El Niño-Southern Oscillation-driven rainfall pattern variability in the Plain of Reeds¹

Variabilidade do padrão de precipitação impulsionado pelo fenômeno El Niño-Oscilação Sul na Planície de Reeds

ABSTRACT

The Plain of Reeds, a crucial agricultural region in the Vietnamese Mekong Delta, is experiencing rainfall pattern variability under the impacts of climate variability. It has shown substantial evidence of a demonstrable link between El-Niño Southern Oscillation (ENSO) and regional rainfall patterns in recent decades.This study investigates the influence of the ENSO on rainfall pattern variability within the Plain of Reeds by analyzing daily data series from 11 rainfall gauge stations, spanning from 1985 to 2020, based on non-parametric statistical methods and the rainfall anomaly index (RAI), examining rainfall pattern variation in relation to ENSO phases. The close links between ENSO events on rainfall pattern variability is verified. During dry periods, there is a decrease in annual rainfall, a delay in the onset of the rainy season (ORS), and an earlier end to the rainy season (DRS), resulting in fewer rainy days. In contrast, humid periods are marked by increased annual rainfall, an earlier ORS, and a later DRS, leading to more rainy days. Furthermore, rainfall intensity exhibited substantial inter-annual variability linked to ENSO. The findings reveal a complex interplay between ENSO and rainfall patterns in the Plain of Reeds. These results have critical implications for water resource management and agricultural planning in the region.

Key words:
drought; rainfall anomaly index; rainfed; ENSO; trend

HIGHLIGHTS:

Seasonal rainfall is strongly linked to El Niño-Southern Oscillation (ENSO) phenomena across the Plain of Reeds.

Significant variability in rainy season rainfall was detected during typical ENSO events.

Seasonal rainfall patterns trend exhibited a later onset and demise while shorter rainy season during the period 1985-2020.

RESUMO

A Planície de Reeds, uma região agrícola crucial no Delta do Mekong vietnamita, está experimentando variabilidade do padrão de precipitação sob os impactos da variabilidade climática. Tem sido verificada evidências substanciais de uma ligação demonstrável entre a Oscilação Sul El-Niño (ENSO) e os padrões regionais de precipitação nas últimas décadas. Este estudo investiga a influência do ENSO na variabilidade dos padrões de precipitação na Planície de Reeds. Neste estudo, foram avaliadas séries de dados diários de 11 estações pluviométricas, abrangendo de 1985 a 2020, com base em métodos estatísticos não paramétricos e no índice de anomalia de precipitação (RAI) para examinar a variação dos padrões de precipitação em relação às fases do ENSO. Verificou-se ligações estreitas entre os eventos do ENSO na variabilidade dos padrões de precipitação. Durante o período seco, houve diminuição na precipitação anual, um atraso no início da estação chuvosa (ORS) e um fim mais precoce da estação chuvosa (DRS), resultando em menos dias chuvosos. Em contraste, os períodos úmidos foram marcados por aumento da precipitação anual, um ORS mais cedo e um DRS mais tarde, levando a mais dias chuvosos. Além disso, a intensidade da precipitação exibiu variabilidade interanual substancial ligada ao ENSO. As descobertas revelam uma interação complexa entre o ENSO e os padrões de precipitação na Planície de Reeds. Esses resultados têm implicações críticas para o gerenciamento de recursos hídricos e planejamento agrícola na região.

Palavras-chave:
seca; índice de anomalia de precipitação; sequeiro; ENSO; tendência

Introduction

El Niño-Southern Oscillation (ENSO) is known as an ocean-atmosphere phenomenon characterised by fluctuating sea surface temperatures and air pressure in the equatorial Pacific Ocean; ENSO triggers a complex chain of atmospheric responses that extend far beyond the equatorial region (Aggarwal et al., 2010; Cai et al., 2021). These climate anomalies are characterized by significant alterations in rainfall regimes, temperature anomalies, and atmospheric circulation patterns across vast geographical areas (Cai et al., 2021). Numerous studies have documented the substantial influence of ENSO on local weather patterns in diverse regions, including Africa, India, the Americas, and various parts of Asia (Tao et al., 2011; Sazib & Bolten, 2020; Supriyasilp & Pongput, 2021; Athira et al., 2023). These impacts are not merely climatic; they have profound socioeconomic consequences, particularly within agricultural sectors, which are often the most vulnerable to climate variability (Li et al., 2020).

