2.1 Study Region
The study area, Fiji, is located at 17.7134° S and 178.0650° E. It occupies a total area of 18,270 square kilometres, with a maximum elevation of 1324 metres (Neall and Trewick 2008). The nation consists of 322 islands with two main islands, Viti Levu and Vanua Levu. The region falls within the tropics and therefore experiences wet and dry seasons. The mean annual temperature is 25 degrees Celsius (Neall and Trewick 2008) and the mean precipitation ranges from 1676 to 3544 mm at different locations (Kumar et al. 2014). The Southern winter season observes movement of the SPCZ northward, away from Fiji; therefore, rainfall during winter is a result of regional features such as orographic lift. Fiji consists of mountainous topography with prevailing south-easterly trade winds which results in an orographic lift (Terry 2005). This is highly evident in the dry season whereby the leeward side (Western and Northern Divisions) receives 20% of the annual rainfall, while the windward side (Central Division) receives 33% of the annual rainfall (Terry 2005). Most of the rain-fed agriculture is located in the Western and Northern Divisions, including the sugar industry. The intra-seasonal movement of the SPCZ northward and southward results in a warm-phase El Niño and a cool-phase La Nina event, respectively, causing unexpected rainfall anomalies (Juillet-Leclerc et al. 2006). Precipitation overall in the country is driven by the South Pacific Convergence Zone, El Niño–Southern Oscillation (ENSO) and tropical cyclones, which are accompanied by heavy rain events during the wet season.
2.2 Data
2.2.1 Satellite Rainfall Data
The satellite precipitation data were selected on the basis of the start date of the record and the spatial resolution (Table 1). Because Fiji occupies a small area, we needed to ensure that products with fine resolutions were selected. Rainfall can be highly variable in different areas despite being in the same zone; therefore, the smallest spatial scale was chosen for validation (Xu et al. 2015). Following the methods of Dembélé and Zwart (2016), satellite data were extracted using the point to pixel method based on the precise observation station coordinates.
Table 1 Information summary of the satellite precipitation products
Product
|
Spatial Resolution
|
Temporal Resolution
|
Start Date
|
PERSIANN-CDR
|
0.25°
|
Daily
|
1983
|
CPC-Precipitation
|
0.5°
|
Daily
|
1979
|
CHIRPS
|
0.05°
|
Daily
|
1981
|
PERSIANN-CDR: PERSIANN-CDR data (Ashouri et al. 2015; Hsu et al. 1997) were retrieved from http://chrsdata.eng.uci.edu/. The dataset has a resolution of 0.25° with a spatial extent of 60° S to 60° N, and is available at hourly, monthly and daily time steps. One of the reasons for selecting this dataset was its long record, starting from 1983 to the near-present, making it ideal for calculation of the hydrological indices. This product uses GEO infrared imaging technology and microwave sensors to compute an estimate of rainfall at each grid pixel (Ashouri et al. 2015). Since its development, the PERSIANN-CDR has been used to carry out streamflow modelling (Liu et al. 2017), monitor rainfall patterns (Arvor et al. 2017) and perform drought modelling (Rhee and Yang 2018b).
CPC unified data: CPC US unified precipitation data (referred to as CPC data in the paper) were obtained from the NOAA/OAR/ESRL Physical Sciences Division (PSD), Boulder, Colorado, United States, and their website https://www.esrl.noaa.gov/psd/. The temporal coverage of the CPC daily dataset started from January 1979 to the present time with a spatial resolution of 0.5°. The dataset is created using the optimal interpolation objective analysis technique derived by Chen et al. (2008). The dataset was validated using 16,000 global gauge stations but now uses reports from 30,000 stations (Sun et al. 2018).
