Effect of extreme El Ni&ntilde;o events on the precipitations of Ecuador

Thielen, Dirk; Ramoni-Perazzi, Paolo; Puche, Mary L.; Marquez, Marco; Quintero, José I.; Rojas, Wilmer; Quintero, Alberto; Bianchi, Guillermo; Soto-Werschitz, Irma A.; Arizapana-Almonacid, Marco Aurelio

doi:https://doi.org/10.5194/egusphere-2022-763

Preprints

https://doi.org/10.5194/egusphere-2022-763

Preprints

17 Aug 2022

| 17 Aug 2022

Effect of extreme El Niño events on the precipitations of Ecuador

Dirk Thielen, Paolo Ramoni-Perazzi, Mary L. Puche, Marco Marquez, José I. Quintero, Wilmer Rojas, Alberto Quintero, Guillermo Bianchi, Irma A. Soto-Werschitz, and Marco Aurelio Arizapana-Almonacid

Abstract. Extreme El Niño events stand out not only for their powerful impacts but also because they are significantly different from other El Niños. In Ecuador, such events are accountable for impacting negatively the economy, infrastructure, and population. Spatial-temporal dynamics of precipitation anomalies from various types of extreme El Niño events are analyzed and compared. Results show that for Eastern Pacific and Coastal El Niño types, most precipitation extremes occur in the first half of the second year of the event. Any significant difference between events becomes more evident at this stage. Spatially, for any event, 50 % of all extreme anomalies occurred at elevations <150 m. Difference between events was significant when considering the altitude when reaching 80 % of all extreme anomalies: EP-EN 97/98 at 500 m, COA-EN 17 at 800 m, and EN 82/83 at 1000 m. Nevertheless, in some sectors of the Andean Cordillera, the ENSO signal could be detected at 3200–3900 m. Distance to coastline and steepness of relief may play determining role. At lowlands, anomalies are most severe in regions where seasonality index is the highest. These results are useful at different decision-making levels for identifying most appropriate practices reducing vulnerability from a potential increase in extreme El Niño frequency and intensity.

Received: 07 Aug 2022 – Discussion started: 17 Aug 2022

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Journal article(s) based on this preprint

24 Apr 2023

Effect of extreme El Niño events on the precipitation of Ecuador

Dirk R. Thielen, Paolo Ramoni-Perazzi, Ezequiel Zamora-Ledezma, Mary L. Puche, Marco Marquez, José I. Quintero, Wilmer Rojas, Alberto Quintero, Guillermo Bianchi, Irma A. Soto-Werschitz, and Marco Aurelio Arizapana-Almonacid

Nat. Hazards Earth Syst. Sci., 23, 1507–1527, https://doi.org/10.5194/nhess-23-1507-2023,https://doi.org/10.5194/nhess-23-1507-2023, 2023

Short summary

Dirk Thielen et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-763', Anonymous Referee #1, 08 Nov 2022

It is timely to revise the impacts of different extreme El Niño types in western South America, particularly Ecuador and northern Peru where they can produce extreme precipitation and associated hazards. Thielen et al. describe the precipitation anomalies in Ecuador during three El Niño events in Ecuador. Their analysis focuses on the Standardized Pluviometric Drought Index (SPDI), a normalized precipitation index calculated from the CHIRPS satellite product at the monthly level. My assessment is that the study can be improved so that it can be more reliable and useful for disaster risk reduction. My specific recommendations are:

1) Normalized precipitation indices such as the SPDI are particularly useful for drought monitoring as they measure the precipitation relative to what is expected climatologically for a specific location and season. However, it is not adequate for addressing the temporal variability of hazards associated with extreme precipitation. It is not the same to have an SPDI >2 during the rainy season, which could imply pluvial and fluvial flooding and debris flows, and during the dry season, in which the precipitation is unlikely to represent a similar hazard. It is important that the authors include the analysis of indices specific for extreme precipitation events (e.g. http://etccdi.pacificclimate.org/list_27_indices.shtml)

2) It is necessary that that the authors include a validation of the CHIRPS satellite product using rain gauge data, at least for a few representative sites, particularly in arid regions where it might be less reliable, and for extreme events on a daily scale (e.g. https://doi.org/10.1016/j.pce.2022.103184).

