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63. Improving the implementation of kangaroo mother care

Author

Hailegebriel, Tedbabe Degefie, Bergh, Anne-Marie, Zaka, Nabila, Roh, Jung Min, Gohar, Fatima, Rizwan, Samia, Asfaw, Atnafu Getachew, Heidarzadeh, Mohammad, and Zeck, Willibald

Subjects
Toiletries industry, Public health, Infants (Premature), Health, World Health Organization, United Nations. Children's Fund
Abstract

In recent years, studies have found evidence that kangaroo mother care is a cost-effective, high-impact intervention that reduces mortality and morbidity in preterm infants. [1] A cornerstone of kangaroo mother [...]

Published
2021
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68. Small babies, big risks: global estimates of prevalence and mortality for vulnerable newborns to accelerate change and improve counting

Author

Lawn, Joy E, primary, Ohuma, Eric O, additional, Bradley, Ellen, additional, Idueta, Lorena Suárez, additional, Hazel, Elizabeth, additional, Okwaraji, Yemisrach B, additional, Erchick, Daniel J, additional, Yargawa, Judith, additional, Katz, Joanne, additional, Lee, Anne C C, additional, Diaz, Mike, additional, Salasibew, Mihretab, additional, Requejo, Jennifer, additional, Hayashi, Chika, additional, Moller, Ann-Beth, additional, Borghi, Elaine, additional, Black, Robert E, additional, Blencowe, Hannah, additional, Ashorn, Per, additional, Lawn, Joy E, additional, Ashorn, Ulla, additional, Klein, Nigel, additional, Hofmeyr, G Justus, additional, Temmerman, Marleen, additional, Askari, Sufia, additional, Chakwera, Samuel, additional, Hussain-Alkhateeb, Laith, additional, Lewin, Alexandra, additional, Retno Mahanani, Wahyu, additional, White Johansson, Emily, additional, Lavin, Tina, additional, Estevez Fernandez, Diana, additional, Gatica Domínguez, Giovanna, additional, de Costa, Ayesha, additional, Cresswell, Jenny A, additional, Krasevec, Julia, additional, Moran, Allisyn C, additional, Pingray, Veronica, additional, Cormick, Gabriela, additional, Gibbons, Luz, additional, Belizan, José, additional, Guevel, Carlos, additional, Warrilow, Kara, additional, Gordon, Adrienne, additional, Flenady, Vicki, additional, Sexton, Jessica, additional, Lawford, Harriet, additional, Paixao, Enny S., additional, Rocha Falcão, Ila, additional, Lima Barreto, Mauricio, additional, Lisonkova, Sarka, additional, Wen, Qi, additional, Mardones, Francisco, additional, Caulier-Cisterna, Raúl, additional, Acuña, José, additional, Velebil, Petr, additional, Jirova, Jitka, additional, Horváth-Puhó, Erzsébet, additional, Sørensen, Henrik Toft, additional, Sakkeus, Luule, additional, Abuladze, Liili, additional, Gissler, Mika, additional, Moradi-Lakeh, Maziar, additional, Heidarzadeh, Mohammad, additional, Khalili, Narjes, additional, A. Yunis, Khalid, additional, Al Bizri, Ayah, additional, Nakad, Pascale, additional, Devi Karalasingam, Shamala, additional, R Jeganathan, J Ravichandran, additional, binti Baharum, Nurakman, additional, Suárez-Idueta, Lorena, additional, Barranco Flores, Arturo, additional, Gonzalez Roldan, Jesus F, additional, Lopez Alvarez, Sonia, additional, van Dijk, Aimée E., additional, Broeders, Lisa, additional, Huicho, Luis, additional, Quezada Pinedo, Hugo G, additional, Cajachagua-Torres, Kim N, additional, Carrillo-Larco, Rodrigo M, additional, Tarazona Meza, Carla Estefania, additional, Guzman-Vilca, Wilmer Cristobal, additional, Olukade, Tawa O., additional, Ali, Hamdy A., additional, Alyafei, Fawziya, additional, AlQubaisi, Mai, additional, Alturk, Mohamad R, additional, Kim, Ho Yeon, additional, Cho, Geum Joon, additional, Razaz, Neda, additional, Söderling, Jonas, additional, Smith, Lucy K, additional, Kurinczuk, Jennifer J, additional, Matthews, Ruth J, additional, Manktelow, Bradley N, additional, Draper, Elizabeth S, additional, Fenton, Alan C, additional, Lowry, Estelle, additional, Rowland, Neil, additional, Wood, Rachael, additional, Monteath, Kirsten, additional, Pereyra, Isabel, additional, Pravia, Gabriella, additional, Davis, Celina, additional, Clarke, Samantha, additional, Wu, Lee S.F., additional, Yoshida, Sachiyo, additional, Bahl, Rajiv, additional, Grandi, Carlos, additional, Labrique, Alain B, additional, Rashid, Mabhubur, additional, Ahmed, Salahuddin, additional, Roy, Arunangshu D., additional, Haque, Rezwanul, additional, Shaikh, Saijuddin, additional, Baqui, Abdullah H., additional, Saha, Samir K., additional, Khanam, Rasheda, additional, Rahman, Sayedur, additional, Shapiro, Roger, additional, Zash, Rebecca, additional, Silveira, Mariângela