CHIRPS: This dataset, starting from 1981 with a spatial extent ranging from 50° S to 50° N, was retrieved from http://chg.ucsb.edu/data/. CHIRPS data have a resolution of 0.05° and use thermal infrared bands and in situ station data to create the final gridded product. The satellite information is used to account for sparsely gauged locations, and precipitation estimates are available on daily, pentadal and monthly scales (Funk et al. 2015). CHIRPS data have not been explored before for Pacific island topographies; however, they have been tested in other areas such as the Caribbean. Evaluation of the CHIRPS in Mozambique has indicated a good ability to detect rainfall during hurricanes (Toté et al. 2015), which is especially useful given Fiji’s proneness to flooding during cyclones.
2.2.2 In Situ Rainfall Data
The daily and monthly precipitation dataset was obtained from Fiji Meteorological Services, which operates over 20 manual and automatically telemetered rain gauges over the Fiji islands. Six high-quality stations were used in the current evaluation: Labasa, Nadi, Navua, Ba, Matei and Nausori (Fig. 1). These meteorological stations were selected considering the variation in local climate, topography and orographic effects observed in Fiji (Table 2). The Central Division stations, Navua and Nausori, are in the windward region characterised mainly with wet weather conditions (See Table 2 and Fig. 2 for Station climatology). The Western Division is the drier side of the island, represented by the stations Rararwai and Nadi. Labasa station is the main station on the island of Vanua Levu, while Matei is a town situated on Taveuni (an outer smaller island); both serve to provide datasets for the Northern Division of Fiji. The data were checked for homogeneity, and missing values were replaced with the long-term monthly averages. The number of missing data ranged from 0 – 5 points for all the stations, while Matei had 31 missing data points.
Table 2 Station climatology for monthly rainfall in Fiji for the wet and dry seasons
Station
|
Longitude
|
Latitude
|
Wet Mean
|
Wet Max
|
Wet Min
|
Dry Mean
|
Dry Max
|
Dry Min
|
|
|
|
(mm)
|
(mm)
|
(mm)
|
(mm)
|
(mm)
|
(mm)
|
Nausori
|
178.5591
|
−18.0464
|
313.9
|
1216.8
|
27.9
|
175.1
|
913.6
|
12.1
|
Nadi
|
177.4439
|
−17.7543
|
246.6
|
1180.6
|
3.6
|
71.5
|
341.8
|
0.0
|
Labasa
|
179.3396
|
−16.4689
|
298.7
|
1068.2
|
15.2
|
77.7
|
490.9
|
0.0
|
Matei
|
179.8667
|
−16.6833
|
299.2
|
918.1
|
23.1
|
151.4
|
728.4
|
7.6
|
Rarawai
|
177.6814
|
−17.5564
|
282.6
|
1907.8
|
0.0
|
73.8
|
402.4
|
0.0
|
Navua
|
178.1700
|
−18.2186
|
343.1
|
843.6
|
78.8
|
237.1
|
593.6
|
41.1
|
2.2.3 Sea Surface Temperature Data
The Kaplan SST data used in this analysis were retrieved from https://climatedataguide.ucar.edu/climate-data/kaplan-sea-surface-temperature-anomalies. The dataset has a temporal coverage starting from 1856 and a global spatial extent with a spatial resolution of 5.0 x 5.0 units. The data are produced by various steps, such as Empirical Orthogonal Functions (EOF) projection, optimal interpolation (OI), the Kalman Filter (KF) forecast, KF analysis and an optimal smoother (OS), which ultimately minimise the number of missing data and error values (Reynolds and Smith 1994). A bounding box was created in Arc GIS, ranging from 150° E to 80° W between the Tropic of Cancer and the Tropic of Capricorn, which resulted in 259 grid points. This ensured that the SST would be extracted for the region surrounding Fiji. The series length extracted was the same for the EDI and SST, from 1980 to 2017 (Fig. 2). The data were checked for homogeneity, and grids with missing data were omitted.