3) The study by Kiefer and Karamperidou (2019; https://doi.org/10.1029/2018PA003423) is very relevant and the authors should compare the results to theirs in the manuscript.

Citation: https://doi.org/10.5194/egusphere-2022-763-RC1
- AC1: 'Reply on RC1', Paolo Ramoni-Perazzi, 09 Dec 2022
  
  1) Normalized precipitation indices such as the SPDI are particularly useful for drought monitoring as they measure the precipitation relative to what is expected climatologically for a specific location and season. However, it is not adequate for addressing the temporal variability of hazards associated with extreme precipitation. It is not the same to have an SPDI >2 during the rainy season, which could imply pluvial and fluvial flooding and debris flows, and during the dry season, in which the precipitation is unlikely to represent a similar hazard. It is important that the authors include the analysis of indices specific to extreme precipitation events (e.g. http://etccdi.pacificclimate.org/list_27_indices.shtml)
  Response. We do agree with this important observation from Referee 1. We have generated important previous experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020, Thielen et al. 2021, among other works which are cited in the original version of the manuscript). From this experience, we realized that SPDI could be an appropriate analysis tool since El Niño generates in this region, rather than several isolated events, a prolonged and continuous extremely humid (SPDI >2) pulse encompassing, firstly the wet season and, later in a lesser degree, the dry season. Precipitations generated from a mega-Niño event are expected to occur in these conditions. SPDI, as in SPI, is designed to analyze accumulative processes rather than the effects of isolated extreme precipitation events. As shown by Thielen et al. 2020 and Thielen et al. 2021, as well as the present study, the main hazards, and affectations from the occurrence of an extreme El Niño event, occur during this prolonged wet pulse, and while SPDI >2. It is projected, as a second stage of the present study, to analyze with the assistance of other indexes) such as the ones recommended by Referee 1, the precipitation dynamics on a daily bases during the prolonged wet pulses of the different mega-Niño events.
  2) It is necessary that the authors include a validation of the CHIRPS satellite product using rain gauge data, at least for a few representative sites, particularly in arid regions where it might be less reliable, and for extreme events on a daily scale (e.g. https://doi.org/10.1016/j.pce.2022.103184).
  Response. We completely agree with this important recommendation from Referee 1. Thus, as for section 2.2 Data, the entire text has been restated as follows in the new version of the manuscript:
  “Precipitation data was obtained from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS V2.0, https://iridl.ldeo.columbia.edu/SOURCES/.UCSB/.CHIRPS/.v2p0/.monthly/.global/). CHIRPS V2.0 is a quasi-global gridded rainfall time series dataset, spanning 50°S-50°N, from 1981 to near-present, with 0.05° resolution satellite imagery with in situ station data, with great applications in monitoring precipitation extremes (Funk et al., 2015). Precipitation layers derived from interpolations of data from climate gauge networks have proven to have some limitations (Deblauwe et al., 2016). CHIRPS provides reliable precipitation observations with high accuracy and is particularly suitable for areas with few rainfall gauges (Paredes-Trejo et al., 2016; Beck et al., 2017), especially over montane (López-Bermeo et al. 2022) or arid regions (Paredes-Trejo et al. 2017; Ramoni-Perazzi et al. 2021) where extreme events may be rather common. According to Beck et al. (2017), in a global-scale evaluation of 23 precipitation datasets, CHIRPS V2.0 tends to perform the best in the hydrological modeling of tropical regions, specifically in Central and South America. As for Ecuador, Thielen et al. (2021a) successfully tested its applicability in the spatial/temporal analysis of hydroclimatological extreme events in one of the most important and extended basins of the Ecuadorian Pacific slope. For the present study, monthly data for the time series Jan-1981/Dec-2018 were obtained from 456 rasters. Monthly and annual mean, as well as some other basic precipitation parameters, were obtained through GIS applications.”
  Now, these modifications in the text imply four new citations. Thus, they have been accordingly included in the REFERENCES section of the new version of the manuscript. These are as follows:
  Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach-Overgaard, A., Svenning, J.-C., Wieringa, J. J., Ramesh, B. R., Stévart, T., and Couvreur, T. L. P.: Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Glob. Ecol. Biogeogr., 25, 443–454. https://doi.org/10.1111/geb.12426, 2016.
  López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J., and Díaz-García, J. A.: Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America, Phys. Chem. Earth, 127, 10.1016/j.pce.2022.103184, 2022.
  Paredes-Trejo, F. J., Alves-Barbosa, H., and Lakshmi-Kumar, T. V.: Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil, Journal of Arid Environments, 139, 26-40, 2017.
  Ramoni-Perazzi, P., Passamani, M., Thielen, D. R., Padovani, C., and Arizapana, M. A.: BrazilClim: The overcoming of limitations of preexisting bioclimate data. Int. J. Clim., 42, doi: 10.1002/joc.7325, 2021.
  Regarding extreme events on a daily scale, it is projected that for next necessary step in this research is to analyze the dynamics of daily precipitations. Several authors state that CHIRPS may be also considered a reliable satellite-based rainfall data source for many geographical regions (eg. Valdés-Pineda et al. 2016; Baez-Villanueva et al. 2018).
  3) The study by Kiefer and Karamperidou (2019; https://doi.org/10.1029/2018PA003423) is very relevant and the authors should compare the results to theirs in the manuscript.
  Response. We completely agree with the recommendation of Referee 1 about comparing our results to those from Kiefer and Karamperidou (2019). In this regard, in the Discussion section, references have been made regarding the similarities in the results between both studies specific to the effects of different ENSO flavors on precipitation dynamics, as well as to the altimetric response of precipitation anomalies to EP and COA events.
  The citation Kiefer and Karamperidou (2019) has been properly added to the REFERENCES section as:
  Kiefer, J., and Karamperidou, C.: High‐resolution modeling of ENSO‐induced precipitation in the tropical Andes: Implications for proxy interpretation. Paleoceanography and Paleoclimatology, 34, 217–236, doi:10.1029/2018PA003423, 2019.
  
