the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Aug 2025
A shifting pattern of tropical cyclone induced high river discharges in the Greater Mekong Region, 1970–2019
Abstract. On average flood events impact over 100 million people globally every year, and because of demographic changes and economic development in flood-prone areas, as well as climate change, the population exposed to flood risk is expected to double by 2050. Under anthropogenic climate change it is expected that flood events previously considered extreme will be occurring with more frequency, due to changing patterns of precipitation in a warming climate. It is, therefore, critically important to better understand how extreme weather events generate high river flows in exposed regions. Here we look specifically at the influence of precipitation from tropical cyclone (TC) activity on high river flows within one such exposed region: a 1.2 million km2 area of South-east Asia encompassing the entirety of the Mekong and Red River catchments, plus 13 smaller catchments along the coastal fringe of Vietnam (collectively referred to here as the Greater Mekong region, or GMR). We use a hydrological model (GM-HYPE) with ERA5 precipitation data to simulate streamflows over the last 50 years (1970–2019) with, and without, TC-linked precipitation. Our results demonstrate that TC-linked precipitation around the GMR generate notable increases in high (95th percentile) streamflows, and this is most notable in the steep sub-catchments draining to Vietnam’s northern coastline. These locations are more exposed to TC activity, and we determine that the elevated soil moisture levels there from monsoonal precipitation, prior to the typhoon season, are an exacerbating factor. Furthermore, trend analysis also shows that shifts in the spatial locations of TC-induced high river flows have been occurring since the 1970s: while statistically significant increases in TC-induced high river discharges are evident in localised regions of the GMR including the highlands of Laos and the Mekong’s delta region, declines in TC-induced high river discharges are much more widespread, with notable declines in the headwater and middle reaches of the Red and Mekong Rivers. Our findings on the changing pattern of high river flows in recent decades, in a region highly exposed to TCs, will be of great interest to strategic planners and flood managers. We conclude with a discussion on the impact of global climate model precipitation projections for this region, contrasting past/present (1980–2014), and future (2016–2050), GM-HYPE model results.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-3506', Anonymous Referee #1, 08 Oct 2025
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AC1: 'Reply on RC1', Melissa Wood, 11 Jun 2026
1. The use of a 500km radius to crop TC related precipitation in the article does not fully demonstrate the applicability of this radius in the GMR region.
We acknowledge that the use of a search radius is a simplification. Reviewer 2 (point 1) raises a similar question concerning the use of the 500km search radius when quantifying the TC related precipitation, and we therefore discuss the implications of using the 500km radius fully in that response (below).
2. Table 1 only considers three variables (precipitation, soil moisture, slope), ignoring possible important factors such as land use, reservoir regulation, and previous rainfall. Suggest explaining the possible impacts of these potential factors in the discussion.
Thank you for these useful comments. Whilst these other factors (especially land use and antecedent rainfall) are potentially important, the role that they play in controlling flow generation is largely manifest through their impacts on soil moisture. We have updated the following text in the manuscript to make this clearer for the reader (L200-L214):
“Our results also indicate that the key factor driving excess stream flow is excess soil saturation and slope-soil saturation interactions. Such excess soil saturation evidently arises due to wetting in the monsoonal months (May to September) prior to the cyclone season (June to November), in addition to wetting from TC-linked precipitation. Thus the wet season creates ideal conditions for maximum rainfall runoff impact from TC-linked precipitation. Any future climate where TC-linked precipitation occurs with greater intensity, after a monsoon season, will increasingly impact populations located in the 1 in 100 year coastal zone.
We use soil moisture as a proxy here for a range of contributing soil-related sub-processes in the GMR, such as extent of soil saturation, soil type, or land use type. We make the assumption that low soil moisture content indicates soils that are capable of detaining rainfall runoff, and a high soil moisture value indicates the soil is nearing its maximum capacity to hold onto water. Once exceeded (increasingly possible in the future GMR), TC-linked rainfall would afterwards directly runoff instead of being absorbed and mitigated, effecting downstream communities. Saturated soils might also presage higher landslip risk, if other conditions allow. In finding that soil moisture is a controlling factor in our study area, the next phase of research might reasonably be to examine the role of sub-factors, such as soil types and class, aquifer proximity/capacity, degree of urbanisation, role of vegetation and tree cover (and associated evapotranspiration), and how land use has contributed.”
