Analysis of the trend of dry spells and how ocean factors affect its patterns during the summer monsoon in Bangladesh using the Mann-Kendall and Frontier Atmospheric General Circulation Model

Analysis of the trend of dry spells and how ocean factors affect its patterns during the summer monsoon in Bangladesh using the Mann-Kendall and Frontier Atmospheric General Circulation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of the trend of dry spells and how ocean factors affect its patterns during the summer monsoon in Bangladesh using the Mann-Kendall and Frontier Atmospheric General Circulation Model Md. Moniruzzaman Monir, Subaran Chandra Sarker, Md. Mostafizur Rahman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4368007/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To assess drought risk, susceptibility to food security, and water resource utilization, it is crucial to comprehend dry spell patterns from a hydrological perspective. Some regional studies have noted an extension of dry spells on a global and regional scale, but it is still unclear how often dry spells occur during the summer monsoon season, which is dominated by rainfall. This study uses the Mann-Kendall trend test to examine the trend of dry spells during Bangladesh's summer monsoon from 1985 to 2022 to close this gap. Using the Frontier Atmospheric General Circulation model and remote sensing methods to examine the effects of ocean elements such as Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), El Niño-Southern Oscillation (ENSO) conditions, and the zonal wind. Daily rainfall data for 34 weather stations were obtained from the Bangladesh Meteorological Department, while surface water occurrence and change intensity data were retrieved from the JRC Global Surface Water Mapping Layers, v1.3 (FAO, UN). The NOAA Physical Sciences Laboratory (PSL) and the Tokyo Climate Center/WMO Regional Climate Centre in RA II (Asia) provided the IOD, SST, ENSO, and zonal wind data. A notable dry spell anomaly over Bangladesh was also noted in this research, with the short, medium-length, and long dry spells increasing in 82.35%, 73.53%, and 50% of weather stations. When El Niño was present, there was less of a dry spell and more during La Niña. The climatic variability of IOD events and SST anomalies in the eastern and western tropical Indian Ocean were also noted by this study to be connected to these anomalous events. The correlation coefficient between summer monsoon rainfall and DMI is 0.34. Throughout the study period, there were changes in the upper atmosphere's and lower troposphere's wind circulation. The study allows the prioritization of regions for drought, effective water resource management, and food scarcity preparedness. Rainfall Dry Spell Indian Ocean Dipole Sea Surface Temperature ENSO Zonal wind Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction The global water cycle is becoming more intense due to climate change (Allan et al. 2020 ), which is also enhancing the differences between the wet and dry seasons and increasing the intensity of sub-seasonal precipitation while reducing its frequency (Konapala et al. 2020 ; Liu & Allan 2013; Schurer et al. 2020 ). Regional variations in atmospheric circulation, the tropical rain belt, and the timing of the seasons lengthen the dry season (Mamalakis et al. 2021 ). The total number of days during a dry spell that has daily precipitation amounts below a predetermined threshold (Agbazo et al. 2021 ) and is important to examine different features of drought periods (Lana et al. 2015 ; Wang et al. 2022 ). A dry spell is a period of unusually dry weather that is shorter than drought and not as severe (Mathugama & Peiris 2021 ). A dry spell is a stretch of three or more days during the wet season when there hasn't been any rain. A pentad dry period lasts for five days. (Sanchi et al. 2021 ). The frequency and severity of dry spells are rising, which is one effect of climate change in tropical areas (Sanchi et al. 2021 ). Dry spells are crucial for controlling the dynamics of soil moisture, terrestrial energy exchange, and vegetation development (Agbazo et al. 2021 ; Sanchi et al. 2021 ). Additionally, dry spells have an impact on the drainage basin's water quality, which could have an impact on society's health and hinder the production of energy in hydroelectric power plants (Whitworth et al. 2012 ; Mahbod et al. 2023 ). The greatest threat to food security in this region is the occurrence of dry spells and changing rainfall frequency and timing during the growing season which results in a lack of soil moisture, also fire risks (Zhang et al., 2018 ; Burton et al., 2021 ). A study on the regional and local-scale dry spell pattern is vital for the appraisal of hydrological results. In tropical regions, the success or failure of the crops, particularly under rainy conditions is largely connected to the distribution of dry spells. Understanding the distribution of dry periods throughout the course of a year helps get the most out of dry land agriculture. Dry spells have an impact on a variety of industries besides agriculture, including fishing, health, and electricity. In light of the aforementioned, the impacts of dry spells in various industries ultimately have a direct effect on a nation's economy (Mishra et al. 2011 ). The length of dry spells can be used to determine the best crop or variety for an area, as well as to breed varieties with different maturation times (Rajeev et al. 2022 ). In this regard, knowledge of wet and dry spell lengths is essential for managing water resources, for optimal planning, and for creating environmental and agricultural applications. The South Asian summer monsoon season brings 85% of the subcontinent's yearly rainfall (Turner & Annamalai 2012 ; Fahad et al. 2022 ). In Bangladesh, the months of June and September saw 57.6% of the country's yearly rainfall (Monir et al. 2023a ). The monsoon is crucial for the agricultural industry since more than 56% of the region's land is used for agriculture (Singh et al. 2014 ; Rawat et al. 2021 ). When prolonged dry periods occur in June and September during the crucial times for planting, soil preparation, and crop growth, the yields of monsoon crops are significantly reduced (Singh et al. 2014 ). Since 1951, the mean June-September rainfall has been dropping at a substantial (10% significance level) in the Indian subcontinent (Rawat et al. 2021 ; Verma et al. 2022 ). Also, in Bangladesh, rainfall is dropping by 75% during the monsoon season (Monir et al. 2023a ). Since the 1970s, the worldwide mean dry spell length (DSL) has grown by 0.46 days each decade (He et al. 2022 ). Droughts are natural climatic catastrophes that occur in specific parts of the world during dry spells with minimal precipitation caused by a lot of ocean and local factors (Breinl et al., 2020 ; Faiz et al., 2020 ). El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), a planetary-scale ocean-atmosphere coupled system, have an impact on dry spell break conditions over the Indian subcontinent (Jha et al. 2016 ; Vengateswari et al. 2019 ; Vishnu et al. 2022 ). The summer monsoon's interannual variability is impacted by numerous forms of climatic variability, including an unusual seasonal sea surface temperature (SST) gradient, which is also related to a positive IOD phase (Zubair et al. 2003 ; Arrigo & Wilson 2008 ; Hussain et al. 2016 ). The amount of moisture carried by low-level winds from the western Indian Ocean determines the intensity of the summer monsoon rains in this area (Pathak et al. 2017 ). Due to the Himalayas, Karakorum, and Hindukush's (HKH) local geography, warm and moist monsoon air cannot be exchanged with cold and dry extratropical air, which determines how the summer monsoon precipitation is distributed over South Asia (Ashfaq 2020 ). Several studies have been undertaken on the impacts of changing precipitation patterns in the Indian subcontinent, specifically in relation to climate change and its geographical distribution (Ghosh et al. 2009 ; Rajeev et al. 2022 ). Few studies observed dry spell break patterns during the summer monsoon in this region (Singh et al. 2014 ; Rajeev et al. 2022 ; Ullah et al. 2023 ). These researches, however, didn't discuss the contributing variables to dry spell patterns. The ENSO-Monsoon relationship's unpredictability includes fluctuations in rainfall observed by Selvaraju ( 2003 ), Geethalakshmi et al. ( 2005 ), Arrigo and Wilson ( 2008 ), Kokilavani et al. ( 2015 ), Jha et al. ( 2016 ), Crétat et al. ( 2017 ), Yadav et al. ( 2018 ), Vengateswari et al. ( 2019 ), Terray et al. ( 2021 ), and Vishnu et al. ( 2022 ) in this region. Few researchers have studied the other factors north-south SST gradient of the Indian Ocean for the summer monsoon dry spell in the Indian subcontinent (Goswami et al. 2006 ; Roxy 2014 ; Noska & Misra 2016 ; Fahad et al. 2022 ; Weldeab et al. 2022 ; Zhang et al. 2023 ). Zubair et al. ( 2003 ), Arrigo and Wilson ( 2008 ), Cai et al. ( 2014 ), Jha et al. ( 2016 ), and Hussain et al. ( 2016 ) evaluated the impact of the formation of a positive Indian Ocean Dipole on summer monsoon rainfall variability. Land surface temperature (LST) (Garai et al. 2022 ), monsoon wind (Pathak et al. 2017 ), and topography (Ashfaq 2020 ; Fahad et al. 2022 ) were also observed for monsoon rainfall variability in this region. Bangladesh is a disaster-prone country in South Asia. Almost