Quantitative effects of antecedent effective rainfall on <em>ID</em> threshold for debris flow

Zhang, Shaojie; Yang, Hongjuan; Liu, Dunlong; Hu, Kaiheng; Wei, Fangqiang

doi:https://doi.org/10.5194/hess-2022-57

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https://doi.org/10.5194/hess-2022-57

Preprints

16 Feb 2022

| 16 Feb 2022

Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Quantitative effects of antecedent effective rainfall on ID threshold for debris flow

Shaojie Zhang, Hongjuan Yang, Dunlong Liu, Kaiheng Hu, and Fangqiang Wei

Abstract. Studies have shown that the antecedent effect precipitation (AEP) is closely related to rainfall intensity-duration (ID) threshold of debris flow. However, the quantitative relationship between the AEP and ID threshold is still undetermined. In this study, a hydrological process based numerical model (Dens-ID) that can derive the ID threshold curve is adopted to address this issue. Jiangjia Gully (JJG) in Dongchuan District of Yunnan Province was chosen as the study area, Dens-ID was used to derive a series of ID threshold curves corresponding to different AEP. Based on calculated data sets including AEP, ID curves, parameters of ID curve equation (α and β), and debris flow density, the influence of AEP on the ID threshold curve is deeply explored. We found that although solid materials and runoff are the two necessary conditions for the formation of debris flow, the specific roles played in which are different: the volume of loose solid sources provides a basal condition for debris flow and determines the scale of debris flow, while the runoff volume will have a sudden change during the rainfall process, which is a key factor promoting the formation of debris flow. In the condition of AEP ranging from 20 mm to 90 mm, AEP and α can be described by the equation α = −0.0078AEP²+ 0.68AEP + 6.43, and β shows a linear change law with AEP. The error of the two equations were evaluated using 45 historical rainfall data that triggered debris flows, which is equal to 37.85 % and 11.1 %. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold.

Received: 07 Feb 2022 – Discussion started: 16 Feb 2022

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Shaojie Zhang, Hongjuan Yang, Dunlong Liu, Kaiheng Hu, and Fangqiang Wei

Status: closed

RC1:
'Comment on hess-2022-57', Anonymous Referee #1, 23 Mar 2022

Dear authors,

I carefully read your manuscript entitled Quantitative effects of antecedent effective precipitation on ID threshold 1 for debris flow.

The scope of the work is to explore the effect of antecedent effective precipitations on ID threshold for the initiation of debris flows in Jiangjia, using Dens-ID model.

The topic of the paper is interesting and fits with the scopes of the journal.

I believe that there are several issues with this manuscript.

The main issue, in my opinion, is the way you conducted your analyses.

The main outcome of the work is that antecedent rainfall plays an important role in debris flows triggering and can also reduce the rainfall amount of the critical event that trigger the debris flow; this is not a novelty, there are several literature papers addressing this topic (I added some references in the attached document); furthermore, according to figure 4, the ID thresholds for AEP= 60 mm have higher alfa values than the thresholds defined for AEP=20 mm, in contrast with your conclusions.

When comparing AEP with alfa and beta values, you used only 1-hour duration, but then you generalised your outcomes without any further investigation. Alfa and beta are empirical parameters of a threshold, hence they are not dependent on duration, but you considered alfa as representative of the Intensity, which varies with duration.

I agree that alfa is equal to the maximum intensity of a threshold when D=1, but this is not the maximum intensity of a rain event.

You have to consider that the intensity is dependent on duration and considering only one duration is not correct; you also have only 1 event associated with 1-hour rainfall.

The outcomes are not fully supported by the analyses, since you analysed only few condition (e.g. 1 duration only and 1 density only) and in some cases, results and conclusions are conflicting.

Some results are very obvious statement and cannot be considered as outcomes of your work. E.g.

“These results indicate that the supply of loose solid material is essential to debris flow formation, but the decisive factor in debris flow occurrence is the sharp increase in runoff.”

You wrote that duration is dependent on rainfall intensity, but it's the other way around. Intensity depends on rainfall amount and duration.

It is not clear if the Dens-ID model is a literature model (I did not find any reference about it) or of you developed it. You assumed some boundary condition (e.g. max and minimum density) without any scientific support about them.

I believe that this paper cannot be considered for publication.

