Characterization of Long-Term Annual and Seasonal Rainfall Trends in Coastal Areas of Bangladesh

doi:10.21203/rs.3.rs-3882001/v1

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Research Article

Characterization of Long-Term Annual and Seasonal Rainfall Trends in Coastal Areas of Bangladesh

https://doi.org/10.21203/rs.3.rs-3882001/v1

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Bangladesh has a highly vulnerable 710 km long low-lying coastline with over 29% of the country’s population residing in coastal districts. While existing literature examines inland rainfall patterns, analysis of rainfall variability along the coast is lacking. This study analyzes annual and seasonal rainfall trends across 17 coastal meteorological stations over 1948–2021. The non-parametric Mann-Kendall test and Sen’s Slope estimator are applied for trend detection and quantification.

The findings reveal high interannual and spatial rainfall variability across stations, governed primarily by monsoonal dynamics. A seasonal analysis shows noticeable winter rainfall increases at Khulna and Satkhira at rates of 0.38 mm/year and 0.32 mm/year respectively. The critical monsoon months exhibit substantial incremental tendencies at Khepupara (11.77 mm/year), Hatiya (10.54 mm/year) and Kutubdia (15.50 mm/year). Additionally, post-monsoon rainfall rises significantly at Hatiya (5.25 mm/year). For annual totals, significant rising trends are observed at Khulna (7.69 mm/year), Khepupara (16.43 mm/year), Hatiya (21 mm/year) and Sandwip (12.45 mm/year). Across the seasonal and annual timescales, widespread non-significant increasing tendencies dominate over declining behaviors for most stations. This study provides key inputs for planning and policies to build climate resilience of vulnerable coastal populations.

Annual Rainfall

Seasonal Rainfall

Mann-Kendall Test

Sen’s slope estimator

Bangladesh, a low-lying deltaic country situated in south Asia, is highly vulnerable to natural disasters and climate change impacts, including shifts in rainfall patterns (Rahman et al., 2007; Kulp & Strauss, 2019). Coastal areas, in particular, face unique challenges due to their susceptibility to sea-level rise and extreme weather events (Azevedo & Mostafavi, 2016; Brown et al., 2013; Toimil et al., 2020; Nicholls et al., 2007; Wong et al., 2014). Specifically, vulnerabilities include sea-level rise, altered precipitation patterns, increase in severe precipitation events, shifts in seasonality, rising temperatures, and a rise in the frequency of extreme climatic events like floods and cyclones (Othman et al., 2015; Hossain et al., 2012; Pal et al., 2023; Climate: Observations, projections, and impacts, 2011).

The anticipated rise in frequent tropical cyclones, higher water levels, and intensified rainfall due to climate change pose increased threats to housing, food security, agriculture, and human health in Bangladesh (Kabir et al., 2016; MoEF, 2008). According to Intergovernmental Panel on Climate Change (2007), Bangladesh is projected to see a 5–6% rise in rainfall by 2030. Additionally, by 2050 and 2100, a sea level rise of 0.30 meters and 0.74 meters respectively could force 0.9 million and 2.1 million Bangladeshis living along the coast to relocate inland (Davis et al., 2018).

Rainfall has significant implication for coastal Bangladesh, impacting those who reside there and depend on the local environment for their livelihoods (Shahid, 2010). It is crucial for crop cultivation, from sowing seeds to harvesting. However, unpredictable extreme precipitation can lead to disasters like landslides, floods, waterlogging, erosion, and saline infiltration into freshwater bodies (Matsumoto, 1989). In 2018, Bangladesh launched the Bangladesh Delta Plan 2100 to address rising sea levels in the low-lying areas of Bangladesh (Integrated Water Management, 2019). Simultaneously, the Bangladesh government is developing climate change adaptation strategies. Deep understanding of rainfall patterns is important for effective resource management and climate planning.