every year, the country is hit by a natural disaster of some form, such as drought, resulting in significant losses in agricultural productivity (Dastagir, 2015 ; Thomas et al. 2023 ). The analysis of the dry spell during the monsoon season is crucial in Bangladesh since 56% of the land is used for agriculture during this period, and it also influences surface water storage and groundwater storage changes (Zhang et al., 2018 ; Rawat et al. 2021 ; Monir et al. 2023b ). However, there has been no research on the pattern of dry spells, particularly in Bangladesh, despite some studies in this region (Singh et al. 2014 ; Rajeev et al. 2022 ; Ullah et al. 2023 ). Even though some study examines both ENSO and IOD for the variability of the summer monsoon rainfall in this region (Arrigo & Wilson 2008 ; Weldeab et al. 2022 ), no research has ever examined all of the ocean and land components together. Therefore, knowledge of the zonal long-term pattern of the summer monsoon dry spell and its relationship to ocean elements is essential for Bangladesh's agro-ecosystem sustainability. Such a research hasn't been conducted yet. So, to fill this research gap, the present novel study aims to analyze the long-term (1985–2022) trend of the dry spell with rainfall occurrences and changes during the summer monsoon in different climate zones of Bangladesh. To understand the variability of dry periods, this study additionally considers ocean elements IOD, SST, ENSO, and monsoon wind. This study also looked at the surface water changes due to the changing pattern of dry spells. This study used the Mann-Kendall trend test to detect dry spell trends and the Frontier Atmospheric General Circulation model to evaluate a relationship between monsoon rainfall variability and ocean and land factors. 2. Data Sources and Methods 2.1 The research area's background and prior knowledge Bangladesh is situated in South Asia's northeast region, located between latitudes 20°34′ and 26°38′ N and longitudes 88°01′ and 92°41′ E. The magnificent Himalayas are located to the north, and the Bay of Bengal is located to the south (Fig. 1 ) (Monir et al. 2023a ). Bangladesh has a warm, humid climate that is regularly affected by tropical cyclones and high precipitation. It is also subject to pre-, monsoon, and post-monsoon circulations. The average temperature in Bangladesh has historically been about 26°C with 2200 mm of annual precipitation. This study adopts the commonly accepted definition of a dry spell, which is the number of consecutive days with precipitation less than 1 mm/day (Agnese et al., 2014 ; Brunetti et al., 2004 ; Groisman & Knight, 2008 ). To identify the trend in dry spells and possible connections with IOD, SST, ENSO, and zonal wind, we concentrated our investigation on the summer monsoon season, which runs from June through September in Bangladesh. 2.2 Observational data and quality checks In this study, we used daily precipitation, water occurrence, and change intensity data for Bangladesh. IOD, SST, ENSO conditions, and the zonal wind speeds data were also evaluated to analyze the effect of ocean factors on dry spell patterns in Bangladesh. The daily rainfall data (1985–2022) were collected from the Bangladesh Meteorological Department ( https://www.bmd.gov.bd/ ) for the 34 weather stations in Bangladesh. 38 weather monitoring stations are located across Bangladesh; however, we have excluded 4 of them as their rainfall data was not consistently collected during the study period. The name and spatial distribution of these selected weather stations are presented in Fig. 1 . The surface water occurrence and change intensity data for Bangladesh (1985–2022) extracted from JRC Global Surface Water Mapping Layers, v1.3, which was collected from the Food and Agricultural Organization of the United Nations ( https://data.apps.fao.org/catalog/dataset/jrc-global-surface-water-mapping-layers-v1-3 ). The IOD, ENSO, and zonal wind speeds data were collected from the Tokyo Climate Centre/ WMO Regional Climate Centre in RA II (Asia) ( https://www.data.jma.go.jp/tcc/tcc/index.html ). The SST data collected from the NOAA Physical Sciences Laboratory (PSL) ( https://psl.noaa.gov/ ) and Tokyo Climate Centre/ WMO Regional Climate Centre in RA II (Asia) ( https://www.data.jma.go.jp/tcc/tcc/products/elnino/ocean/sst-global_tcc.html ). This study examines Bangladesh's daily precipitation amounts to determine the type of dry spells from June through September, during the summer monsoon season. The Indian Subcontinent (including Bangladesh) normally experiences the summer monsoon from June to September, during which time they get over 90% of their yearly precipitation (Xavier et al. 2007 ; Patwardhan et al. 2014 ). The missing rainfall data was estimated using the usual ratio approach. 2.3 Dry spell calculation The length of a dry period is defined as the number of days in a row with precipitation of less than 1 mm/day (Brunetti et al., 2004 ; Groisman & Knight, 2008 ; Agnese et al., 2014 ). Wainwright et al. ( 2021 ) discuss the computation of the mean length of dry periods. There are four seasonality groups for dry spells (Pascale et al., 2016 ; Dunning et al., 2016 ), and we employed dry-year-round analysis. A dry period is described as having two or more consecutive dry days (Giorgi et al., 2019; Wainwright et al. 2022 ). In this study, we analyze dry spells into three classes: 2 to 4 days, 5 to 7 days, and more than 7 days. 2.3.1 Methods for analyzing dry spell trends The non-parametric Mann-Kendal (MK) (Mann, 1945 ; Kendall, 1975 ) test was used to evaluate trends in the number of dry spells and dry spell days The MK test works well for identifying patterns in time series data since it is less likely to outline (Monir et al. 2023a , a ). The indicator function \(sign ({x}_{i}- {x}_{j}\) ) must first be calculated from Eq. 1 to conduct the MK test on a time series of length \({x}_{1}\) , \({x}_{2}\) , \({x}_{3}\) , …., \({x}_{n}\) (Drapela & Drapelova, 2011 ). \(sign ({x}_{i}- {x}_{j}\) ) = \(\left\{\begin{array}{cc}1& {x}_{i}- {x}_{j}>0\\ 0& {x}_{i}- {x}_{j}=0\\ -1& {x}_{i}- {x}_{j}<0\end{array}\right.\) (1) This indicates whether there is a positive, negative, or zero difference between the values during periods i and j. Next, the previously described quantity's mean and variance are ascertained. The average E[S] is given by Eq. 2 (Anand et al. 2020 ). E[S] = \(\sum _{i=1}^{n-1}\sum _{j=i+1}^{n}sign ({x}_{i}- {x}_{j})\) (2) Moreover, the variance VAR[S] is given by the following Eq. 3 : $$VAR\left[S\right]= \frac{1}{18}(n\left(n-1\right)\left(2n+5\right)-\sum _{k=1}^{p}qk(qk-1)(2qk+5)$$ 3 Where p is the total number of tie groups in a data collection, and qk is the number of data points in the kth tie group. To compute the MK test statistic, we use the variance VAR[S] and the mean E[S]. For large sample sizes, the following adjustment ensures that the test statistic Z MK (Eq. 4 ) is distributed approximately normally (Monir et al., 2024a ). $${Z}_{MK}= \left\{\begin{array}{cc}\frac{E\left[S\right]-1}{\sqrt{VAR\left[S\right]}}& E\left[S\right]>0\\ 0& E\left[S\right]=0\\ \frac{E\left[S\right]+1}{\sqrt{VAR\left[S\right]}}& E\left[S\right]<0\end{array}\right\}$$ 4 This Z MK value ranges from − 1 to 1. A Z MK of 0 implies no trend, while a Z MK of -1 or 1 indicates a completely negative or positive trend, respectively (Anand et al. 2020 ). Z MK values ranging from 0 to 0.164 suggest a moderately increasing trend, while values greater than 0.165 indicate a strong increasing trend. Similarly, a declining trend is indicated by 0 to -0.164 for moderate decline and − 0.165 for strong decline (Kandya et al. 2021 ). 2.4 Examine the relationship between IOD and dry spell pattern Using composite analysis and Kendall's tau-based slope estimator, the correlations between the IOD and the dry spell pattern were examined. The composite approach was suggested as an alternative to the correlation method since it does not assume that any relationship between two variables is linear (Harou et al. 2006 ). When dealing with a dataset that has a lot of linked ranks, one non-parametric correlation coefficient called Kendall's tau might be utilized in place of Spearman's coefficient (Balacco et al. 2022 ). According to Zhao et al. (2005), this approach is insensitive to outliers. The relationship between the IOD and the dry periods was calculated using the following formulation (Eq. 5) of Kendall's tau-based slope estimator (Monir et al. 2024b). $$\tau = \frac{{N}_{c}-{N}_{d}}{\frac{n(n-1)}{2}}$$ 1 where the numerator is split by the total number of potential matches of data pairs (xi, yi) among the n observations, and N c and N d represent the concordance and discordance, respectively, among the n observations. The traditional t-test was used to confirm the data's significance at 95 and 99 percentiles. 2.5 Analysis of the effect of SST anomaly on summer monsoon rainfall This study has employed version 1 of the Frontier Atmospheric General Circulation Model. Previous research has effectively replicated the observed impact of the IOD on the South Asian area and the tropical Indian Ocean region (Ashok et al. 2001 ; Guan et al. 2003). An indicator of ENSO was the SST anomaly averaged over the Nino 3.4 area (5° S–5° N, 170–120° W) in the Pacific Ocean. El Nino (La-Nina) years are defined as those in which the average SST anomaly, measured from June to September, is more than + 0.5°C or less than − 0.5°C (Fahad et al. 2020; Vishnu et al. 2022 ). 