New Approach Evaluating Peatland Fires in Indonesian Factors
Round 1
Reviewer 1 Report
I tried to read the article with much interest but found it very difficult to access the pertinent details I am afraid. Therefore, I cannot recommend the article to be published in its present state. It needs a rewrite to be clear, concise, use relevant information and structure. I think it would also benefit from an editor for English language input.
Author Response
(C)
I tried to read the article with much interest but found it very difficult to access the pertinent details I am afraid. Therefore, I cannot recommend the article to be published in its present state. It needs a rewrite to be clear, concise, use relevant information and structure. I think it would also benefit from an editor for English language input.
(A1)
I asked for English proof reading and made revised manuscript.
I think you can read my manuscript now.
So please start to review my manuscript.
Author Response File: 
Author Response.docx
Reviewer 2 Report
80 – 94 The objectives of this work are not very clearly stated (style). It should be emphasized something like: Since the fire in 2019 occurred regardless of the ENSO index, in this work we demonstrated a new OLR-MC (a rain-related index derived from outgoing longwave radiation) and the fire occurrence was analysed first that could explain occurrence of peatland fires.
125 Please provide more specific name of Modis product used in this study (What is Modis HS data ?)
127 “We use only the spatial and temporal hotspot data 127 in this study. “, Please be more specific…
130 Please provide a web link or the reference for “OLR-MC (rain-related index derived from Outgoing Longwave Radiation) data”
130 A more in detail presentation of OLR-MC index is required for the readers auditorium because it is the core topics of the work. What was the authors motivation to use this index? Are there other similar indices existing? Are there any similar work or references related to this index?
199-204 What are the spatial variations of OLR-MC index? Is it homogeneous over the various locations in the broad area of Kalimantan to be representative with only one time series?
216 Is there any better and more advanced method of describing of the relationship between Hotspots (Fires) and OLR-MC index? Simple regression analysis based on ordinary least squares is not suitable for monthly time series because of the seasonal autocorrelation in the data.
218-219 “suggests that there was a good correlation…”, Why correlation coefficient is not shown?
239 The same statistical issue as in line 216
303-304 More detail description and presentation of the GWL simulation results is required. Authors used their own method for estimation of groundwater level without any validation of the results.
Author Response
(C) 80 – 94 The objectives of this work are not very clearly stated (style).
It should be emphasized something like:
Since the fire in 2019 occurred regardless of the ENSO index, in this work we demonstrated a new OLR-MC (a rain-related index derived from outgoing longwave radiation) and the fire occurrence was analysed first that could explain occurrence of peatland fires.
(A1) Thank you for your suggestion.
I revised them like in the below.
(line 94-98)
In this paper, we discussed two issues related to peatland fires in Indonesia. Firstly, we introduce one of existing indicators to explain active fires in 2019 occurred regardless of ENSO-neutral. The correlation between the OLR-MC and the fire occurrence was analyzed. The OLR-MC is one of indices issued by JMA and a rain-related index derived from outgoing longwave radiation (OLR) in Indonesia (MC: Maritime Continent, area from Kalimantan to west Papua).
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(C) 125 Please provide more specific name of Modis product used in this study (What is Modis HS data ?)
(A2)
MODIS, spectroradiometer , could detect temperature anomaly on the surface of earth.
High temperature areas are usually referred to as “hotspot”.
NASA FIRMS (Fire Information for Resource Management System) provide us such data and we called them “MODIS Hotspot Data”.
I rewrite 2.2.1. like in the below.
(line 139-144)
2.2.1. Hotspot (fire) Data
Eighteen years of hotspot (HS, temperature anomaly) data detected by moderate resolution imaging spectroradiometer (MODIS) on the Terra and Aqua satellites are used to evaluate fires in boreal forests. MODIS HS data collected from 2002 to 2019 were obtained from NASA FIRMS (Fire Information for Resource Management System, https://firms2.modaps.eosdis.nasa.gov/download/(Accessed 6 June 2020)). We use only the spatial and temporal (latitude, longitude, and acquisition date and time) hotspot data in this study. The number of daily hotspots is used to identify fire activities and the fire year.
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(C) 127 “We use only the spatial and temporal hotspot data 127 in this study. “, Please be more specific…
(A3)
MODIS hotspot data contains information such as latitude, longitude, brightness, acquisition date and time, satellite name, confidence, etc.
