A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia

Hariadi, Mugni Hadi; van der Schrier, Gerard; Steeneveld, Gert-Jan; Sutanto, Samuel; Sutanudjaja, Edwin; Ratri, Dian Nur; Sopaheluwakan, Ardhasena; Klein Tank, Albert

doi:https://doi.org/10.5194/hess-2023-14

Preprints

https://doi.org/10.5194/hess-2023-14

Preprints

16 Jan 2023

| 16 Jan 2023

Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia

Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank

Abstract. This article provides high-resolution information on the projected changes in annual extreme rainfall and high and low streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that over Indochina, Myanmar faces more challenges in the near future. The east coast of Myanmar will experience more extreme high rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from the increase in dry spell length of up to 40 %, while the increase of extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low flow event than in a high flow event. The majority of rivers in the central Mekong catchment, Sumatra, the Malaysian peninsula, Borneo, and Java will experience more extreme low flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low flow occurrences, which reaches 101 % and 122 % on average over Sumatra and Java, respectively. Finally, the changes in extreme high and low streamflow events are more pronounced in rivers with steep hydrographs, while rivers with shallow hydrographs have a higher risk in the probability of low flow change. Our study highlights the importance of catchment properties in aggregating and/or buffering the impact of extreme climate change.

Received: 10 Jan 2023 – Discussion started: 16 Jan 2023

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Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank

Status: closed

RC1: 'Comment on hess-2023-14', Anonymous Referee #1, 07 Feb 2023

Review on “A high-resolution perspective of extreme rainfall and river flow

under extreme climate change in Southeast Asia” by Hariadi et al.

The manuscript provided a synthesis on the streamflow response under HigResMIP climate change scenario. Southeast Asia was chosen for testing the hydrological model called PCR-GLOBWB. The authors focused on how the extreme rainfall may influence the changes in low and high flow events. The manuscript may need a substantial revision before publication in HESS journal.

The message conveyed by the authors was not clear, does extreme rainfall influence changes in low and high flows, or increased low flow under climate change scenario? Or something else. I suggest the author to better rephrase it throughout.
The research gap being addressed in the current form seemed too weak and not clear. Para 2 and 5 in the introduction may summarized previous facts. But it still not clears why the research is important to do. Also, does it any benefit using CMIP for the analysis compared to other datasets.
It seemed that the authors would like to deliver the message on the importance of catchment properties to flows dynamics under climate change scenario (L15). But it may not be supported by strong methods and findings. Is there any calibration/validation on the hydrological model? If yes, which stations were used for this? Findings in Figs 3-6 may not show the changes in low/high flow, but it indicates the changes in water depth/storage in the river networks. River discharge is measured in fixed station not along the river network.
I appreciate that authors mentioned about uncertainty of the work. My question is how the uncertainty influences the conclusion/findings. Better elaborate on it will improve the readability of the manuscript.
Please elaborate what PCR-GLOBWB model can do and how does it work? Are there any assumptions for the model.
L131-137 not part of data, I suggest the authors to provide new sub-section about statistical analysis.
Hydrological drought was firstly mentioned in L55, which may not coherence with the previous paragraph. Please revise to better connect with the previous one. Does hydrological drought become a focus of study? I think is not as not many supports afterwards.
L386-395 shall be in the method section to represent the catchment properties influence. Also, how steep or shallow hydrograph was determined was not found anywhere in the method.

L2 does any climate change is not extreme?

L9 more prominent, how much?

L129 the authors used Hamon method for calculating potential ET may need elaboration (e.g. why not other methods?) the method may not commonly used n Southeast Asia.

L145 aggregated?

L158 put a comma after Indonesia

L534 PhD thesis?

Citation: https://doi.org/10.5194/hess-2023-14-RC1

AC1: 'Reply on RC1', Mugni Hariadi, 13 Mar 2023

We would like to thank the reviewer for valuable suggestions and comments. In this document, Prefers to the page number and L refers to the line number in the recent paper. For example, P3L65-70, refers to page 3, lines 65-70.

