1 Introduction

The southwest Pacific (SWP) Island nations (Fig. 1) are prone to catastrophic impacts of strong winds and storm surges, as well as extreme rainfall and associated flooding, during the passage of tropical cyclones (TCs). For example, severe TC Winston (February 2016) caused several fatalities and substantial damage to the infrastructure and agricultural sectors across the Fiji Islands. The direct economic loss (or damage)Footnote 1 (hereafter referred to as ‘economic loss’ or simply ‘loss’) resulting from this cyclone accounted for ~ 20% of the country’s gross domestic product (GDP) (Esler et al. 2016). A year earlier, the economic loss from severe TC Pam (March 2015) exceeded 60% of Vanuatu’s GDP, derailing the country’s budget and fiscal position, and posing serious consequences for growth and development (McGee et al. 2015; World Bank 2018).

Fig. 1
figure 1

Map of SWP Island nations

Full size image

The impacts of TCs on societies in SWP Island nations are likely to be further exacerbated in a warming climate due to the increasing influences of greenhouse anthropogenic climate change, and through growing coastal population and infrastructure development (Lal 2011). For example, a number of studies have indicated that TCs are likely to become more intense with warming, and their rain-carrying capacity is also likely to increase substantially (see, for example, a review by Knutson et al. (2020)). Such changes, combined with accelerated sea-level rise, can cause increased rates of coastal flooding and storm surges, with potentially drastic economic impacts on societies and the country as a whole (Fig. 2, Woodruff et al. (2013)). Several studies have investigated the trends in the normalised economic loss (i.e. taking into account inflation, increasing population and wealth) due to TCs over the past decades in different regions around the globe. For example, in a recent study over the USA, Grinsted et al. (2019) revealed an emergent increasing trend in economic loss attributed to a detectable change in extreme storms due to global warming. In India, Raghavan and Rajesh (2003) also showed an increasing trend in the normalised economic loss due to TCs over Andhra Pradesh, but the trend there was attributable mainly to economic and demographic factors rather than any long-term changes in TC characteristics. Similarly, over the mainland of China, Zhang et al. (2009) examined (without normalisation) the direct economic losses and casualties caused by landfalling TCs during the 1983–2006 period. They reported that even though the mortality from TCs shows no trend, the economic costs have increased rapidly during the nation’s productivity boom.

Fig. 2
figure 2

Conceptual diagram of likely changes in TC-induced risks in a warming climate for two cases (based on Woodruff et al. 2013): case 1 represents a condition in present-climate without any TC, and case 2 represents a likely condition in future-climate in presence of a severe TC event

Full size image

In the SWP region, Mohan and Strobl (2017) used satellite measures of nightlight intensity to show the short-term impact of TC Pam on the economic activity of the affected islands across Vanuatu. However, as far as we are aware, no literature as yet exists that addresses the long-term changes in TC-related economic losses over the past decades for the SWP region. The overall objective of this work is twofold. We first examine the link between severe TCs and the consequent economic and human losses over several SWP island nations. In order to better capture year-to-year variability of the impacts (both economic losses and fatalities) associated with severe TCs, normalisation of the losses is performed to take into consideration inflation and changes in population and wealth (e.g. Pielke and Landsea (1998)). The other objective is to understand the role of local agencies (such as the National Disaster Management Office, NDMO and the National Hydrological and Meteorological Services, NHMS) in mitigating the losses through provisions of early-warning systems and planning strategies at various stages of a TC event.

The rest of the paper is as follows. Section 2 presents the data and methodology, and Sect. 3 provides results and discussion. Section 4 summarises the findings of this research.

2 Data and methodology

Estimates of TC economic loss were obtained from the respective Government’s TC Disaster Assessment and NHMS reports for the study period 1970–2018. Several of these reports are archived at the United Nations Office for the Coordination of Humanitarian Affairs’ (UN-OCHA) ‘ReliefWeb’ portal.Footnote 2 Comprehensive TC impact loss data, such as those over the USA (e.g. Pielke et al. (2008)), are also archived at the Pacific Damage and Loss (PDaLo) portal,Footnote 3 but due to some discrepancies with the available Government and NHMS reports (e.g. for TC Gita and TC Winston in 2018), such data are only used here for subjective verification purposes.