Among the key drivers of regional climate variability, the ENSO stands out as a dominant force, exerting a profound influence on weather patterns across the globe (Asadi et al., 2017; Li et al., 2020). Studies across diverse geographical regions have confirmed ENSO’s profound influence on the local weather (Bogale & Temesgen, 2021; Cai et al., 2021). While the impacts of ENSO phases on rainfall distribution and intensity are well-documented (Chen et al., 2014; Han et al., 2016), cold periods are often associated with increased rainfall in south-eastern regions, while warm periods of drought are associated with eastern tropical areas (Lengaigne & England, 2015; Ayarzagüena et al., 2019). Studies have shown rainfall reductions during warm phases, leading to reductions of crop yield (4.5%) in Brazil, 3.7% in Indonesia, 2.9% in Bangladesh, and up to 10.7% in Nicaragua (Sazib & Bolten, 2020; Cherian et al., 2021). The most severe drought in over 90 years struck Vietnam from 2014 to 2016, prompting the Vietnam Government to declare a state of emergency in 18 provinces (Dinh & Dang, 2022). This devastating drought had a profound impact on the agricultural sector, resulting in substantial losses and far-reaching consequences for the region’s farmers (Lee & Dang, 2020; Dinh & Dang, 2022). The prolonged dry spell had a particularly dire effect on the Plain of Reeds, a fertile and vital agricultural area, exacerbating crop failures and severely affecting the livelihoods of rural communities (Dinh & Dang, 2022). In contrast, cold phases have been associated with increased rainfed, resulting in rice (Oryza sativa) yield increase in countries such as the Philippines (4.9%), Cambodia (14.7%), and Vietnam (5.6%) (Lengaigne & England, 2015; Cherian et al., 2021).

Within the context of the Plain of Reeds, one of the two critical deltas in the Vietnamese Mekong Delta, understanding the intricate climate-agriculture interactions is paramount (Dinh & Dang, 2022; Phung, 2024). However, the Plain of Reeds is highly vulnerable to climate change impacts. Severe typical El-Niño events, such as those of 2014-2016 and 2018-2019, have caused significant droughts in the Plain of Reeds, highlighting the urgent need for a detailed understanding of ENSO’s influence on local rainfall patterns (Dang, 2021; Dinh & Dang, 2022). This study addresses this gap by investigating the relationship between ENSO events and rainfall variability in the Plain of Reeds of the Vietnamese Mekong Delta, focusing on its implications for agricultural land area of approximately 700,000 hectares, with an average annual rice yield of up to 5.60 tons per hectare.

Material and Methods

The Plain of Reeds, a crucial delta in the Vietnam’s Mekong Delta, spans Dong Thap, Tien Giang, and Long An Provinces (10° 04’ to 11° 00’ N latitude and 105° 03’ to 106° 09’ E longitude). This low-lying floodplain, with elevations ranging from 0.5-3.5 m above sea level, plays a vital role in the region’s hydrology and agriculture (Figure 1).

Figure 1
Map of the Plain of Reeds with rainfall gauge stations marked in red color (Source: authors)

The study area is affected by a tropical monsoon climate with two distinct seasons (Köppen, 1936; Lee & Dang, 2020). The Southwest monsoon (May to mid-November) brings abundant rainfall, while the Northeast monsoon (late November to April) introduces drier conditions (Figure 2).