We also write in the conclusion section that other factors such as antecedent conditions, land use, and soil type, would all be appealing areas for future study.
3. The author points out that the reliability of data in the 1970s and 1980s was low, but does not evaluate the specific impact on trend analysis.
(This response is also applicable for Reviewer 2, Point 5, below).
Thank you for this comment. Indeed ERA5 precipitation reanalysis data prior to the 1980s is recognised to be less accurate, as it relied on fewer ground weather stations, radiosondes, and satellite retrievals for pressure, humidity and temperature (Bell et al., 2021[1]). To respond to this comment the authors explored the impact of incorporating pre-satellite era data in the trend analysis, by estimating, and then comparing, the Mann Kendall (S) trend signal for highest (95th percentile) river discharges, for the periods 1971-1984 (Fig RC1 below), and 1985-2019 (Fig RC2 below), relative to the full 1971-2019 dataset we used in the manuscript (Fig 4 in the manuscript = Fig RC3 below). In all figures, the panels show the trend direction With- (panel a), and Without- (panel b), TC-linked precipitation. Panel c shows the influence of TC-linked precipitation in those trends.
Figure RC1 - Trends 1971-1984: Mann Kendall (S), on 95th percentile flows, with significance.
Figure RC2 - Trends 1985-2019: Mann Kendall (S), on 95th percentile flows, with significance
Figure RC3 - Trends 1971-2019: Mann Kendall (S), on 95th percentile flows, with significance
These era-divided trend plots have also been added to Supplementary Material 4 (‘S’ statistic for mean and 95th percentile flows in the GMR: Figs S4.3-S4.6) for reference, and we refer to the issue in the manuscript L156-L162.
We find that using only data for 1985-2019 subtly alters the trend analysis results in a couple of ways. Firstly, river discharge trends at many sub-catchments in the Mekong/Red River headwaters within China (i.e. above 26° N), show a switch from small positive-statistically-significant in all data (Fig. RC3 panels a,b), to small negative-not significant trend in the post 1985 years only data (Fig. RC2 panels a,b). Here, TC-linked precipitation has consistently trended positive (panel c), therefore factors unrelated to TC-precipitation could be influencing this declining behaviour in discharge extremes at this location. The cause, if it is not related to the ERA5 data quality prior to 1985, could possibly be the impact of dam installation (which the GM-HYPE model does incorporate) in this region which occurred in the 1980s onwards.
Secondly, overall, in smaller headwater sub-catchments beyond the main channels, many statistically significant discharge trends are more positive in the 1985+ era data compared with the original dataset. There are modest new positive trends in highest river flows, both inland (upper Laos, Thailand) and in the steep coastal sub-catchments around Vietnam’s northern and central coastlines. The upward trend is only statistically significant north of Vientiane, Laos ( ~19° N), where highest flows inland tracks with an uptick in TC-linked precipitation inland post 1985 (Fig 2c). Nevertheless, the (not statistically significant) upward trend around the coastline cannot be similarly ascribed to TC-linked precipitation, because in this area this is shown to be decreasing over the same timescale. One possible explanation of positive statistically significant (and not-statistically significant) trends here is that TC-linked rainfall distribution has been changing in the last decades: future climate projections for this region suggest TC events are slightly reducing in frequency, but becoming more intense (and thus travelling further inland). These ERA5 linked model results may possibly show an early signal of this behaviour.