Several studies have investigated rainfall patterns and climate change impacts on inland areas of Bangladesh. Mondal et al. (2013) analyzed southwest regional hydrological trends, while Mondal et al (2018) examined precipitation concentration rates across different regions of Bangladesh. Hossain et al. (2017) utilized countrywide historical data to project future rainfall shifts. Rahman et al (2017) also used national rainfall records to predict trajectory changes. Additionally, Shahid (2010) emphasized understanding rainfall variability and extreme event trends to guide water resources, agriculture, and disaster preparation policies. Shahid (2011) also investigated extreme rainfall event nationwide to determine association between event frequency and increased amount of annual/seasonal rainfall. Other analyses focused on the seasonality, monsoon variability and ENSO correlations related to precipitation, mainly using historical datasets spanning Bangladesh (Rahman et al., 2017; Ahmed, 1989; Ahmed et al., 1996; Ahmed, 1994; Ahmed & Karmakar, 1993; Ahmed & Kim, 2003; Ahasan, 2003).

Existing literature overwhelmingly examines inland rainfall patterns, although some studies focused on some part of coastal areas in Bangladesh, rather than complete coastal dynamics. For example, Hossain et al. (2014) investigated spatial and temporal variability of rainfall over the south-west of Bangladesh. Countrywide or region-specific inland assessments dominate to determine seasonal fluctuations, long-term shifts, and disaster risk relationships related to Bangladesh’s climate.

Coaster weather, specifically rainfall patterns, investigations are comparatively lacking despite acute vulnerability of coastal communities to rainfall disruptions. Understanding precipitation patterns is crucial for effective adaptation strategies, agricultural planning, disaster risk reduction, and water resource management (Rahman et al., 2007; Yang & Liu, 2020). Analyzing rainfall trends equips policy makers with actionable insights needed for climate-resilient development (Hayhoe & Stoner, 2020).

Monitoring coastline rainfall trends enables targeted response plans to manage coastal erosion, saline intrusion, storm surge, infrastructure damage, and population displacement risks (Focardi & Pepi, 2023; Griggs & Reguero, 2021). Tracking rainfall changes along the coast is vital for strengthening resilience against these multifaceted threats. Granular coastline rainfall analysis informs migration and rehabilitation strategies for displaced populations (Barrett et al., 2021). Therefore, in this study, we investigate the long-term changes in the spatial and temporal rainfall patterns in the coastal region. Specifically, we evaluate and assess the long-term variations of rainfall in coastal regions. By analyzing time-series rainfall trends in the coastal region of Bangladesh, the study aims to contribute valuable insights and bridge existing research gaps.

2.1 Study Areas Description

The coastal area of Bangladesh is about 47,203 km², consisting of 19 coastal districts. The coastal areas of Bangladesh accounts for 32% of the country’s land area. Around 35 million people, representing 29% of Bangladesh’s population, reside in the coastal districts including Jessore, Narail, Gopalganj, Shariatpur, Chandpur, Sathkira, Khulna, Bagerhat, Pirozpur, Jhalakati, Barguna, Barisal, Patuakhali, Bhola, Lakshmipur, Noakhali, Feni, Chittagong, and Cox’s Bazar (Uddin & Kaudstaal, 2003; Mia & Islam, 2005; Ahmad, 2019; Hossain, 2001; Shampa et al., 2023). Bangladesh has a 710 km long coastline comprising diverse ecosystems like the world’s largest mangrove forest spanning 6017 km², tidal flats, estuaries, sea grass meadows, around 70 islands, accreted lands, beaches, and peninsula (Ahmad, 2019; Shampa et al., 2023). A significant percentage of coastal inhabitants live in poverty, vulnerable to both natural disasters. Major climate change driven events include sea level rise, cyclones, storm surges, coastal flooding, salinity intrusion and erosion (Ahmad, 2019; Iftekhar, 2006). For this research, daily rainfall data over the period 1948–2021 was obtained from 17 meteorological stations located along the coastal region of Bangladesh. The study period and latitude and longitude of each station is presented in the following Table 1. The precise locations of these 17 coastal weather monitoring sites are shown in Fig. 1.