  Citation: https://doi.org/10.5194/egusphere-2022-763-AC1
RC2:
'Comment on egusphere-2022-763', Anonymous Referee #2, 15 Nov 2022

It is necessary to assess the effects of extreme El Niño events on the precipitations of Ecuador under the background of climate change.

I have the following comments.

1. Why you use SPDI index to monitor precipitation spatial-temporal dynamicsï¼It seems that this index is more appropriate for drought monitoring instead of precipitation monitoring. In the introduction part and material part, there is no review about the applicability of SPDI for extreme precipitation monitoring.

2. Following above comment , as shown in Figure 2. (Ia, Ib, IIa and IIb), even though most precipitation extremes occur in the first half of the second year of the event, the SPDI index still indicates very humid in the second half year especially in the 82/83 and 97/98 EI Niño events, under the situation that the precipitation is almost zero. Thus, I further suspect the applicability of SPDI for extreme precipitation monitoring in this study.

3.Labels in Figure 2 and Figures should be clearer.

4.Obviously SPDI index is a very important part in this study, but in the abstract, there is no introduction about SPDI. Please add the information of SPDI in the abstract.

5.In session 2.4, why not re-sample SPDI estimation at 30 m resolution instead of re-sampling DEM at 0.05°, by which the altitudinal dynamics estimations would be more correct.

6.Because Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño are mentioned many times in the paper, please make a figure to depict where are the regions of Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño.

7.In the page 9. Line 17 to 18, it is confused that the sum of precipitation is only 17%, not 100%. Please clarify it.

Citation: https://doi.org/10.5194/egusphere-2022-763-RC2
- AC2: 'Reply on RC2', Paolo Ramoni-Perazzi, 09 Dec 2022
  