To best represent year-to-year variability, economic losses due to TCs are normalised to take into consideration inflation and changes in wealth and population over time. We use here the conventional approach (e.g. Pielke et al. (2008) and Grinsted et al. (2019)) to normalise TC impact data relative to the 2016 values (note here that 2016 is chosen subjectively as the reference year due to the impact of severe TC Winston in that year over the SWP region). The normalisation methodology assumes that losses are proportional to three factors: inflation, wealth and population. The general formula to normalise the loss in a particular year relative to the year of interest (in our case 2016) is as follows:

$${\mathrm{NL}}_{16}={L}_{y}\times {I}_{y}\times {W}_{y}\times {P}_{y}$$
(1)

  • • NL16 is the normalised loss relative to the 2016 values.

  • • y is the year of TC impact.

  • • Ly is the loss in year y, in current-year dollars (and not adjusted for inflation).

  • • Iy is the inflation adjustment factor. This factor is computed using the Implicit Price Deflator for Gross Domestic Product (IPDGDP) and is the ratio of the 2016 IPDGDP to that in the TC impact year (e.g. Pielke et al. (2008)).

  • • Wy is the wealth factor, computed using the nominal GDP, the inflation adjustment factor, Iy, and the population.

  • • Py is the population factor.

A detailed description of the normalisation procedure could be found in Pielke et al. (2008) and Grinsted et al. (2019). For the purpose of this study, IPDGDP, GDP and population data were obtained from the World Bank Open Data Online Portal.Footnote 4 Moreover, TC intensity and track data were obtained from the Southwest Pacific Enhanced Archive for Tropical Cyclones (SPEArTC: Diamond et al. (2012)) database.

3 Results and discussion

3.1 Severe TCs in SWP island nations

This study focuses on TC-associated losses over three Pacific Island Countries, namely Fiji, Samoa, Tonga and Vanuatu (FSTV) mainly because economic loss data are readily available for these nations. The time series of the number of severe TCs (i.e. category 3 and above based on the Australian and Fiji TC intensity scale, Table 1), passing within 100, 200 and 500 km from the FSTV region for the period 1970–2018 are shown in Fig. 3. Over Fiji and Vanuatu, the number of severe TCs passing in close proximity shows a slight decreasing trend whereas for Samoa and Tonga there is a slight increasing trend (note, these trends are not statistically significant at the 95% confidence level).

Table 1 Australian and Fiji Region Intensity Scale of Tropical Storms (FMS 2009)
Full size table
Fig. 3
figure 3

Time series of number of major (category 3 and above) TCs passing within 100, 200 and 500 km from Fiji (a), Samoa (b), Tonga (c) and Vanuatu (d)

Full size image

Table 2 shows the meteorological parameters of severe TCs passing within 50 km of the FSTV region, and the respective tracks of those TCs are shown in Fig. 4(a-d). Of all TCs during the period 1970–2018, Winston was the most severe TC in the region with a record minimum seal level pressure of 884 hPa and 10-min sustained windspeed of 278 km h−1 which directly impacted Fiji on the 20th of February 2016. TC Pam, with a minimum sea level pressure of 896 hPa and 10-min sustained windspeed of 250 km h−1, was the most severe storm that has impacted Vanuatu on the 13th of March 2015. TC Ofa, with a minimum pressure of 925 hPa, was the most severe TC to affect Samoa on the 4th of February 1990 whereas Gita (with a minimum pressure of 927 hPa and 10-min sustained windspeed of 204 km h−1 at some point in its lifetime) was the most severe storm to affect Tonga on the 12th of February 2018. All of these TCs had significant impact over the respective regions as will be shown in the following sections.

Table 2 List of severe TCs (i.e. category 3 and above) passing within 50 km from the FSTV region for the period 1970–2018
Full size table
Fig. 4
figure 4

(a–d) Tracks of severe TCs over the FSTV region (a–d) listed in Table 2 and (e) track of TC Winston (2016) also shown separately. The blue diamond markers show genesis positions

Full size image

3.2 Economics losses over the FSTV region

Table 3 shows the estimated and normalised losses, adjusted to the 2016 values, associated with severe TCs (listed in Table 2) impacting the FSTV region. The estimated losses show large temporal differences, even after taking into consideration inflation, wealth and population factors. Losses (i.e. inflation corrected) due to the most recent TCs are very high over some of the nations. Over Fiji, losses due to TC Winston in 2016 amounted to FJD $1.990 billion compared with severe TCs Oscar (1983) and Kina (1993) where the losses were FJD 0.649 billion and FJD 0.513 billion respectively; clearly, the loss associated with TC Winston was over three times greater than the other two severe events. The total loss due to TCs in the last decade (i.e. 2010–2019) is also significantly larger than that in the previous decades: FJD 2.363 billion (2010–2019); FJD 0.294 billion (2000–2009); FJD 0.6934 billion (1990–1999); 0.884 billion (1980–1989); and FJD 0.350 billion (1970–1979).