Figure 2
Distribution of monthly rainfall across the Plain of Reeds in the period of 1985- 2020

This dichotomy results in a rainy season accounting for approximately 85% of the annual precipitation, crucial for agricultural activities (Figures 3A and B). Temperatures in the Plain of Reeds remain relatively stable year-round, averaging between 26.0 to 28.2 °C (Dang, 2021). The region experiences significant spatial variation in rainfall patterns, with annual precipitation ranging from 1467.4 mm in the southwest (Cao Lanh station) to 1650.8 mm in the northeast (Moc Hoa station) (Figure 3). Hydrologically, the Plain of Reeds is influenced by both the Mekong River’s flow regimes, local precipitation and tides from the east and west seas (Nguyen & Dang, 2024). This unique combination of factors contributes to the area’s importance as a major rice-growing region.

Figure 3
Spatial distribution of dry season rainfall (A) and rainy season rainfall (B) across the Plain of Reeds in the period of 1985- 2020

The Plain of Reed’s distinct geographical and climatic characteristics make it particularly susceptible to the impacts of climate change, especially concerning rainfall patterns and water resource management (Dang, 2021). This study used a dataset of daily rainfall records from 11 observation stations across the Plain of Reeds between 1985 and 2020, (Table 1). The dataset provides comprehensive coverage of the area’s diverse microclimates.

Table 1
Basic patterns at rain gauge stations across the Plain of Reeds during 1985-2020

Divisions	Stations	Latitude (N)	Longitude (E)	Altitude (m)	Duration	Quality
Western	Hong Ngu	10° 47' 45''	105° 22' 36''	1.64	1986-2020	Homogeneous
	Cao Lanh	10° 33' 24''	105° 39' 48''	1.02	1985-2020	Homogeneous
	My Tho	10° 40' 46''	105° 46' 47''	1.02	1985-2020	Discontinuous
	Tam Nong	10° 45' 41''	105° 31' 12''	1.95	1985-2020	Discontinuous
Northern	Tan Hung	10° 58' 23''	105° 45' 49''	0.87	1985-2020	Discontinuous
	Moc Hoa	10° 47' 45''	105° 59' 28''	0.84	1985-2020	Homogeneous
Eastern	Ben Luc	10° 41' 48''	106° 24' 39''	0.35	1985-2020	Discontinuous
	Thu Thua	10° 42' 33''	106° 17' 57''	0.40	1985-2020	Discontinuous
	Tan Phuoc	10° 29' 6''	106° 14' 49''	0.60	1985-2020	Discontinuous
	Chau Thanh	10° 22' 13''	106° 16' 15''	0.72	1985-2020	Discontinuous
Southern	Cai Lay	10° 22' 46''	106° 6' 50''	0.86	1985-2020	Homogeneous

To ensure the quality of input data series, a rigorous quality control process was implemented. Each station’s data underwent thorough examination for homogeneity, employing statistical techniques to identify and address any inconsistencies (Nguyen et al., 2022). This process involved detecting abrupt changes, gradual shifts, and outliers in the time series (Nguyen et al., 2022).

The Mann-Kendall test and Sen’s slope estimator have been widely employed to identify trends in rainfall characteristics (Lee & Dang, 2020). These tests offer several advantages, such as being non-parametric, straightforward to implement, not requiring the assumption of normality, being robust against outliers, and capable of handling missing values (Lee & Dang, 2020). One notable advantage of the Mann-Kendall test is its ability to handle abrupt breaks in data series, making it less sensitive to inhomogeneous data (Nguyen et al., 2022). Additionally, the Mann-Kendall test is considered to be highly reliable when multiple points are tested within a single data series.

The Mann-Kendall test can be defined by Eq. 1.

$$S=\sum _{i=1}^{n-1}\sum _{j=i+1}^n\mathrm{sgn}(X_j-X_i)$$ (1)

where:

S - the test statistic;

n - the total of observed annual rainfall variable, and

X_j, X_i - the observated data series of annual rainfall variables.