Table 1

Latitudes and Longitudes of Meteorological Stations and Study Period
Station	Period	Latitude	Longitude
Khulna	1948–2021	22.83	89.58
Satkhira	1948–2021	22.68	89.17
Mongla	1991–2021	22.42	89.67
Barisal	1949–2021	22.67	90.40
Patuakhali	1973–2021	22.36	90.34
khepupara	1974–2021	21.93	90.23
Bhola	1966–2021	22.33	89.42
M.court	1952–2021	22.83	91.13
Feni	1973–2021	23.00	91.40
Hatia	1966–2021	22.43	91.10
Sandwip	1966–2021	22.42	91.42
Sitakunda	1977–2021	22.53	91.58
Patenga	1949–2021	22.34	91.79
Ambagan	1999–2021	22.35	91.81
Kutubdia	1977–2021	21.83	91.83
Cox’s Bazar	1948–2021	21.42	92.03
Teknaf	1977–2021	20.87	92.26

2.2 Data Description and Preparation for Analysis

Time series of daily rainfall recorded from 1948 to 2021 at each of 17 weather stations are obtained from the Bangladesh Meteorological Department (BMD). Inspection of the daily rainfall time series data indicates that less than 1% of the data is missing. To compute annual and seasonal total rainfall for each station, the daily rainfall time series for each month is aggregated. According to Islam & Uyeda (2007), the monthly total rainfalls are grouped into four seasonal total and an annual total. Appropriate pre-whitening measures are applied to the series of annual and seasonal total rainfall data to reduce or eliminate serial autocorrelations when present.

2.3 Methods for Trend Detection and Characterization

Different statistical methods have been used for detecting trends and shifts in climate and hydrologic variables (Sonali & Kumar, 2013). These methods can be broadly categorized as parametric and non-parametric. Some studies have recommended using non-parametric approaches for analyzing hydrometeorological data trends (Sonali & Kumar, 2013; Mirabbasi et al., 2020).

In this article, the non-parametric Mann-Kendall trend test (Mann, 1945; Kendall, 1948) was used for assessing the presence of significant long-term monotonic upward or downward trends in rainfall. As Mann-Kendall trend test does not presume any specific probability distribution of the data, it is better suited for non-normal data with outliers- typical characteristics of hydro-climatic records. The test compares relative ranks of sample data rather than absolute values. This provides greater robustness over parametric alternatives that make strict assumptions about normality, stationary, and independence.

Moreover, the magnitude of the trends in annual and seasonal rainfall was quantified using the Theil-Sen slope estimator. Being based on medians of pairwise slopes, the Theil-Sen method demonstrates greater resilience to outliers and extreme values compared to ordinary least squares regression slopes. Its robustness and reliability for both normal and non-normal data make it appropriate for climate applications (Hirsch et al., 1982).

Analysis of long-term rainfall records from 17meteorological stations along the coastal of Bangladesh showed spatial and temporal variations in rainfall patterns. The investigation focused on annual and seasonal aggregated rainfall metrics over the period from 1948 to 2021.

Summary statistics were computed to study the statistical properties of the seasonal and annual rainfall time series at each station. The summary statistics included mean, standard deviation, and coefficient of variation. Table 2, 3, 4, and 5 present these metrics for the seasonal coastal rainfall. Table 6, on the other hand, shows summary statistics of annual coastal rainfall. The results show differences in the mean rainfall received across seasons and locations.

As evident in Table 2, the winter season receives the lowest rainfall among all seasons, with mean winter total rainfall ranging from 26.57 mm (Ambagan) to 43.64 mm (Satkhira). The interannual variability in winter rainfall, measured using the coefficient of variation (CV), is high - with CV values ranging from 88.41% at Mongla to 155.59% at Sitakunda. Out of the 17 stations analyzed, 14 have winter rainfall CV higher than 100%, indicating high year - to - year fluctuations in winter seasons totals across most sites. In contrast, the pre-monsoon season receives moderate rainfall quantities, with average rainfall ranging from 241.29 mm at Satkhira to 552.04 mm at Feni station. Interannual fluctuations vary across sites as indicated by CV values between 34.37% (Ambagan) and 63.14% (Patuakhali). Most of the locations (14 out of 17) demonstrate a CV of 50% or above (Table 3).