Table 3 TC-associated economic loss over the FSTV region—in millions of local currency (LC) and USD. Ly is the loss for the year ‘y’ whereas NL is the loss, Ly, normalised with respect to 2016 values
Full size table

Similarly, based on the data analysed here, the highest loss for Tonga was due to TC Gita in 2018 (~ TOP 0.317 billion) followed by TC Waka in 2001 (TOP 0.231 billion). Samoa did not experience any major TC-related losses in the last two decades. However, during the period 1990–1999, we saw two back-to-back severe TCs over the country: TC Ofa (1990) and TC Val (1991) that caused damages close to WST 2.3 billion and WST 4.5 billion respectively.

TC-associated loss over Vanuatu, similar to Samoa, does not show any trend over the past decades. While loss due to the recent TC Pam (2015) was the largest in the current-year vatu (VUT 63.20 billion), loss after normalisation was the largest (VUT 95.78 billion) for TC Uma (1987). Following TC Uma and Pam, the major normalised impact losses were 2.62 and 1.91 billion vatu associated with TC Prema (1993) and TC Dani (1999) respectively. These findings in general show that, except for Fiji, clear increasing trends in TC economic losses for other countries are absent. While we acknowledge that the lack of complete economic data for some countries may affect the trends, improved DRR efforts by various sectors may have played a critical role in supressing economic loss and fatalities (see subsequent discussions).

3.3 Disaster response and fatalities

The number of severe TC-related fatalities can provide an indirect measure of a country’s level of preparedness and response for a particular event. Table 4 shows the fatalities related to the severe TCs (that are listed in Table 2) impacting the FSTV region. For Fiji, the largest TC-related fatality was 52 during TC Meli (1979), followed by 44 fatalities that were during the passage of severe TC Winston (2016). TC Eric (1985) and TC Kina (1993) recorded total fatalities of 28 and 26 respectively, whereas those associated with other TC events were below 20 deaths. For Samoa, the number of recorded TC-related deaths was mainly below 10, except for the severe TC Evan (2012) that caused 14 deaths in the country. For Tonga, the largest number of fatalities (i.e. 6) was also during the passage of TC Evan (2012), whereas, for Vanuatu, the largest number of TC-related deaths was 50 during the passage of TC Uma (1987), followed by 15 fatalities during TC Pam (2015).

Table 4 Fatalities associated with severe TCs listed in Table 2
Full size table

Losses in human lives translate to losses in GDP over time. However, economic loss values due to TCs, such as those provided in Table 3, do not take this into consideration (Raghavan and Rajesh 2003). Moreover, trends in fatalities are an excellent indicator of the improvements in the overall DRM and DRR over time. Reasonable metrics (or indices) to measure this are as follows: (i) the ratio of fatality (Dy) to economic loss (NLy) and (ii) the product of the two (i.e. Dy × NLy: Raghavan and Rajesh (2003)). A decreasing trend in the former means fewer deaths per unit of economic loss which would signify improvements in DRM and DRR. In the latter metric, a decreasing trend would signify either a decrease in Dy or NLy or both.

Table 4 shows the fatality indices related to TCs that are listed in Table 2. Over Fiji, the \({~}^{D}\!\left/ \!{~}_{NL}\right.\) index shows a decreasing trend that peaked during TC Gavin (1997), and this indicates an overall improvement of DRR over time. The \(D\times NL\) factor peaks during TC Winston (2016), which could be associated with an exceptionally large impact loss. For Tonga and Vanuatu, both the indices show a decreasing trend, signifying improvements in DRR, whereas for Samoa, the results are mixed: the \({~}^{D}\!\left/ \!{~}_{NL}\right.\) index shows no trend but the \(D\times NL\) shows a decreasing trend, though a larger sample size is needed to make any confident conclusion here.