Thirdly, whilst many locations described above appear to show a small positive trend in river flows in the post-1985 data, relative to the prior 1971-1984 years data, this is not the case for main channels of the Red and Mekong Rivers: in these headwaters (21° N - 25° N), and around the Mekong River sub-catchments upstream and downstream of Tonle Sap Lake in Cambodia, there is a negative statistically significant trend. This is consistent with the all-years dataset. A negative statistically significant trends occur for the Saigon River, Vietnam (11° N - 13°N), upstream of Ho Chi Minh City also. These negative statistically significant trends in post 1985 sub-catchment extremes are unlikely to be linked to TC activity, as a high proportion of these locations have increased TC-linked discharges over that time period (Fig R2, panel c). Again, these negative trends may be anthropogenic in origin, give the locations.
The findings outlined above highlight there is some sensitivity of the GM-HYPE model outputs to the choice of ERA5 period. ERA5 data before 1985 does appear to be slightly under-representing precipitation, potentially reducing flows in the model for that period. However, we do not have up-to-date river gauge measurements throughout the GMR to corroborate our assumptions as to the source of these differences. Abstraction, dams, and reservoir installation in the GMR could be an alternative plausible explanation. On balance, the authors believe that trends from ~50 years of data may be more reliable than trends obtained from ~35 years of data. The statistically significant trends observed in 1971-2019 data still closely match those found for the 1985-2019 period. Therefore the authors have not modified the data years used in the study. Instead we have updated the manuscript to highlight again that there is potential tendency for pre-satellite data to be less reliable (e.g. L127-L129, L156-L162), and point to the new figures in Supplementary Materials 4.
4. Figure 5 shows future changes, but does not provide confidence intervals or inter model differences (such as multi model sets).
We thank you for this comment. The authors have clarified the nature of our study to avoid misunderstandings; we are not able to provide confidence intervals or inter model differences for future changes because our study employs just a single HYPE model. In our discussion section we contemplate what mean and highest river discharges in the future GMR might look like due to a changing climate, given what we discovered for the past/present climate. Figure 5 illustrates findings for highest river discharges when our GM-HYPE model is forced with the Roberts et al. (2019) Global Climate Model data results (SSP5-8.5 scenario). Modelling the future flows GMR was never an objective of the study, so this paragraph is merely for discussion. We hope that our changes to this section of the manuscript makes this clearer.
5. The abstract should succinctly summarize the research objectives, methods, key findings, and conclusions. I suggest reducing background information in the abstract and focusing more on the study's highlights and outcomes.
Thank you for this recommendation. We have reworked the abstract to reduce the background information and to add extra lines of text to draw the focus more towards the study’s objectives, key findings, and simple conclusions.
6. Some newest research work related with this paper can be added in the introduction. Diffusion evolution rules of grouting slurry in mining-induced cracks in overlying strata. Water injection softening modeling of hard roof and application in Buertai coal mine.
Thank you for this comment. This research does indeed look to be valuable, however given the limited scope of this study, the authors don’t feel that this additional material is needed within the current outline. It could, however, be usefully employed in a future study, which extends our work, perhaps going into more detail of the impact of groundwater influences, soil type, and land use within the GMR.
7. The conclusion mentions “useful for planners and managers”, but does not provide specific recommendations (such as which areas should prioritize strengthening flood control facilities). The authors should provide a summary of their main findings, the limitations of the study, and recommendations for future research in the conclusion.
Thank you for these constructive comments, the authors have updated and amended the conclusion section as suggested. For specific impacts we inserted:
“Our findings on historic extreme river flow trends will be of interest to urban planners, flood specialists, and river catchment managers throughout the GMR, and particularly in the smaller coastal sub-catchments south of the Red River delta where soil saturation appears to be driving an upwards trend in highest river flows.”
And there is new text which outlines a limitation of our study:
“…hydrological models do not provide insights of river hydraulics and flooding mechanisms, and as such this study is limited to mapping sub-regional river flow excess and trends, which we hope will form a basis for more detailed examination in future work..”