With sgn (X_j-X_i) is defined based on Eq. 2

$$sign(X_j-X_i)=\left\{\begin{array}{c}+1\text{ if }{x}_j-x_i>0\\ 0\text{ if }{x}_j-x_i=0\\ -1\text{ if }{x}_j-x_i<0\end{array}\right.$$ (2)

If n ≥ 10, sign (X_j-X_i) is considered as a standard distribution

When the statistics of the standard test, denoted as Z_s and given by Eq. 3.

$$Z_S=\left\{\begin{array}{c}\frac{S-1}{\sqrt{Var\left(S\right)}}\text{ if }S>0\\ 0\text{ if }S=0\\ \frac{S-1}{\sqrt{Var\left(S\right)}}\text{ if }S<0\end{array}\right.$$ (3)

If Z_S is a standard normal distribution. To assess the significance of the trend, a comparison can be made between the value of Z_S and the critical value α associated with the specified significance level. This approach, founded on the premise of independence, is known as the original Man-Kendall trend test.

When tied ranks occur, the variance of the sum of ranks (S) in Eq. 3 is defined by Eq. 4.

$$Var\left(S\right)=\frac{1}{18}[n(n-1)(2n+5)-\sum _{j=1}^m{t}_j(t_j-1)(2t_j+5)]$$ (4)

where:

m - the number of groups of ties;

t_j - the number of tied observations of each group, and

S - distribution of the sum of data ranks.

The Z_s test is commonly employed to evaluate the significance of trends. If the Zs value is positive, the considered data series expresses an upward trend, while the Zs value is negative, the considered data series shows a downward trend (Lee & Dang, 2020).

Researchers across various fields, including meteorology, hydrology and environmental science, frequently employ Sen’s slope to analyze trends and make decisions based on trend analysis (Lee & Dang, 2020; Nguyen et al., 2022). Sen’s slope is computed by determining the median of all possible pairwise slopes between data points, providing a more reliable estimate of the trend’s direction and magnitude (Lee & Dang, 2020). By focusing on the relative differences between data points rather than the actual values, Sen’s slope offers a more stable and accurate representation of the trend present in the dataset (Nguyen et al., 2022).

Sen’s slope is calculated based on Eq. 5:

$$\beta =Median\left(\frac{X_i-X_j}{i-j}\right)\text{ with }j<i$$ (5)

where:

β - Sen’s slope, and

x_i and x_j - data series at time scales t_i and t_j, respectively.

The null hypothesis indicating no trend is dismissed if the absolute Z_S value surpasses 1.96, at p ≤ 0.05 (Lee & Dang, 2020).

The rainfall anomaly index (RAI) was developed by Van Rooy (1965). RAI considers two anomalies, i.e., positive and negative anomaly (Asadi et al., 2017). First, the rainfall data series are arranged in descending order (Nguyen et al., 2022). The ten highest values are averaged to form a threshold for positive anomaly and the ten lowest values are averaged to form a threshold for negative anomaly (Asadi et al., 2017; Alamgir, 2022). Where the thresholds are defined by Eq. 6.

$$RAI=\pm 3\left[\frac{(p-\overline{p})}{\overline{x}-\overline{p}}\right]$$ (6)

where:

p and p̅ - rainfall value and the average value of the observed rainfall data series, and

x̅ - average value of the 10 highest or lowest rainfall data samples

Table 2 shows the rainfall anomaly index classification used in the present study (Nguyen et al., 2022).

Table 2
Rainfall Anomaly Index (RAI) classification (Aryal et al., 2022)

Values	Category classes
> 4.0	Extreme humid
2.0 ~ 4.0	Very humid
0.0 ~ 2.0	Humid
-2.0 ~ 0.0	Dry
-4.0 ~ -2.0	Very dry
< -4.0	Extreme dry

Results and Discussion

Analysis of the statistical characteristics of rainfall at four representative stations across the Plain of Reeds, are presented in Table 3. The minimum annual rainfall (MIR) exhibits notable variation across stations, ranging from 717.0 mm (at Hong Ngu) to 1046.5 mm (at Moc Hoa). In contrast, the maximum annual rainfall (MAR) shows great differentiation, spanning from 1854.0 mm (at Hong Ngu) to 2420.9 mm (at Moc Hoa), indicating moderate overall rainfall variation in the study area. The standard deviation (SD) of MER ranges from 238.3 mm to 335.3 mm, suggesting considerable inter-annual variability. This variability is further quantified by the coefficient of variation (CV), which ranges from 0.163 at Cai Lay to 0.227 at Hong Ngu, indicating strong differentiation in rainfall distribution across the Plain of Reeds.

