Islands in the South Pacific are vulnerable to climate change [1
]. The climate in the South Pacific has become drier by 15% and warmer by 0.8 °C, compared to the earlier 20th century [2
]. Fiji, one of the key Pacific Island countries, experiences easterly trade winds on most calendar days. The easterly trade winds or the northeasterly monsoon, when lifted by high mountains, causes moisture condensation and produces heavy rainfall on the windward eastern side of Fiji. The subsidence of the relatively dry air produces less rainfall on the leeward western side.
From a large-scale viewpoint, the El Nino Southern Oscillation (ENSO) is the main cause of climate variability over this region at interannual timescales. La Nina events dominated the interannual sea surface temperature (SST) anomaly (SSTA) over the central Equatorial Pacific during 1950 and 1975; after that time, El Nino events became more frequent [3
]. The Pacific Decadal Oscillation (PDO) dominates the climate variability at decadal timescales [4
]. PDO was mostly positive prior to 1998 and then shifted to a strong negative phase [5
]. Positive PDO is characterized by the similar SSTA of El Nino over the Equatorial Pacific, and thus shifts the weather systems northeastward, but on a decadal timescale. The South Pacific Convergence Zone (SPCZ) is a reverse-oriented monsoon trough with strong low-level convergence and a rainfall band that extends from the Warm Pool southeastward to French Polynesia [6
]. The interferential impact of ENSO and PDO on the SPCZ is complex [8
]. El Nino events weaken the strength of the Walker Circulation and shift the dominant weather systems over the Equatorial Pacific toward areas in the northeast such as the SPCZ. When El Nino takes place during the positive PDO, the SPCZ moves northeast towards the equator, and its intensity becomes stronger [8
]. The large-scale convection departure decreases precipitation over Fiji and leads to droughts [10
Fiji has observed more frequent dry conditions since the 1950’s compared to previous decades in the western and northern areas based on analysis performed using the Standardized Precipitation Index (SPI). Analysis of observed monthly rainfall for Fiji over the period 1949–2008 showed downward trends at a 99% confidence level with decreases in rainfall of approximately 13–47 mm per year [11
]. Although no significant long-term trends were observed in annual rainfall [12
], there were more frequent dry seasons during the last 50 years compared to the first 50 years when the nearly 100 years of data since 1900 were examined [13
]. The local temperature also increased due to the effects of climate change [14
]. The most impacted stations were located in western and northern Fiji, where deficiency in rainfall from 1969–1988 caused an increase in moderate and severe droughts [11
]. Risbey et al. [15
] projected an increase in rainfall of approximately 3.3% by 2025 and 9.7% by 2100 using a global climate model (GCM). Feresi et al. [16
] and Agrawala et al. [17
] did not project a definitive change in rainfall. IPCC [18
] projected that Fiji will experience an intensified seasonal cycle, i.e., a rainfall decrease in the dry season and a rainfall increase in the wet season. The shift towards extended periods of dry spells causes loss of soil fertility, which could impact negatively on agriculture [1
Since 1940, severe droughts have occurred in 1942, 1958, 1969, 1978, 1983, 1987, 1992, 1997–1998, 2003, and 2010 [16
]. Severe droughts can cause serious socio-economic loss as well as physical damages as drought conditions persist. The ENSO event of 1997–1998 caused a severe drought with damages of up to Fiji $
100 million. Rainfall failure occurred across two successive dry seasons, and more significantly during the intervening wet season when precipitation is normally reliable [16
]. Since many rural communities are reliant on rainwater, streams, and shallow wells for domestic use, watering crop gardens, and livestock, these communities are especially vulnerable to periods of drought when surface water resources are at a minimum [19
]. Schools and businesses were forced to close and caused disruption to residential areas. Such impacts made extreme difficulties for Fiji since the resources of an island country are limited. External aid and governmental assistance were required to ensure supply of sustenance and facilitate recovery in the worst-hit parts of Fiji, which included the western and northern divisions and outer islands.
Drought conditions in Fiji are currently monitored using the 3-, 6-, and 12-month SPI calculated for weather stations with long historical data [20
]. The monitoring network over Fiji with long data is quite sparse though, resulting in considerable uncertainty in the estimates of extreme wet and dry events. Evidence shows that estimation of the historical trends has a large noise-to-signal ratio over regions with sparse data networks [21
]. Furthermore, most Fiji weather stations with long data are located along the coastline, so the sparse network cannot capture small-scale convective precipitation over land and precipitation from orographic lifting at mountains. Rainfall variability in the high mountains is greater than the variability in cities.
The limited variables and inconsistency in duration of satellite observation introduces difficulties and uncertainties in methods and analysis. For example, the Climate Prediction Center Morphing Technique (CMORPH) data is only available from 1998 onward. Due to the limited number or variables being observed, it is difficult to prepare for droughts because the response of rainfall distribution to large-scale dynamics is unclear. In addition, unlike other types of disasters, the onset and termination of droughts is not always clear. The increase in uncertainty of climate variability makes the reduction of drought impacts even more difficult.
Drought outlook of Fiji is also provided based on SPI: SPI predictions for weather stations are based on the statistically downscaled seasonal forecast data from the Seasonal Climate Outlooks for Pacific Island Countries developed by the Bureau of Meteorology of Australia. If spatially distributed drought prediction is available, possibly reflecting the orographic effect of the main island, it would be helpful to prevent and minimize the adverse impacts of droughts in Fiji. Drought prediction data only available for weather stations or obtained based on low-resolution bias-corrected seasonal forecast data are not sufficient for effective decision making.
