the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Feb 2026
Characterising runoff processes for Australia: Insights from a parsimonious rainfall-runoff event identification method
Abstract. Rainfall-runoff events are widely used in hydrological applications, from flood estimation and flood forecasting, to understanding catchment responses in a climate/anthropogenic affected world. The majority of methods used to identify rainfall-runoff events are statistical in nature, relying on subjective, user-defined ‘rules’ (i.e., parameters) that define a rainfall-runoff event. Since no ground-truth information is available to confirm the exact beginning and end of rainfall-runoff events, there is noticeable inconsistency (i.e. uncertainty) within the results. In this study, we propose the Robust Variance-based Event Identification Method (RVEIM), a new parsimonious rainfall-runoff event identification method which uses fewer parameters and better mimics the natural runoff generation process; decreasing the uncertainty in rainfall-runoff event identification. RVEIM detects runoff events by focusing on changes of streamflow variance and pairs to the corresponding rainfall event(s) simultaneously. RVEIM was compared to a benchmarking event identification method in 8 representative catchments in Australia. A sensitivity analysis was performed using a comprehensive set of plausible event identification parameter values for both methods. Results revealed that the variation of rainfall-runoff events characteristics – including annual number, length of runoff events, and the mean volume of runoff events – showed limited uncertainty from the RVEIM (standard deviation within ±15 % of the mean across 8 representative Australian catchments). This demonstrates robustness of RVEIM, while the benchmarking method exhibited considerably greater uncertainty in the event characteristics, with coefficients of variation exceeding 23 % and more than an order of magnitude difference across catchments. Using RVEIM, we present the first comprehensive summary of rainfall-runoff event characteristics across 467 Australian catchments. The distribution of event-scale runoff coefficients for individual catchments shows a strong climate gradient. Such systematic shifts in the distribution of runoff coefficient across climate regions indicate that climate variables play an important role in catchment response and point to potential contrasts in dominating runoff generation mechanisms across climate zones.
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Status: open (until 23 Mar 2026)
- CC1: 'On the validation and transferability of RVEIM', Yiwen Mei, 24 Feb 2026 reply
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CC2: 'On the validation and transferability of RVEIM', Yiwen Mei, 24 Feb 2026
reply
I would like to thank HESS Discussions for providing such a great platform for interactive discussions with authors. I apologize for any additional workload this may create for the authors. I was not officially invited to review this manuscript, but the topic aligns closely with my research interests, which is why I took the opportunity to offer my perspective.
The manuscript “Characterising runoff processes for Australia: Insights from a parsimonious rainfall-runoff event identification method” by Khoie introduces a new parsimonious (two-parameter) rainfall-runoff event identification method called RVEIM. Unlike existing pairing methods, which typically search for rainfall events preceding a runoff event, RVEIM starts the search from a rainfall event and looks forward for the corresponding runoff event. It also employs a variable-length searching window, in contrast to the fixed-length windows used by other methods. RVEIM was applied to 467 Australian catchments, and the resulting event identifications were compared to those obtained using the “local maxima method” (though no citation was provided for this method). The comparison focused on event characteristics such as the number of events per year, runoff coefficient (RC), event length, and event volume.
Overall, the manuscript is well-written. However, I have two major comments that I would like to discuss with the authors, along with several specific comments and editorial suggestions annotated in the manuscript.
- Validation strategy for event identification
The manuscript compares the number of events, event length, and event volume between RVEIM and the local maxima method (Figure 6). One finding is that RVEIM-derived characteristics exhibit lower interannual variability. The manuscript interprets this as evidence that RVEIM introduces less “uncertainty” than the benchmarking method. I question this interpretation because it is reasonable to expect fewer events, shorter durations, and lower flow volumes during dry years. Higher variability in the benchmarking method may actually reflect a better representation of contrasting hydrological conditions between dry and wet years. The fact that RVEIM produces similar event characteristics regardless of wet or dry conditions does not, in my view, appear to be a favorable outcome.
Another finding concerns the event runoff coefficient (RC). The manuscript reports that RVEIM yields significantly fewer instances of RC > 1 compared to the local maxima method (Figure 7). I agree that a method producing fewer violations of RC > 1 is preferable. However, a key point here is ensuring that both methods start from the same baseline, particularly in how baseflow is separated from streamflow when calculating RC. Both methods use the Lyne-Hollick filter for baseflow separation, but I would like to know whether the same alpha parameter values were used for both methods when producing the comparison in Figure 7. If different values were used, this could explain the observed differences in RC > 1 occurrences.
Additionally, I note that no mathematical definition of RC is provided in the manuscript. I assume that the event RC is calculated as the ratio of quick-flow volume (streamflow minus baseflow) to rainfall volume. Clarifying this would be helpful.
Finally, I believe there are other validation approaches that could strengthen the analysis. Event characteristics should covary with catchment physiographic conditions. For example, one might expect fewer events in larger catchments or lower peak flows in more elongated catchments (Shen et al., 2016). Exploring such relationships could provide additional insight into the performance of RVEIM.