Citation: https://doi.org/10.5194/hess-2022-57-RC1
- AC1: 'Reply on RC1', Shaojie Zhang, 12 Apr 2022
  
  Dear Reviewer 1:
  The authors would like to thank the reviewers for his detailed review of our manuscript. Regardless of whether or not this manuscript can be reviewed again, the author will carefully revise the draft with reference to the reviewers' comments one by one.
  The authors agree with most of the issues raised by Reviewer 1. But we need to declare one point: this manuscript is not intended to repeat the qualitative description of the influence of AEP on the ID threshold curve. The author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system. AEP will significantly change the position of the threshold curve in the I-D coordinate system, and the change law of the position of the ID threshold curve can be described by the functions of α~AEP and β~AEP, α and β are the two parameters of the ID threshold equation. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold. Therefore, the function of AEP and these two parameters can quantitatively describe the influence of AEP on the ID threshold curve. However, no scholars have conducted relevant research on how to construct the functional relationship between AEP~alpha and AEP~beta.
  The author has problems in presentation and analysis in the current manuscript, which results in reviewer 1 not really getting the innovations in the text. Therefore, the author will make a thorough revision of this draft based on the revision comments of Reviewer 1. The comments made by reviewer 1 are very helpful to improve the readability of our manuscript, and the author is very grateful. Thanks again!
  Best regards!
  
  Citation: https://doi.org/10.5194/hess-2022-57-AC1
RC2:
'Comment on hess-2022-57', Anonymous Referee #2, 11 Apr 2022
The manuscript aims at determining thresholds for debris flow initiation accounting for the current and the antecedent precipitation conditions using a physically based and a statistically based approach. The topic is relevant and has been previously addressed by several authors. However, I am not sure what is the novelty contribution of the work and I have several concerns:

The structure of the manuscript, the clarity of the text, tables, and figure captions need to be significantly improved. Currently, the ideas are very difficult to follow throughout the manuscript.

I think there is a confusion between antecedent precipitation (AEP) and antecedent soil moisture conditions (line 31- 35 and 168-169). Antecedent precipitation is the rainfall that falls over a catchment during a given amount of time before the triggering rainfall event happens. AEP is only a proxy for soil moisture conditions and does not always correspond well with the measured soil moisture at the sites which depends on soil properties.
Additionally, the definition of antecedent precipitation (AEP) and triggering rainfall event is not clear. The manuscript does not state for how long the antecedent precipitation is accumulated and the minimum time between two consecutive rainfall events is not specified. This information is essential as the aim of the manuscript is to find rainfall I-D thresholds for different AEP values.

The data used to fit the thresholds shows almost no variation. This is surprising as usually there is a larger variation in the recorded rainfall conditions that have triggered landslides and debris flows (e.g. Marra et al 2017, Brunetti et al 2010, Guzzetti et al. 2008). Which data has been used to obtain the R-squared values?

Another of my main concerns is in the validation of the obtained thresholds. The authors base their validation on the comparison between the two types of thresholds they have obtained. However, the performance of neither of the thresholds has been evaluated using independent inventory data. Additionally, the thresholds have not been compared with similar thresholds that have already been obtained in literature.

Finally, one of the main conclusions of the manuscript is that antecedent precipitation plays an important role in debris flow initiation and less intense rainfall is required to trigger a debris flow when the antecedent precipitation is significant. This conclusion has been reached by several authors in the past and I agree. However, the results presented in section 4.3 contradict this conclusion. Table 5, figure 8 and figure 9 show that for a given duration of 1 h the intensity required to trigger a landslide event is higher when AEP is equal to 60 mm than when AEP is equal to 20 mm.

For the above-stated reasons, I do not recommend the current manuscript for publication in HESS.

Additional comments:

Line 42: Are you sure? If I recall well Marra et al. (2017) uses rainfall depth and rainfall duration.

Fig4: What do the blue and red points represent? Are those points the debris flows used to fit the thresholds?

Fig 5. Rainfall intensity depends on rainfall duration and should be expressed in [mm/h]. I think that you are not plotting the intensity but the rainfall accumulation.

Table 4: Why is the duration interval for which the thresholds are defined different for all the considered antecedent precipitation values and the two debris flow densities?

Fig 6: I could not find any reference to this figure in the text.

Fig 7: What do the dots represent?

Fig 8: What are the black points? What is P?

Line 437: Units are missing "when D is larger than 3."

Line 458-459: It is stated that debris flow events are plotted in figure 12 however they are not.