This study aims to develop a drought prediction model that can be used for areas with sparse monitoring networks. Fiji is a case study area. By providing spatially detailed drought prediction data, vulnerability to droughts may be reduced while resiliency may be increased. Multi-Model Ensemble seasonal climate forecast data from APEC Climate Center (APCC MME) are used to provide up to 6 months-lead climate forecasting. Machine learning models are used to provide spatially distributed drought information for ungauged areas. In order to overcome the limitation of sparse monitoring networks, dynamically downscaled historical climate data from the Weather Research and Forecasting (WRF) model are used to train machine learning models instead of in-situ data as reference data.
This study ultimately targets national, provincial, and regional officials whose main duties include water resources and agricultural management. The final beneficiaries of the output are residents of the area; water users and farmers for whom decision-making can be helped by drought prediction information with finer spatial resolution.
2. Study Area
Fiji has a total area of about 194,000 km2
of which approximately 10% is land. Fiji consists of 332 islands. The two largest islands are Viti Levu and Vanua Levu, which account for about three-quarters of the total land area of Fiji [22
]. Figure 1
shows the topography of Fiji’s main islands. The largest island, Viti Levu, which has an area of 10,388 km2
, is covered with thick tropical forest. The island has a considerable area higher than 500 m in elevation with the peak of Mount Tomanivi at 1324 m above sea level. Viti Levu hosts the capital city of Suva, which contains about three-quarters of the population. Other important towns include Nadi, where the international airport is located, and Lautoka.
Fiji has a tropical marine climate and is warm year-round with minimal extremes. The warm season lasts from November to April and the cool season lasts from May to October. Temperatures in the cool season average 22 °C. Winds are moderate, though cyclones occur about once a year (10–12 times per decade). Viti Levu is a mountainous volcanic island with a wet-dry tropical climate. The southeast side of the island faces the predominant trade winds and therefore receives more precipitation than the northwest side, which is rain-shadowed by interior highlands. The volcanic mountains force orographic lifting of the saturated air, which can produce extremely heavy rainfall on the windward side of the mountain. Rainfall on the leeward side is much lighter due to the subsidence of the dry air, which largely influences agriculture in those areas. In the dry season, the uneven distribution of rainfall can cause a prolonged lack of moisture on the leeward side. The leeward side only receives 20% of the annual total rainfall in the dry season, compared to 33% received on the windward side [23
Sugar export is an important source of foreign exchange for Fiji, as sugar cane processing makes up one-third of industrial activity. Coconut, ginger, and copra are also significant industries. These agricultural products are highly influenced by climate extremes; the sugar industry was damaged by drought in 1998.
We developed hybrid drought prediction models using APCC MME seasonal climate forecasts and machine learning models and examined their performance for the case study area of Fiji. The purpose of the models is to provide spatially distributed detailed drought prediction data of SPI6 for the area. The APCC MME provides up to 6-month lead precipitation forecast data. Remote sensing data were used to bias-correct the forecast data as well as to train machine learning models; machine learning models of ERT and Adaboost were used to provide spatially distributed drought information for ungauged areas. In order to overcome the limitation of sparse monitoring network, dynamic downscaling of historical climate with the WRF model was used to produce reference data.
When compared to the performance of the hybrid models trained based on different reference data, the models trained using the WRF model outputs performed better than the models trained using in-situ data: ERT_WRF outperformed ERT_INSITU in all cases, and Adaboost_WRF outperformed Adaboost_INSITU except for Drought MAE and Drought Accuracy of 1-month lead predictions, Total MAE and Total Accuracy of 2-month lead predictions, and Total Accuracy of 3-month lead predictions. The superiority of the models trained based on the WRF model outputs indicates that the spatial extent of the training data is important because in-situ data are from only four weather stations. The added value caused by the topography is clear, especially in the convergence/divergence field over the islands; this crucially impacted inland and coastal precipitation and caused greater detail in precipitation to be found in the WRF model outputs [24
The use of the ERT_WRF model produced better results compared to Adaboost_WRF in terms of Total MAE, Total Accuracy, and Drought Accuracy for all lead times, as well as in terms of Drought MAE of 1-month lead predictions. For other lead times, no statistical difference between ERT_WRF and Adaboost_WRF were found (2- to 4-month lead predictions) or ERT_WRF showed larger error than Adaboost_WRF (5- to 6-month lead predictions) in terms of Drought MAE. It shows that the choice of the machine learning model matters; the use of simulated input data with added noise to attain the same numbers of samples between drought categories may have improved the performance of ERT and surpassed the advantage of Adaboost, supporting weak learners.
Compared to FCST_ONLY, ERT_WRF performed better in terms of Total MAE and Total Accuracy for all lead times as well as in terms of Drought Accuracy for 2- to 6-month lead predictions. Although there was no statistically significant difference for 1-month and 3-month lead predictions in terms of Drought MAE and the error of ERT_WRF was larger for 2-month and 4- to 6-month lead predictions, Drought Accuracy of ERT_WRF for 2- to 6-month lead predictions was higher than FCST_ONLY. The hybrid model, especially ERT_WRF, showed good performance compared to simply bias corrected forecasts.
Hybrid models with better performance than simply bias corrected forecasts in most cases for areas with sparse monitoring networks were successfully developed. It should be noted that the performance of the compared methods may be evaluated differently according to the purpose of the study with the appropriate choice of a performance measure. In future studies, the use of more diverse input variables related to drought for machine learning models need to be investigated. Only SPI based on precipitation data was examined in this study; drought prediction based on drought indices considering the effect of evapotranspiration, such as the Standardized Precipitation-Evapotranspiration Index [49
] or the Standardized Evapotranspiration Deficit Index [50
], may also help to reduce vulnerability to droughts.