General comment: The manuscript provided a synthesis on the streamflow response under HigResMIP climate change scenario. Southeast Asia was chosen for testing the hydrological model called PCR-GLOBWB. The authors focused on how the extreme rainfall may influence the changes in low and high flow events. The manuscript may need a substantial revision before publication in HESS journal.

reply: We would like to point out that this manuscript is not intended to test the performance of the PCR-GLOBWB hydrological model but we used the model as a tool to simulate the streamflow. This manuscript focused on the impact of extreme climate change (RCP 8.5) on extreme rainfall and furthermore on the low and high streamflow. The model validations are available from the previous paper by Sutanudjaja et al. (2018). Previous studies by Trambauer et al., (2014) and Ward et al., (2013) show that PCR-GLOBWB is reliable for extreme studies.

Major comments:

No	Comment	reply
1	The message conveyed by the authors was not clear, does extreme rainfall influence changes in low and high flows, or increased low flow under climate change scenario? Or something else. I suggest the author to better rephrase it throughout.	We understand the reviewer's concern about the message of the paper. However, we stated our main finding in the abstract “the impact of climate change is more prominent in a low flow event than in a high flow” (P1L9-10). Moreover, we also discuss this in Section 4.2, e.g., ”the impact of changes in climate indices in SEA also affects the changes in hydrological extremes. The increase of CDD over Northern MEB results in declining LFD over the central part of MEB” (P12). We will change the title of the sub-chapter for clarification.
2	The research gap being addressed in the current form seemed too weak and not clear. Para 2 and 5 in the introduction may summarized previous facts. But it still not clears why the research is important to do. Also, does it any benefit using CMIP for the analysis compared to other datasets.	The reviewer is correct that paragraphs 2 and 5 summarized the previous studies and this study differs compared to those in the sense that we used the latest version of CMIP model results (HighResMIP) and the PCRGLOBWB model to simulate the hydrological extremes. The use of HighResMIP models generates more accurate monsoon characteristics and extreme rainfall results compared to the downscaling result of CMIP5 (CORDEX) (Hariadi et al. (2021,2022)). We will add this statement to address this feedback.
3	It seemed that the authors would like to deliver the message on the importance of catchment properties to flows dynamics under climate change scenario (L15). But it may not be supported by strong methods and findings. Is there any calibration/validation on the hydrological model? If yes, which stations were used for this? Findings in Figs 3-6 may not show the changes in low/high flow, but it indicates the changes in water depth/storage in the river networks. River discharge is measured in fixed station not along the river network.	The importance of catchment properties to streamflow changes due to climate change is not the main message of our paper. The objective of our paper is to evaluate the changes in future extreme rainfall and streamflow. However, we found that not all river basins in SEA follow the changes in extreme rainfall and our result suggests that the catchment properties/memory may play a significant role here. Therefore, we discuss the importance of catchment properties in the discussion. We will revise our manuscript accordingly thus the main message of our is clearly stated and the discussion on catchment properties will not overcome the main message. We used the river recession constant (C) to indicate the catchment properties. The method and the map of the recession constants are available in the supplement material (Supplementary 7). The validation of the PCR-GLOBWB hydrological model is available in the previous paper by Sutanudjaja et al. (2018) and in Section 2.2.2 (P4L131-137). The river discharge (streamflow) is calculated using the kinematic wave for the routing method (P4L130), which allows flow and area to vary both spatially and temporally within a conduit. Thus, the PCR-GLOBWB simulates the river discharge in m3/s for all river networks. We will add this statement in the revised version.
4	I appreciate that authors mentioned about uncertainty of the work. My question is how the uncertainty influences the conclusion/findings. Better elaborate on it will improve the readability of the manuscript	The analysis is based on one climate change scenario (RCP 8.5) which means that the result is more applicable to this scenario. Although, IPCC (2021) reported a relatively small difference between RCP scenarios in the near future (until 2050) rainfall projection. In addition, the hydrological simulation is excluded the water demand, which mean is not consider the change in water demand in the future. This is because we would like to focus on climate forcing. We will elaborate more on how the uncertainty influences the conclusion/findings in the manuscript.
5	Please elaborate what PCR-GLOBWB model can do and how does it work? Are there any assumptions for the model.	We will add more explanations of the PCR-GLOBWB model in chapter 2.2.2. The PCR-GLOBWB is essentially a leakybucket type of model [Bergström, 1976] applied on a cell-by-cell basis. Daily for each grid cell, PCR-GLOBWB calculates the water storage in two vertically stacked soil layers (max. depth 0.3 and 1.2 m) and an underlying groundwater layer, as well as the water exchange between the layers and between the top layer and the atmosphere (rainfall, evaporation and snow melt).
6	L131-137 not part of data, I suggest the authors to provide new sub-section about statistical analysis.	L131-137 is the validation of the PCR-GLOBWB hydrological model summarized in the previous paper (Sutanudjaja et al., 2018). We will make it clear in the manuscript that the detailed validation is available from the previous paper and it is publicly available.
7	Hydrological drought was firstly mentioned in L55, which may not coherence with the previous paragraph. Please revise to better connect with the previous one. Does hydrological drought become a focus of study? I think is not as not many supports afterwards.	We thank you for the useful feedback. Indeed, the reviewer is correct that this study analyzes the low flow and not necessarily drought. We will revise this part.
8	L386-395 shall be in the method section to represent the catchment properties influence. Also, how steep or shallow hydrograph was determined was not found anywhere in the method.	L386-395 is brief information for the river recession constant. The detailed method for how calculating the river recession constant is presented in the supplement material (Supplementary 8).

