Journal of Climate, 30(9), pp.3185-3196.
Wang, Quan J., Yawen Shao, Yong Song, Andrew Schepen, David E. Robertson, Dongryeol Ryu, and Florian Pappenberger. "An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm." Environmental Modelling & Software 122 (2019): 104550.
Huang, Zeqing, and Tongtiegang Zhao. "pyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts." Environmental Modelling & Software 166 (2023): 105732.
Citation: https://doi.org/10.5194/egusphere-2025-2506-RC1
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