Table 3
Statistical patterns of rainfall across the study area during the period 1985-2020

Station	MIR (mm)	MAR (mm)	MER (mm)	SD (mm)	CV (%)	Skewness	Kurtosis	τ	p	β
Hong Ngu	717.0	1854.0	1253.0	284.4	0.227	-0.019	-0.465	-1.645	0.269	-1.738
Cao Lanh	996.0	2387.8	1467.9	277.8	0.189	0.975	1.883	0.041	0.713	1.133
Moc Hoa	1046.5	2420.9	1620.8	335.3	0.206	0.317	-0.506	-0.133	0.258	-6.386
Cai Lay	757.7	1884.2	1454.2	238.3	0.163	-0.697	1.220	0.160	0.182	4.300

• MIR - Minimum annual rainfall; MAR - Maximum annual rainfall; MER - Mean annual rainfall; SD - Standard deviation; CV - Coefficient of variation; τ - Kendall’s tau; p - Critical value and β-Sen’s slope

The distribution of rainfall data shows interesting spatial patterns. Hong Ngu and Cai Lay stations exhibit negative skewness (-0.019 and -0.697, respectively), indicating a left-skewed distribution with a tendency towards lower extreme values. Conversely, Cao Lanh and Moc Hoa stations display positive skews (0.975 and 0.317, respectively), suggesting a right-skewed distribution with a propensity for higher extreme values. The kurtosis values provide insights into the shape of the rainfall distribution. Hong Ngu and Moc Hoa stations show negative kurtosis (-0.465 and -0.506, respectively), indicating a relatively flat distribution with a lower likelihood of extreme values. In contrast, Cao Lanh and Cai Lay stations exhibit positive kurtosis (1.883 and 1.220, respectively), suggesting a more peaked distribution with a higher probability of extreme events. A study results on extreme rainfall trends in the Mekong Delta by Lee & Dang Lee (2019) recorded that Cao Lanh and Cai Lay stations are increasing extreme rainfall events in recent decades. The results highlight the spatial heterogeneity of rainfall patterns across the study area.

The negative values of both Kendall’s tau and Sen’s slope at Moc Hoa station (τ = -0.133, β = -6.386) and Hong Ngu station (τ = -1.645, β = -1.738) indicate a decreasing trend in rainfall over time. The magnitude of Sen’s slope reveals that the trend of decrease is more pronounced at Moc Hoa station compared to Hong Ngu station (Table 3). The positive values of Kendall’s tau and Sen’s slope vary from 0.041 to 0.160 and 1.133 to 4.300 at Cao Lanh and Cai Lay stations, respectively, suggesting an upward trend in rainfall (Table 3). This implies that the rainfall at Cao Lanh and Cai Lay is increasing over time.

Figure 4 illustrates the simulated results of the weather patterns, across four representative stations in the study area during the period 1985-2020, as represented by the RAI. Throughout this period, the study area experienced 15.3 episodes of weather ranging from dry to extreme dry, with RAI values varying from -0.51 to -8.16 across the study area. For Cao Lanh station, the result recorded seven instances of dryness, six instances of very dryness and two instances of extreme dryness. For Moc Hoa station, it recorded eight instances of dry, eight very dry and one of extreme dryness. For the western part (Hong Ngu station), there were seven instances of dry, six very dry and one of extreme dryness. For the southeast part, Cai Lay station showed 11 instances of dry, three very dry and three of extreme dryness (Table 4). Notably, during the strong El-Niño event of 2015, the RAI values were consistently negative, ranging from -0.98 to -4.14, with specific values of -0.98 at Cai Lay, -2.08 at Hong Ngu, -3.37 at Cao Lanh, and -4.14 at Moc Hoa (Figure 5A). These results imply that there is a significant change, towards a decline of both seasonal and annual rainfall in the study area and this has led to the more frequent occurrence of dry to extremely dry weather. A study on rainfall trends in the Vietnamese Mekong Delta under the impacts of climate change by Lee and Dang (2019) also confirmed the results presented above.