3. Result 3.1 Historical summer-monsoon rainfall On an annual basis, the summer monsoon rainfall in Bangladesh varies from values close to 1000 in the western area to 4000 in the northeastern region (Fig. 2 ). Generally speaking, the western and northern areas have less rainfall at this time of year; the southern region has mid-level rainfall, while the northeastern region has higher levels. Up to 1994, similar geographical trends were noted from the beginning of the research period. Nonetheless, this area's rainfall patterns varied between 1994 and 2005. From 1994 to 2005, comparatively more summer monsoon rainfall was recorded in the northern area. In Bangladesh's north and west, less summer monsoon rainfall was recorded after 2005. Rainfall in the southern and southeast was relatively more significant for a few years, including 2001–2002, 2006–07, 2012–13, and 2015–18. 3.1.1 Water occurrence and change intensity The Surface Water Occurrence map shows us how frequently water has been seen on Bangladesh's land surface between 1985 and 2022 (Fig. 3 ). The northeastern portion remains submerged for most of the year, as this map makes abundantly obvious (Fig. 3 A). The Surface Water Occurrence Change Intensity (1985–2022) indicates that surface water levels are declining in Bangladesh's central and northeastern areas (Fig. 3 B). Also, the northeastern region faced the rising trend of dry spells during the summer monsoon, when most of the rainfall occurs. The loss of surface water in the central region occurs due to the decrease in the open surface for urbanization. On the other hand, the southwest is seeing an increase in the change intensity. Because these locations are low terrain and subject to tidal surges, the reverse scenario occurs when sea levels rise. 3.2 Total different types of dry spells and their trend In 67.65% of weather stations, more than eight short dry spells (2–4 days long) and more than one medium dry spell (5–7 days long) were observed yearly (Table 1 ). In 41.17% of weather stations, the mean long dry spell (more than 7 days long) is observed more than 0.5. In 55.88% of weather stations, more than 35 dry spell days were observed yearly. The height means short dry spells, medium dry spells, long dry spells, and total dry spell days were observed in Chuadanga (10.06), Rangpur (2.21), Rajshahi (1.75), and Syedpur (52.73) (Table 1 ). These stations are located in the western and northern regions of Bangladesh. On the other hand, the lowest means of short dry spells, medium dry spells, long dry spells, and total dry spell days were observed in Sylhet (3.98), Teknaf (0.67), Teknaf (0.15), and Sylhet (15.76). Table 1 Number of dry spells and trend in dry spells during the summer monsoon in Bangladesh (1985–2022). Weather Station Mean of Dry Spell Trend of Dry Spell (MK Z) 2–4 days 5–7 days 7 + days Total dry spell days 2–4 days 5–7 days 7 + days Total dry spell days Dhaka 8.66 1.2 0.29 35.74 0.434 2.537** -0.019 2.591** Tangail 9.56 2 0.34 49.95 0.672 0.985 -0.119 1.254 Mymenshing 7.69 1.14 0.26 32.36 1.44 -0.748 0.743 1.737* Faridpur 9.26 1.41 0.36 39.64 3.306*** 0.026 0.678 2.496** Madaripur 8.18 1.39 0.54 37.96 1.296 0.077 -0.87 0.777 Srimangal 7.64 0.69 0.18 28.78 0.171 -1.077 -0.932 -1.131 Sylhet 3.98 0.8 0.25 15.76 1.373 1.042 0.265 1.323 Bogra 9.48 1.54 0.85 46.65 1.423 1.54 -0.458 1.648 Rajshahi 9.56 1.72 1.75 51.03 1.803** 1.401 1.408 2.864*** Ishurdi 9.56 2.21 0.9 50.45 0.086 2.502** -0.155 2.17** Dinajpur 9.44 2 1.03 50.05 0.574 2.589** 2.184** 4.086*** Rangpur 9.23 2.21 0.97 50.18 2.071** 1.741* 1.553 2.727*** Sydpur 9.09 1.727 1.59 52.73 1.503 1.967** 0.102 1.903* Chuadanga 10.06 1.35 0.82 46.12 -2.214** 2.75 1.28 1.055 Jessore 8.42 1.42 0.82 41.59 -0.143 1.44 1.15 2.084** Khulna 8.46 1.09 0.58 37.38 0.755 -0.082 1.418 1.741* Mongla 7.03 0.94 0.5 31.47 0.689 2.057** -0.316 1.629 Satkhira 8.46 1.3 0.73 40.09 0.774 0.91 1.475 2.724*** Barisal 7.94 1.06 0.46 34.5 0.445 1.177 2.155** 1.779* Bhola 7.96 1.16 0.31 33.16 1.26 1.335 1.13 1.82* Khepupara 6.52 0.93 0.39 36.24 0.322 1.56 1.92 1.56 Chandpur 9.42 1.32 0.92 44.59 -0.243 1.45 1.21 2.81*** Teknaf 8.87 0.67 0.15 25.78 -0.201 -0.97 1.13 -1.01 Chittagong 7.75 0.89 0.23 32.71 -0.078 1.01 -0.59 -0.598 Comilla 8.57 1.01 0.32 33.57 1.28 1.75* -1.28 1.09 Cox's Bazar 7.79 0.98 0.31 29.78 0.24 1.761* -1.599 -0.792 Feni 8.27 1.24 0.24 34.58 1.432 -1.867* -0.525 -0.638 Hatiya 9.21 1.09 0.19 31.43 1.552 -1.761* -0.492 -0.531 Kutubdia 8.52 1.3 0.46 37.68 1.006 -0.323 -0.99 0.124 M. court 7.3 0.94 0.24 29.85 0.985 2.569** -0.483 2.061** Rangamati 6.95 0.89 0.22 25.29 0.873 1.852* -0.329 1.795* Sandwip 8.61 1.25 0.35 38.94 1.27 -0.423 -0.782 0.274 Sitakunda 8.94 0.88 0.41 36.03 0.042 -0.405 -0.244 -0.166 Patuakhali 5.35 0.77 0.53 25.68 -0.469 0.786 0.94 0.827 *** 99% Significant level; ** 95% Significant level; * 90% Significant level Most of the short-length dry spells occur in western and northern Bangladesh. The southwestern, northcentral, and southcentral regions have significantly higher short-length dry spells. Significantly less amount of this type of dry spell occurs in northeastern, southcentral, and southern regions. Very few short dry spells occur in the eastern and southcentral regions (Fig. 4 A). Medium-length dry spells mainly occur in the northern region, and almost a higher amount is observed in the northwestern region. The eastern and southern regions have very few medium-length dry spells. There are moderate medium-length dry periods in the remaining middle area (Fig. 4 B). Similarly, extended dry spells are more common in Bangladesh's northern and northwest areas. The yearly mean number of dry spell days is mostly observed in the northern and northwestern regions, and moderately higher dry spell days are observed in the southwestern and central regions (Fig. 4 C). The northeastern, southeastern, and southern regions have moderately fewer, and the eastern region has very few yearly mean dry spell days (Fig. 4 D). 82.35% of weather stations are rising, and 17.65% have a declining trend in short dry spells (Table 1 ). Among the rising trends, 3.57% are high rising, 7.14% are moderate rising, and 89.29% are low rising trends. On the other hand, 16.67% are moderately declining, and 83.33% have a low declining trend. 73.53% of weather stations show a rising tendency for medium-length dry spells, while 26.47% have a declining trend. 8% are high rising trends, 36% are moderate, and 56% are low rising trends (Table 1 ). On the other side, all the declining trends are low. For long dry spells, half of the weather stations have a rising trend, and others have a declining trend. Among the rising trends, 17.64% are moderate rising, and 82.36% are low rising trends. On the other side, all the declining trends are low. For the total dry spell days, 79.41% of weather stations have a rising trend, and 20.59% have a declining trend. Among the rising trends, 18.51% are high rising, 40.74% are moderate rising, and 40.76% are low rising trends (Table 1 ). On the other side, all the declining trends are low. A high-rising trend for short dry spells is observed in the northern and northcentral regions. The rising trend is observed in the southwestern region (Fig. 5 A). The rising trend for medium-length dry spells is observed in the western and northern regions (Fig. 5 B). On the other hand, the rising trend for long dry spells is observed in the northern, southwestern, and southcentral regions (Fig. 5 C). Also, a rising trend was observed from central to western and northern regions for the total dry spell days (Fig. 5 D). The rising trend in all classes of dry spell and total dry spell days is observed in the northern and northwestern regions. 3.3 Relationship between Indian Ocean Dipole (IOD) and summer monsoon rainfall in Bangladesh Figure 6 displays the total quantity of summer-monsoon rainfall in Bangladesh (1985–2022) together with the yearly IOD indices. There is a statistically significant positive correlation coefficient between the IOD score and the country's total monsoon rainfall (Fig. 6 ). This correlation is ongoing through the month of September, beginning in the month of May. The correlation between monthly rainfall and DMI during summer monsoon is 0.39 for June, 0.31 for July, 0.33 for August, and 0.38 for September in Bangladesh. The standard deviation criterion's sigma was used to choose IOD occurrences. Nine positive (1993, 1994, 1997, 2006, 2007, 2015, 2018, 2019, 2020) and ten negative (1985, 1992, 2995, 1996, 1998, 2005, 2010, 2016, 2021, 2022) years were chosen from the IOD time series study (Fig. 6 ). The majority of the years during the IOD index's positive phases revealed a positive correlation between Bangladesh's DMI index and monsoon rainfall, however one year revealed a negative correlation. The time series for the years 1992, 1994, 1996, 1997, 2005, 2010, 2012, 2015, 2016, 2020, and 2022 showed extremely distinct patterns: the amount of monsoon rainfall was higher when the IOD index was higher and lower when the IOD index was lower. 