Being the major wet period, the monsoon season receives abundant rainfall with station averages spanning from 1129.88 mm (Satkhira) to 3340.27 mm (Teknaf). However, monsoonal rainfall variability measured by CV lies in a lower range of 21.6% (Khepupara) to 33.62% (Feni) relative to other seasons (Table 4). This indicates consistently heavy rainfall during the critical monsoon season. During the post-monsoon period, coastal stations receive moderate mean rainfall ranging from 164.43 mm to 324.33 mm. The CV ranges between 54.10% (Patenga) and 83.70% (Bhola), with most sites (15 out 17) showing a CV greater than 60% (Table 5). This signals high interannual fluctuations post-monsoon similar to the pre-monsoon season.

On an annual timescale, mean rainfall levels across 17 stations vary between 1581.53 mm (Satkhira) to 4017.02 mm (Teknaf). The range of coefficient of variation lies between 19.88% (Khepupara) to 31.74% (Kutubdia). All stations demonstrate a CV less than 35% for yearly aggregated rainfall, highlighting lower interannual fluctuations (Table 6).

The following subsections discuss long-term trends and variability over the annual and seasonal time scales for the 17 coastal stations, based on the statistical trend detection methodology outlined in the previous section.

3.1 Long-Term Trends of Seasonal Rainfall

Table 2–5 present the results of the Mann-Kendall test for detecting monotonic trends in the seasonal rainfall records of the 17 coastal stations. Statistically significant increasing rainfall trends at the 5% level are discernible in the winter season at Khulna and Satkhira stations. The monsoon rainfall exhibits upward tendencies at Khepupara, Hatiya, and Kutubdia. Additionally, the post-monsoon rainfall shows a rising trend only at Hatiya station.

However, most stations demonstrate positive, though non-significant, tendencies in winter season rainfall over the analysis period. These stations include-Barisal, Khepupara, Maijdee Court, Hatiya, Sitakunda, Patenga, Ambagan, Kutubdia, Cox’s Bazar, and Teknaf. The pre-monsoon and monsoon seasonal rainfall for all stations also display positive but non-significant trends. Similar behavior is observed in the post monsoon rainfall for all stations except Hatiya. In these cases, while data exhibits a general inclination toward increasing rainfall over time, the trends are neither consistent throughout the period nor statistically significant according to the Mann-Kendall test.

In contrast, a minority of stations show insignificant declining tendencies in some seasons- Mongla, Khepupara, Bhola, Feni, Sitakunda and Ambagan in pre-monsoon; Barisal, Bhola, Maijdee Court, Feni, Ambagan and Cox’s Bazar in monsoon. For the post-monsoon season, negative rainfall trends are visible at Satkhira, Barisal, Bhola, Patenga and Ambagan station records. This decreasing statistical behavior indicates potential drying signals amidst rainfall fluctuations.

The Mann-Kendall test reveals that most stations exhibit no significant trends in majority of the seasons. Spatially and seasonally limited pockets of rising and declining behaviors stand out as exceptions.

The Sen’s slope estimates also in Table 2–5 quantify the rates of increase or decrease in the seasonal rainfall trends across the stations. Significant rising winter rainfall tendencies are observed at Khulna (0.38 mm/year) and Satkhira (0.32 mm/year). During the monsoon season, rainfall exhibits substantial upward trends at Khepupara (11.77 mm/year), Hatiya (10.54 mm/year), and Kutubdia (10.50 mm/year). The post-monsoon rainfall series shows a significant positive trend solely at the Hatiya station, increasing at 5.25 mm/year.