Small comments:

No	Small comment	Reply
1	L2 does any climate change is not extreme?	We refer the extreme climate change to RCP 8.5
2	L9 more prominent, how much?	We used prominent to highlight that the change of low flow is higher compared to high flow. The value is available in chapter 3.2 (P9L253-308)
3	L129 the authors used Hamon method for calculating potential ET may need elaboration (e.g. why not other methods?) the method may not commonly used n Southeast Asia.	We only used rainfall and temperature in the analysis, this limit us to using more advanced methods like the Penman-Monteith method.
4	L145 aggregated?	The number of events is cumulated per year.
5	L158 put a comma after Indonesia	We will add the comma
6	L534 PhD thesis?	It is a master's thesis. We will revise the manuscript

Citation: https://doi.org/10.5194/hess-2023-14-AC1

RC2: 'Comment on hess-2023-14', Anonymous Referee #2, 13 Feb 2023

Review on hess-2023-14 “A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia” by Hariadi et al

General comments:

Based on HighResMIP CMIP6 models this study came up with the projection of extreme rainfall and river flow under extreme future climate change over Southeast Asia. While the study is useful, I have some concerns that the authors need to consider and address.

Specific comments

1. I think the literature review was not broad enough. As far as I know CORDEX – SEA has carried out multimodels downscaling projection over Southeast Asia. In fact, according to one of their papers (Tangang et al. 2020-- Climate Dynamics,55, 1247-1267), the projected future changes are consistent with what described in this study. However, I find there was lack of discussion on the comparison of the two simulations i.e. CORDEX-SEA vs HighRESMIP.

2. The mean rainfall and extremes are very seasonally dependent. Yet, this study only considers annual extremes. I propose the authors to consider the changes in different seasons in addition to annual timescale.

3. This study appears to have lack of model validations. I think it is useful to validate the HighResMIP models in terms of their ability in simulating the seasonal mean climate, including the monsoonal circulations.

4. The study used bias-corrected outputs to evaluate the projected changes. Yet, there was no analysis (other than Fig 1) on how bias-correction changes the future projected as compared to the raw outputs. It seems from a recently published paper ( Ngai et al. 2022 -- Weather and Climate Extremes, 37, 100484) inconsistency in the magnitude and direction of future change can occur between the bias-corrected and the raw outputs of CORDEX-SEA simulations. The authors need to provide information how bias-correction can affect the projected climate change signals in HighResMIP models.

5. I also find lack of validation in the river flow simulation during the historical period. I think this is needed before the models can be confidently used for projections.