3.4 Effect of SST anomaly on summer monsoon rainfall In this study, there is a strong correlation between SST and summer monsoon rainfall anomalies (Fig. 7 ). The results of this study point to more rainfall under a changing climate with rising SSTs (Fig. 7 A). However, these changes in SSTs affect the monsoon rainfall pattern. Most weather stations observed rising dry spells during monsoon according to the changes in SSTs. There is a pocket region near Bangladesh where SSTs are trending upward (Fig. 7 B). Throughout the research period, Bangladesh's monsoon rainfall pattern is directly impacted by the shifting SSTs. 3.5 ENSO effects on dry spells during summer monsoon SST changes and their persistence over the Indian Ocean are the basis for the operational definition of ENSO conditions. El Niño and La Niña were the two categories into which ENSO years were divided, with the other years falling into the neutral category. In El Niño years, the dry spell lasted an average of 35 days, ranging from 32 to 45 days. The dry period in the La Niña phase lasted 31 to 49 days, with an average of 41 days, whereas the dry spell in the neutral phase lasted 32 to 47 days, with an average of 38 days (Fig. 8 ). 3.6 Zonal mean atmospheric circulation's sensitivity to SST anomalies Figure 9 presents the wind (anomaly) composites for the four deficient monsoon season years (1992, 2002, 2012, and 2022). The composites are shown for various pressure levels of UT/LS. It is also evident that during the deficient (dry) composites over the Indian subcontinent, there are significant changes in the circulation patterns in the high atmosphere (850 hPa) and lower troposphere (200 hPa). Wind circulation in the upper atmosphere, lower troposphere, and the SST anomalies all changes during the research period (Fig. 9 ). 4. Discussion The maximum dry/wet spell duration has altered dramatically, according to a spatiotemporal analysis of Bangladesh's precipitation patterns, however, the total number of precipitation events from 1906 to 2010 showed very little change (Dash & Maity, 2019 ; Monir et al. 2023b ). The significance of the summer monsoon rains on the economy and civilization of the region has made it a key factor in climate change. The Indian Subcontinent has seen a dramatic decrease in rainfall, with an average of − 0.4 ± 0.15 mm/decade (Subrahmanyam et al. 2023 ). The South Asian summer monsoon season accounts for 85% of the subcontinent's annual rainfall (Turner & Annamalai 2012 ; Fahad et al. 2022 ). In Bangladesh, June and September received 57.6% of the country's annual rainfall (Monir et al. 2023a ). Annual summer monsoon rainfall in Bangladesh ranges from around 1000 in the west to 4000 in the northeast (Fig. 2 ). In general, the western and northern regions receive less rainfall at this time of year; the southern region receives medium-level rainfall, while the northeastern region receives more (Fig. 2 ). Since 1951, the mean summer monsoon rainfall has been dropping at a substantial in the Indian subcontinent (Rawat et al. 2021 ; Verma et al. 2022 ). Also, in Bangladesh, rainfall is dropping by 75% during the monsoon season (Monir et al. 2023a ). About 80% of the yearly precipitation across the Indian subcontinent falls during the Indian Summer Monsoon, which also saw unusual sub-seasonal changes in rainfall throughout the summer monsoon season, including a rapid change of 54% in summer monsoon rainfall (Vibhute et al. 2023 ). The Surface Water Occurrence Change Intensity (1985–2022) shows that surface water levels are decreasing in Bangladesh's center and northeastern regions (Fig. 3 B), where dry spells increase during the summer monsoon. A total of around 90,000 sq km of permanent surface water vanished between 1984 and 2015, While in other places, new, permanent bodies of water spanning 184,000 square km appeared (Pekel et al. 2016 ). In this study, more than eight short dry spells and more than one medium dry spell were seen each year in 67.65% of weather stations (Table 1 ). According to the present study, 82.35% of weather stations are rising for short dry spells, 73.53% in medium-length dry spells, and 50% in long dry spells. Also, since the 1970s, the worldwide mean dry spell length (DSL) has grown by 0.46 days each decade (He et al. 2022 ). Dry season spells across South America and southern Africa are getting longer, up to about two days every decade, while those over West Africa are getting shorter, around the same length (Wainwright et al. 2022 ). Oceanic moisture sources dominate the Indian Subcontinent’s summer monsoon rains. However, land surface processes play a substantial role in precipitation formation across the Indian subcontinent. Evapotranspiration over a region adds moisture to the atmosphere, which may result in precipitation in the same location (Pathak et al. 2014 ). The IOD score has a statistically significant positive correlation with the country's total monsoon rainfall (Fig. 6 ). The correlation coefficient between monthly rainfall and DMI during the summer monsoon is 0.39 in June, 0.31 in July, 0.33 in August, and 0.38 in September. This conclusion is consistent with earlier research showing that seasonal rainfall in Northeast India (May–September) is negatively impacted by greater positive IOD (Sarkar et al. 2021 ). While some research (Ashok et al., 2001 ) find a favorable link between IODM and monsoon rainfall over the Indian subcontinent, others (Saji & Yamagata, 2003 ) report a weak and inconsequential relationship. Therefore, it is still unclear how the IODM and monsoon rainfall are related. There is a larger correlation between East Asia and the monsoon than there is with South Asia, according to a recent study (Kripalani et al, 2005 ). It makes sense to look at how the monsoon affects the dipole since the primary Indian monsoon peaks in the boreal summer, June through September, while the IOD develops in the summer and peaks in the autumn. The sliding correlations between monsoon rainfall and the dipole mode index suggest that the impact of monsoon over dipole is weakening after the 1960s. This weakening relationship has been evidenced by the composites of sea-surface temperature anomalies and circulation patterns (Kulkarni et al. 2006 ). This present study found a substantial association between SST and summer monsoon rainfall anomalies (Fig. 7 ). The findings of this study hint at increased rainfall in a changing climate with rising SSTs (Fig. 7 A). Despite prior research indicating that the summer monsoon variability is not actively influenced by SST, the mean SSTs over the Asian monsoon basins (Indian Ocean and north-west Pacific) are primarily over the threshold. It also suggests that rising sea surface temperatures brought on by climate change may not always translate into more monsoon precipitation (Roxy, 2014 ). For the past three decades, it has been maintained that there is a threshold of 28–29.5°C (Rajendran et al. 2012 ) at which there is no longer any meaningful relationship between precipitation and SST (Sabin et al. 2012 ). SST is a slave over the region and has no active influence on the summer monsoon's variability since the mean SSTs over the monsoon basins (the Bay of Bengal) are generally higher than the threshold (Gadgil et al. 1984 ). However, the present study found a pocket region near Bangladesh where SSTs are trending upward (Fig. 7 B). Throughout the research period, Bangladesh's monsoon rainfall pattern is directly impacted by the shifting SSTs. According to the present study, the dry spell lasted in El Niño years an average of 35 days, in the La Niña phase 41 days, and in the neutral phase 38 days (Fig. 8 ). Previously it has been discovered that the East Asian summer monsoon is significantly influenced by the ENSO (Xie et al., 2009 ), a predominant interannual fluctuation climatic pattern in the tropical Pacific (Wu et al., 2009 ; Xie et al., 2016 ). The current analysis indicates that there are notable changes in the lower troposphere (200 hPa) circulation patterns and upper atmosphere (850 hPa) during the deficient (dry) composites over the Indian subcontinent (Fig. 9 ). From 10° N to 30° N, there are positive westerly wind anomalies, with a noticeable anomaly of the westerly wind over Southeast Asia and the Bay of Bengal. This indicates that, in comparison to years with negative IOD, there are stronger westerly winds over Southeast Asia and the Bay of Bengal in positive IOD years (Saji & Yamagata, 2003 ). However, during the summer, from high pressure zones wind are originate and moisture is transported from the Arabian Sea in a southeasterly direction, this wind direction mechanism is entirely reversed, leading to more rainfall across the Indian region (Hussain et al. 2016 ). 