References:

Brunetti, M., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., & Guzzetti, Fausto. (2010). Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences, 10(3), 447–458. https://doi.org/10.5194/nhess-10-447-2010

Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2008). The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides, 5(1), 3–17. https://doi.org/10.1007/s10346-007-0112-1

Marra, F., Destro, E., Nikolopoulos, E. I., Zoccatelli, D., Creutin, J. D., Guzzetti, F., & Borga, M. (2017). Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds. Hydrology and Earth System Sciences, 21(9), 4525–4532. https://doi.org/10.5194/hess-21-4525-2017
Citation: https://doi.org/10.5194/hess-2022-57-RC2
- AC2: 'Reply on RC2', Shaojie Zhang, 12 Apr 2022
  
  Dear Reviewer 2:
  The author is very grateful to your careful review of this manuscript. The authors believe that the comments made by the reviewers will be useful in improving the manuscript. The author answers several major questions raised by reviewer 2 one by one.
  The first concerns：The reviewer is not sure what is the novelty contribution of the work and suggested that structure of the manuscript, the clarity of the text, tables, and figure captions need to be significantly improved. It may be because of writing problems that the reviewer 2's understanding of innovation is biased. This manuscript is not intended to repeat the qualitative description of the influence of AEP on the ID threshold curve. The author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system. AEP will significantly change the position of the threshold curve in the I-D coordinate system, and the change law of the position of the ID threshold curve can be described by the functions of α~AEP and β~AEP, α and β are the two parameters of the ID threshold equation. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold. Therefore, the function of AEP and these two parameters can quantitatively describe the influence of AEP on the ID threshold curve. However, no scholars have conducted relevant research on how to construct the functional relationship between AEP~alpha and AEP~beta.
  The second concern is about the confusion between antecedent precipitation (AEP) and antecedent soil moisture conditions. The authors believe that AEP can reflect the level of water content in the topsoil within a watershed. However, the authors do not intend to stress that AEP is equivalent to soil water content. There is definitely a big difference between these two physical parameters. In particular, the empirical decay formula for calculating AEP is subjective, so it is difficult to have a regular correspondence with the measured soil water content. The authors will revise the relating sentences in order to avoid this misleading.
  The third concern is about the data used to fit the thresholds shows almost no variation. In our current manuscript, two types of data are used for getting the ID threshold curves. The first data groups are obtained from our numerical model, and the second first data group is from the field observation data. Large different of ID curves under different AEP between our study and the others e.g., Marra et al 2017, Brunetti et al 2010, Guzzetti et al. 2008. The authors guess that the main reason is due to the different methods for dealing rainfall data. In the references, they used one same debris flow event but the rainfall events standard is different, different recognition of rainfall process can cause big difference in the I and D, so the ID curves from the references have big difference. However, our study set the same recognition standard. We guess this is the main cause.
  The fourth concern is the performance of neither of the thresholds has been evaluated using independent inventory data. Additionally, the thresholds have not been compared with similar thresholds that have already been obtained in literature. Actually, the performance of the thresholds has been evaluated using debris flow inventory data. This work had been published in another Journal, e.g., Geomorphology (A physics-based model to derive rainfall intensity-duration threshold for debris flow) and Journal of Hydrology (A hydrology-process based method for correlating debris flow density to rainfall parameters and its application on debris flow prediction). And also, we compared with similar thresholds that have already been obtained in literature, please also check the published paper “A physics-based model to derive rainfall intensity-duration threshold for debris flow”. The main focus of the current manuscript is to use the database constructed by numerical model to quantify effects of antecedent effective rainfall on ID threshold for debris flow, especially on how to construct the functions of AEP~alpha and AEP~beta, in order to describe the change law of the position of the ID threshold curve in the I-D coordinate system.
  The fifth concern is the results in figure 4 is contrast with the conclusions. The authors agreed this point, and after careful consideration, we think the current conclusion is not suitable. As mentioned in the “first concern”, the author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system.
  As for other detailed comments, the authors will amend them according to your suggestions. Thanks again!
  Best regards!
  
  Citation: https://doi.org/10.5194/hess-2022-57-AC2

Status: closed

RC1:
'Comment on hess-2022-57', Anonymous Referee #1, 23 Mar 2022

Dear authors,

I carefully read your manuscript entitled Quantitative effects of antecedent effective precipitation on ID threshold 1 for debris flow.

The scope of the work is to explore the effect of antecedent effective precipitations on ID threshold for the initiation of debris flows in Jiangjia, using Dens-ID model.