We used latitude, longitude, and acquisition date and time.
“the spatial and temporal hotspot data” means them (latitude, longitude, and acquisition date and time).
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(C) 130 Please provide a web link or the reference for “OLR-MC (rain-related index derived from Outgoing Longwave Radiation) data”
(A4)
Web link is already in References [24]. But I move it into main text near line 142.
https://www.data.jma.go.jp/gmd/cpd/db/diag/2018/index/html/soiolru/index_html_soiolru_2018.html (Accessed 6 June 2020)
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(C) 130 A more in detail presentation of OLR-MC index is required for the readers auditorium because it is the core topics of the work.
What was the authors motivation to use this index?
Are there other similar indices existing?
Are there any similar work or references related to this index?
(A5)
(c) 1. What was the authors motivation to use this index?
From a few years ago, I started to use OLR Data from NOAA to estimate rainfall in Indonesia. I am familiar with OLR and also know it is effective. So, firstly I introduce the OLR-MC index.
(c) 2. Are there other similar indices existing?
I am searching to find similar indices. However, at the moment, I could not find a better index than the OLR-MC index.
(c) 3. Are there any similar work or references related to this index?
Basically, the OLR-MC index is developed to observe monsoon activity.
Most monsoon researchers are only interest in Monsoon, not fires.
Most fire researchers are completely dependent on ENSO index and are not paying attention to alternative indices.
So, it is s little bit difficult to find similar works or references.
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(C) 199-204 What are the spatial variations of OLR-MC index? Is it homogeneous over the various locations in the broad area of Kalimantan to be representative with only one time series?
(A6)
The cover area of the OLR-MC index is from Kalimantan to west of Papua (5 N-5 S, 110-135 E). I describe cover area in the manuscript (line 144).
I don’t know how researcher in Japan Meteorological agency caluculate monthly value of the OLR-MC index exactly.
And I am not meteorologist. So, I just introduce you to the below web site.
https://www.data.jma.go.jp/gmd/cpd/diag/note.html
**** OLR index
(translation from Japanese to English done by Google translation)
It is an index for monitoring convective activity near the Philippines, Indonesia, and the date line. As for the calculation method, the OLR normal deviation with the sign reversed is area averaged and standardized with the standard deviation. Because the sign of the normal deviation is reversed, positive values are more active than normal and negative values are less active.
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(C) 216 Is there any better and more advanced method of describing of the relationship between Hotspots (Fires) and OLR-MC index? Simple regression analysis based on ordinary least squares is not suitable for monthly time series because of the seasonal autocorrelation in the data.
(A7)
Thank you for your suggestion.
Due to “the seasonal autocorrelation”, OLR-MC index and Nino index show a good correlation with fire.
I try to find benefit of “the seasonal autocorrelation” in the next step.
There is a chance to develop to make a fire forecast.
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(C) 218-219 “suggests that there was a good correlation…”, Why correlation coefficient is not shown?
(A8)
Correlation coefficient is simply calculated by square root of decision coefficient (R2=0.8389). It is 0.9159.
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(C) 239 The same statistical issue as in line 216
(A9)
I could not find “the same statistical issue”.
Correlation coefficient (for Niño 3.4 Index) is 0.7765 (R2=0.6030).
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(C) 303-304 More detail description and presentation of the GWL simulation results is required. Authors used their own method for estimation of groundwater level without any validation of the results.
(A10)
(c) 1. More detail description:
I describe detail of the GWL simulation.
******(line 330-334)
Equation (3) ((GWL of the day)=(GWL of the previous day)+ R – 8) is used to estimate daily GWL. Calculation starts from June 19. Initial GWL on June 19 is set to 0 mm from averaged groundwater level [28]. GWL of next day (June 20) is +3.3 mm (=0+R–8 (where R (rainfall on June 20) is 11.3 mm from average TRMS rainfall data). Like this manner, daily GWL is estimated. GWL becomes under -500 mm on September 1 (DN=244).
(c) 2. Varidation:
Model-0 is a very simple model. As I assumed evapotranspiration (E) in Equation (2) is 8 mm day-1, rainfall is only one variable. Validation of Model-0 were carried out three cases in 2015, 2006, and 2019. Comparison of measured and estimated GWL in Figures 8, 9, and 10 showed Model-0 could estimate GWL well.