5. Conclusion In this study, the trend in historical dry spell trend (1985–2022) during the summer monsoon and how different ocean factors such as IOD, SST anomalies, ENSO conditions, and the zonal wind circulation affect the summer monsoon rainfall pattern in Bangladesh were explored. The following is a summary of the primary findings from this study: Generally speaking, the western and northern areas have less rainfall during summer monsoon. Rainfall in the southern and southeast was relatively more significant for a few years, including 2001–2002, 2006–07, 2012–13, and 2015–18. The center and northeastern regions are experiencing a decrease in surface water levels. The short-length, medium-length, and extended dry spells are more common in Bangladesh's northern and northwest areas, and these dry spells increase in 82.35%, 73.53%, and 50% of weather stations respectively. The total amount of monsoon rainfall is positively correlated (statistically significant) with the IOD score and DMI and the monthly rainfall correlation coefficient is 0.39 in June, 0.31 in July, 0.33 in August, and 0.39 in September. A strong correlation between summer monsoon rainfall and SST anomalies, SSTs rise and the climate changes, there may be more rainfall. The dry spell lasted El Niño years an average of 35 days, in the La Niña phase 41 days, and in the neutral phase lasted 38 days. From 10° N to 30° N, there are positive westerly wind anomalies, with a notable westerly wind anomaly over Southeast Asia and the Bay of Bengal. The study's conclusions imply that the timing of rainfall is changing. Over the past few decades, Bangladesh's annual average rainfall has been relatively constant; however, during the summer monsoon, when the majority of rainfall typically falls, there have been more dry spells. Understanding the spatiotemporal variance of compound dry-hot extremes is made easier by looking at the temporal evolutions of dry spells. The increased likelihood of drought and crop failure suggests that more urgent work needs to be done on developing better, immediately transferable plans for managing climate risk. However, there are certain limits to the results of this study, and more research will help to achieve the objective of comprehending how Pakistan's precipitation is affected by air circulation and how it is physically related to the IOD mode. The current study recommends that future researchers focus on dry and rainy spells concurrently for different seasons. Declarations Data availability: Due to constraints imposed by the BMD authority, the rainfall datasets analyzed during the current work are not publicly available. However, the accompanying author is willing to provide them upon reasonable request. Other data sets are publicly available. CRediT author statement: Monir, M.M.: Conceptualization, Methodology, Software, Data curation, Visualization, and Writing- Original draft. Sarker, S.C.: Conceptualization, Supervision, Validation, Reviewing and Editing. Ripon, M.M.: Reviewing. Islam, M.N.: Reviewing and Editing. Conflict of interest: The authors affirm that they have no known financial or interpersonal conflicts that would have seemed to have an impact on the research presented in this study. 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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , 42 (3/W6), 63–65. https://doi.org/10.5194/isprs-archives-XLII-3-W6-63-2019 Verma, S., Bhatla, R., Shahi, N.K., Mall, R.K. 2022. Regional modulating behavior of Indian summer monsoon rainfall in context of spatiotemporal variation of drought and flood events. Atmos. Res., 274. https://doi.org/10.1016/j.atmosres.2022.106201. Vibhute, A.S., Chowdary, J.S., Darshana, P. 2023. Abrupt sub-seasonal rainfall variability over India during summer monsoon 2021: Interaction between midlatitude and tropical circulation. Atmospheric Research, 292 (1). https://doi.org/10.1016/j.atmosres.2023.106869 Vishnu, S., Chakraborty, A., Srinivasan, J. 2022. Why the droughts of the Indian summer monsoon are more severe than the floods. Clim. Dyn., 58 (11–12), 3497–3512. https://doi.org/10.1007/s00382-021-06111-1 Vishnu, S., Chakraborty, A., Srinivasan, J. 2022. Why the droughts of the Indian summer monsoon are more severe than the floods. Clim. Dyn. , 58 (11–12), 3497–3512. https://doi.org/10.1007/s00382-021-06111-1 Wainwright, C.M., Allan, R.P., Black, E. 2022. Consistent Trends in Dry Spell Length in Recent Observations and Future Projections. Geophys. Res. Lett. , 49 (12). https://doi.org/10.1029/2021GL097231 Wainwright, C.M., Allan, R.P., Black, E. 2022. Consistent Trends in Dry Spell Length in Recent bservations and Future Projections. Geophysical Research Letters, 49. https://doi.org/10.1029/2021GL097231 Wainwright, C.M., Black, E., Allan, R.P. 2021. Future changes in wet and dry season characteristics in CMIP5 and CMIP6 simulations. J. Hydrometeorol., 22(9), 2339–2357. https://doi.org/10.1175/JHM-D-21-0017.1 Wang, X., Lu, H., Yuan, W. 2022. Inter-Annual Variations of Precipitation Modulate the Dry Spell Length. Geohealth. 18;6(4), e2022GH000611. https://doi.org/10.1029/2022GH000611 Weldeab, S., Rühlemann, C., Ding, Q., Khon, V., Schneider, B., Gray, W.R. 2022. Impact of Indian Ocean surface temperature gradient reversals on the Indian Summer Monsoon. Earth & Planet. Sci. Lett. , 578 , 117327. https://doi.org/10.1016/j.epsl.2021.117327 Whitworth, K.L., Baldwin, D.S., Kerr, J.L. 2012. Drought, Floods and Water Quality: Drivers of a severe Hypoxic Blackwater Event in a Major River System (the Southern Murray-Darling Basin, Australia). J. Hydrol., 450, 190-198. https://doi.org/10.1016/j.jhydrol.2012.04.057 Wu, Z., Wang, B., Li, J., Jin, F.-F. 2009. An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. Journal of Geophysical Research, 114, D18120. https://doi.org/10.1029/2009JD011733. Xavier, P.K., Marzin C., Goswami, B.N. 2007. An objective definition of the Indian summer monsoon season and a new perspective on the ENSO–monsoon relationship. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.45 Xie, S.-P., Hu, K.-M., Hafner, J., Tokinaga, H., Du, Y., Huang, G., Sampe, T. 2009. Indian Ocean capacitor effect on Indo–Western Pacific climate during the summer following El Niño. Journal of Climate, 22, 730–747. https://doi. org/10.1175/2008JCLI2544.1. Xie, S.P., Kosaka, Y., Du, Y., Hu, K.M., Chowdary, J., Huang, G. 2016. Indo-western Pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: a review. Advances in Atmospheric Sciences, 33, 411–432. https://doi.org/10.1007/s00376-015-5192-6. Yadav, R.K., Srinivas, R.K.G., Chowdary, J.S., 2018. Atlantic Niño modulation of the Indian summer monsoon through Asian jet. Clim. Atmos. Sci. 1. https://doi.org/10.1038/s41612-41018-40029-41615. Zhai, P.X., Zhang, H., Pan, X. 2005. Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–110. https://doi.org/10.1175/JCLI-3318.1 Zhang, J., Wu, R., Gu, Q. et al. 2023. Influences of tropical Pacific and North Atlantic SST anomalies on summer drought over Asia. Clim. Dyn. https://doi.org/10.1007/s00382-023-06886-5 Zhang, W., Brandt, M., Tong, X., Tian, Q., Fensholt, R. 2018. Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel. Biogeosciences, 15, 319–330. https://doi.org/10.5194/bg-15-319-2018 Zubair, L., Rao, S., Yamagata, T., 2003. Modulation of Sri Lankan Maha rainfall by the Indian Ocean Dipole. Geophys. Res. Lett., 30: 1063. https://doi.org/10.1029/2002GL015639. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4368007","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307929214,"identity":"6c5d6457-ec28-4eba-b0e6-f90c7bc126be","order_by":0,"name":"Md. Moniruzzaman Monir","email":"","orcid":"","institution":"Begum Rokeya University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Moniruzzaman","lastName":"Monir","suffix":""},{"id":307929215,"identity":"ea412241-b157-40c2-8e3f-2f65939539a4","order_by":1,"name":"Subaran Chandra Sarker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYNCCAwwM/BIwjgSDAXFaJGeQrMXgBrFa5NuPP/xcccYm3/h27zPpCgY7eQbp5g14tRicyTGWPHMjzXLbneNmkmcYkg0bZI4V4NciwcMg2fDhsIHZjTQ2yQYG5gQGiRwCDpvB/vhnw4f/BsYzwFrqCWthuMFgJtlw44CBgQRYy2HCWoB+MbNsOJNsIHHnGLNlg8FxwzZCfgGG2OObDcfsDPhntzHebKiolucnFGLoljIwsJGifhSMglEwCkYBdgAA/95BGpv/q+sAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9390-7098","institution":"Begum Rokeya University","correspondingAuthor":true,"prefix":"","firstName":"Subaran","middleName":"Chandra","lastName":"Sarker","suffix":""},{"id":307929216,"identity":"1ac175cd-223f-4d90-bc89-e1a0d7099390","order_by":2,"name":"Md. Mostafizur Rahman","email":"","orcid":"","institution":"Begum Rokeya University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Mostafizur","lastName":"Rahman","suffix":""},{"id":307929217,"identity":"79011f34-eeaa-4ec5-abf6-920534d4f844","order_by":3,"name":"Md. Nazrul Islam","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Nazrul","lastName":"Islam","suffix":""}],"badges":[],"createdAt":"2024-05-04 10:39:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4368007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4368007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58130263,"identity":"da26fbca-f6cd-4194-b33c-508ef8e272b2","added_by":"auto","created_at":"2024-06-11 14:23:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1404260,"visible":true,"origin":"","legend":"\u003cp\u003eBangladesh: the study area and the geographical distribution of the weather monitoring station. DEM data from the USGS Shuttle Radar Topography Mission, and the map produced using ArcGIS software v10.5.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/c09095929e738c4b2bf45c23.png"},{"id":58130259,"identity":"c959ee2f-b4be-49ea-8676-45feb4efad69","added_by":"auto","created_at":"2024-06-11 14:23:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4627602,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of summer-monsoon rainfall in Bangladesh (1985-2022).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/ba956588ec72826f9bf86fe5.png"},{"id":58130667,"identity":"738fe490-6556-41b7-875d-29cdac4c663a","added_by":"auto","created_at":"2024-06-11 14:31:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":720391,"visible":true,"origin":"","legend":"\u003cp\u003eSurface water occurrence change intensity in Bangladesh (1985-2022): (A) Water occurrence (1985-2022), (B) Water occurrence change intensity (1985-2022).