Table 4
Number of weather events recorded based on rainfall anomaly index classification across the study area during the period 1985-2020

Stations	El-Niño phase			La-Niña phase
	EH	VH	H	D	VD	ED
Hong Ngu	2	6	13	7	6	2
Cao Lanh	4	4	10	9	8	1
Moc Hoa	3	6	10	8	8	1
Cai Lay	4	5	10	11	3	3
Mean	3.25	5.25	10.75	8.75	6.25	1.75

• EH - Extremely humid; VH -Very humid; H - Humid; D - Dry; VD - Very dry; ED - Extremely dry

Figure 4
Results of rainfall anomaly index at Cao Lanh station (A), Moc Hoa station (B), Hong Ngu station (C) and Cai Lay station (D) during the period 1985-2020

Figure 5
Timing of the onset of the rainy season, length of the rainy season and cessation of the rainy season during the 2015 El-Niño event (A) and the 2020 La-Niña event (B) across the Plain of Reeds in the Vietnamese Mekong Delta

In the period of 1985-2020, the study area experienced 15.8 instances of excess moisture, ranging from humid to extremely humid during the same period (Table 4). At the Cao Lanh station, the results detected four instances of humidity, ten of humidity conditions, four very humid and four extremely humid. Moc Hoa station defined 10 humid, six very humid and three extremes humid. Hong Ngu station exhibited 13 humid, five very humid and two extremes humid. Cai Lay station had 10 instances of humidity, five very humid and four extremes humid. The La-Niña year of 2020 was particularly noteworthy, as it exhibited positive RAI values across all stations, ranging from 5.04 to 7.69, indicating above-average rainfall (Figure 6B) that may be due to the impact of climate change.

Figure 6
Temporal distribution of rainy season patterns across the study area during the period 1985-2020

In overall, the findings revealed a significant imbalance in weather patterns over the 36-year study period (1985-2020), with a marked prevalence of both dry and humid weather conditions. Specifically, years controlled by dry and humid weather conditions significantly outnumber those with near normal weather conditions, highlighting the region’s vulnerability to climatic extremes.

Of the 36 years studied, 19.25 years exhibited humid to extremely humid weather conditions, indicating that, for the majority of the time, the Plain of Reeds experienced humid weather conditions. This pattern underscores the necessity of enhancing adaptive solutions to address the impacts of such variability on agricultural practices in the region. The analysis of the relationship between rainfall patterns (ORS, DRS and length of the rainy season) and the RAI is illustrated in Figure 6 and detailed in Table 5. The results indicate a strong correlation between the ORS and ENSO phases, where a delayed ORS is associated with negative RAI values.

Table 5
Simulated results of rainy season rainfall patterns (days) at representative observation stations across the Plain of Reeds during the period 1985-2020

Year	Cao Lanh		Moc Hoa		Hong Ngu		Cai Lay		Year	Cao Lanh		Moc Hoa		Hong Ngu		Cai Lay
	ORS	DRS	ORS	DRS	ORS	DRS	ORS	DRS		ORS	DRS	ORS	DRS	ORS	DRS	ORS	DRS
1985	107	347	107	347	-	-	107	307	2003	129	334	120	305	118	329	122	298
1986	125	332	127	338	128	331	123	325	2004	136	340	131	334	131	333	136	307
1987	150	340	160	334	153	312	160	333	2005	127	341	151	300	153	360	150	355
1988	132	332	131	330	133	330	125	336	2006	93	333	139	330	138	323	137	319
1989	127	322	131	322	138	321	126	321	2007	128	324	129	320	116	323	127	319
1990	138	324	125	317	161	315	140	318	2008	116	331	117	331	126	323	117	341
1991	111	330	137	313	146	313	143	299	2009	109	321	143	310	135	322	121	307
1992	159	328	133	312	161	325	166	308	2010	139	349	139	351	136	351	151	334
1993	131	350	130	301	136	334	135	301	2011	135	332	121	311	144	331	130	330
1994	125	318	125	303	126	318	125	317	2012	118	335	134	332	133	329	113	301
1995	147	330	144	330	153	329	145	320	2013	124	334	153	332	125	331	130	331
1996	135	346	134	343	136	356	134	343	2014	144	338	163	341	158	340	160	343
1997	132	348	133	317	112	340	134	312	2015	163	346	164	342	162	337	160	312
1998	145	350	145	326	133	360	159	350	2016	136	317	136	356	174	356	136	313
1999	112	347	114	333	89	347	94	353	2017	120	339	121	339	124	324	125	338
2000	129	306	138	345	108	352	109	306	2018	113	301	127	332	150	314	156	330
2001	131	311	131	311	114	318	131	308	2019	127	310	147	315	154	331	128	322
2002	130	349	163	352	111	353	162	316	2020	141	309	144	337	162	300	140	335