  1. Why you use SPDI index to monitor precipitation spatial-temporal dynamics? It seems that this index is more appropriate for drought monitoring instead of precipitation monitoring. In the introduction part and material part, there is no review about the applicability of SPDI for extreme precipitation monitoring.
  Response. The SPDI is very similar to the SPI (Standardized Precipitation Index). Although it is originally used for analyzing drought evolution, as in for the SPI, its application is not limited to dry events. It is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. The index is simple enough to be able to be developed routinely and in real-time in wide spaces and for numerous observation stations, which makes it useful for implementation in the monitoring and forecasting of unusually dry and wet conditions at different time and space scales of analysis. We have generated important experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020 and Thielen et al. 2021, both works cited in the original version of the manuscript). In the new version of the manuscript, here provided, additional arguments about the use of the SPDI have been included in the Material & Methods section.
  2. Following above comment, as shown in Figure 2. (Ia, Ib, IIa and IIb), even though most precipitation extremes occur in the first half of the second year of the event, the SPDI index still indicates very humid in the second half year especially in the 82/83 and 97/98 EI Niño events, under the situation that the precipitation is almost zero. Thus, I further suspect the applicability of SPDI for extreme precipitation monitoring in this study.
  Response. As in SPI, the SPDI is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. At this time scale of analysis, SPDI tends to gravitate toward zero unless a distinctive wet or dry trend is taking place. SPDI as in SPI-12 is designed not to analyze the effects of isolated extreme precipitation events, but rather the accumulative effect of an anomalously wet pulse(s) to which a high concomitant SPDI value can be associated with flooding and other hazards in the event of any additional precipitation.
  3.Labels in Figure 2 and Figures should be clearer.
  Response. Labels and characters in Figures 1, 2, and 3 have been modified to be clearer. The resulting figures have been included in the new version of the manuscript, to be provided when requested.
  4. Obviously SPDI index is a very important part in this study, but in the abstract, there is no introduction about SPDI. Please add the information of SPDI in the abstract.
  Response. Although we do agree with Referee 2 about the strategic value of including additional and specific information about SPDI in the Abstract, this section has already reached the maximum allowed number of words. In any case, the present study is about the analysis of precipitation anomalies generated by extreme El Niño events, and not about the analysis tool itself. In the current form, in the Abstract, the results from such anomaly analysis are sufficiently addressed. As mentioned in our response to comment 1, in the Material and Methods section of the new version of the manuscript, additional information has been included regarding different medullar aspects of the SPDI, such as its applicability and performance regarding other important precipitation anomaly indexes such as the SPI.
  5.In session 2.4, why not re-sample SPDI estimation at 30 m resolution instead of resampling DEM at 0.05°, by which the altitudinal dynamics estimations would be more correct.
  Response. The work resolution is determined by the lowest resolution available in any of the considered variables. Thus, any resampling must be performed toward the coarsest resolution. In our study, that of the precipitation anomaly data (ie. CHIRPS with 0.05° of spatial resolution).
  6.Because Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño are mentioned many times in the paper, please make a figure to depict where are the regions of Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño.
  Response: For an El Niño event to be categorized as CP, EP or COA it depends on the SSTA pattern. Along an ENSO event, the warming and presence of seawater may be temporal and spatial very dynamic. Such dynamics may vary a lot, not only among the different types of El Niño (CP, EP vs. COA), but also among El Niño events of the same type (eg. EP-EN82/83 vs. EP-EN97/98). These pattern or dynamics is a topic under a great deal of analysis and discussion. Regarding the objectives of the present study, it is out of our reach to elaborate a map (a sequence of maps, really) depicting the specific oceanic areas where each of the mega-Niño events, considered in the present study, expressed its SSTA dynamics. In any case, as stated in the original text of the manuscript (page 6, lines 18-19), readers can relate the CP-EN events occurring mainly in the central Pacific NIÑO 3.4 region, while the EP-EN and COA-EN occurring to the easterly Pacific region of NIÑO 1+2 region (Larkin and Harrison, 2005; Ashok et al., 2007; Kug et al., 2009).
  7.In the page 9. Line 17 to 18, it is confused that the sum of precipitation is only 17%, not 100%. Please clarify it.
  Response. If March-July (n=5) has 10% each, and the rest of the months (n=7) is around 7% each, the sum is not 17%, but: (10% x 5 months) + (7% x 7 months) ≈ 100%. Now, to prevent any misinterpretation the text in the original manuscript (page 9, lines 17 to 18) has been rephrased as: “The monthly precipitation from March to July is about 10%, that is 50% of the annual total amount. As for the rest of the year, that is from August to February, precipitation discretely drops to around 7% per month.”
  