3.4 Current responsibilities of NHMS

The role of the local NHMS is critical for DRM and DRR, particularly with those associated with TCs and extreme rainfall. The Fiji NHMS (known as the Fiji Meteorological Services or FMS) is also designated as the Regional Specialised Meteorological Centre (RSMC) by the World Meteorological Organization (WMO) under the World Weather Watch Program specialising in TCs. With this designation, the Fiji NHMS is responsible for the provision of ‘first-level’ information on TCs, such as basic information covering TC’s present and forecast positions, as well as movement and intensity in the SWP region, from 0° to − 25°S, and 160°E to 120°W. Nonetheless, the general roles of the individual NHMSs in the region include provision of essential weather, including TC-related services, and climate and hydrological services which we briefly discuss here.

Foremost, the NHMSs are responsible for continuous monitoring of weather and providing daily forecasts to the general public, marine and aviation. The FMS in particular is responsible for providing these services to Fiji, the Cook Islands, Kiribati, Nauru, Niue, Tokelau, Tonga and Tuvalu.

During TCs, the NHMSs issue Special Weather Bulletins (SWB) every 6 h upon issuance of a TC alert, the frequency of which is usually increased to every 3 h with the upgrade to a TC warning. Together with that, TC threat and forecast maps are issued every 6 h. Alerts for heavy rainfall are also issued if the estimated 24-h rainfall exceeds 100 mm within the next 48 h (but not before 24 h) and these could later be upgraded to a warning if the estimated rainfall exceeds 100 mm within the next 24 h.

The NHMSs also have Climate Services Division whose operations include the (1) National Climate Data Centre, and (2) National Climate Monitoring and Prediction Centre. Within the framework of the climate services delivery, the NHMSs are responsible for monitoring of climate variability and change. They also promote the effective use of these data and provide consultative services for planning, research and decision-making processes that are critical for socio-economic development.

The Climate Monitoring and Prediction Centre issues regular climate monitoring products, including updates on El Niño–Southern Oscillation (ENSO) status and monthly-to-seasonal outlooks for atmospheric variables such as rainfall and air temperature. It also issues a regular ocean outlook which includes information on sea surface temperatures (SSTs), fisheries convergence zone, sea level, coral bleaching and astronomical tide predictions. Specialised monthly-to-seasonal products for sectors such as agriculture (e.g. sugar cane in Fiji), energy (e.g. hydroelectricity), health (e.g. Malaria Early Warning System) and climate data are provided when needed or upon request for decision-makings (e.g. engineering designs within infrastructure departments, planning in the agriculture sector, NDMO and health).

Hydrological Services have also been recently introduced in some of the NHMSs (e.g. Fiji) to issue flood advisories. These are mostly based on river-level monitoring through telemetered systems, and the FMS has, in particular, incorporated a flash flood guidance system with the assistance of the WMO.

Most of the above services and products are delivered routinely to all sectors, including communities, government, media organisations and other Meteorological Services in the region and the world. Social media platforms and mobile phone applications, together with animations and videos, are increasingly used to disseminate these weather, climate and hydrological information. While these services are publicly available, the NDMO is one of the key specialised users of it. During any anticipated deterioration of weather, climate and hydrological condition, the NHMS provides regular briefings to NDMO, including face-to-face communication and product updates. It is also important to note here that all the regional NHMSs coordinate their scientific and technical programme and activities through the Pacific Meteorological Council (PMCFootnote 5).

3.5 Preparedness and disaster response: TC Winston (2016) as a case study

To reduce the impacts from TCs, a series of actions is mandatory before, during and after the passage of TCs. Here we use Severe TC Winston (2016) as an example to illustrate the preparedness and response by the NHMS and NDMO during a TC event over the SWP region.

3.5.1 TC history and disaster preparedness

TC Winston, a category 5 system in the Australian TC intensity scale, made landfall on 20 February 2016 at 0600 UTC over Viti Levu, Fiji, directly affecting the region of Rakiraki (see the track in Fig. 4(e)). The central pressure and 10-min sustained windspeed during the landfall were 886 hPa and 280 km h−1 respectively. Following is a brief history of TC Winston before making landfall over Fiji and the disaster preparatory actions undertaken by the relevant organisations such as the NDMO.