Several other stations demonstrate positive rainfall increments with positive slope signs, but they do not show statistical significance at the 5% level. For example, the winter totals display non-significant increasing behaviors at rates ranging from 0.04 mm/year (Cox’s Bazar) to 0.17 mm/year (Khepupara). Similarly, the pre-monsoon estimates show insignificantly rising rainfall tendencies, with slope values spanning 0.43 mm/year (Khulna) to 3.72 mm/year (Kutubdia). During the critical monsoon period, comparable inclining behaviors are observed at multiple stations like Khulna, Satkhira, Mongla etc. with slope estimates between 0.46 mm/year and 12.80 mm/year. Similar non-significant positive trends are detected in the post-monsoon season across majority of the stations as well. While monotonic significant trends are restricted to a handful of station-seasons, the Sen’s slope analysis shows extensive non-significant increasing behaviors in seasonal rainfall totals for the coastal region.

Table 2

Descriptive Statistics and Trend Analysis Results of Winter Rainfall
Station	Mean(mm)	Standard Deviation	CV (%)	Z	P-value	Sen’s Slope
Khulna	38.69	49.86	128.87	3.08	0.00**	0.38
Satkhira	43.64	47.96	109.90	2.06	0.04**	0.32
Mongla	39.97	35.34	88.41	-1.07	0.28	-0.68
Barisal	35.35	40.09	113.40	0.78	0.44	0.08
Patuakhali	34.26	35.94	104.93	-0.98	0.33	-0.26
Khepupara	35.02	37.86	108.11	0.85	0.40	0.17
Bhola	39.71	41.63	104.83	-1.05	0.30	-0.23
M.Court	34.01	40.24	118.31	1.09	0.27	0.06
Feni	39.88	36.01	90.30	-1.67	0.09	-0.42
Hatiya	31.56	39.54	125.30	0.17	0.87	0.00
Sandwip	35.04	35.62	101.68	-0.88	0.38	-0.15
Sitakunda	31.64	49.24	155.59	0.95	0.34	0.16
Patenga	30.59	35.97	117.56	0.87	0.38	0.10
Ambagan	26.57	25.77	97.01	0.00	1.00	0.00
Kutubdia	31.68	38.77	122.38	-0.04	0.97	0.00
Cox’s Bazar	30.34	36.20	119.33	0.78	0.44	0.04
Teknaf	26.84	40.34	150.26	0.11	0.91	0.00
** indicates significant trend at 5% level of significance. Negative Sen’s slope is decreasing trend and positive Sen’s slope means upward trends

Table 3

Descriptive Statistics and Trend Analysis Results of Pre-monsoon Rainfall
Station	Mean(mm)	Standard Deviation	CV (%)	Z	P-value	Sen’s Slope
Khulna	284.34	141.56	49.79	0.53	0.60	0.43
Satkhira	241.29	132.35	54.85	1.67	0.10	1.25
Mongla	273.45	99.34	36.33	-0.75	0.45	-1.41
Barisal	328.72	189.20	57.56	0.83	0.41	0.98
Patuakhali	348.96	220.34	63.14	0.00	1.00	0.00
Khepupara	369.94	151.87	41.05	-0.76	0.45	-1.19
Bhola	388.76	209.67	53.93	-0.17	0.86	-0.33
M.Court	466.70	209.64	44.92	1.44	0.15	1.92
Feni	552.04	240.61	43.59	-0.97	0.33	-2.24
Hatiya	430.98	219.56	50.94	1.25	0.21	2.79
Sandwip	517.17	280.04	54.15	0.86	0.39	2.20
Sitakunda	526.29	253.69	48.20	-0.42	0.67	-1.71
Patenga	414.28	213.52	51.54	1.01	0.31	1.47
Ambagan	475.00	163.25	34.37	-1.19	0.23	-5.00
Kutubdia	375.58	231.97	61.76	0.91	0.36	3.72
Cox’s Bazar	403.01	215.57	53.49	1.54	0.12	1.77
Teknaf	347.93	196.83	56.57	0.57	0.57	1.69
** indicates significant trend at 5% level of significance. Negative Sen’s slope is decreasing trend and positive Sen’s slope means upward trends