2.3 Methods
2.3.1 Satellite Rainfall Validation
Continuous verification statistics were applied to test the satellite-derived precipitation products (Jolliffe and Stephenson 2003) for the six stations (Fig. 1). The following performance measures were used to evaluate the products: coefficient of determination (R2), root-mean-square error (RMSE) and Lin’s concordance. The R2 measures the extent of association between the actual recorded data and the satellite data in this case. The RMSE assesses the variance of errors independently and indicates the inconsistency between the recorded and the satellite values, with lower values indicating minimum error difference between the recorded data and satellite data (Adamowski et al. 2012). Lin’s concordance created by Nickerson (1997), is calculated based on the degree to which two variables fall on the 45° line that passes through the origin. The calculation was undertaken in R using the DescTools package (Signorell et al. 2016). Lin’s concordance have values between 0 and 1 without a unit, and a higher value indicates a strong relationship similar to R2. The unit for RMSE is millimetres. The daily data were aggregated to monthly data and were tested for the identified performance metrics. A complementary check was also made on Nadi station to test for differences in the wet and dry seasons respectively for the CPC product (Results shown in Table 5). The eyeball method is a subjective form of verification (Ebert 2007), and in this study we made scatter plots of observed versus satellite data for Nadi station, as well as created kernel density plots to determine instances of overestimation and underestimation.
2.3.2 Index Calculation
For further analyses, CPC data were used because these data rendered the best correlation results with local station data ( Table 4 ). Therefore, following point extraction, the dataset was checked for quality and homogeneity, whereby the zoo package (Zeileis and Grothendieck 2005) in R was used to carry out linear interpolation in the case of missing precipitation data. Point linear interpolation is calculated using the values measured on either side of the missing data (North and Livingstone 2013). The drought index was calculated on the basis of the principles identified by Byun and Wilhite (1999) for each of the grid cell retrieved from the CPC data. The daily Effective Drought Index (EDI) values were aggregated into monthly EDI values because the climate index data were available at monthly timescales (Fig. 3).
2.3.3 Multivariate Analysis
CCA successfully establishes interrelations to identify linear combinations between more than one response and explanatory variable (Ouarda et al. 2001), while PCA employs the same approach on a single multi-dimensional dataset. The relationships between the weights of the response and explanatory data are known as loading patterns, and these can be used to visualise potential physical mechanisms in large-scale processes (Shabbar and Barnston 1996). The multivariate analysis was performed according to the guidelines provided by Wilks (2011). SST was used as the explanatory variable and the gridded EDI as the response variable, from which a probabilistic forecast was generated. If a dataset x (t) leads to y (t), then CCA can be used to forecast the y variable by using the lagged x variable in a gridded form. A lag of 2 months was introduced in the data matrix between the EDI and the SST, consequently, a forecast of EDI could be provided at short term (2 months ahead). Short term forecasts are not only accurate, but is adequate to assist managers and farmers in making useful management and agronomic decisions (Anshuka et al. 2019). As a first step, the data were standardised and centred to remove any trend. This ensures that the principal components (PCs) are mutually orthogonal (Westra and Sharma 2010). Previous studies have also applied pre-processing of data using empirical orthogonal analysis (Landman and Mason 1999; Tang et al. 2000; Yu et al. 1997). This has a number of advantages such as, reduces dimensionality and noise in the data, and gives equal opportunity for predictors to contribute to forecasting models (Shabbar and Barnston 1996).
We selected the first 10 PCs from the SST and EDI to perform the CCA. Forecast evaluation was performed by splitting the data into two parts, whereby a hindcast was generated which entailed model training on data from 1980 to 2004 and the forecast was tested using the data from 2005 to 2017. The forecast skill was verified using the coefficient of determination (R2) and mean square error (MSE). A spatial plot was generated to determine how well the forecast and the hind cast predicted the EDI classes as shown in Table 3.
Table 3 EDI drought classes
Value
|
Category
|
EDI
|
3
|
Extremely Wet
|
≥2
|
2
|
Very Wet
|
1.5 to 1.99
|
1
|
Moderately Wet
|
1.0 to 1.49
|
0
|
Normal
|
-0.99 to 0.99
|
-1
|
Moderately Dry
|
-1.0 to -1.49
|
-2
|
Severely Dry
|
-1.5 to -1.99
|
-3
|
Extremely Dry
|
≤-2
|