In addition to this text which relates to opportunities / areas to consider for potential future research :
“We hope our findings will contribute to the understanding of the nature of contemporary flood hazard from rainfall runoff, and to the discussions around future proofing river flood defences. Further research work could focus on refining assumptions of a 500km radius to represent TC-linked precipitation within the GMR, or on the soil-related aspects driving the observed upward trend in parts of the GMR – e.g. whether soil saturation in these locations is linked to soil type, land use, vegetation or even soil-groundwater interactions. Alternatively, it would be interesting research to explore in more depth the TC-linked rainfall runoff risk in a future warming climate, using ensembles to better represent the uncertainty of projected TC activity in this region”
8. There are several grammar mistakes in the paper, please revise and double-check throughout the whole manuscript. The language could benefit from further editing and polishing.
Thank you for these comments. We have checked the manuscript carefully and taken the opportunity to try and improve the readability of our manuscript.
----------------------------------------[1] Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, S., Bidlot, J.‐R., Haimberger, L., Woollen, J., Buontempo, C., & Thépaut, J.‐N. (2021). The ERA5 global reanalysis: Preliminary extension to 1950. Quarterly Journal of the Royal Meteorological Society, 147(741), 4186–4227. https://doi.org/10.1002/qj.4174
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AC1: 'Reply on RC1', Melissa Wood, 11 Jun 2026
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RC2: 'Comment on egusphere-2025-3506', Anonymous Referee #2, 15 Apr 2026
This manuscript investigates the influence of tropical cyclone (TC)–induced precipitation on high river discharges across the Greater Mekong Region using the GM-HYPE hydrological model forced with ERA5 data, with and without TC precipitation data. In the following, some points that would benefit from further consideration and clarification.
- A central element of the study is the identification and removal of TC-induced precipitation using a fixed 500 km radius around the cyclone centre. While this approach follows previous studies, it represents a strong simplification as the use of a fixed buffer may therefore lead to the inclusion of non-TC precipitation and, conversely, the exclusion of TC-related rainfall occurring outside the selected radius. This introduces a source of uncertainty that could affect the estimated contribution of TCs to streamflow. It would be helpful if the authors could discuss this limitation more explicitly and, if possible, explore the sensitivity of the results to the choice of the buffer size or consider alternative, more physically based approaches for attributing precipitation to cyclones.
- While the overall performance of the GM-HYPE model is described as satisfactory, the authors does not provide a targeted validation of the TC-related signal itself. It would strengthen the study to demonstrate that the model is capable of reproducing observed discharge responses during TC events. For example, a comparison with documented flood events associated with specific cyclones, or an event-based evaluation of model performance during TC periods, would provide stronger support for the interpretation of the results
- The analysis of the drivers of excess high flows identifies antecedent soil moisture as the dominant explanatory factor. This finding is consistent with hydrological understanding; however, it is derived from a relatively simple regression framework that does not include interaction terms and may not fully capture the non-linear nature of these hydrological processes. Clarifying this aspect and exploring temporal dynamics (e.g. antecedent conditions with lags) would strengthen the analysis.
- The characterization of high flows is mainly based on the 95th percentile of discharge, which provides a useful first-order indicator of extremes but does not fully describe flood behaviour. Approaches based on on return periods, for istance, could allow a more reliable assessment of flood hazard and its changes under TC influence.
- The analysis is based on the use of long-term dataset spanning 1970–2019. The authors acknowledge that both ERA5 and IBTrACS data are less reliable before the satellite era, but this potential limitation is not explored further in the analysis. Given the known limitations of pre-satellite data, the authors could maybe consider restricting the analysis to a more reliable period, applying correction techniques, or at least explicitly testing the sensitivity of the results to the inclusion of earlier data.
- From my understanding, the analysis of future conditions is not fully consistent with the historical framework as TC precipitation cannot be isolated in the GCM simulations. Future projections, indeed, include all sources of precipitation, making the comparison with the TC-specific historical results not consistent. Clarifying this point and carefully framing the interpretation would improve the coherence of the study. I also feel that this analysis focused on the use of future projections should be deepened if the authors would include it within the manuscript (e.g., which projections have been used, have they been corrected in some way, etc.) as otherwise this part seems to be extremely marginally.