- Novelty and transferability of RVEIM
The manuscript claims three novelties for RVEIM: (a) it is parsimonious, requiring only two parameters; (b) it is rainfall-centered, initiating the search from rainfall rather than runoff events; and (c) it features a variable-length searching window. The authors suggest that these features make RVEIM more transferable than existing methods. I do not see a clear connection between each of these claimed novelties and enhanced transferability.
Regarding point (a), having fewer parameters could imply that the method is less adaptable to catchments with hydrological regimes that differ substantially from those used in method development. For instance, would RVEIM be suitable for snow-influenced catchments? Regarding point (b), whether the search begins from rainfall or runoff appears to be a design choice rather than a feature that inherently improves transferability. I see no clear advantage of one approach over the other in this regard.
Moreover, RVEIM is designed for daily time-series data. However, there are existing event identification methods developed for hourly data (e.g., Mei and Anagnostou, 2015; Thiesen et al., 2019). Given that sub-daily rainfall and streamflow data are available for Australia, I wonder whether RVEIM could be adapted to use higher-resolution inputs, and if so, what advantages or challenges that might present.
Thank you for considering my comments. I hope they contribute constructively to the improvement of the manuscript.
Yiwen Mei
References
Shen, X., Mei, Y., & Anagnostou, E. N. (2017). A Comprehensive Database of Flood Events in the Contiguous United States from 2002 to 2013. Bulletin of the American Meteorological Society, 98(7), 1493-1502. https://doi.org/10.1175/BAMS-D-16-0125.1,
Mei, Y. and Anagnostou, E. N.: A hydrograph separation method based on information from rainfall and runoff records, J. Hydrol., 523, 636–649, https://doi.org/10.1016/j.jhydrol.2015.01.083, 2015.
Thiesen, S., Darscheid, P., and Ehret, U.: Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory, Hydrol. Earth Syst. Sci., 23, 1015–1034, https://doi.org/10.5194/hess-23-1015-2019, 2019.
Citation: https://doi.org/10.5194/egusphere-2025-6241-CC2 -
RC1: 'Comment on egusphere-2025-6241', Anonymous Referee #1, 27 Feb 2026
reply
This manuscript provides a very useful contribution by introducing a novel, physically based and parsimonious event identification method that exhibits excellent robustness. This offers a standardised and transferable approach for event identification that forms the starting point of much hydrological analysis. However, some aspects of the methodology would benefit from further elucidation.
Major Comments:
- The algorithm flow chart in Figure 2 is a very useful inclusion that I commend, however, please consider the following:
- Consider spacing out the boxes within the “Streamflow statistics” window to allow better interpretation of the flow arrows.
- The left arrow from the first diamond “n_exceed >= len_rain & length(QD[idx_candidate]) >0” is misleading as it says, “We have no streamflow event” yet it flows to “Calculate streamflow event characteristics”.
- For both diamonds it is unclear under what condition you should follow which arrow.
- Please clarify the basis for the *3 scaling factor in equation 4.
- Can you please add further explanation as to why n_exceed >= len_rain is a necessary condition. Are there any situations, such as on a small catchment, where this may not be the case? Does this add any uncertainty? I am not suggesting any further analysis is required, rather just some further discussion. For example, my understanding is that “best_lag” will vary based on the catchment and this should ensure that on average n_exceed >= len_rain. Some words to this effect would be beneficial, I think.
- Line 254-255: I’m concerned about the lack of sensitivity testing for alpha in the local maxima event identification. Whilst I appreciate that it would unlikely change the results significantly or the conclusion of the study, I think for completeness alpha should either be sampled for both or neither algorithm, as it will have an impact on event identification.
- Could you please clarify the ranges of parameters for Local maxima in Table 3, particularly for delta.y? Often the delta.y parameter is specified as a proportion of the peak to account for different catchment scales, however, here it is flow value. Given the range of catchment sizes investigated, i.e. 102-105, is it possible that the uncertainty of the local maxima algorithm is inflated by sampling across an implausible range for some catchments? I also note that in the author’s cited paper from 2025, in Table 1 the parament range for delta.y in the local maxima algorithm is the proportion 0.9-0.7 whilst the delta.y for a different algorithm, local minima, is 0-30 which is what is used in this manuscript. Can you please clarify this?
- Looking at the results in Figure 5 is it possible that RVEIM is truncating the start of events by starting at i, where QDi is first positive, which is resulting in the shorter event lengths and lower volumes in Figure 6 b) & c), and typically lower RC values in Figure 7)? Could you consider starting at i-1 instead?
- I think the results shown in figure 5.2 should be moved out of the discussion and up into the results.
- It would be worth commenting in the discussion whether RVEIM would generalise to use on subdaily data.
Minor Comments:
- Please review the manuscript and ensure correct spelling and grammar is used throughout.
- Please rephrase lines 98-100 “We demonstrated that the proposed rainfall-runoff event identification method leads to noticeably lower percentage of RCs better reflecting physical processes” as it is hard to interpret.