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/ccb617b287e574ac466f8233.png"},{"id":58130260,"identity":"26e6876c-8658-41dc-85d0-6624c0bd0e4a","added_by":"auto","created_at":"2024-06-11 14:23:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":447366,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean yearly dry spell and total dry spell days during the summer monsoon in Bangladesh (1985-2022): (A) Short dry spell (2-4 days), (B) Medium dry spell (5-7 days), and (C) Long dry spell (More than 7 days), (D) Total dry spell days.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/24355f86eaf662f3b07eb343.png"},{"id":58130267,"identity":"c78a3d4a-b93c-4a16-8f24-29bd49cebd8a","added_by":"auto","created_at":"2024-06-11 14:23:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":542490,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend in dry spell and total dry spell days during the summer monsoon in Bangladesh (1985-2022): (A) Short dry spell (2-4 days), (B) Medium dry spell (5-7 days), and (C) Long dry spell (More than 7 days), (D) Total dry spell days.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/3c376cf9a1e6a1fd349e0ab3.png"},{"id":58130261,"identity":"0120c9b2-012f-47ad-a2e0-775ef7f7fbff","added_by":"auto","created_at":"2024-06-11 14:23:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1291136,"visible":true,"origin":"","legend":"\u003cp\u003eDipole Mode Index (DMI) and Monsoon Rainfall time series presentation: the blue and orange lines indicate monthly mean DMI and yearly average monsoon rainfall respectively, while the light orange and light blue shading denotes positive and negative IOD periods respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/3d27a8e1821b672b7f13165b.png"},{"id":58130669,"identity":"396281a8-fc20-4544-95f4-cf1451ab304d","added_by":"auto","created_at":"2024-06-11 14:31:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":645075,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between SST anomalies and summer monsoon rainfall in Bangladesh: (A) Time series of SST differences from average data (black columns) and summer monsoon rainfall (blue line), (B) SST anomaly in the Indian Ocean from the NOAA coral reef surveillance satellite (1985–2022) during the summer monsoon.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/653023f995812b7f89b243c5.png"},{"id":58130668,"identity":"52fb1cf3-f53a-469f-a21e-0ee1549e61e6","added_by":"auto","created_at":"2024-06-11 14:31:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27798,"visible":true,"origin":"","legend":"\u003cp\u003eTime series observations of ENSO and Bangladesh's summer monsoon dry spell pattern.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/c506ea8a68aa18cdc076ed02.png"},{"id":58130265,"identity":"e85fed91-8056-482a-8b43-d36d05aa117d","added_by":"auto","created_at":"2024-06-11 14:23:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3189874,"visible":true,"origin":"","legend":"\u003cp\u003eRegression using JJAS IMR for the pair model run yielded the zonal wind speeds of U850 hpa and U200 hpa over 5°–15° N, as well as the cross-sectional time-longitude of SST anomalies in 1992, 2002, 2012, and 2022.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/84f9223cb994d6a68c94a329.png"},{"id":67227443,"identity":"0f19e30b-68b9-44f9-8b5e-5f79091ff1c5","added_by":"auto","created_at":"2024-10-22 15:38:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14568986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4368007/v1/3ff1b111-f176-46c7-992e-770b25635c09.pdf"}],"financialInterests":"","formattedTitle":"Analysis of the trend of dry spells and how ocean factors affect its patterns during the summer monsoon in Bangladesh using the Mann-Kendall and Frontier Atmospheric General Circulation Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global water cycle is becoming more intense due to climate change (Allan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is also enhancing the differences between the wet and dry seasons and increasing the intensity of sub-seasonal precipitation while reducing its frequency (Konapala et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu \u0026amp; Allan 2013; Schurer et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regional variations in atmospheric circulation, the tropical rain belt, and the timing of the seasons lengthen the dry season (Mamalakis et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The total number of days during a dry spell that has daily precipitation amounts below a predetermined threshold (Agbazo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and is important to examine different features of drought periods (Lana et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A dry spell is a period of unusually dry weather that is shorter than drought and not as severe (Mathugama \u0026amp; Peiris \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A dry spell is a stretch of three or more days during the wet season when there hasn't been any rain. A pentad dry period lasts for five days. (Sanchi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The frequency and severity of dry spells are rising, which is one effect of climate change in tropical areas (Sanchi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dry spells are crucial for controlling the dynamics of soil moisture, terrestrial energy exchange, and vegetation development (Agbazo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sanchi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, dry spells have an impact on the drainage basin's water quality, which could have an impact on society's health and hinder the production of energy in hydroelectric power plants (Whitworth et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mahbod et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The greatest threat to food security in this region is the occurrence of dry spells and changing rainfall frequency and timing during the growing season which results in a lack of soil moisture, also fire risks (Zhang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Burton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study on the regional and local-scale dry spell pattern is vital for the appraisal of hydrological results. In tropical regions, the success or failure of the crops, particularly under rainy conditions is largely connected to the distribution of dry spells. Understanding the distribution of dry periods throughout the course of a year helps get the most out of dry land agriculture. Dry spells have an impact on a variety of industries besides agriculture, including fishing, health, and electricity. In light of the aforementioned, the impacts of dry spells in various industries ultimately have a direct effect on a nation's economy (Mishra et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The length of dry spells can be used to determine the best crop or variety for an area, as well as to breed varieties with different maturation times (Rajeev et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this regard, knowledge of wet and dry spell lengths is essential for managing water resources, for optimal planning, and for creating environmental and agricultural applications.\u003c/p\u003e \u003cp\u003eThe South Asian summer monsoon season brings 85% of the subcontinent's yearly rainfall (Turner \u0026amp; Annamalai \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fahad et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Bangladesh, the months of June and September saw 57.6% of the country's yearly rainfall (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The monsoon is crucial for the agricultural industry since more than 56% of the region's land is used for agriculture (Singh et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rawat et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When prolonged dry periods occur in June and September during the crucial times for planting, soil preparation, and crop growth, the yields of monsoon crops are significantly reduced (Singh et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since 1951, the mean June-September rainfall has been dropping at a substantial (10% significance level) in the Indian subcontinent (Rawat et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verma et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, in Bangladesh, rainfall is dropping by 75% during the monsoon season (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Since the 1970s, the worldwide mean dry spell length (DSL) has grown by 0.46 days each decade (He et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Droughts are natural climatic catastrophes that occur in specific parts of the world during dry spells with minimal precipitation caused by a lot of ocean and local factors (Breinl et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Faiz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). El Ni\u0026ntilde;o-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), a planetary-scale ocean-atmosphere coupled system, have an impact on dry spell break conditions over the Indian subcontinent (Jha et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vengateswari et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vishnu et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The summer monsoon's interannual variability is impacted by numerous forms of climatic variability, including an unusual seasonal sea surface temperature (SST) gradient, which is also related to a positive IOD phase (Zubair