In summary, the study addresses an interesting topic and presents a promising modeling strategy, but some key methodological assumptions and limitations should be more thoroughly examined.
Citation: https://doi.org/10.5194/egusphere-2025-3506-RC2 -
AC2: 'Reply on RC2', Melissa Wood, 11 Jun 2026
1. A central element of the study is the identification and removal of TC-induced precipitation using a fixed 500 km radius around the cyclone centre. While this approach follows previous studies, it represents a strong simplification as the use of a fixed buffer may therefore lead to the inclusion of non-TC precipitation and, conversely, the exclusion of TC-related rainfall occurring outside the selected radius. This introduces a source of uncertainty that could affect the estimated contribution of TCs to streamflow. It would be helpful if the authors could discuss this limitation more explicitly and, if possible, explore the sensitivity of the results to the choice of the buffer size or consider alternative, more physically based approaches for attributing precipitation to cyclones.
(This response is also applicable for Reviewer 1, Point 1, above).
We acknowledge this source of uncertainty. It is not practical at this stage to undertake a sensitivity analysis using different values of the search radius because the staff who undertook the HYPE simulations have now moved on to new roles. However, our selection of the 500 km value was deliberately undertaken to ensure that a search radius at the lower end of the 500-1000 km radii that have been used in prior studies was used. This ensures that our analysis of the effects of TCs on river flows is conservative, because the TC related precipitation we estimate is likely at the lower bound. We now include specific discussion of this in the revised manuscript at L116- L128.
2. While the overall performance of the GM-HYPE model is described as satisfactory, the authors does not provide a targeted validation of the TC-related signal itself. It would strengthen the study to demonstrate that the model is capable of reproducing observed discharge responses during TC events. For example, a comparison with documented flood events associated with specific cyclones, or an event-based evaluation of model performance during TC periods, would provide stronger support for the interpretation of the results.
Thank you for your comment about validation of the TC-related signal on its own. Indeed, in Supplementary Material 1 there is information that the GM-HYPE model performs well in simulating river discharges at 20 gauging stations within the Greater Mekong River domain (Table S1.1), between 2002 and 2007. But we had not included any information for specific TC events. Therefore we have added to Supplementary Material 1 to provide more focussed information on GM-HYPE model performance against recorded TC events, if the TC-linked rain fields and gauge location do coincide within this period.
Plots comparing observed and simulated streamflow at three gauge locations - Pakse, Khong Chiam and Vientiane - are now included in an updated Supplementary Material 1 (Fig S1.3), with approximately 3 to 4 specific TC events occurring each year to highlight model aptitude in the typhoon season. A Kling-Gupta Efficiency (KGE) box plot is also included to demonstrate the overall effectiveness of the GM-HYPE model in simulating river discharge across 20 distinct storm events captured at gauged locations (copied below). As can be seen, KGE values range between 0 and 0.5 score, which indicates a good fit against observations given incalculable limitations of (i) storm duration/extent/direction and (ii) expected lag times between storm occurrence and river response (Fig RC4 below/ Fig. S1.2 in the manuscript).
Figure RC3 – box plot of KGE ‘goodness of fit’ score at 18 gauge locations, showing the averages obtained in simulating ~20 TC events within the GMR. A KGE score of 1 is perfect skill, a score greater than -0.4 indicates performance is better than using mean flow as a predictor.
3. The analysis of the drivers of excess high flows identifies antecedent soil moisture as the dominant explanatory factor. This finding is consistent with hydrological understanding; however, it is derived from a relatively simple regression framework that does not include interaction terms and may not fully capture the non-linear nature of these hydrological processes. Clarifying this aspect and exploring temporal dynamics (e.g. antecedent conditions with lags) would strengthen the analysis.