F., additional, Buffarini, Romina, additional, Kolsteren, Patrick, additional, Lachat, Carl, additional, Huybregts, Lieven, additional, Roberfroid, Dominique, additional, Zeng, Lingxia, additional, Zhu, Zhonghai, additional, He, Jianrong, additional, Qui, Xiu, additional, Gebreyesus, Seifu H., additional, Tesfamariam, Kokeb, additional, Bekele, Delayehu, additional, Chan, Grace, additional, Baye, Estifanos, additional, Workneh, Firehiwot, additional, Asante, Kwaku P., additional, Boanmah-Kaali, Ellen, additional, Adu-Afarwuah, Seth, additional, Dewey, Kathryn G., additional, Gyaase, Stephaney, additional, Wylie, Blair J., additional, Kirkwood, Betty R., additional, Manu, Alexander, additional, Thulasiraj, Ravilla D, additional, Tielsch, James, additional, Chowdhury, Ranadip, additional, Taneja, Sunita, additional, Babu, Giridhara R, additional, Shriyan, Prafulla, additional, Maleta, Kenneth, additional, Mangani, Charles, additional, Acevedo-Gallegos, Sandra, additional, Rodriguez-Sibaja, Maria J., additional, Khatry, Subarna K., additional, LeClerq, Steven C., additional, Mullany, Luke C., additional, Jehan, Fyezah, additional, Ilyas, Muhammad, additional, Rogerson, Stephen J., additional, Unger, Holger W., additional, Ghosh, Rakesh, additional, Musange, Sabine, additional, Ramokolo, Vundli, additional, Zembe-Mkabile, Wanga, additional, Lazzerini, Marzia, additional, Mohamed, Rishard, additional, Wang, Dongqing, additional, Fawzi, Wafaie W., additional, Minja, Daniel T.R., additional, Schmiegelow, Christentze, additional, Masanja, Honorati, additional, Smith, Emily, additional, Lusingu, John P.A., additional, Msemo, Omari A., additional, Kabole, Fathma M., additional, Slim, Salim N., additional, Keentupthai, Paniya, additional, Mongkolchati, Aroonsri, additional, Kajubi, Richard, additional, Kakuru, Abel, additional, Waiswa, Peter, additional, Walker, Dilys, additional, Hamer, Davidson H., additional, Semrau, Katherine E.A., additional, Chaponda, Enesia B., additional, Chico, R. Matthew, additional, Banda, Bowen, additional, Musokotwane, Kebby, additional, Manasyan, Albert, additional, Pry, Jake M., additional, Chasekwa, Bernard, additional, Humphrey, Jean, additional, Shamim, Abu Ahmed, additional, Christian, Parul, additional, Ali, Hasmot, additional, Klemm, Rolf D.W., additional, Massie, Alan B., additional, Mitra, Maithili, additional, Mehra, Sucheta, additional, Schulze, Kerry J., additional, Shamim, Abu Amed, additional, Sommer, Alfred, additional, Ullah, Barkat, additional, West, Keith P., additional, Begum, Nazma, additional, Chowdhury, Nabidul Haque, additional, Islam, Shafiqul, additional, Mitra, Dipak Kumar, additional, Quaiyum, Abdul, additional, Diseko, Modiegi, additional, Makhema, Joseph, additional, Cheng, Yue, additional, Guo, Yixin, additional, Yuan, Shanshan, additional, Roro, Meselech, additional, Shikur, Bilal, additional, Goddard, Frederick, additional, Haneuse, Sebastien, additional, Hunegnaw, Bezawit, additional, Berhane, Yemane, additional, Worku, Alemayehu, additional, Kaali, Seyram, additional, Arnold, Charles D., additional, Jack, Darby, additional, Amenga-Etego, Seeba, additional, Hurt, Lisa, additional, Shannon, Caitlin, additional, Soremekun, Seyi, additional, Bhandari, Nita, additional, Martines, Jose, additional, Mazumder, Sarmila, additional, Ana, Yamuna, additional, R, Deepa, additional, Hallamaa, Lotta, additional, Pyykkö, Juha, additional, Lumbreras-Marquez, Mario I., additional, Mendoza-Carrera, Claudia E., additional, Hussain, Atiya, additional, Karim, Muhammad, additional, Kausar, Farzana, additional, Mehmood, Usma, additional, Nadeem, Naila, additional, Nisar, Muhammad Imran, additional, Sajid, Muhammad, additional, Mueller, Ivo, additional, Ome-Kaius, Maria, additional, Butrick, Elizabeth, additional, Sayinzoga, Felix, additional, Mariani, Ilaria, additional, Urassa, Willy, additional, Theander, Thor, additional, Deloron, Phillippe, additional, Nielsen, Birgitte Bruun, additional, Muhihi, Alfa, additional, Noor, Ramadhani Abdallah, additional, Bygbjerg, Ib, additional, Moeller, Sofie Lykke, additional, Aftab, Fahad, additional, Ali, Said M., additional, Dhingra, Pratibha, additional, Dhingra, Usha, additional, Dutta, Arup, additional, Sazawal, Sunil, additional, Suleiman, Atifa, additional, Mohammed, Mohammed, additional, Deb, Saikat, additional, Kamya, Moses R., additional, Nakalembe, Miriam, additional, Mulowooz, Jude, additional, Santos, Nicole, additional, Biemba, Godfrey, additional, Herlihy, Julie M., additional, Mbewe, Reuben K., additional, Mweena, Fern, additional, Yeboah-Antwi, Kojo, additional, Bruce, Jane, additional, Chandramohan, Daniel, additional, and Prendergast, Andrew, additional