Minor comment:

1. I suggest the authors to use “Malay Peninsula” instead of “Malaysian Peninsula”. Alternatively “Peninsular Malaysia” can be used if the authors was referring to the west Malaysia. However, if the landmasses they are referring to include the southern Thailand then “Malay Peninsula” would be most appropriate.

Citation: https://doi.org/10.5194/hess-2023-14-RC2

AC2: 'Reply on RC2', Mugni Hariadi, 13 Mar 2023

We would like to thank the reviewer for the comments, valuable suggestions, and acknowledgement of this study. In this document, P

refers to the page number and L refers to the line number in the recent paper. For example, P3L65-70 relates to page 3, lines 65-70.

No	Comments	reply
1	I think the literature review was not broad enough. As far as I know CORDEX – SEA has carried out multimodels downscaling projection over Southeast Asia. In fact, according to one of their papers (Tangang et al. 2020-- Climate Dynamics,55, 1247-1267), the projected future changes are consistent with what described in this study. However, I find there was lack of discussion on the comparison of the two simulations i.e. CORDEX-SEA vs HighRESMIP.	The comparisons of CORDEX-SEA vs HighRESMIP are conducted in our previous paper (Hariadi et. al., 2021 and 2022). We will elaborate more on the comparison between CORDEX-SEA vs HighResMIP in the revised version.
2	The mean rainfall and extremes are very seasonally dependent. Yet, this study only considers annual extremes. I propose the authors to consider the changes in different seasons in addition to annual timescale.	We do agree with the reviewer that means rainfall and extremes are very seasonally dependent. However, in this paper, we would like to focus on the general changes in extreme events for both rainfall and streamflow.
3	This study appears to have lack of model validations. I think it is useful to validate the HighResMIP models in terms of their ability in simulating the seasonal mean climate, including the monsoonal circulations.	The performance of the HighResMIP model and the comparison between monsoon characteristics and rainfall indices are available in the previous paper (Hariadi et. al., 2021 and 2022). We will summarize the findings in the introduction (P3L64-75).
4	The study used bias-corrected outputs to evaluate the projected changes. Yet, there was no analysis (other than Fig 1) on how bias-correction changes the future projected as compared to the raw outputs. It seems from a recently published paper (Ngai et al. 2022 -- Weather and Climate Extremes, 37, 100484) inconsistency in the magnitude and direction of future change can occur between the bias-corrected and the raw outputs of CORDEX-SEA simulations. The authors need to provide information how bias-correction can affect the projected climate change signals in HighResMIP models.	We thank the reviewer for the feedback. We used the same bias correction method as Ngai et al. (2022), so the impact of bias correction on the simulations can be expected to be significant, as Ngai et al. (2022) showed. In addition, the objective of the paper is to document the changes in hydrology. Quantifying the impact of bias correction is out of the scope of this study. Furthermore, we will refer to Ngai et al. (2022) to show the impact of bias correction on the simulations in the revised manuscript.
5	I also find lack of validation in the river flow simulation during the historical period. I think this is needed before the models can be confidently used for projections.	The detailed validation of the PCR-GLOBWB hydrological model is available in the previous paper by Sutanudjaja et al. (2018). We refer to this in P5L131-137. In addition, previous studies by Trambauer et al., (2014) and Ward et al., (2013) show that PCR-GLOBWB is reliable for extreme studies.

Small comment:

I suggest the authors to use “Malay Peninsula” instead of “Malaysian Peninsula”. Alternatively “Peninsular Malaysia” can be used if the authors was referring to the west Malaysia. However, if the landmasses they are referring to include the southern Thailand then “Malay Peninsula” would be most appropriate.

Reply:

We thank the reviewer for the feedback. We agree with the reviewer and thus we will use “Peninsular Malaysia” for referring west Malaysia in the revised manuscript

Citation: https://doi.org/10.5194/hess-2023-14-AC2

Status: closed

RC1: 'Comment on hess-2023-14', Anonymous Referee #1, 07 Feb 2023

Review on “A high-resolution perspective of extreme rainfall and river flow

under extreme climate change in Southeast Asia” by Hariadi et al.