  Citation: https://doi.org/10.5194/egusphere-2022-763-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-763', Anonymous Referee #1, 08 Nov 2022

It is timely to revise the impacts of different extreme El Niño types in western South America, particularly Ecuador and northern Peru where they can produce extreme precipitation and associated hazards. Thielen et al. describe the precipitation anomalies in Ecuador during three El Niño events in Ecuador. Their analysis focuses on the Standardized Pluviometric Drought Index (SPDI), a normalized precipitation index calculated from the CHIRPS satellite product at the monthly level. My assessment is that the study can be improved so that it can be more reliable and useful for disaster risk reduction. My specific recommendations are:

1) Normalized precipitation indices such as the SPDI are particularly useful for drought monitoring as they measure the precipitation relative to what is expected climatologically for a specific location and season. However, it is not adequate for addressing the temporal variability of hazards associated with extreme precipitation. It is not the same to have an SPDI >2 during the rainy season, which could imply pluvial and fluvial flooding and debris flows, and during the dry season, in which the precipitation is unlikely to represent a similar hazard. It is important that the authors include the analysis of indices specific for extreme precipitation events (e.g. http://etccdi.pacificclimate.org/list_27_indices.shtml)

2) It is necessary that that the authors include a validation of the CHIRPS satellite product using rain gauge data, at least for a few representative sites, particularly in arid regions where it might be less reliable, and for extreme events on a daily scale (e.g. https://doi.org/10.1016/j.pce.2022.103184).

3) The study by Kiefer and Karamperidou (2019; https://doi.org/10.1029/2018PA003423) is very relevant and the authors should compare the results to theirs in the manuscript.

Citation: https://doi.org/10.5194/egusphere-2022-763-RC1
- AC1: 'Reply on RC1', Paolo Ramoni-Perazzi, 09 Dec 2022
  
  1) Normalized precipitation indices such as the SPDI are particularly useful for drought monitoring as they measure the precipitation relative to what is expected climatologically for a specific location and season. However, it is not adequate for addressing the temporal variability of hazards associated with extreme precipitation. It is not the same to have an SPDI >2 during the rainy season, which could imply pluvial and fluvial flooding and debris flows, and during the dry season, in which the precipitation is unlikely to represent a similar hazard. It is important that the authors include the analysis of indices specific to extreme precipitation events (e.g. http://etccdi.pacificclimate.org/list_27_indices.shtml)
  Response. We do agree with this important observation from Referee 1. We have generated important previous experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020, Thielen et al. 2021, among other works which are cited in the original version of the manuscript). From this experience, we realized that SPDI could be an appropriate analysis tool since El Niño generates in this region, rather than several isolated events, a prolonged and continuous extremely humid (SPDI >2) pulse encompassing, firstly the wet season and, later in a lesser degree, the dry season. Precipitations generated from a mega-Niño event are expected to occur in these conditions. SPDI, as in SPI, is designed to analyze accumulative processes rather than the effects of isolated extreme precipitation events. As shown by Thielen et al. 2020 and Thielen et al. 2021, as well as the present study, the main hazards, and affectations from the occurrence of an extreme El Niño event, occur during this prolonged wet pulse, and while SPDI >2. It is projected, as a second stage of the present study, to analyze with the assistance of other indexes) such as the ones recommended by Referee 1, the precipitation dynamics on a daily bases during the prolonged wet pulses of the different mega-Niño events.
  2) It is necessary that the authors include a validation of the CHIRPS satellite product using rain gauge data, at least for a few representative sites, particularly in arid regions where it might be less reliable, and for extreme events on a daily scale (e.g. https://doi.org/10.1016/j.pce.2022.103184).
  Response. We completely agree with this important recommendation from Referee 1. Thus, as for section 2.2 Data, the entire text has been restated as follows in the new version of the manuscript:
  “Precipitation data was obtained from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS V2.0, https://iridl.ldeo.columbia.edu/SOURCES/.UCSB/.CHIRPS/.v2p0/.monthly/.global/). CHIRPS V2.0 is a quasi-global gridded rainfall time series dataset, spanning 50°S-50°N, from 1981 to near-present, with 0.05° resolution satellite imagery with in situ station data, with great applications in monitoring precipitation extremes (Funk et al., 2015). Precipitation layers derived from interpolations of data from climate gauge networks have proven to have some limitations (Deblauwe et al., 2016). CHIRPS provides reliable precipitation observations with high accuracy and is particularly suitable for areas with few rainfall gauges (Paredes-Trejo et al., 2016; Beck et al., 2017), especially over montane (López-Bermeo et al. 2022) or arid regions (Paredes-Trejo et al. 2017; Ramoni-Perazzi et al. 2021) where extreme events may be rather common. According to Beck et al. (2017), in a global-scale evaluation of 23 precipitation datasets, CHIRPS V2.0 tends to perform the best in the hydrological modeling of tropical regions, specifically in Central and South America. As for Ecuador, Thielen et al. (2021a) successfully tested its applicability in the spatial/temporal analysis of hydroclimatological extreme events in one of the most important and extended basins of the Ecuadorian Pacific slope. For the present study, monthly data for the time series Jan-1981/Dec-2018 were obtained from 456 rasters. Monthly and annual mean, as well as some other basic precipitation parameters, were obtained through GIS applications.”
  Now, these modifications in the text imply four new citations. Thus, they have been accordingly included in the REFERENCES section of the new version of the manuscript. These are as follows:
  Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach-Overgaard, A., Svenning, J.-C., Wieringa, J. J., Ramesh, B. R., Stévart, T., and Couvreur, T. L. P.: Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Glob. Ecol. Biogeogr., 25, 443–454. https://doi.org/10.1111/geb.12426, 2016.
  López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J., and Díaz-García, J. A.: Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America, Phys. Chem. Earth, 127, 10.1016/j.pce.2022.103184, 2022.
  Paredes-Trejo, F. J., Alves-Barbosa, H., and Lakshmi-Kumar, T. V.: Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil, Journal of Arid Environments, 139, 26-40, 2017.
  Ramoni-Perazzi, P., Passamani, M., Thielen, D. R., Padovani, C., and Arizapana, M. A.: BrazilClim: The overcoming of limitations of preexisting bioclimate data. Int. J. Clim., 42, doi: 10.1002/joc.7325, 2021.
  Regarding extreme events on a daily scale, it is projected that for next necessary step in this research is to analyze the dynamics of daily precipitations. Several authors state that CHIRPS may be also considered a reliable satellite-based rainfall data source for many geographical regions (eg. Valdés-Pineda et al. 2016; Baez-Villanueva et al. 2018).
  3) The study by Kiefer and Karamperidou (2019; https://doi.org/10.1029/2018PA003423) is very relevant and the authors should compare the results to theirs in the manuscript.
  Response. We completely agree with the recommendation of Referee 1 about comparing our results to those from Kiefer and Karamperidou (2019). In this regard, in the Discussion section, references have been made regarding the similarities in the results between both studies specific to the effects of different ENSO flavors on precipitation dynamics, as well as to the altimetric response of precipitation anomalies to EP and COA events.
  The citation Kiefer and Karamperidou (2019) has been properly added to the REFERENCES section as:
  Kiefer, J., and Karamperidou, C.: High‐resolution modeling of ENSO‐induced precipitation in the tropical Andes: Implications for proxy interpretation. Paleoceanography and Paleoclimatology, 34, 217–236, doi:10.1029/2018PA003423, 2019.
  