The FMS started monitoring tropical disturbance (TD) ‘09F’ on 7 February 2016, which had developed near 8.4°S and 170.6°E (far northwest of Fiji: FMS (2016m)). Early on 11 February, the FMS upgraded the TD to a category 1 TC and named it ‘Winston’ (FMS 2016g). Later, around 1200 UTC on the same day, Winston intensified into a category 2 TC, as a small, well-defined eye developed within the deepening convection (FMS 2016h). On 12 February, Winston rapidly intensified into a category 3 TC by 0600 UTC and then a category 4 TC by 1200 UTC (FMS 2016i). By 0000 UTC on 15 February, TC Winston reduced in intensity and became a category 2 system (FMS 2016j) before upgrading to a category 3 system at 1800 UTC on the 16th (FMS 2016k). By 18 February, the intensifying system was translating towards Fiji (FMS 2016f). It reached category 5 level at 0600 UTC on the 19th, with 10-min sustained winds reaching 205 km h−1 (FMS 2016l). Around 1800 UTC, Winston passed over the Fijian island of Vanuabalavu with a momentary wind gust of 306 km h−1 (FMS 2016a). TC Winston attained its maximum (sustained) intensity around 0000 UTC on 20 February with 10-min sustained winds of 280 km h−1 and a central pressure of 884 hPa (SPEArTC). It made landfall over Rakiraki around 0600 UTC at peak intensity (SPEArTC) making it the only known category 5 TC (until 2018), to directly impact Fiji, and therefore the most intense storm on record to strike the nation (SPEArTC). It also marked the strongest landfall by any TC in the South Pacific basin (SPEArTC), and one of the strongest landfalls worldwide since the modern era of global records began in 1970 (Liberto 2016).

The FMS started issuing TC warnings for the Fiji group on 14 February (FMS 2016c). However, these were called off on 16 February as Winston moved away from the nation (FMS 2016b). TC warnings commenced again on 18 February after Winston started moving towards Fiji for the second time and these warnings were issued for the northern and eastern islands (FMS 2016d). The northern islands in the TC’s direct path were placed under severe hurricane warnings on 19 February (FMS 2016e). The NDMO activated, in total, more than 700 evacuation centres throughout the nation (RNZ 2016) and the Republic of Fiji Military Forces (RFMF) were placed on standby for relief efforts (Talei 2016). On the afternoon of 20 February, a state of emergency was declared (Swami 2016), a nationwide curfew was enacted starting at 1800 LT (Singh 2016) and public transportation services were suspended across Viti Levu.

3.5.2 Fatalities

A total of 44 fatalities resulted as a direct consequence of TC Winston. While there is no difference in fatality based on gender, differences do exist when age and ethnicity factors are considered (Esler et al. 2016). For the age group below 15 years, the fatality is ~ 13%, but it is 50% for the 15–64-year group. For the age group 65 and above, which makes just 4% of the total Fijian population, the mortality is disproportionally high at ~ 37% (Esler et al. 2016).

With respect to ethnicity, a larger percentage of fatalities was observed for the iTaukei (first nation) group in comparison with the Indian descendant (Indo-Fijian) group. For the iTaukei group, which comprises 57% of the Fijian population, this figure stands at 97% in comparison to 3% for the Indo-Fijian group that make 37% of the population (Esler et al. 2016). Even after stratifying into regions based on ethnicity, this is still valid. Areas such as the Koro Island, where 21% of deaths occurred, are largely inhibited by iTaukei. On the other hand, in areas which are primarily populated by Indo-Fijians, e.g. Ba (54%), the mortality amongst the iTaukei group was still higher (Esler et al. 2016). This discrepancy in the fatality between the ethnic groups requires further investigation and appropriate measures for future improved TC-related disaster responses.

3.5.3 Implications for NDMO and NMHS and their production and delivery of climate services to support decision-making in DRM: lessons learned and future directions

While necessary preparatory and disaster response actions, which included early warnings both to decision-makers and to the general public, were undertaken, the resultant 44 deaths (in particular, the high rate of mortality in the iTaukei group) show that there are still some shortcomings in the approach and appropriate actions need to be taken to correct these shortcomings. Some of the actions that could be stipulated to help improve DRR and disaster preparedness are outlined as follows:

  • • Increase public awareness and individual preparedness

    Since TCs are a common phenomenon in the SWP region, science-based services such as the NHMS could work closely with the Ministry of Education to develop meteorology-related syllabuses for schools. Schools could also organise short visits to the NHMS throughout the academic year to give more insight into what is being taught about meteorology in their curriculum. Schools could also annually invite NHMSs to deliver presentations on early warning alerts and potential actions expected from the general public, particularly if there is no meteorology-related curriculum. Moreover, science-based services (e.g. NHMS) and other relevant organisations (e.g. NDMO) could organise workshops discussing key concepts surrounding TCs, DRR, preparedness and response. A short book or pamphlet about TCs in various languages could also help increase public awareness particularly with regard to the impacts of TCs.