Published
2023
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69. Observation and modelling of the storm surges generated by the September 2022 Hurricane Ian in Florida

Author

Heidarzadeh, Mohammad, Iwamoto, Takumu, Sepic, Jadranka, and Mulia, Iyan E.

Abstract

The September 2022 Hurricane Ian, which made a landfall as a Category four hurricane, with wind speeds reaching 240 km/h, was among the most destructive hurricanes to hit Florida: at least 77 deaths were reported in Florida and North Carolina, and the total damage was estimated to approximately US$ 63 billion. Along the coast Hurricane Ian generated both a normal (positive) and a reverse (negative) storm surge. A comprehensive data analysis and modelling efforts were undertaken to illuminate processes leading to two types of surges. Mean sea level pressure and wind data from 49 meteorological NOAA and ASOS stations, measured with 1-6 min time step, along with sea level data from 11 NOAA tide gauges, measured with a 1 min time step, were analysed. The ERA5 Reanalysis data were used to assess propagation parameters and synoptic scale properties of Hurricane Ian. Numerical weather prediction High-Resolution Rapid Refresh (HRRR) model, and a parametric wind model of tropical cyclones were both used to estimate temporal evolution of the 10 m wind and mean sea level pressure fields near and over Florida. The models were then used separately to force the Regional Ocean Modelling System (ROMS), and to reproduce positive and negative surges. Differences between two sets of simulations are discussed in detail. It was shown that the main factor governing appearance of positive and negative surge was high spatial changeability of wind field over a relatively small (O(200 km)) area., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

70. Vulnerable newborn types:Analysis of population-based registries for 165 million births in 23 countries, 2000 to 2021