  Citation: https://doi.org/10.5194/egusphere-2022-763-AC1
RC2:
'Comment on egusphere-2022-763', Anonymous Referee #2, 15 Nov 2022

It is necessary to assess the effects of extreme El Niño events on the precipitations of Ecuador under the background of climate change.

I have the following comments.

1. Why you use SPDI index to monitor precipitation spatial-temporal dynamicsï¼It seems that this index is more appropriate for drought monitoring instead of precipitation monitoring. In the introduction part and material part, there is no review about the applicability of SPDI for extreme precipitation monitoring.

2. Following above comment , as shown in Figure 2. (Ia, Ib, IIa and IIb), even though most precipitation extremes occur in the first half of the second year of the event, the SPDI index still indicates very humid in the second half year especially in the 82/83 and 97/98 EI Niño events, under the situation that the precipitation is almost zero. Thus, I further suspect the applicability of SPDI for extreme precipitation monitoring in this study.

3.Labels in Figure 2 and Figures should be clearer.

4.Obviously SPDI index is a very important part in this study, but in the abstract, there is no introduction about SPDI. Please add the information of SPDI in the abstract.

5.In session 2.4, why not re-sample SPDI estimation at 30 m resolution instead of re-sampling DEM at 0.05°, by which the altitudinal dynamics estimations would be more correct.

6.Because Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño are mentioned many times in the paper, please make a figure to depict where are the regions of Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño.

7.In the page 9. Line 17 to 18, it is confused that the sum of precipitation is only 17%, not 100%. Please clarify it.