et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Arrigo \u0026amp; Wilson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hussain et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The amount of moisture carried by low-level winds from the western Indian Ocean determines the intensity of the summer monsoon rains in this area (Pathak et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to the Himalayas, Karakorum, and Hindukush's (HKH) local geography, warm and moist monsoon air cannot be exchanged with cold and dry extratropical air, which determines how the summer monsoon precipitation is distributed over South Asia (Ashfaq \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have been undertaken on the impacts of changing precipitation patterns in the Indian subcontinent, specifically in relation to climate change and its geographical distribution\u003c/p\u003e \u003cp\u003e(Ghosh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rajeev et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Few studies observed dry spell break patterns during the summer monsoon in this region (Singh et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rajeev et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These researches, however, didn't discuss the contributing variables to dry spell patterns. The ENSO-Monsoon relationship's unpredictability includes fluctuations in rainfall observed by Selvaraju (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Geethalakshmi et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), Arrigo and Wilson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Kokilavani et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Jha et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Cr\u0026eacute;tat et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Yadav et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Vengateswari et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Terray et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Vishnu et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in this region. Few researchers have studied the other factors north-south SST gradient of the Indian Ocean for the summer monsoon dry spell in the Indian subcontinent (Goswami et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Roxy \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Noska \u0026amp; Misra \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fahad et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weldeab et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zubair et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Arrigo and Wilson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Cai et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Jha et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Hussain et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) evaluated the impact of the formation of a positive Indian Ocean Dipole on summer monsoon rainfall variability. Land surface temperature (LST) (Garai et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), monsoon wind (Pathak et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and topography (Ashfaq \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fahad et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were also observed for monsoon rainfall variability in this region.\u003c/p\u003e \u003cp\u003eBangladesh is a disaster-prone country in South Asia. Almost every year, the country is hit by a natural disaster of some form, such as drought, resulting in significant losses in agricultural productivity (Dastagir, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The analysis of the dry spell during the monsoon season is crucial in Bangladesh since 56% of the land is used for agriculture during this period, and it also influences surface water storage and groundwater storage changes (Zhang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rawat et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Monir et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). However, there has been no research on the pattern of dry spells, particularly in Bangladesh, despite some studies in this region (Singh et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rajeev et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Even though some study examines both ENSO and IOD for the variability of the summer monsoon rainfall in this region (Arrigo \u0026amp; Wilson \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Weldeab et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), no research has ever examined all of the ocean and land components together. Therefore, knowledge of the zonal long-term pattern of the summer monsoon dry spell and its relationship to ocean elements is essential for Bangladesh's agro-ecosystem sustainability. Such a research hasn't been conducted yet. So, to fill this research gap, the present novel study aims to analyze the long-term (1985\u0026ndash;2022) trend of the dry spell with rainfall occurrences and changes during the summer monsoon in different climate zones of Bangladesh. To understand the variability of dry periods, this study additionally considers ocean elements IOD, SST, ENSO, and monsoon wind. This study also looked at the surface water changes due to the changing pattern of dry spells. This study used the Mann-Kendall trend test to detect dry spell trends and the Frontier Atmospheric General Circulation model to evaluate a relationship between monsoon rainfall variability and ocean and land factors.\u003c/p\u003e"},{"header":"2. Data Sources and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The research area's background and prior knowledge\u003c/h2\u003e \u003cp\u003eBangladesh is situated in South Asia's northeast region, located between latitudes 20\u0026deg;34\u0026prime; and 26\u0026deg;38\u0026prime; N and longitudes 88\u0026deg;01\u0026prime; and 92\u0026deg;41\u0026prime; E. The magnificent Himalayas are located to the north, and the Bay of Bengal is located to the south (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Bangladesh has a warm, humid climate that is regularly affected by tropical cyclones and high precipitation. It is also subject to pre-, monsoon, and post-monsoon circulations. The average temperature in Bangladesh has historically been about 26\u0026deg;C with 2200 mm of annual precipitation. This study adopts the commonly accepted definition of a dry spell, which is the number of consecutive days with precipitation less than 1 mm/day (Agnese et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Brunetti et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Groisman \u0026amp; Knight, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To identify the trend in dry spells and possible connections with IOD, SST, ENSO, and zonal wind, we concentrated our investigation on the summer monsoon season, which runs from June through September in Bangladesh.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Observational data and quality checks\u003c/h2\u003e \u003cp\u003eIn this study, we used daily precipitation, water occurrence, and change intensity data for Bangladesh. IOD, SST, ENSO conditions, and the zonal wind speeds data were also evaluated to analyze the effect of ocean factors on dry spell patterns in Bangladesh. The daily rainfall data (1985\u0026ndash;2022) were collected from the Bangladesh Meteorological Department (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bmd.gov.bd/\u003c/span\u003e\u003cspan address=\"https://www.bmd.gov.bd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the 34 weather stations in Bangladesh. 38 weather monitoring stations are located across Bangladesh; however, we have excluded 4 of them as their rainfall data was not consistently collected during the study period. The name and spatial distribution of these selected weather stations are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The surface water occurrence and change intensity data for Bangladesh (1985\u0026ndash;2022) extracted from JRC Global Surface Water Mapping Layers, v1.3, which was collected from the Food and Agricultural Organization of the United Nations (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.apps.fao.org/catalog/dataset/jrc-global-surface-water-mapping-layers-v1-3\u003c/span\u003e\u003cspan address=\"https://data.apps.fao.org/catalog/dataset/jrc-global-surface-water-mapping-layers-v1-3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The IOD, ENSO, and zonal wind speeds data were collected from the Tokyo Climate Centre/ WMO Regional Climate Centre in RA II (Asia) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.jma.go.jp/tcc/tcc/index.html\u003c/span\u003e\u003cspan address=\"https://www.data.jma.go.jp/tcc/tcc/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The SST data collected from the NOAA Physical Sciences Laboratory (PSL) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psl.noaa.gov/\u003c/span\u003e\u003cspan address=\"https://psl.noaa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Tokyo Climate Centre/ WMO Regional Climate Centre in RA II (Asia) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.jma.go.jp/tcc/tcc/products/elnino/ocean/sst-global_tcc.html\u003c/span\u003e\u003cspan address=\"https://www.data.jma.go.jp/tcc/tcc/products/elnino/ocean/sst-global_tcc.