  • • Upgrade the current tools used to observe and forecast weather

    There is a need to improve the equipment, programmes and forecasting tools used to provide weather information. These include, for example, the installation of Automatic Weather Systems (AWSs), sourcing of satellite products, improving and upgrading Numerical Weather Prediction (NWP) models and installation of a network of weather radars.

  • • Home retrofitting and building back better

    While not associated with NHMS, it is important to mention here that home retrofitting could also aid mitigation, prevention and preparedness. Approximately 30,369 houses, 495 schools and 88 health clinics and medical facilities were damaged or destroyed during the passage of TC Winston. Assessing the photos (e.g. Fuata (2016)) show that some structures have suffered major damages mostly to the roofing and openings (e.g. windows). To mitigate such effects of strong winds, owners could have their buildings assessed by professionals and accordingly retrofit these structures with necessary reinforcing materials (such as hurricane straps and roofing screws). While there is no guarantee that these measures will provide an absolute protection during the passage of a TC, they nonetheless substantially decrease the likelihood of damage during these events compared to when not implementing them.

3.6 TC decision-support framework in the context of a changing climate

Understanding the likely changes in TC characteristics at regional and local scales due to global warming is challenging. This is at least in part due to the lack of long-term TC records in the Pacific for detection and attribution of climate change signals (Knutson et al. 2019) and partially because of the large deficiencies and biases in climate model simulations of regional and local-scale TCs (Wang et al. 2014). Reliable observations of TCs became available only from around the 1970s when satellite monitoring became operational, but these few decades of data are not sufficient to adequately resolve climate change signals particularly in the presence of large climate variability across and between decades (Klotzbach and Landsea 2015; Moon et al. 2019).

Moreover, global climate models are not typically run at scales needed to resolve changes in TC characteristics (particularly intensity) at island-scale, substantially limiting our ability to understand the effects of climate change on TCs over small island countries in the SWP region. Nevertheless, from past literature (Vecchi et al. 2006; Power and Kociuba 2011; Sugi and Yoshimura 2012; Tokinaga et al. 2012; Tory et al. 2013; Woodruff et al. 2013; Kossin et al. 2014; Lucas et al. 2014; Walsh et al. 2016; Chand et al. 2017; Chand 2018; Kossin 2018; Sharmila and Walsh 2018; Lee et al. 2021), trends in observation records, climate model simulations and theoretical understanding, we have a good understanding of how various climatic conditions may interact to affect TCs and associated impacts in the SWP region as depicted in Fig. 5. It is important to note here that this schematic does not make any attempt to quantify the changes, but instead demonstrates how different climatic factors may interact to affect TC characteristics and associated impacts in the SWP region.

Fig. 5
figure 5

Schematic representation of the linkages between natural climate variability, human-induced global warming and tropical cyclones. Note that this diagram is not exclusive and does not quantify the changes, but instead demonstrates how different climatic factors may interact to affect TC characteristics and associated impacts over the SWP region (1Vecchi et al. 2006; 2Yeh et al. 2009; 3Power and Kociuba 2011; 4Kim and Yu 2012; 5Sugi et al. 2012; 6Sugi and Yoshimura 2012; 7Tokinaga et al. 2012; 8Church et al. 2013; 9Hartmann et al. 2013; 10Tory et al. 2013; 11Woodruff et al. 2013; 12Cai et al. 2014; 13Kossin et al. 2014; 14Lucas et al. 2014; 15Walsh et al. 2016; 16Chand et al. 2017; 17Taupo and Noy 2017; 18Chand 2018; 19Kossin 2018; 20Sharmila and Walsh 2018; 21Andrew et al. 2019; 22Chand et al. 2020)

Full size image

The level of risk associated with a TC event is already very large across the SWP region as shown earlier, and TC-induced risks are likely to exacerbate further as a consequence of global warming, particularly during future-climate El Niño periods. The increasing threats from storm surge and coastal flooding due to sea-level rise should be of concern for atolls and islands, particularly those located within the TC-impact zones (e.g. Cook Islands, Fiji, Samoa, Solomon Islands, Tonga and Vanuatu). Such threats can also have direct consequences on long-term food and water security for the atoll and island communities, for example through intrusion of saltwater into freshwater storages and cultivable land, creating irrecoverable damages to the livelihood, and in some cases through permanent loss of islets (Hisabayashi et al. 2018).