- Please check that the correct conversion of units has been undertaken for Mean annual streamflow in Table 2.
Citation: https://doi.org/10.5194/egusphere-2025-6241-RC1 - The algorithm flow chart in Figure 2 is a very useful inclusion that I commend, however, please consider the following:
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I would like to thank HESS Discussions for providing such a great platform for interactive discussions with authors. I apologize for any additional workload this may create for the authors. I was not officially invited to review this manuscript, but the topic aligns closely with my research interests, which is why I took the opportunity to offer my perspective.
The manuscript “Characterising runoff processes for Australia: Insights from a parsimonious rainfall-runoff event identification method” by Khoie introduces a new parsimonious (two-parameter) rainfall-runoff event identification method called RVEIM. Unlike existing pairing methods, which typically search for rainfall events preceding a runoff event, RVEIM starts the search from a rainfall event and looks forward for the corresponding runoff event. It also employs a variable-length searching window, in contrast to the fixed-length windows used by other methods. RVEIM was applied to 467 Australian catchments, and the resulting event identifications were compared to those obtained using the “local maxima method” (though no citation was provided for this method). The comparison focused on event characteristics such as the number of events per year, runoff coefficient (RC), event length, and event volume.
Overall, the manuscript is well-written. However, I have two major comments that I would like to discuss with the authors, along with several specific comments and editorial suggestions annotated in the manuscript.
The manuscript compares the number of events, event length, and event volume between RVEIM and the local maxima method (Figure 6). One finding is that RVEIM-derived characteristics exhibit lower interannual variability. The manuscript interprets this as evidence that RVEIM introduces less “uncertainty” than the benchmarking method. I question this interpretation because it is reasonable to expect fewer events, shorter durations, and lower flow volumes during dry years. Higher variability in the benchmarking method may actually reflect a better representation of contrasting hydrological conditions between dry and wet years. The fact that RVEIM produces similar event characteristics regardless of wet or dry conditions does not, in my view, appear to be a favorable outcome.
Another finding concerns the event runoff coefficient (RC). The manuscript reports that RVEIM yields significantly fewer instances of RC > 1 compared to the local maxima method (Figure 7). I agree that a method producing fewer violations of RC > 1 is preferable. However, a key point here is ensuring that both methods start from the same baseline, particularly in how baseflow is separated from streamflow when calculating RC. Both methods use the Lyne-Hollick filter for baseflow separation, but I would like to know whether the same alpha parameter values were used for both methods when producing the comparison in Figure 7. If different values were used, this could explain the observed differences in RC > 1 occurrences.
Additionally, I note that no mathematical definition of RC is provided in the manuscript. I assume that the event RC is calculated as the ratio of quick-flow volume (streamflow minus baseflow) to rainfall volume. Clarifying this would be helpful.
Finally, I believe there are other validation approaches that could strengthen the analysis. Event characteristics should covary with catchment physiographic conditions. For example, one might expect fewer events in larger catchments or lower peak flows in more elongated catchments (Shen et al., 2016). Exploring such relationships could provide additional insight into the performance of RVEIM.
The manuscript claims three novelties for RVEIM: (a) it is parsimonious, requiring only two parameters; (b) it is rainfall-centered, initiating the search from rainfall rather than runoff events; and (c) it features a variable-length searching window. The authors suggest that these features make RVEIM more transferable than existing methods. I do not see a clear connection between each of these claimed novelties and enhanced transferability.
Regarding point (a), having fewer parameters could imply that the method is less adaptable to catchments with hydrological regimes that differ substantially from those used in method development. For instance, would RVEIM be suitable for snow-influenced catchments? Regarding point (b), whether the search begins from rainfall or runoff appears to be a design choice rather than a feature that inherently improves transferability. I see no clear advantage of one approach over the other in this regard.
Moreover, RVEIM is designed for daily time-series data. However, there are existing event identification methods developed for hourly data (e.g., Mei and Anagnostou, 2015; Thiesen et al., 2019). Given that sub-daily rainfall and streamflow data are available for Australia, I wonder whether RVEIM could be adapted to use higher-resolution inputs, and if so, what advantages or challenges that might present.
Thank you for considering my comments. I hope they contribute constructively to the improvement of the manuscript.
Yiwen Mei
References
Shen, X., Mei, Y., & Anagnostou, E. N. (2017). A Comprehensive Database of Flood Events in the Contiguous United States from 2002 to 2013. Bulletin of the American Meteorological Society, 98(7), 1493-1502. https://doi.org/10.1175/BAMS-D-16-0125.1,
Mei, Y. and Anagnostou, E. N.: A hydrograph separation method based on information from rainfall and runoff records, J. Hydrol., 523, 636–649, https://doi.org/10.1016/j.jhydrol.2015.01.083, 2015.
Thiesen, S., Darscheid, P., and Ehret, U.: Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory, Hydrol. Earth Syst. Sci., 23, 1015–1034, https://doi.org/10.5194/hess-23-1015-2019, 2019.