Author

Suárez-Idueta, Lorena, Yargawa, Judith, Blencowe, Hannah, Bradley, Ellen, Okwaraji, Yemisrach B., Pingray, Veronica, Gibbons, Luz, Gordon, Adrienne, Warrilow, Kara, Paixão, Enny S., Falcão, Ila Rocha, Lisonkova, Sarka, Wen, Qi, Mardones, Francisco, Caulier-Cisterna, Raúl, Velebil, Petr, Jírová, Jitka, Horváth-Puhó, Erzsebet, Sørensen, Henrik Toft, Sakkeus, Luule, Abuladze, Lili, Gissler, Mika, Heidarzadeh, Mohammad, Moradi-Lakeh, Maziar, Yunis, Khalid A., Al Bizri, Ayah, Karalasingam, Shamala D., Jeganathan, Ravichandran, Barranco, Arturo, Broeders, Lisa, van Dijk, Aimée E., Huicho, Luis, Quezada-Pinedo, Hugo Guillermo, Cajachagua-Torres, Kim Nail, Alyafei, Fawziya, AlQubaisi, Mai, Cho, Geum Joon, Kim, Ho Yeon, Razaz, Neda, Söderling, Jonas, Smith, Lucy K., Kurinczuk, Jennifer, Lowry, Estelle, Rowland, Neil, Wood, Rachael, Monteath, Kirsten, Pereyra, Isabel, Pravia, Gabriella, Ohuma, Eric O., and Lawn, Joy E.

Abstract

Objective To examine prevalence of novel newborn types among 165 million livebirths in 23 countries from 2000 to 2021. Design Population-based, multi-country analysis. Setting National data systems in 23 middle and high-income countries.Population Liveborn infants Methods Country teams with high-quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified livebirths by six newborn types based on gestational age information (preterm, PT, 90th centile) for gestational age, according to INTERGROWTH-21st standards. We considered small newborn types of any combination of preterm or SGA and Term+LGA was considered large. Time trends were analysed using three-year moving averages for small and large types.Main Outcome Measures Prevalence of six newborn types.Results We analysed 165,017,419 livebirths and the median prevalence of small types was 11.7% (highest in Malaysia (26%) and Qatar (15.7%)). Overall, 18.1% of newborns were large (T+LGA), and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. Conclusions The distribution of newborn types varies across the 23 middle and high-income countries. Small newborn types were highest in West-Asian countries and large types in Europe. To better understand the global patterns of these novel newborn types, more information is needed especially from low- and middle- income countries.

Published
2023
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74. Vulnerable newborn types:Analysis of population-based registries for 165 million births in 23 countries, 2000–2021

Author

Suárez-Idueta, Lorena, Yargawa, Judith, Blencowe, Hannah, Bradley, Ellen, Okwaraji, Yemisrach B., Pingray, Veronica, Gibbons, Luz, Gordon, Adrienne, Warrilow, Kara, Paixao, Enny S., Falcão, Ila Rocha, Lisonkova, Sarka, Wen, Qi, Mardones, Francisco, Caulier-Cisterna, Raúl, Velebil, Petr, Jírová, Jitka, Horváth-Puhó, Erzsebet, Sørensen, Henrik Toft, Sakkeus, Luule, Abuladze, Lili, Gissler, Mika, Heidarzadeh, Mohammad, Moradi-Lakeh, Maziar, Yunis, Khalid A., Al Bizri, Ayah, Karalasingam, Shamala D., Jeganathan, Ravichandran, Barranco, Arturo, Broeders, Lisa, van Dijk, Aimée E., Huicho, Luis, Quezada-Pinedo, Hugo Guillermo, Cajachagua-Torres, Kim Nail, Alyafei, Fawziya, AlQubaisi, Mai, Cho, Geum Joon, Kim, Ho Yeon, Razaz, Neda, Söderling, Jonas, Smith, Lucy K., Kurinczuk, Jennifer, Lowry, Estelle, Rowland, Neil, Wood, Rachael, Monteath, Kirsten, Pereyra, Isabel, Pravia, Gabriella, Ohuma, Eric O., Black, Robert E., Suárez-Idueta, Lorena, Yargawa, Judith, Blencowe, Hannah, Bradley, Ellen, Okwaraji, Yemisrach B., Pingray, Veronica, Gibbons, Luz, Gordon, Adrienne, Warrilow, Kara, Paixao, Enny S., Falcão, Ila Rocha, Lisonkova, Sarka, Wen, Qi, Mardones, Francisco, Caulier-Cisterna, Raúl, Velebil, Petr, Jírová, Jitka, Horváth-Puhó, Erzsebet, Sørensen, Henrik Toft, Sakkeus, Luule, Abuladze, Lili, Gissler, Mika, Heidarzadeh, Mohammad, Moradi-Lakeh, Maziar, Yunis, Khalid A., Al Bizri, Ayah, Karalasingam, Shamala D., Jeganathan, Ravichandran, Barranco, Arturo, Broeders, Lisa, van Dijk, Aimée E., Huicho, Luis, Quezada-Pinedo, Hugo Guillermo, Cajachagua-Torres, Kim Nail, Alyafei, Fawziya, AlQubaisi, Mai, Cho, Geum Joon, Kim, Ho Yeon, Razaz, Neda, Söderling, Jonas, Smith, Lucy K., Kurinczuk, Jennifer, Lowry, Estelle, Rowland, Neil, Wood, Rachael, Monteath, Kirsten, Pereyra, Isabel, Pravia, Gabriella, Ohuma, Eric O., and Black, Robert E.