Citation: https://doi.org/10.5194/egusphere-2022-763-RC2
- AC2: 'Reply on RC2', Paolo Ramoni-Perazzi, 09 Dec 2022
  
  1. Why you use SPDI index to monitor precipitation spatial-temporal dynamics? It seems that this index is more appropriate for drought monitoring instead of precipitation monitoring. In the introduction part and material part, there is no review about the applicability of SPDI for extreme precipitation monitoring.
  Response. The SPDI is very similar to the SPI (Standardized Precipitation Index). Although it is originally used for analyzing drought evolution, as in for the SPI, its application is not limited to dry events. It is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. The index is simple enough to be able to be developed routinely and in real-time in wide spaces and for numerous observation stations, which makes it useful for implementation in the monitoring and forecasting of unusually dry and wet conditions at different time and space scales of analysis. We have generated important experience regarding the applicability of the SPDI in both, extreme wet or dry precipitation conditions (eg. Thielen et al. 2020 and Thielen et al. 2021, both works cited in the original version of the manuscript). In the new version of the manuscript, here provided, additional arguments about the use of the SPDI have been included in the Material & Methods section.
  2. Following above comment, as shown in Figure 2. (Ia, Ib, IIa and IIb), even though most precipitation extremes occur in the first half of the second year of the event, the SPDI index still indicates very humid in the second half year especially in the 82/83 and 97/98 EI Niño events, under the situation that the precipitation is almost zero. Thus, I further suspect the applicability of SPDI for extreme precipitation monitoring in this study.
  Response. As in SPI, the SPDI is a monthly rainfall index that is based on the calculation of accumulated monthly rainfall anomalies. At this time scale of analysis, SPDI tends to gravitate toward zero unless a distinctive wet or dry trend is taking place. SPDI as in SPI-12 is designed not to analyze the effects of isolated extreme precipitation events, but rather the accumulative effect of an anomalously wet pulse(s) to which a high concomitant SPDI value can be associated with flooding and other hazards in the event of any additional precipitation.
  3.Labels in Figure 2 and Figures should be clearer.
  Response. Labels and characters in Figures 1, 2, and 3 have been modified to be clearer. The resulting figures have been included in the new version of the manuscript, to be provided when requested.
  4. Obviously SPDI index is a very important part in this study, but in the abstract, there is no introduction about SPDI. Please add the information of SPDI in the abstract.
  Response. Although we do agree with Referee 2 about the strategic value of including additional and specific information about SPDI in the Abstract, this section has already reached the maximum allowed number of words. In any case, the present study is about the analysis of precipitation anomalies generated by extreme El Niño events, and not about the analysis tool itself. In the current form, in the Abstract, the results from such anomaly analysis are sufficiently addressed. As mentioned in our response to comment 1, in the Material and Methods section of the new version of the manuscript, additional information has been included regarding different medullar aspects of the SPDI, such as its applicability and performance regarding other important precipitation anomaly indexes such as the SPI.
  5.In session 2.4, why not re-sample SPDI estimation at 30 m resolution instead of resampling DEM at 0.05°, by which the altitudinal dynamics estimations would be more correct.
  Response. The work resolution is determined by the lowest resolution available in any of the considered variables. Thus, any resampling must be performed toward the coarsest resolution. In our study, that of the precipitation anomaly data (ie. CHIRPS with 0.05° of spatial resolution).
  6.Because Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño are mentioned many times in the paper, please make a figure to depict where are the regions of Central Pacific El Niños, Eastern Pacific El Niño, and Coastal El Niño.
  Response: For an El Niño event to be categorized as CP, EP or COA it depends on the SSTA pattern. Along an ENSO event, the warming and presence of seawater may be temporal and spatial very dynamic. Such dynamics may vary a lot, not only among the different types of El Niño (CP, EP vs. COA), but also among El Niño events of the same type (eg. EP-EN82/83 vs. EP-EN97/98). These pattern or dynamics is a topic under a great deal of analysis and discussion. Regarding the objectives of the present study, it is out of our reach to elaborate a map (a sequence of maps, really) depicting the specific oceanic areas where each of the mega-Niño events, considered in the present study, expressed its SSTA dynamics. In any case, as stated in the original text of the manuscript (page 6, lines 18-19), readers can relate the CP-EN events occurring mainly in the central Pacific NIÑO 3.4 region, while the EP-EN and COA-EN occurring to the easterly Pacific region of NIÑO 1+2 region (Larkin and Harrison, 2005; Ashok et al., 2007; Kug et al., 2009).
  7.In the page 9. Line 17 to 18, it is confused that the sum of precipitation is only 17%, not 100%. Please clarify it.
  Response. If March-July (n=5) has 10% each, and the rest of the months (n=7) is around 7% each, the sum is not 17%, but: (10% x 5 months) + (7% x 7 months) ≈ 100%. Now, to prevent any misinterpretation the text in the original manuscript (page 9, lines 17 to 18) has been rephrased as: “The monthly precipitation from March to July is about 10%, that is 50% of the annual total amount. As for the rest of the year, that is from August to February, precipitation discretely drops to around 7% per month.”
  