Table 4

Descriptive Statistics and Trend Analysis Results of Monsoon Rainfall
Station	Mean(mm)	Standard Deviation	CV (%)	Z	P-value	Sen’s Slope
Khulna	1178.46	388.75	32.99	1.87	0.06	4.78
Satkhira	1129.88	350.13	30.99	0.23	0.82	0.46
Mongla	1369.65	331.66	24.21	0.20	0.84	1.37
Barisal	1427.03	388.10	27.20	-0.42	0.68	-0.95
Patuakhali	1773.68	576.45	32.50	0.15	0.88	0.83
Khepupara	1946.29	420.48	21.60	2.77	0.01**	11.77
Bhola	1534.62	375.85	24.49	-1.34	0.18	-4.83
M.Court	2064.16	660.30	31.99	-0.27	0.79	-0.97
Feni	1955.02	657.20	33.62	-0.12	0.90	-1.28
Hatiya	2238.06	690.27	30.84	1.96	0.05**	10.54
Sandwip	2624.07	673.55	25.67	1.58	0.11	8.15
Sitakunda	2215.11	680.86	30.74	1.48	0.14	12.8
Patenga	2040.64	576.76	28.26	0.96	0.34	4.02
Ambagan	2103.30	581.71	27.66	-0.21	0.83	-2.33
Kutubdia	2245.73	748.41	33.33	1.96	0.05**	15.50
Cox’s Bazar	2800.92	756.20	27.00	-0.35	0.73	-1.19
Teknaf	3340.27	810.15	24.25	1.41	0.16	9.69
** indicates significant trend at 5% level of significance. Negative Sen’s slope is decreasing trend and positive Sen’s slope means upward trends

Table 5

Descriptive Statistics and Trend Analysis Results of Post-monsoon Rainfall
Station	Mean(mm)	Standard Deviation	CV (%)	Z	P-value	Sen’s Slope
Khulna	164.43	114.97	69.92	1.15	0.25	0.86
Satkhira	166.72	115.16	69.07	-0.1	0.92	-0.08
Mongla	211.45	128.67	60.85	0.00	1.00	0.00
Barisal	227.62	143.16	62.89	-0.41	0.68	-0.34
Patuakhali	256.26	158.96	62.03	1.87	0.06	3.59
Khepupara	324.33	207.71	64.04	1.59	0.11	3.68
Bhola	245.80	205.73	83.70	-0.23	0.82	-0.25
M.Court	246.34	188.82	76.65	1.41	0.16	1.53
Feni	255.43	184.33	72.17	0.97	0.33	2.05
Hatiya	294.38	207.75	70.57	2.44	0.01**	5.25
Sandwip	301.78	195.52	64.79	0.71	0.48	1.27
Sitakunda	269.64	207.33	76.89	0.33	0.74	0.96
Patenga	257.43	139.27	54.10	-0.22	0.82	-0.25
Ambagan	291.74	175.15	60.03	-0.24	0.81	-0.77
Kutubdia	259.35	154.52	59.58	0.61	0.54	1.46
Cox’s Bazar	288.04	183.08	63.56	0.71	0.48	0.59
Teknaf	301.98	187.94	62.24	0.89	0.37	2.02
** indicates significant trend at 5% level of significance. Negative Sen’s slope is decreasing trend and positive Sen’s slope means upward trends

3.2 Annual Rainfall Trends and Characterization

The results of the Mann-Kendall test on the annual rainfall records are presented in Table 6. Out of the 17 coastal stations, 14 locations show positive trends in annual rainfall amounts over the analysis period. These include Khulna, Satkhira, Mongla, Barisal Patuakhali, Khepupara, Maijdee Court, Hatiya, Sandwip, Sitakunda, Patenga, Kutubdia, Cox’s Bazar and Teknaf. However, Bhola, Feni and Ambagan experience declining tendencies in annual totals, though not statistically significant.