• ORS - The onset of the rainy season; DRS - The demise of the rainy season; RAI - Rainfall anomaly index

During dry years, the ORS was delayed by an average of 5.9 days compared to the long-term average from 1985 to 2020. Because of the El-Niño event of 2015, the ORS was delayed by 27.8 days relative to the long-term average (Figure 6). Conversely, in years characterized by excess humidity, the ORS occurred earlier, averaging 4.3 days ahead of the long-term average, with the La-Niña year of 2020 seeing an early onset of 12.3 days. Similarly, the DRS also demonstrated a significant relationship with ENSO phases, where a later DRS correlated with negative RAI values. In dry weather years, the DRS occurred earlier by an average of 3.1 days compared to the long-term average. During the El-Niño event in 2015, the DRS ended 5.9 days earlier than the long-term average (Figure 6). In contrast, during years of excess humidity, the DRS was delayed by an average of 3.9 days, with the La-Niña year of 2020 showing an early onset of 4.7 days compared to the long-term average. Regarding the length of the rainy season, the analysis revealed that during dry weather periods, the length of the rainy season was generally 9.3 days shorter than the long-term average. Conversely, in years with abundant humidity, the length of the rainy season extended by an average of 8.6 days beyond the long-term average.

Overall, these results reveal that weather conditions have a direct influence on rainfall patterns across the Plain of Reeds, although the impact varies across different areas within the study region. These results demonstrate a clear influence of ENSO phases on rainfall patterns across the Plain of Reeds. During El-Niño events, characterized by negative RAI values, the rainy season generally onsets later, demise earlier, and has a shorter duration. Conversely, La-Niña events, associated with positive RAI values, tend to bring an earlier onset, later demise, and longer duration of the rainy season.

Conclusions

1. Over the past 36 years (1985-2020), the Plain of Reeds has experienced significant imbalance in its weather patterns, with a marked prevalence of both dry and humid weather conditions.

2. The frequency of ENSO increases has been recorded in recent years. Typically, the 2015-2016 period saw an extremely serious El-Niño event in Vietnam’s 92-year history, while the 2019-2020 period continued to record an extreme El-Niño event.

3. This study contributes to improving our understanding of the impacts of ENSO events on agriculture, the environment, and society in the Plain of Reeds. The findings can be applied to develop early warning systems for farmers, policymakers, and other stakeholders to prepare for and respond to ENSO-related events as well as promoting sustainable development in the region.

Acknowledgments

The author would like to sincerely thank the reviewers as well as the editorial board for reviewing and providing feedback to help improve the manuscript.

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• ¹ Research developed at Dong Thap University, Faculty of Social Sciences Education, Viet Nam

Supplementary documents

• Author confirms that there is no supplementary research data.

Financing statement

• Author has not received research grants from any agency or organization.

Edited by

• Editors: Antônio Gustavo de Luna Souto & Walter Esfrain Pereira

Data availability

Author confirms that there is no supplementary research data.

Publication Dates

• Publication in this collection
  23 May 2025
• Date of issue
  Sept 2025

History

• Received
  09 Nov 2024
• Accepted
  10 Mar 2025
• Published
  02 Apr 2025