  Citation: https://doi.org/10.5194/egusphere-2022-763-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (28 Dec 2022) by Paolo Tarolli

AR by Paolo Ramoni-Perazzi on behalf of the Authors (01 Feb 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 Feb 2023) by Paolo Tarolli

RR by Anonymous Referee #3 (01 Mar 2023)

Suggestions for revision or reasons for rejection

Dear authors, I did not review the first version of the manuscript. I give here a feedback on the way you address the previous comments (as requested by the editor) plus a few suggestions from my reading of the paper.
As you will see most comments are relatively minor, hence my recommendation.

The main comment from both reviewers (the use of SPDI for the evaluation of positive precipitation extremes) has been addressed with the addition of some text. No new analyses nor changes were done.
I find the motivations given in the replies and in the text convincing, with some exceptions (see comment 1), so I believe the main issue is solved.

Overall I find the topic interesting. The presentation, however, requires some work because the paper is currently very difficult to read. I give some suggestions below.

1. Many more details are needed to understand how the SPDI works. Specifically,
a) how long do you aggregate in time (eq. 2)? since the beginning of the timeseries?
b) in page 5 line 5 you say that SPDI is similar to SPI12. Why 12? Are you accumulating (eq. 2) only for 12 months? is there something I am missing?
c) line 19 same page: what does it mean from i positive to i negative? what is i?
these questions make it clear that you need to describe better this index. Otherwise the non expert user will not understand what is happening.

2. in page 6 lines 21 and 23 you define 2 times the same acronym

3. I found the reading extremely difficult due to the use of boldface long ‘abbreviations’ to distinguish between only 4 events. Please use shorter and more reader friendly symbols and avoid boldface. For instance, why not EP83, EP98, MIX15 and COA17?
The same holds for the names of the catchments. Is it necessary to have both name and number?

4. page 7 and following, usually the symbol for p-values is not in capitals

5. page 8 line 1. I don’t follow how p=0.097 is considered significant when before you said you were looking for p<0.05. please rephrase

6. please change the labels in Fig. 2 to a, b, c. having I-a II-a is confusing

7. Fig. 2: it seems there is a mistake in the map in the last panel: the title says Amazon slope but the inset shows the Pacific slope

8. page 11: why are the events discussed in order that is not chronological nor of event type? what is the rationale for having 3.4.3 before 3.4.4?

Hide

ED: Publish subject to minor revisions (review by editor) (13 Mar 2023) by Paolo Tarolli

AR by Paolo Ramoni-Perazzi on behalf of the Authors (18 Mar 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (20 Mar 2023) by Paolo Tarolli

AR by Paolo Ramoni-Perazzi on behalf of the Authors (27 Mar 2023)

Journal article(s) based on this preprint

24 Apr 2023

Effect of extreme El Niño events on the precipitation of Ecuador

Nat. Hazards Earth Syst. Sci., 23, 1507–1527, https://doi.org/10.5194/nhess-23-1507-2023,https://doi.org/10.5194/nhess-23-1507-2023, 2023

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Dirk Thielen et al.

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Extreme El Niño events are significantly different from other El Niños, standing out for their powerful impacts, and are predicted to increase in frequency and intensity. In this regard, we provide valuable and highly strategic information on the similarities and differences between the effects on precipitation from types of extreme El Niño, highlighting spatially and quantitatively, those regions where most extreme precipitation anomalies are most likely to occur in extreme events.


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