Thank you for this comment. On this advice we explored the temporal dynamics of the GM-HYPE model outputs using lags and have updated the results from this exploration into Supplementary Materials 5, and updated our table and text within the main manuscript as follows:
“To identify the potential factors driving the excess streamflow, we evaluated the role of: (1) catchment excess precipitation; (2) catchment excess soil moisture; and (3) mean catchment slope using Ordinary Least Squares (OLS) regression. Modelled data were moderately to highly right skewed, which were log10 transformed to correct skew, thus setting up analysis of a log-linearised power law model. We included all three main effects and all combinations of their two-way interactions in the initial model before reducing the model to the most significant terms.
Table 1 shows the three significant terms contributing to excess streamflow as excess soil moisture, mean catchment slope, and their two-way interaction, which has an explained variation of 68.3% in the log-linearized power law model. Inference of the final model is that, on average, holding for mean catchment slope, for every mm increase in excess soil moisture there is a 1.34E-2 m3 s-1 increase in mean excess streamflow, and while holding for excess soil moisture, for every m m-1 increase in mean catchment slope, there is a 8.83E-4 m3 s-1 increase in mean excess streamflow.”
Table 1 - Results for OLS of a log-linearized power law model, where all variables were log10 transformed. Fitting statistics are adjusted R2 = .68, max VIF = 9.4 (tolerable value), and DOF = 1051 for residual error. Table also shows units, parameter estimates and evaluations.
Term
units
Estimate
Standard Error
Statistic
p-Value
Intercept
-
-3.441
0.062
-50.857
<0.001
Excess soil moisture
mm
0.904
0.073
12.451
<0.001
Mean sub-catchment slope
m/m
-0.277
0.052
-5.299
<0.001
Soil Moisture x Slope
mm x m/m
0.370
0.063
5.884
<0.001
4. The characterization of high flows is mainly based on the 95th percentile of discharge, which provides a useful first-order indicator of extremes but does not fully describe flood behaviour. Approaches based on return periods, for instance, could allow a more reliable assessment of flood hazard and its changes under TC influence.
Thank you for this valuable comment. After giving this question some considerable thought, the authors respectfully disagree that adding additional approaches describing flood behaviour, such as return periods, would be useful in this study. The motivation of the study was to explore whether TC-linked precipitation would or would not impact the higher river discharges in the various river sub-catchments of the GMR. This question is especially relevant in our warming climate. We chose to specifically focus on the interactions between TC-linked precipitation and the sub-catchment response (i.e. ignoring contributions from monsoonal or minor storms) as a first examination of a hypothesis, with the intension that this could lead to further work if proven correct. With our semi-distributed GM-HYPE model we showed that there is an excess that can be linked to TC rainfall occurrence, and we mapped at a sub-catchment scale where this excess was most evident over our time period.
The GM-HYPE model can only provide summary information for sub-catchment model outlet points, where river gauge networks often would not coincide. Our particular study set up does not particularly align with flood hazard mapping and therefore any return period estimation could be unreliable. Consequently, we feel flood hazard estimation approaches such as return period calculations could be a possible ‘next-step’ beyond this study.
5. The analysis is based on the use of long-term dataset spanning 1970–2019. The authors acknowledge that both ERA5 and IBTrACS data are less reliable before the satellite era, but this potential limitation is not explored further in the analysis. Given the known limitations of pre-satellite data, the authors could maybe consider restricting the analysis to a more reliable period, applying correction techniques, or at least explicitly testing the sensitivity of the results to the inclusion of earlier data.
Thank you for this great comment. Reviewer 1 (point 3) asked a very similar question about the reliability and sensitivity of using ERA5 data from before the 1980’s. We refer the reviewer to our response therein, which describes the effect on results, of excluding data from 1970-1984. We have also expanded Supplementary Material 4 to show our findings (‘S’ statistic for mean and 95th percentile flows in the GMR) for the years 1970-1984 vs 1985-2019, and the impact of choice of input data in our model set up.