Rising annual rainfall trends are found to be significant at 5% level at the stations-Khulna, Khepupara, Hatiya and Sandwip, as shown Table 6 and Fig. 2. This implies the presence of consistent upward monotonic patterns in the annual rainfall levels at these sites. On the other hand, the remaining 10 stations with increasing tendencies demonstrate fluctuating annual behaviors amidst the positive trend.

Table 6

Descriptive Statistics and Trend Analysis Results of Annual Rainfall
Station	Mean(mm)	Standard Deviation	CV (%)	Z	P-value	Sen’s Slope
Khulna	1665.91	485.06	29.12	2.36	0.02**	7.69
Satkhira	1581.53	459.05	29.03	0.84	0.40	1.83
Mongla	1894.52	386.01	20.38	0.00	1.00	0.21
Barisal	2018.72	517.87	25.65	0.28	0.78	0.72
Patuakhali	2413.15	717.60	29.74	0.99	0.32	5.77
Khepupara	2675.58	531.98	19.88	3.27	0.00**	16.43
Bhola	2208.89	520.32	23.56	-1.24	0.21	-6.26
M.Court	2811.22	819.99	29.17	1.41	0.16	1.53
Feni	2802.37	857.37	30.59	-0.03	0.97	-0.27
Hatiya	2994.98	842.23	28.12	2.86	0.00**	21.00
Sandwip	3478.06	846.64	24.34	2.01	0.04**	12.45
Sitakunda	3042.69	882.43	29.00	1.00	0.32	10.14
Patenga	2742.94	660.15	24.07	1.17	0.24	4.59
Ambagan	2896.61	697.19	24.07	-0.48	0.63	-5.64
Kutubdia	2912.33	924.25	31.74	1.46	0.15	16.97
Cox’s Bazar	3522.31	877.30	24.91	0.42	0.67	1.15
Teknaf	4017.02	925.49	23.04	1.54	0.12	12.49
** indicates significant trend at 5% level of significance. Negative Sen’s slope is decreasing trend and positive Sen’s slope means upward trends

The Sen’s slope estimates quantified the true slope of the annual and seasonal trends. As evident from Table 6, annual rainfall exhibits significant rising trends at rates varying from 7.69 mm/year (Khulna) to 21 mm/year (Hatiya). The seasonal slope estimates provide further insight into the rainfall variability across different stations and seasons. For instance, winter rainfall is found to grow significantly only at Khulna and Satkhira, while most other stations show non-significant inclining behaviors. In contrast, substantial increasing tendencies are discernible in the monsoon totals at various stations, mirroring the importance of this critical wet season.

Although the annually aggregated rainfall increases across majority of the 17 coastal stations, as depicted in Table 6, Figs. 3 and 4, there was considerable heterogeneity regarding the significance of these trends. Similarly, spatio-temporal disparities are manifested in the seasonal trend patterns and slope estimates.

Though this study leverages extensive rainfall data collected from coastal meteorological stations to quantify the long-term rainfall trends across coastal regions of Bangladesh, it has some limitations. Seasonal and annual rainfall data series are constructed from the daily rainfall measurements with less than 1% of missing recorded at stations located throughout the coastal regions. Accurately estimating rainfall trends is critical for modeling hydrological systems, water resources planning and management, developing effective adaptation strategies, agricultural planning, and disaster risk reduction (Rahman et al., 2007; Yang & Liu, 2020; Duarte et al., 2022). Ignoring or removing missing data introduces two problems: it propagates errors through the analysis and reduces sample sizes, negatively impacting the precision of estimates (Fagandini et al., 2023). In future studies, estimates could be improved by imputing missing values.

This study analyzed the annual and seasonal rainfall records across 17 meteorological stations in the coastal region of Bangladesh. The extensive historical data spanning 1948–2021 was analyzed for significant trends using the non-parametric Mann-Kendall and Sen’s Slope estimator methods.

