*3.3. Temporal Clustering*

After spatial cluster analysis had been obtained, a Euclidean distance timed and spaced with average linkage was next applied to the monthly climate factors for each SC to find temporal clusters (TCs) within each SG. Normally, Thailand has three seasons, summer (February–May), rainy (May–October), and winter (October–February). To compare temporal clusters of the ENSO phenomenon, three TCs within each SC were compared in this study. TC1, TC2, and TC3, which were sorted according to the amount of ascending precipitation, were represented by orange, blue, and green, respectively. TCs corresponding to each SG is shown in the dendrogram to depict the groups of clusters and their combination, indicating dissimilarity in the vertical scale and the samples (months) in clustering order on the horizontal axis. They help to see how long each season lasts and the different period of seasons in each spatial grouping (Figure 7).

**Figure 7.** Dendrograms of TGs for the five different SCs discovered by ENSO events, years and SCs: (**a**) SC1; (**b**) SC2; (**c**) SC3; (**d**) SC4; (**e**) SC5.

For example, in 1982, TC1 and TC2 in SC1 depicted a very dry season with average precipitation intensity of less than 2 mm/day (Table 5). They were composed of three months. Months of TC1 were December and of TC2 were January and February. TC3, on the other hand, was a slightly wet season with an average precipitation of 2 mm/day or more for 9 months, March–November.


TC1 and TC2 in SC2, 1982 depicted a very dry season with average precipitation intensity of less than 2 mm/day. TC1 was composed of February–March. TC2 was December and January. TC3, on the other hand, was a wet season with an average precipitation of 4.51 mm/day for 8 months, April–November.

TC1 and TC2 in SC3, 1982 was a dry season for three months, December (TC1) and January–February (TC2), with an average precipitation of less than 2 mm/day, while TC3 was a wet season with an average precipitation of 4 mm/day or more for 9 months, March–November.

TCs in SC4 and SC5, 1982 were the same. TC1 was a dry season, January–February, with an average precipitation of less than 2 mm/day, while TC2 and TC3 were a wet season with an average precipitation of 2 mm/day or more for 10 months, December (TC2) and March–November (TC3).

TCs in each SC in La Niña were similar to those in El Niño. Nevertheless, there was higher average precipitation intensity in La Niña phenomenon, than those in El Niño phenomenon. Furthermore, the rainy season was a longer period in SC4 and SC5 for both events of ENSO.

#### **4. Discussion**

The highest average rainfall in 1982, 1997, and 2015 (5.64–11.06 mm/day) was less than that of in 1999, 2007, and 2011 (9.87–14.74 mm/day). This corresponds to the Oceanic Niño Index (ONI), showing that ONI in 1982, 1997, and 2015 was greater than 0.5 ◦C, meaning that El Niño occurred, and in 1999, 2007, and 2011 was below −0.5 ◦C, meaning that La Niña occurred [29].

Lower rainfall than usual was found, so there was a widespread drought in almost all regions of Thailand in 1982 and 1997, especially in Northeast [30]. There also was a severe El Niño effect in 2015, causing very low precipitation across the country (*x* = 2.28 mm/day).

Five spatial clusterings were formed. SC5 with the highest average precipitation was formed by only one station in Khlong Yai District, Trat Province, in every year whether there was an El Niño or La Niña phenomenon (*x* = 5.64 − 14.74 mm/day). The topography of Khlong Yai District is a coastline fully influenced by the southwest monsoon from the Gulf of Thailand; consequently, it has abundant rainfall for most of the year. This is consistent with the Trat Agricultural Meteorological Document that reports that Khlong Yai District, Trat Province, is the wettest area in Thailand [31].

There were approximately 80 stations in SC1 and SC2 with low average precipitation and especially low in 2015, mostly in the Central, North, and Northeast. It was consistent with a report that rainfall in these three regions when El Niño occurred was less than the average 30 years of rainfall of normal years.

There were three TCs in each SC. When the El Niño phenomenon occured, Thailand rainfall tended to be lower than normal, especially during the summer and early rainy season (mid-February–June). The dry season in El Niño was longer and less than average rainfall than TCs for the La Niña phenomena.

Most stations in the south were clustered into SC3 and SC4 with moderate and high rainfall, respectively, for both El Niño and La Niña phenomena. Usually, rainfall in Thailand, especially in the southeast coast, is high during October–December. In addition, some parts of Thailand were not affected by the ENSO phenomenon (El Niño and La Niña), such as Trat in SC5 with the highest rainfall, and Tak, Chiang Rai, Chiang Mai, Phayao, and Lampang in SC1 with the least rainfall. This may be due to their topography.

There are 35 provinces with more than one meteorological station of TMD. Of these, stations in 34 provinces were grouped into different SCs. This may be due to their topography affecting a different climate.

Spatial clusters were similar for both El Niño and La Niña except in 2015, when severe El Niño occurred. This might be the Euclidian distance matrix tending to cluster the samples with climate variables having similar mean. This suggests that other similarity matrices, such as correlation, may be possible to group samples based on trends and variation over time [11].

#### **5. Conclusions**

This paper employed multivariate cluster analysis with the average linkage to analyze the spatial and temporal grouping, using climate factors which are rainfall, relative humidity, average temperature, maximum temperature, lowest temperature, solar radiation, and wind speed at 124 locations over Thailand from CCAM (10 km), for the years 1982, 1997, and 2015 (El Niño) and 1999, 2007, and 2011 (La Niña).

Five SCs with a distance between a cluster of 12.5 were compared. It was observed that SCs were similar for both El Niño and La Niña except in 2015, when severe El Niño occurred. This indicated the more severe El Niño, the more spatial variation. The main difference between SC1–SC5 was the ascending amount of precipitation, where SC1 had the least amount of rainfall and SC5 had the heaviest rainfall.

In addition, three TC patterns in each SC were similar for both El Niño and La Niña. Nevertheless, the average precipitation intensity in La Niña was higher than that in El Niño.

This paper implements cluster analysis on atmospheric panel data. Even multivariable panel data is more complicated, but it is practical to cluster. Cluster results arealso more realistic than cross-sectional data and avoid information loss.

Future studies may focus on using future climate factors from the weather forecast models for clustering to study the spatial and temporal distributions. Other than the correlation distance suggested, the robust distance, for example the absolute distance or the Canberra distance to deal with outliers, should be further studied. Furthermore, as there might be extreme whether events in the ENSO phenomenon, for example less or abundant precipitation, which may affect the clustering, outliers should be detected and handled prior.

**Author Contributions:** Conceptualization, P.D.; methodology, P.D.; software, P.D., N.J., P.Y. and S.P.; validation, P.D.; formal analysis, P.D., N.J., P.Y. and S.P.; investigation, P.D.; resources, P.D., N.J., P.Y. and S.P.; data curation, P.D., N.J., P.Y. and S.P.; writing—original draft preparation, P.D., N.J., P.Y. and S.P.; writing—review and editing, P.D., U.H. and S.I.; visualization, P.D.; supervision, P.D.; project administration, P.D., U.H. and S.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research project was supported by the Agricultural Research De-velopment Agency (ARDA) [PRP6405031190] and Thailand Sciece Research and Innovation (TSRI). Basic Research Fund: Fiscal year 2022 under project number FRB650048/0164.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