The topic of the paper is interesting and fits with the scopes of the journal.

I believe that there are several issues with this manuscript.

The main issue, in my opinion, is the way you conducted your analyses.

The main outcome of the work is that antecedent rainfall plays an important role in debris flows triggering and can also reduce the rainfall amount of the critical event that trigger the debris flow; this is not a novelty, there are several literature papers addressing this topic (I added some references in the attached document); furthermore, according to figure 4, the ID thresholds for AEP= 60 mm have higher alfa values than the thresholds defined for AEP=20 mm, in contrast with your conclusions.

When comparing AEP with alfa and beta values, you used only 1-hour duration, but then you generalised your outcomes without any further investigation. Alfa and beta are empirical parameters of a threshold, hence they are not dependent on duration, but you considered alfa as representative of the Intensity, which varies with duration.

I agree that alfa is equal to the maximum intensity of a threshold when D=1, but this is not the maximum intensity of a rain event.

You have to consider that the intensity is dependent on duration and considering only one duration is not correct; you also have only 1 event associated with 1-hour rainfall.

The outcomes are not fully supported by the analyses, since you analysed only few condition (e.g. 1 duration only and 1 density only) and in some cases, results and conclusions are conflicting.

Some results are very obvious statement and cannot be considered as outcomes of your work. E.g.

“These results indicate that the supply of loose solid material is essential to debris flow formation, but the decisive factor in debris flow occurrence is the sharp increase in runoff.”

You wrote that duration is dependent on rainfall intensity, but it's the other way around. Intensity depends on rainfall amount and duration.

It is not clear if the Dens-ID model is a literature model (I did not find any reference about it) or of you developed it. You assumed some boundary condition (e.g. max and minimum density) without any scientific support about them.

I believe that this paper cannot be considered for publication.

Citation: https://doi.org/10.5194/hess-2022-57-RC1
- AC1: 'Reply on RC1', Shaojie Zhang, 12 Apr 2022
  
  Dear Reviewer 1:
  The authors would like to thank the reviewers for his detailed review of our manuscript. Regardless of whether or not this manuscript can be reviewed again, the author will carefully revise the draft with reference to the reviewers' comments one by one.
  The authors agree with most of the issues raised by Reviewer 1. But we need to declare one point: this manuscript is not intended to repeat the qualitative description of the influence of AEP on the ID threshold curve. The author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system. AEP will significantly change the position of the threshold curve in the I-D coordinate system, and the change law of the position of the ID threshold curve can be described by the functions of α~AEP and β~AEP, α and β are the two parameters of the ID threshold equation. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold. Therefore, the function of AEP and these two parameters can quantitatively describe the influence of AEP on the ID threshold curve. However, no scholars have conducted relevant research on how to construct the functional relationship between AEP~alpha and AEP~beta.
  The author has problems in presentation and analysis in the current manuscript, which results in reviewer 1 not really getting the innovations in the text. Therefore, the author will make a thorough revision of this draft based on the revision comments of Reviewer 1. The comments made by reviewer 1 are very helpful to improve the readability of our manuscript, and the author is very grateful. Thanks again!
  Best regards!
  
  Citation: https://doi.org/10.5194/hess-2022-57-AC1
RC2:
'Comment on hess-2022-57', Anonymous Referee #2, 11 Apr 2022
The manuscript aims at determining thresholds for debris flow initiation accounting for the current and the antecedent precipitation conditions using a physically based and a statistically based approach. The topic is relevant and has been previously addressed by several authors. However, I am not sure what is the novelty contribution of the work and I have several concerns:

The structure of the manuscript, the clarity of the text, tables, and figure captions need to be significantly improved. Currently, the ideas are very difficult to follow throughout the manuscript.

I think there is a confusion between antecedent precipitation (AEP) and antecedent soil moisture conditions (line 31- 35 and 168-169). Antecedent precipitation is the rainfall that falls over a catchment during a given amount of time before the triggering rainfall event happens. AEP is only a proxy for soil moisture conditions and does not always correspond well with the measured soil moisture at the sites which depends on soil properties.
Additionally, the definition of antecedent precipitation (AEP) and triggering rainfall event is not clear. The manuscript does not state for how long the antecedent precipitation is accumulated and the minimum time between two consecutive rainfall events is not specified. This information is essential as the aim of the manuscript is to find rainfall I-D thresholds for different AEP values.