Climate change adaptation planning for managing future TC-induced impacts can be very complicated for highly vulnerable atoll and island countries in the SWP region. However, some recent events, such as those associated with TC Pam and TC Winston, can present a window of opportunity to trigger transformational changes in the communities’ approach to TC-induced risks and impacts. These transformational changes can range from developing quintessential strategies for mitigating immediate impacts of TCs—such as emergency shelters and effective evacuation procedures—to long-term technological and engineering solutions—such as seawalls, levees, desalination plants and storm water harvesting—to mitigate effects of rising sea-level and storm surges, and their potential impacts on food and water security.

3.7 Recommendations

Pacific Island Countries and Territories (PICTs) are taking collective action towards developing a regional TC preparedness and response framework via the Pacific Meteorological Council (PMC). The PMC is a specialised body of the Secretariat of the Pacific Regional Environment Programme (SPREP) comprised of the directors or heads of PICT meteorological services that was established in 2011 to facilitate and coordinate the scientific and technical programme and activities of the Pacific island region’s meteorological services. At the most recent biennial PMC meeting held in Samoa in 2019, the Pacific Islands Climate Services (PICS) PanelFootnote 6 of the PMC identified the need to work towards an agreement between PICTs’ National Meteorological and Hydrological Services (NMHS) and regional providers (i.e. Fiji Meteorological Service, National Institute of Water and Atmospheric Research (NIWA) of New Zealand and the Australian Bureau of Meteorology) on a more coordinated release process and timeline for SWP TC outlooks.

The PMC identified two priority opportunities for improving seasonal TC preparedness communication; (i) regional providers were prone to disseminate regional and country TC outlooks before NMHS have had an opportunity to prepare their own TC outlook statements and media releases, and (ii) each provider uses a different methodology and different boundaries leading to different outlooks and thus some confusion amongst users. To address the first issue, PMC has worked with regional providers to make embargoed draft TC outlooks available to NMHS a week prior to release, enabling NMHS to tailor local language media releases that facilitate local understanding and uptake of regional TC outlook products. On the second issue, PICS has released good practice guidelines (PICS Panel 2020) for producers and users of SWP TC outlooks that include recommendations to prioritise user needs over scientific interest and to engage with stakeholders to determine their capacity to interpret scientific and multi-model information.

To further enhance communication of seasonal TC preparedness, the WMO Regional Association Five (RA-V) Pacific Regional Climate Centre (RCC) NetworkFootnote 7 has published an online ENSO tracker (RCC 2020) product that gathers and summarises the status of ENSO according to different global institutions and combined with other educational and awareness products, like the TC outlooks, can help PICTs’ stakeholders better understand their seasonal TC risk. The Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) together with SPREP and other partners have also published a Pacific-themed, animated video called the ‘Adventures of the Climate Crab’ (CSIRO 2013) in multiple Pacific languages that explain to a general audience ENSO and its effects on PICTs’ Seasonal TC risk and how to prepare. It is important to ensure that such products and services are culturally appropriate, understandable by the intended audience, and tailored to stakeholders’ needs.

While NMHS are responsible for transmitting seasonal and event-scale TC information, many Pacific communities have difficulty understanding the information provided, have delayed access to weather and climate information, have access but mistrust the accuracy of the information by NMHS, or have no access to any information provided by NMHS due to remoteness or isolation of communities (Lui et al. 2017; Plotz et al. 2017; Chambers et al. 2019). Thus, many remote Pacific communities rely on weather and climate forecasts based on traditional knowledge (TK) alone or in combination with contemporary (NMHS) forecasts (Magee et al. 2016; Chambers et al. 2019). When category 5 TC Zoe (2002), the second most intense TC ever recorded in the Southern Hemisphere at that time, devastated the Solomon Islands outliers of Tikopia and Anuta in 2002, islanders without access to NMHS forecasts or warnings relied on TK to survive the disaster without the loss of a single life (France-Presse 2003; SPREP 2019). Tikopian Elders were able to use TK of unusual movements of ocean currents and bird behaviour to forecast the coming TC and evacuate the community to caves where they sheltered safely until TC Zoe passed (SPREP 2019).