Author Response File: Author Response.docx
Reviewer 3 Report
see attached
Comments for author File: 
Comments.pdf
Author Response
Minor comments
(C) l. 13 – I think “were” is a better choice than “could be” because this implies that the ENSO method was completely satisfactory, but R2 was not as high as with your replacement index.
(A1) Thank you. I changed it.
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(C) l. 17 – what’s MC? Define here as you did for OLR.
(A2) (Line-16)
The OLR-MC Index is one of the rain-related indices derived from OLR (Outgoing Longwave Radiation) in MC (Maritime Continent) area in Indonesia.
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(C) l. 24 – why are only two depths given for the three fire stages? Shouldn’t it be 0, -300 and -500 mm?
(A3) As you know if ground water level (GWL) is 0mm, peat is too wet to burn. So, GWL= 0mm is for a surface fire not for peat fire. As shown in Figures 7,8,9, and 10, shallow and deep peat fire starts from GWL less than -300 mm and -500 mm. Two depths are used to evaluate peat fire activities.
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(C) l. 42-50 – this material would be better presented in a table
(A4) (Line-54- )
I insert Table 1.
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(C) l. 77, 404 – Usup et al. (2004). I don’t think the co-authors would appreciate not being cited.
(A5) I delete it and other authors names from main sentences.
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(C) l.429-432 Suggested rewrite -
A method for estimating rainfall using the OLR distribution map was developed. Rainfall at any arbitrary location can be estimated with this method. MODEL-0 can be applied in any target region to determine the fire stages from the estimated GWL (Figures 4 and 5).
(A6) Thank you. I follow your suggestion. I insert them into Conclusion (7)
(7) (Line-460- )
A method for estimating rainfall using the OLR distribution map was developed. Rainfall at any arbitrary location can be estimated with this method. MODEL-0 can be applied in any target region to determine the fire stages from the estimated GWL (Figures 4 and 5).
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Authors provide a quality feedback to provided questions. The Manuscript, under Editorial approval can proceed to publishing.
Author Response
Thank you for reviewing.
Reviewer 3 Report
Clarifications I thought necessary for the previous version were added. Some very minor points to be addressed include:
MC is defined as Maritime Continent in one place and as Middle Corridor in another. Please make up your mind.
It might help readers who are not familiar with the OLR index to add a sentence or two in Section 2.2.2 describing its connection to convective activity. Also add a sentence explaining the values shown in Table 1.
Better explanation of the OLR index might encourage its wider use and of your own work.
Author Response
(C1) MC is defined as Maritime Continent in one place and as Middle Corridor in another. Please make up your mind.
(A1) Maritime Continent is the correct name. I unified them.
Line 17, 99, and 142.
(C2) It might help readers who are not familiar with the OLR index to add a sentence or two in Section 2.2.2 describing its connection to convective activity. Also add a sentence explaining the values shown in Table 1.
(A2)
(1) I insert the blow sentence near Table 1 in Introduction.
Line 53
There are two papers showing that correlation between OLR and rainfall in tropical regions [9,10].
(2) In Section 2.2.2, I insert the blow sentence.
Line 145
One paper showed a high negative correlation between OLR and rainfall in South East Asia [9].
(3) I added two papers in References.
[9] Lim, E.S., Wong, C.J., Abdullah, K., Poon, W.K. Relationship between outgoing longwave radiation and rainfall in South East Asia by using NOAA and TRMM satellite, 2011 IEEE Colloquium on Humanities, Science and Engineering, Penang, pp. 785-790, 2011. doi: 10.1109/CHUSER.2011.6163843.
[10] Liebmann, B., Marengo, J.A., Glick, J.D., Kousky, V.E., Wainer, I.C., Massambani, O. A Comparison of Rainfall, Outgoing Longwave Radiation, and Divergence over the Amazon Basin, Journal of Climate, 11, pp.2898-2909, 1998.
(C3) Better explanation of the OLR index might encourage its wider use and of your own work.
(A3) Thank you.
I will do fire forecast using the OLR index and GWL estimation using MODEL-0 from this year.