The data used to fit the thresholds shows almost no variation. This is surprising as usually there is a larger variation in the recorded rainfall conditions that have triggered landslides and debris flows (e.g. Marra et al 2017, Brunetti et al 2010, Guzzetti et al. 2008). Which data has been used to obtain the R-squared values?

Another of my main concerns is in the validation of the obtained thresholds. The authors base their validation on the comparison between the two types of thresholds they have obtained. However, the performance of neither of the thresholds has been evaluated using independent inventory data. Additionally, the thresholds have not been compared with similar thresholds that have already been obtained in literature.

Finally, one of the main conclusions of the manuscript is that antecedent precipitation plays an important role in debris flow initiation and less intense rainfall is required to trigger a debris flow when the antecedent precipitation is significant. This conclusion has been reached by several authors in the past and I agree. However, the results presented in section 4.3 contradict this conclusion. Table 5, figure 8 and figure 9 show that for a given duration of 1 h the intensity required to trigger a landslide event is higher when AEP is equal to 60 mm than when AEP is equal to 20 mm.

For the above-stated reasons, I do not recommend the current manuscript for publication in HESS.

Additional comments:

Line 42: Are you sure? If I recall well Marra et al. (2017) uses rainfall depth and rainfall duration.

Fig4: What do the blue and red points represent? Are those points the debris flows used to fit the thresholds?

Fig 5. Rainfall intensity depends on rainfall duration and should be expressed in [mm/h]. I think that you are not plotting the intensity but the rainfall accumulation.

Table 4: Why is the duration interval for which the thresholds are defined different for all the considered antecedent precipitation values and the two debris flow densities?

Fig 6: I could not find any reference to this figure in the text.

Fig 7: What do the dots represent?

Fig 8: What are the black points? What is P?

Line 437: Units are missing "when D is larger than 3."

Line 458-459: It is stated that debris flow events are plotted in figure 12 however they are not.

References:

Brunetti, M., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., & Guzzetti, Fausto. (2010). Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences, 10(3), 447–458. https://doi.org/10.5194/nhess-10-447-2010

Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2008). The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides, 5(1), 3–17. https://doi.org/10.1007/s10346-007-0112-1

Marra, F., Destro, E., Nikolopoulos, E. I., Zoccatelli, D., Creutin, J. D., Guzzetti, F., & Borga, M. (2017). Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds. Hydrology and Earth System Sciences, 21(9), 4525–4532. https://doi.org/10.5194/hess-21-4525-2017
Citation: https://doi.org/10.5194/hess-2022-57-RC2
- AC2: 'Reply on RC2', Shaojie Zhang, 12 Apr 2022
  