The message conveyed by the authors was not clear, does extreme rainfall influence changes in low and high flows, or increased low flow under climate change scenario? Or something else. I suggest the author to better rephrase it throughout.
The research gap being addressed in the current form seemed too weak and not clear. Para 2 and 5 in the introduction may summarized previous facts. But it still not clears why the research is important to do. Also, does it any benefit using CMIP for the analysis compared to other datasets.
It seemed that the authors would like to deliver the message on the importance of catchment properties to flows dynamics under climate change scenario (L15). But it may not be supported by strong methods and findings. Is there any calibration/validation on the hydrological model? If yes, which stations were used for this? Findings in Figs 3-6 may not show the changes in low/high flow, but it indicates the changes in water depth/storage in the river networks. River discharge is measured in fixed station not along the river network.
I appreciate that authors mentioned about uncertainty of the work. My question is how the uncertainty influences the conclusion/findings. Better elaborate on it will improve the readability of the manuscript.
Please elaborate what PCR-GLOBWB model can do and how does it work? Are there any assumptions for the model.
L131-137 not part of data, I suggest the authors to provide new sub-section about statistical analysis.
Hydrological drought was firstly mentioned in L55, which may not coherence with the previous paragraph. Please revise to better connect with the previous one. Does hydrological drought become a focus of study? I think is not as not many supports afterwards.
L386-395 shall be in the method section to represent the catchment properties influence. Also, how steep or shallow hydrograph was determined was not found anywhere in the method.

L2 does any climate change is not extreme?

L9 more prominent, how much?

L129 the authors used Hamon method for calculating potential ET may need elaboration (e.g. why not other methods?) the method may not commonly used n Southeast Asia.

L145 aggregated?

L158 put a comma after Indonesia

L534 PhD thesis?

Citation: https://doi.org/10.5194/hess-2023-14-RC1

AC1: 'Reply on RC1', Mugni Hariadi, 13 Mar 2023

Major comments:

No	Comment	reply
1	The message conveyed by the authors was not clear, does extreme rainfall influence changes in low and high flows, or increased low flow under climate change scenario? Or something else. I suggest the author to better rephrase it throughout.	We understand the reviewer's concern about the message of the paper. However, we stated our main finding in the abstract “the impact of climate change is more prominent in a low flow event than in a high flow” (P1L9-10). Moreover, we also discuss this in Section 4.2, e.g., ”the impact of changes in climate indices in SEA also affects the changes in hydrological extremes. The increase of CDD over Northern MEB results in declining LFD over the central part of MEB” (P12). We will change the title of the sub-chapter for clarification.
2	The research gap being addressed in the current form seemed too weak and not clear. Para 2 and 5 in the introduction may summarized previous facts. But it still not clears why the research is important to do. Also, does it any benefit using CMIP for the analysis compared to other datasets.	The reviewer is correct that paragraphs 2 and 5 summarized the previous studies and this study differs compared to those in the sense that we used the latest version of CMIP model results (HighResMIP) and the PCRGLOBWB model to simulate the hydrological extremes. The use of HighResMIP models generates more accurate monsoon characteristics and extreme rainfall results compared to the downscaling result of CMIP5 (CORDEX) (Hariadi et al. (2021,2022)). We will add this statement to address this feedback.
3	It seemed that the authors would like to deliver the message on the importance of catchment properties to flows dynamics under climate change scenario (L15). But it may not be supported by strong methods and findings. Is there any calibration/validation on the hydrological model? If yes, which stations were used for this? Findings in Figs 3-6 may not show the changes in low/high flow, but it indicates the changes in water depth/storage in the river networks. River discharge is measured in fixed station not along the river network.	The importance of catchment properties to streamflow changes due to climate change is not the main message of our paper. The objective of our paper is to evaluate the changes in future extreme rainfall and streamflow. However, we found that not all river basins in SEA follow the changes in extreme rainfall and our result suggests that the catchment properties/memory may play a significant role here. Therefore, we discuss the importance of catchment properties in the discussion. We will revise our manuscript accordingly thus the main message of our is clearly stated and the discussion on catchment properties will not overcome the main message. We used the river recession constant (C) to indicate the catchment properties. The method and the map of the recession constants are available in the supplement material (Supplementary 7). The validation of the PCR-GLOBWB hydrological model is available in the previous paper by Sutanudjaja et al. (2018) and in Section 2.2.2 (P4L131-137). The river discharge (streamflow) is calculated using the kinematic wave for the routing method (P4L130), which allows flow and area to vary both spatially and temporally within a conduit. Thus, the PCR-GLOBWB simulates the river discharge in m3/s for all river networks. We will add this statement in the revised version.
4	I appreciate that authors mentioned about uncertainty of the work. My question is how the uncertainty influences the conclusion/findings. Better elaborate on it will improve the readability of the manuscript	The analysis is based on one climate change scenario (RCP 8.5) which means that the result is more applicable to this scenario. Although, IPCC (2021) reported a relatively small difference between RCP scenarios in the near future (until 2050) rainfall projection. In addition, the hydrological simulation is excluded the water demand, which mean is not consider the change in water demand in the future. This is because we would like to focus on climate forcing. We will elaborate more on how the uncertainty influences the conclusion/findings in the manuscript.
5	Please elaborate what PCR-GLOBWB model can do and how does it work? Are there any assumptions for the model.	We will add more explanations of the PCR-GLOBWB model in chapter 2.2.2. The PCR-GLOBWB is essentially a leakybucket type of model [Bergström, 1976] applied on a cell-by-cell basis. Daily for each grid cell, PCR-GLOBWB calculates the water storage in two vertically stacked soil layers (max. depth 0.3 and 1.2 m) and an underlying groundwater layer, as well as the water exchange between the layers and between the top layer and the atmosphere (rainfall, evaporation and snow melt).
6	L131-137 not part of data, I suggest the authors to provide new sub-section about statistical analysis.	L131-137 is the validation of the PCR-GLOBWB hydrological model summarized in the previous paper (Sutanudjaja et al., 2018). We will make it clear in the manuscript that the detailed validation is available from the previous paper and it is publicly available.
7	Hydrological drought was firstly mentioned in L55, which may not coherence with the previous paragraph. Please revise to better connect with the previous one. Does hydrological drought become a focus of study? I think is not as not many supports afterwards.	We thank you for the useful feedback. Indeed, the reviewer is correct that this study analyzes the low flow and not necessarily drought. We will revise this part.
8	L386-395 shall be in the method section to represent the catchment properties influence. Also, how steep or shallow hydrograph was determined was not found anywhere in the method.	L386-395 is brief information for the river recession constant. The detailed method for how calculating the river recession constant is presented in the supplement material (Supplementary 8).

Small comments:

No	Small comment	Reply
1	L2 does any climate change is not extreme?	We refer the extreme climate change to RCP 8.5
2	L9 more prominent, how much?	We used prominent to highlight that the change of low flow is higher compared to high flow. The value is available in chapter 3.2 (P9L253-308)
3	L129 the authors used Hamon method for calculating potential ET may need elaboration (e.g. why not other methods?) the method may not commonly used n Southeast Asia.	We only used rainfall and temperature in the analysis, this limit us to using more advanced methods like the Penman-Monteith method.
4	L145 aggregated?	The number of events is cumulated per year.
5	L158 put a comma after Indonesia	We will add the comma
6	L534 PhD thesis?	It is a master's thesis. We will revise the manuscript

Citation: https://doi.org/10.5194/hess-2023-14-AC1

RC2: 'Comment on hess-2023-14', Anonymous Referee #2, 13 Feb 2023

Review on hess-2023-14 “A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia” by Hariadi et al

General comments:

Specific comments

5. I also find lack of validation in the river flow simulation during the historical period. I think this is needed before the models can be confidently used for projections.