The results indicate substantial inter-annual and spatial variability in rainfall totals across the stations. However, the annual and seasonal series largely showed non-significant statistical behaviors without clear monotonic trends. Notable exceptions, indicating significant rising rainfall tendencies, are observed at Khulna and Satkhira in the winter season; Khepupara, Hatiya and Kutubdia during the critical monsoon period; and Hatiya in the post-monsoon months. For annual totals, significant rising trends are observed at Khulna, Khepupara, Hatiya and Sandwip.

Integration of the findings across time scales and stations suggests that while temporal rainfall fluctuations and extremes dominate, there are insignificant increasing behaviors in the rainfall records of most stations. Significant declining trends are rarely observed in the analysis. The highest and lowest mean annual rainfall totals over the period are recorded at Teknaf and Satkhira stations, respectively.

The insights into long-term rainfall pattern obtained from this investigation contribute valuable information for informing coastal adaptation policies and water resource planning by the Bangladesh government. A more comprehensive understanding of the rainfall variability and trends along the vulnerable coastline enables targeted measures to address flood risks and enhance water availability. This may entail the implementation of infrastructure projects such as cyclone shelters, seawalls, improved irrigation systems, and rainwater harvesting systems.

A large proportion of Bangladesh's coastal population is engaged in rain-fed agriculture, with rice being the major staple crop. The insights from this rainfall analysis could help guide selection of appropriate rice varieties and sowing schedules to match rainfall variability across seasons. Stress-tolerant and early maturing cultivars may be promoted to safeguard yields under potential water deficits or excesses revealed in certain areas.

Additionally, long-term rainfall statistics allow optimized planning of irrigation infrastructure, water storage systems and groundwater extraction policies by the regional authorities. Effective water resource management supports the vulnerable coastal communities against freshwater shortages or excess flooding during extremes.

The study also equips disaster management agencies to better prepare for intensified rainfall-triggered hazards like cyclones, storm surges, landslides and coastal floods based on historical variability and trends analysis. Preventive strategies could involve establishing robust early warning systems, drainage networks, cyclone shelters and coastal embarkments.

Furthermore, the rainfall trend insights enable designing appropriate measures to tackle the considerable threat of coastal erosion. Solutions may encompass seawalls, coastal afforestation, and mangrove plantations for reducing erosion. Accounting for potential ecological shifts is also critical for the sustainability of coastal settlements relying on fishing and forest produce.

The granular spatio-temporal perspective on rainfall patterns empowers Bangladeshi coastal communities to analyze hazard risks, make informed livelihood decisions and build residence to hydro-climatic change. The findings of this study on rainfall trend detection and measure serve to mitigate existing threats posed to this vulnerable region.

Author Contributions

Tania and Zakaria collected data and performed initial data analysis. Tania wrote initial draft of the manuscript. Islam edited and rewrote the initial draft of manuscript. All authors reviewed the manuscript.

Acknowledgements

The authors are grateful to the Bangladesh Meteorological Department for providing the rainfall data. This work was conducted under the framework of the coastline survey initiatives of the Oceanographic Data Center, Bangladesh Oceanographic Research Institute (BORI). The authors extend sincere appreciation to BORI for facilitating its completion through technical guidance and institutional infrastructure.

Funding

The authors declare that no financial funds, and grants were received during the preparation of this manuscript. However, the Oceanographic Data Center, Bangladesh Oceanographic Research Institute (BORI) provided authors Mst Tania Islam, Md Zakaria with technical guidance and access to institutional infrastructure in support of this research under the framework of BORI’s coastline survey initiatives.

Declaration of Conflicting Interests

The authors have no potential conflicts of interests with respect to the research, authorship, and /or publication of the article.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Characterization of Long-Term Annual and Seasonal Rainfall Trends in Coastal Areas of Bangladesh

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Study Areas Description

2.2 Data Description and Preparation for Analysis

2.3 Methods for Trend Detection and Characterization

3. Results and Discussions

3.1 Long-Term Trends of Seasonal Rainfall

3.2 Annual Rainfall Trends and Characterization

4. Conclusions

Declarations

References

Additional Declarations

Status:

Version 1