  Dear Reviewer 2:
  The author is very grateful to your careful review of this manuscript. The authors believe that the comments made by the reviewers will be useful in improving the manuscript. The author answers several major questions raised by reviewer 2 one by one.
  The first concerns：The reviewer is not sure what is the novelty contribution of the work and suggested that structure of the manuscript, the clarity of the text, tables, and figure captions need to be significantly improved. It may be because of writing problems that the reviewer 2's understanding of innovation is biased. This manuscript is not intended to repeat the qualitative description of the influence of AEP on the ID threshold curve. The author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system. AEP will significantly change the position of the threshold curve in the I-D coordinate system, and the change law of the position of the ID threshold curve can be described by the functions of α~AEP and β~AEP, α and β are the two parameters of the ID threshold equation. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold. Therefore, the function of AEP and these two parameters can quantitatively describe the influence of AEP on the ID threshold curve. However, no scholars have conducted relevant research on how to construct the functional relationship between AEP~alpha and AEP~beta.
  The second concern is about the confusion between antecedent precipitation (AEP) and antecedent soil moisture conditions. The authors believe that AEP can reflect the level of water content in the topsoil within a watershed. However, the authors do not intend to stress that AEP is equivalent to soil water content. There is definitely a big difference between these two physical parameters. In particular, the empirical decay formula for calculating AEP is subjective, so it is difficult to have a regular correspondence with the measured soil water content. The authors will revise the relating sentences in order to avoid this misleading.
  The third concern is about the data used to fit the thresholds shows almost no variation. In our current manuscript, two types of data are used for getting the ID threshold curves. The first data groups are obtained from our numerical model, and the second first data group is from the field observation data. Large different of ID curves under different AEP between our study and the others e.g., Marra et al 2017, Brunetti et al 2010, Guzzetti et al. 2008. The authors guess that the main reason is due to the different methods for dealing rainfall data. In the references, they used one same debris flow event but the rainfall events standard is different, different recognition of rainfall process can cause big difference in the I and D, so the ID curves from the references have big difference. However, our study set the same recognition standard. We guess this is the main cause.
  The fourth concern is the performance of neither of the thresholds has been evaluated using independent inventory data. Additionally, the thresholds have not been compared with similar thresholds that have already been obtained in literature. Actually, the performance of the thresholds has been evaluated using debris flow inventory data. This work had been published in another Journal, e.g., Geomorphology (A physics-based model to derive rainfall intensity-duration threshold for debris flow) and Journal of Hydrology (A hydrology-process based method for correlating debris flow density to rainfall parameters and its application on debris flow prediction). And also, we compared with similar thresholds that have already been obtained in literature, please also check the published paper “A physics-based model to derive rainfall intensity-duration threshold for debris flow”. The main focus of the current manuscript is to use the database constructed by numerical model to quantify effects of antecedent effective rainfall on ID threshold for debris flow, especially on how to construct the functions of AEP~alpha and AEP~beta, in order to describe the change law of the position of the ID threshold curve in the I-D coordinate system.
  The fifth concern is the results in figure 4 is contrast with the conclusions. The authors agreed this point, and after careful consideration, we think the current conclusion is not suitable. As mentioned in the “first concern”, the author found that a higher AEP can provide favorable hydrological conditions for runoff generation and solid material resource recharge in JJG, but it do not always mean that the ID threshold condition for triggering debris flow is decreased. As for JJG, only after AEP>40 mm, the AEP and ID threshold condition for triggering debris flow will be completely negatively correlated; when 15 mm≤AEP≤40 mm, the solid material supply is rapidly increased by the AEP, a stronger hydrodynamic condition is required to transform them into debris flow in JJG meaning that the ID threshold condition for triggering debris flow is enhanced, and this positive correlation between them persists until the two ID threshold curves intersect in the I-D coordinate system.
  As for other detailed comments, the authors will amend them according to your suggestions. Thanks again!
  Best regards!
  
  Citation: https://doi.org/10.5194/hess-2022-57-AC2

Shaojie Zhang, Hongjuan Yang, Dunlong Liu, Kaiheng Hu, and Fangqiang Wei

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Jun 2024	8	3	2	13
Jul 2024	8	2	1	11
Aug 2024	12	3	0	15
Sep 2024	9	1	0	10
Oct 2024	9	3	0	12
Nov 2024	3	3	1	7
Dec 2024	2	0	2

Cumulative views and downloads (calculated since 16 Feb 2022)

Month	HTML	PDF	XML	Total
Feb 2022	155	30	3	188
Mar 2022	56	32	2	90
Apr 2022	73	23	10	106
May 2022	33	11	1	45
Jun 2022	11	9	2	22
Jul 2022	25	4	0	29
Aug 2022	12	11	0	23
Sep 2022	15	7	0	22
Oct 2022	12	2	2	16
Nov 2022	10	6	0	16
Dec 2022	9	4	0	13
Jan 2023	9	6	0	15
Feb 2023	11	8	1	20
Mar 2023	12	3	1	16
Apr 2023	6	4	1	11
May 2023	6	6	1	13
Jun 2023	7	2	0	9
Jul 2023	10	10	3	23
Aug 2023	8	4	1	13
Sep 2023	17	6	3	26
Oct 2023	8	8	0	16
Nov 2023	13	0	13
Dec 2023	10	6	1	17
Jan 2024	5	4	0	9
Feb 2024	9	7	1	17
Mar 2024	10	13	2	25
Apr 2024	14	1	4	19
May 2024	4	4	6	14
Jun 2024	8	3	2	13
Jul 2024	8	2	1	11
Aug 2024	12	3	0	15
Sep 2024	9	1	0	10
Oct 2024	9	3	0	12
Nov 2024	3	3	1	7
Dec 2024	2	0	2

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Total article views: 852 (including HTML, PDF, and XML) Thereof 852 with geography defined and 0 with unknown origin.

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Latest update: 13 Dec 2024

Short summary

We use a numerical model to find that the relationships of AEP-α and AEP-β can be respectively described by the specific function. The I-D threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation. This work is helpful to understand the influence mechanism of AEP on I-D threshold curve and are beneficial to improve the prediction capacity of the I-D threshold.


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