Abstract

Objective: To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021. Design: Population-based, multi-country analysis. Setting: National data systems in 23 middle- and high-income countries. Population: Liveborn infants. Methods: Country teams with high-quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm <37 weeks versus term ≥37 weeks) and size for gestational age defined as small (SGA, <10th centile), appropriate (10th–90th centiles), or large (LGA, >90th centile) for gestational age, according to INTERGROWTH-21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3-year moving averages for small and large types. Main outcome measures: Prevalence of six newborn types. Results: We analysed 165 017 419 live births and the median prevalence of small types was 11.7% – highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. Conclusions: The distribution of newborn types varies across the 23 middle- and high-income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low- and middle-income countries.

Published
2023

76. Stillbirths: contribution of preterm birth and size-for-gestational age for 119.6 million total births from nationwide records in 12 countries, 2000 to 2020

Author

Okwaraji, Yemisrach Behailu, primary, Suarez Idueta, Lorena, additional, Ohuma, Eric, additional, Bradely, Ellen, additional, Yargawa, Judith, additional, Pingray, Veronica, additional, Cormick, Gabriela, additional, Flenady, Vick, additional, Gordon, Adrienne, additional, Horvath-Puho, Erzsebet, additional, Sorensen, Henrik T, additional, Abuladze, Lilli, additional, Sakkeus, Luule, additional, Karalasingam, Shamala Devi, additional, Jeganathan, J Ravichandran R, additional, Olukade, Tawa O, additional, Alyafei, Fawziya, additional, Barranco, Arturo, additional, Khalili, Narjes, additional, Heidarzadeh, Mohammad, additional, Van Dijk, Aimee E., additional, Broeders, Lisa, additional, Wood, Rachael, additional, Monteath, Kirsten, additional, Razaz, Neda, additional, Soderling, Jonas, additional, Yunis, Khalid, additional, Pereyra, Isabella, additional, Pravia, Maria, additional, Lisonkova, Sarka, additional, Wen, Qi, additional, Smith, Lucy, additional, Matthews, Ruth J, additional, Lawn, Joy E, additional, and Blencowe, Hannah, additional

Published
2023
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89. The landslide source of the eastern Mediterranean tsunami on 6 February 2023 following the Mw 7.8 Kahramanmaraş (Türkiye) inland earthquake.

Author

Heidarzadeh, Mohammad, Gusman, Aditya Riadi, and Mulia, Iyan E.

Subjects
TSUNAMI warning systems, TSUNAMIS, EARTHQUAKES, LANDSLIDES, TRAVEL time (Traffic engineering)
Abstract

This paper presents the first example of how to systematically identify the submarine landslide source of a tsunami using an innovative hybrid approach. This ground-breaking method is developed to resolve the puzzle around the source mechanism of the mysterious tsunami observed on 6th February 2023 in the Eastern Mediterranean Sea. The tsunami followed the two inland Mw 7.8 and Mw 7.5 Türkiye–Syria earthquakes, which occurred consequently with a 9 h interval on this day. The first earthquake (Mw 7.8) had an epicentral distance of 90 km from the nearest coast, which is closer than the second one (Mw 7.5) to the coast and yet its crustal deformation was almost entirely limited to inland. Therefore, the co-seismic surface displacement generated by the earthquake was ruled out as the source of the tsunami, confirmed by numerical modelling. Here, we hypothesized that the tsunami was most likely generated by a submarine landslide triggered by the earthquake. Analysis of tide gauge observations revealed that the waves arrived from 27 min to 48 min after the first earthquake (Mw 7.8) at different coastal locations, implying that the potential submarine landslide was triggered by the first earthquake (Mw 7.8). Backward tsunami travel time mapping using tide gauge observations guided us to constrain the area of the potential landslide. We approximated the dimensions of the landslide using spectral analysis of the tsunami observations. Consequently, an iterative trial-and-error approach was employed to confirm the landslide source of the tsunami by defining various informed alternative landslide scenarios and applying numerical modeling. Modelling showed that a submarine landslide can reproduce the tsunami observations reasonably well. It is located on a steep slope of the seafloor approximately 50 km from Arsuz. The submarine landslide is estimated to have caused a seafloor deformation measuring approximately 16 km in length and 4.0 km in width. [ABSTRACT FROM AUTHOR]