Minor comment:

Citation: https://doi.org/10.5194/hess-2023-14-RC2

AC2: 'Reply on RC2', Mugni Hariadi, 13 Mar 2023

No	Comments	reply
1	I think the literature review was not broad enough. As far as I know CORDEX – SEA has carried out multimodels downscaling projection over Southeast Asia. In fact, according to one of their papers (Tangang et al. 2020-- Climate Dynamics,55, 1247-1267), the projected future changes are consistent with what described in this study. However, I find there was lack of discussion on the comparison of the two simulations i.e. CORDEX-SEA vs HighRESMIP.	The comparisons of CORDEX-SEA vs HighRESMIP are conducted in our previous paper (Hariadi et. al., 2021 and 2022). We will elaborate more on the comparison between CORDEX-SEA vs HighResMIP in the revised version.
2	The mean rainfall and extremes are very seasonally dependent. Yet, this study only considers annual extremes. I propose the authors to consider the changes in different seasons in addition to annual timescale.	We do agree with the reviewer that means rainfall and extremes are very seasonally dependent. However, in this paper, we would like to focus on the general changes in extreme events for both rainfall and streamflow.
3	This study appears to have lack of model validations. I think it is useful to validate the HighResMIP models in terms of their ability in simulating the seasonal mean climate, including the monsoonal circulations.	The performance of the HighResMIP model and the comparison between monsoon characteristics and rainfall indices are available in the previous paper (Hariadi et. al., 2021 and 2022). We will summarize the findings in the introduction (P3L64-75).
4	The study used bias-corrected outputs to evaluate the projected changes. Yet, there was no analysis (other than Fig 1) on how bias-correction changes the future projected as compared to the raw outputs. It seems from a recently published paper (Ngai et al. 2022 -- Weather and Climate Extremes, 37, 100484) inconsistency in the magnitude and direction of future change can occur between the bias-corrected and the raw outputs of CORDEX-SEA simulations. The authors need to provide information how bias-correction can affect the projected climate change signals in HighResMIP models.	We thank the reviewer for the feedback. We used the same bias correction method as Ngai et al. (2022), so the impact of bias correction on the simulations can be expected to be significant, as Ngai et al. (2022) showed. In addition, the objective of the paper is to document the changes in hydrology. Quantifying the impact of bias correction is out of the scope of this study. Furthermore, we will refer to Ngai et al. (2022) to show the impact of bias correction on the simulations in the revised manuscript.
5	I also find lack of validation in the river flow simulation during the historical period. I think this is needed before the models can be confidently used for projections.	The detailed validation of the PCR-GLOBWB hydrological model is available in the previous paper by Sutanudjaja et al. (2018). We refer to this in P5L131-137. In addition, previous studies by Trambauer et al., (2014) and Ward et al., (2013) show that PCR-GLOBWB is reliable for extreme studies.

Small comment:

Reply:

We thank the reviewer for the feedback. We agree with the reviewer and thus we will use “Peninsular Malaysia” for referring west Malaysia in the revised manuscript

Citation: https://doi.org/10.5194/hess-2023-14-AC2

Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank

Supplement

https://doi.org/10.5194/hess-2023-14-supplement

Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank

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Jan 2024	12	16	0	28
Feb 2024	13	17	3	33
Mar 2024	28	13	3	44
Apr 2024	14	2	3	19

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Month	HTML	PDF	XML	Total
Jan 2023	222	67	4	293
Feb 2023	110	27	6	143
Mar 2023	59	21	5	85
Apr 2023	23	14	0	37
May 2023	33	14	2	49
Jun 2023	18	13	0	31
Jul 2023	49	18	2	69
Aug 2023	42	13	1	56
Sep 2023	26	20	1	47
Oct 2023	13	16	0	29
Nov 2023	15	5	0	20
Dec 2023	18	16	2	36
Jan 2024	12	16	0	28
Feb 2024	13	17	3	33
Mar 2024	28	13	3	44
Apr 2024	14	2	3	19

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Total article views: 960 (including HTML, PDF, and XML) Thereof 960 with geography defined and 0 with unknown origin.

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Latest update: 09 Apr 2024

Short summary

We utilize the high-resolution CMIP6 on extreme rainfall and extreme streamflow projection over Southeast Asia. This region will experience an increase in both dry and wet extremes in near future. We found a more extreme low flow and high flow, respectively along with the increasing probability of low flow and high flow events. We reveal that the changes in low flow events and their probabilities are not only influenced by extreme dry climates but also by the catchment characteristics.


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