When designing a TC decision-support framework for PICTs, the incorporation of traditional forecast methods into contemporary forecast systems can lead to forecasts that are locally relevant and better trusted by the users, which in turn could significantly improve the communication and application of climate and weather information, especially to remote communities (Chand et al. 2014; Plotz et al. 2017). Thus, Plotz et al. (2017) recommend that such a framework comprises of four main decision points: (1) consideration of the level of involvement of traditional-knowledge experts or the community that is required; (2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity; (3) the availability of long-term data—both traditional-knowledge and contemporary-forecast components; and (4) the level of resourcing available.

There is no one-size-fits-all approach when it comes to developing climate change adaptation and mitigation strategies, particularly for highly vulnerable communities where TC-induced impacts are of a major concern. Drawing on experiences from past TC events as analogies to what may happen in the warming climate is imperative. Such activity should emphasise a ‘bottom-up’ approach (Pielke Sr et al. 2012) that requires local community and stakeholder-led discussion to first determine and evaluate the level of TC-induced threats to critical local factors like food and water security, and then develop relevant and relatable adaptation and mitigation strategies for each factor. Moreover, strategy development for adaptation planning processes needs to be an iterative process to take into account updated science on climate extremes. Adaptation decisions are not static but need to be themselves ‘adaptive’ to new information and knowledge (Morioka et al. 2020). Moving forward, it is critical that relevant national agencies (such as NHMS, NDMO and Department of Climate Change) understand the differences in the likelihood of TC occurrences between different climatic conditions (e.g. El Niño and La Niña periods) and clearly communicate this to sector stakeholders and the wider community through their information products and awareness-raising. Such approaches are believed to be a more inclusive way of assessing impacts of TCs and adopting more effective adaptation methodologies to deal with the complexity of potential extreme events affecting the SWP region.

4 Summary

Normalised economic losses (based on wealth and population) and fatalities during the passage of TCs in close proximity to the Fiji, Samoa, Tonga and Vanuatu (FSTV) region for the period 1970–2018 have been investigated in this study. An increasing trend in normalised economic loss due to TCs over the study period is not evident for most nations, except for Fiji where TC Winston in 2016 caused the most damage (USD 0.9 Billion) to the nation. Moreover, TC Val (1991), Gita (2018) and Uma (1987) caused the most extensive economic losses over Samoa (USD 1.782 Billion), Tonga (USD 0.146 Billion) and Vanuatu (USD 0.922 Billion) respectively. Hence, TC Val (1991) could be considered the costliest TC over the FSTV region. In terms of fatalities, the largest number of severe TC-associated deaths has occurred over Fiji and Vanuatu at 52 and 50 during TC Meli (1979) and TC Uma (1987) respectively. For Samoa and Tonga, the highest numbers of fatalities of 14 and 6 were due to TC Evan (2018) and TC Isaac (1982) respectively. Trends in fatalities are an important indicator of the overall improvements in the DRR and DRM over time, and this was examined here using the ratio of fatality to normalised economic loss. A decreasing trend in this index is generally evident, which signifies that there have been improvements in DRR and DRM over time in the FSTV region.

TC Winston has been used as a case study to illustrate the disaster preparedness and response during a TC event over the FSTV region and future recommendations have been suggested to reduce impacts during the passage of TCs. These recommendations include (i) increasing public awareness and individual preparedness, (ii) upgrading the current tools used to observe and forecast weather and (iii) home retrofitting. The discrepancy in fatalities between ethnic groups during TC Winston also requires further investigation and appropriate measures for future disaster TC responses.

Finally, we discussed the collective action taken by Pacific Island Countries and Territories (PICTs) towards developing a regional TC preparedness and response framework via the Pacific Meteorological Council (PMC). In particular, when designing a TC decision-support framework for PICTs, the incorporation of traditional forecast methods into contemporary forecast systems can lead to forecasts that are locally relevant and better trusted by the users, which in turn could significantly improve the communication and application of climate and weather information, especially to remote communities. Such a framework should comprise four main decision points: (1) consideration of the level of involvement of traditional-knowledge experts or the community that is required, (2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity, (3) the availability of long-term data—both traditional-knowledge and contemporary-forecast components—and (4) the level of resourcing available.