Published
2023
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95. The Impact of Psychiatric Care Program on Empathy of Neonatal Intensive Care Nurses in Iran.

Author

Heidarzadeh, Mohammad, Heidari, Haydeh, Heidary, Shima, and Ahmadi, Ali

Subjects
*NEONATAL nursing, *INTENSIVE care nursing, *NEONATAL intensive care, *NEONATAL intensive care units, *EMPATHY, *PREMATURE infants
Abstract

BACKGROUND: The main role of nurses is not only to inform about the disease and treatment of the patient but also to establish an effective therapeutic relationship to address concerns and provide empathy, comfort, and support. This issue is very prominent in neonatal intensive care units (NICUs) and doubles the importance of empathetic communication between nurses and parents and promoting empathy skills in nurses working in neonatal intensive care units. AIM: This study aimed to evaluate the effect of a support program on the empathy of neonatal intensive care nurses across the Iran. METHODS: This study was conducted in 2021 as a semiexperimental intervention in a group of 166 nurses working in the neonatal department all over Iran who met the inclusion criteria. Jefferson's empathy questionnaire was completed electronically by the participants before and after the online education program start. Data were analyzed using SPSS software (V 24.0). RESULTS: The empathy score of nurses was 60.31 ± 5.76 before and 66.47 ±6.60 after the intervention. The empathy scores of nurses after the intervention increased statistically significantly. CONCLUSION: Nurses can communicate empathically with parents by training their verbal and nonverbal communication skills and gaining a common understanding of the feelings of parents of premature infants. [ABSTRACT FROM AUTHOR]

Published
2023
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100. Characteristics of two tsunamis generated by successive Mwg 7.4 and Mwg 8.1 earthquakes in the Kermadec Islands on 4 March 2021

Author

Wang, Yuchen, Heidarzadeh, Mohammad, Satake, Kenji, and Hu, Gui

Subjects
Earth and Planetary Sciences(all)
Abstract

Data availability: The datasets were derived from sources in the public domain. The tide gauge data used in this research were provided by the Sea Level Station Monitoring Facility of the Intergovernmental Oceanographic Commission (http://www.ioc-sealevelmonitoring.org/list.php; IOC, 2022). We used bathymetric and topographic data of the General Bathymetric Chart of the Ocean (https://www.gebco.net/data_and_products/gridded_bathymetry_data/; GEBCO, 2022). We used the earthquake focal mechanism catalog of the Global Centroid Moment Tensor Project (https://www.globalcmt.org/CMTsearch.html; Global CMT, 2022) and the earthquake source model of the United States Geological Survey (https://earthquake.usgs.gov/earthquakes/eventpage/us7000dflf/executive; USGS, 2022). We used the GMT software for drafting some of the figures (Wessel and Smith, 1998). Copyright © The Author(s) 2022. On 4 March 2021, two tsunamigenic earthquakes (Mw 7.4 and Mw 8.1) occurred successively within 2 h in the Kermadec Islands, offshore New Zealand. We examined sea level records at tide gauges located at ∼100 to ∼2000 km from the epicenters, conducted Fourier and wavelet analyses as well as numerical modeling of both tsunamis. Fourier analyses indicated that the energy of the first tsunami is mainly distributed over the period range of 5–17 min, whereas it is 8–32 min for the second tsunami. Wavelet plots showed that the oscillations of the first tsunami continued even after the arrival of the second tsunami. As the epicenters of two earthquakes are close to each other (∼55 km), we reconstructed the source spectrum of the second tsunami by using the first tsunami as the empirical Green's function. The main spectral peaks are 25.6, 16.0, and 9.8 min. The results are similar to those calculated using tsunami-to-background ratio method and are also consistent with the source models. Japan Society for the Promotion of Science (grant no. 19J20293); Royal Society (grant no. CHL\R1\180173).

Published
2022
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