6. From my understanding, the analysis of future conditions is not fully consistent with the historical framework as TC precipitation cannot be isolated in the GCM simulations. Future projections, indeed, include all sources of precipitation, making the comparison with the TC-specific historical results not consistent. Clarifying this point and carefully framing the interpretation would improve the coherence of the study. I also feel that this analysis focused on the use of future projections should be deepened if the authors would include it within the manuscript (e.g., which projections have been used, have they been corrected in some way, etc.) as otherwise this part seems to be extremely marginally.
The authors appreciate this reviewer comment. It is completely correct that using the GM-HYPE model with Roberts et al. (2019) SSP5-8.5 global climate model data (split into past 1980-2014, and future 2016-2050, inputs) is limited because TC-events cannot be parsed out and interrogated. We did not include these findings in our results section for this reason. Instead by including this suggestion in the discussion section we hoped to apply some context and food for further discussion. On this reviewer advice we have updated this section of the manuscript to better frame and explain this discussion addendum, with the following text:
“…Sub-catchments exposed to more intense TC activity in future years need to prepare for the potential for new excesses in high streamflows over the coming decades. Modelling future TC events is beyond the scope of this study, and it is not an objective. However, it would be interesting future work to consider the implications of a warming climate on river discharges in the GMR, given our results above. We re-ran the GM-HYPE model very simply, with the local precipitation fields from a high-resolution GCM (Roberts et al., 2019 - representing SSP5-8.5 scenario projections, 1950 to 2050) to gain an elementary insight. We parsed this GCM data into two parts - a past/present (1980 to 2014) climate, and a future (2016 to 2050) climate. Contrasting the GM-HYPE model outputs from these two climate scenarios suggests that future high streamflows may tend to increase relative to the past/present baseline. We fully acknowledge that this set up is a highly simplified representation of the GMR, as unlike our prior analysis, it cannot isolate the influence of unique TC events. Hence, the local precipitation fields in the GCM data not only represent all rainfall (monsoon, storm, and TC-linked all combined), but also the climatological influences on the precipitation for this Shared Socioeconomic Pathway, over the study period. Consequently, model A and B results are incompatible comparisons with a future analysis. But contrasting past and future GCM model output may suggest interesting routes for potential future research. Accepting the above broad assumptions, the GM-HYPE model outputs using SSP5-8.5 GCM projection data (Fig. 5c), suggest that highest river flows in the GMR may increase generally in most locations, but more in the main Mekong and Red River sub-catchments.”
Furthermore, we removed text referring to future climate modelling from the introduction section, as it may have been distracting from the core aims and objectives of the study.
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The paper quantifies the influence of TC activity on high river flows within a 1.2 million km2 area of southeast Asia encompassing the Mekong and Red River catchments, plus 13 smaller catchments located along the coastal fringe of Vietnam. In 2020, this Greater Mekong Region (GMR) supported a population 85 of over 160 million people.
1.The use of a 500km radius to crop TC related precipitation in the article does not fully demonstrate the applicability of this radius in the GMR region.
2.Table 1 only considers three variables (precipitation, soil moisture, slope), ignoring possible important factors such as land use, reservoir regulation, and previous rainfall. Suggest explaining the possible impacts of these potential factors in the discussion.
3.The author points out that the reliability of data in the 1970s and 1980s was low, but does not evaluate the specific impact on trend analysis.
4.Figure 5 shows future changes, but does not provide confidence intervals or inter model differences (such as multi model sets).
5.The abstract should succinctly summarize the research objectives, methods, key findings, and conclusions. I suggest reducing background information in the abstract and focusing more on the study's highlights and outcomes.
6.Some newest research work related with this paper can be added in the introduction. Diffusion evolution rules of grouting slurry in mining-induced cracks in overlying strata. Water injection softening modeling of hard roof and application in Buertai coal mine.
7.The conclusion mentions “useful for planners and managers”, but does not provide specific recommendations (such as which areas should prioritize strengthening flood control facilities). The authors should provide a summary of their main findings, the limitations of the study, and recommendations for future research in the conclusion.
8. There are several grammar mistakes in the paper, please revise and double-check throughout the whole manuscript. The language could benefit from further editing and polishing.