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This study examines Bangladesh's daily precipitation amounts to determine the type of dry spells from June through September, during the summer monsoon season. The Indian Subcontinent (including Bangladesh) normally experiences the summer monsoon from June to September, during which time they get over 90% of their yearly precipitation (Xavier et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Patwardhan et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The missing rainfall data was estimated using the usual ratio approach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Dry spell calculation\u003c/h2\u003e \u003cp\u003eThe length of a dry period is defined as the number of days in a row with precipitation of less than 1 mm/day (Brunetti et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Groisman \u0026amp; Knight, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Agnese et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Wainwright et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) discuss the computation of the mean length of dry periods. There are four seasonality groups for dry spells (Pascale et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dunning et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and we employed dry-year-round analysis. A dry period is described as having two or more consecutive dry days (Giorgi et al., 2019; Wainwright et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, we analyze dry spells into three classes: 2 to 4 days, 5 to 7 days, and more than 7 days.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Methods for analyzing dry spell trends\u003c/h2\u003e \u003cp\u003eThe non-parametric Mann-Kendal (MK) (Mann, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) test was used to evaluate trends in the number of dry spells and dry spell days The MK test works well for identifying patterns in time series data since it is less likely to outline (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003ea\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe indicator function \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(sign ({x}_{i}- {x}_{j}\\)\u003c/span\u003e\u003c/span\u003e) must first be calculated from Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e1\u003c/span\u003e to conduct the MK test on a time series of length \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{3}\\)\u003c/span\u003e\u003c/span\u003e, \u0026hellip;., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{n}\\)\u003c/span\u003e\u003c/span\u003e (Drapela \u0026amp; Drapelova, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(sign ({x}_{i}- {x}_{j}\\)\u003c/span\u003e \u003c/span\u003e) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{\\begin{array}{cc}1\u0026amp; {x}_{i}- {x}_{j}\u0026gt;0\\\\ 0\u0026amp; {x}_{i}- {x}_{j}=0\\\\ -1\u0026amp; {x}_{i}- {x}_{j}\u0026lt;0\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eThis indicates whether there is a positive, negative, or zero difference between the values during periods i and j. Next, the previously described quantity's mean and variance are ascertained. The average E[S] is given by Eq.\u0026nbsp;2 (Anand et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eE[S] = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{n-1}\\sum _{j=i+1}^{n}sign ({x}_{i}- {x}_{j})\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003eMoreover, the variance VAR[S] is given by the following Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$VAR\\left[S\\right]= \\frac{1}{18}(n\\left(n-1\\right)\\left(2n+5\\right)-\\sum _{k=1}^{p}qk(qk-1)(2qk+5)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere p is the total number of tie groups in a data collection, and qk is the number of data points in the kth tie group. To compute the MK test statistic, we use the variance VAR[S] and the mean E[S]. For large sample sizes, the following adjustment ensures that the test statistic Z\u003csub\u003eMK\u003c/sub\u003e (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e) is distributed approximately normally (Monir et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${Z}_{MK}= \\left\\{\\begin{array}{cc}\\frac{E\\left[S\\right]-1}{\\sqrt{VAR\\left[S\\right]}}\u0026amp; E\\left[S\\right]\u0026gt;0\\\\ 0\u0026amp; E\\left[S\\right]=0\\\\ \\frac{E\\left[S\\right]+1}{\\sqrt{VAR\\left[S\\right]}}\u0026amp; E\\left[S\\right]\u0026lt;0\\end{array}\\right\\}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis Z\u003csub\u003eMK\u003c/sub\u003e value ranges from \u0026minus;\u0026thinsp;1 to 1. A Z\u003csub\u003eMK\u003c/sub\u003e of 0 implies no trend, while a Z\u003csub\u003eMK\u003c/sub\u003e of -1 or 1 indicates a completely negative or positive trend, respectively (Anand et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Z\u003csub\u003eMK\u003c/sub\u003e values ranging from 0 to 0.164 suggest a moderately increasing trend, while values greater than 0.165 indicate a strong increasing trend. Similarly, a declining trend is indicated by 0 to -0.164 for moderate decline and \u0026minus;\u0026thinsp;0.165 for strong decline (Kandya et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Examine the relationship between IOD and dry spell pattern\u003c/h2\u003e \u003cp\u003eUsing composite analysis and Kendall's tau-based slope estimator, the correlations between the IOD and the dry spell pattern were examined. The composite approach was suggested as an alternative to the correlation method since it does not assume that any relationship between two variables is linear (Harou et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). When dealing with a dataset that has a lot of linked ranks, one non-parametric correlation coefficient called Kendall's tau might be utilized in place of Spearman's coefficient (Balacco et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Zhao et al. (2005), this approach is insensitive to outliers. The relationship between the IOD and the dry periods was calculated using the following formulation (Eq.\u0026nbsp;5) of Kendall's tau-based slope estimator (Monir et al. 2024b).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\tau = \\frac{{N}_{c}-{N}_{d}}{\\frac{n(n-1)}{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the numerator is split by the total number of potential matches of data pairs (xi, yi) among the n observations, and N\u003csub\u003ec\u003c/sub\u003e and N\u003csub\u003ed\u003c/sub\u003e represent the concordance and discordance, respectively, among the n observations. The traditional t-test was used to confirm the data's significance at 95 and 99 percentiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of the effect of SST anomaly on summer monsoon rainfall\u003c/h2\u003e \u003cp\u003eThis study has employed version 1 of the Frontier Atmospheric General Circulation Model. Previous research has effectively replicated the observed impact of the IOD on the South Asian area and the tropical Indian Ocean region (Ashok et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Guan et al. 2003). An indicator of ENSO was the SST anomaly averaged over the Nino 3.4 area (5\u0026deg; S\u0026ndash;5\u0026deg; N, 170\u0026ndash;120\u0026deg; W) in the Pacific Ocean. El Nino (La-Nina) years are defined as those in which the average SST anomaly, measured from June to September, is more than +\u0026thinsp;0.5\u0026deg;C or less than \u0026minus;\u0026thinsp;0.5\u0026deg;C (Fahad et al. 2020; Vishnu et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Historical summer-monsoon rainfall\u003c/h2\u003e \u003cp\u003eOn an annual basis, the summer monsoon rainfall in Bangladesh varies from values close to 1000 in the western area to 4000 in the northeastern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Generally speaking, the western and northern areas have less rainfall at this time of year; the southern region has mid-level rainfall, while the northeastern region has higher levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUp to 1994, similar geographical trends were noted from the beginning of the research period. Nonetheless, this area's rainfall patterns varied between 1994 and 2005. From 1994 to 2005, comparatively more summer monsoon rainfall was recorded in the northern area. In Bangladesh's north and west, less summer monsoon rainfall was recorded after 2005. Rainfall in the southern and southeast was relatively more significant for a few years, including 2001\u0026ndash;2002, 2006\u0026ndash;07, 2012\u0026ndash;13, and 2015\u0026ndash;18.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Water occurrence and change intensity\u003c/h2\u003e \u003cp\u003eThe Surface Water Occurrence map shows us how frequently water has been seen on Bangladesh's land surface between 1985 and 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The northeastern portion remains submerged for most of the year, as this map makes abundantly obvious (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Surface Water Occurrence Change Intensity (1985\u0026ndash;2022) indicates that surface water levels are declining in Bangladesh's central and northeastern areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Also, the northeastern region faced the rising trend of dry spells during the summer monsoon, when most of the rainfall occurs. The loss of surface water in the central region occurs due to the decrease in the open surface for urbanization. On the other hand, the southwest is seeing an increase in the change intensity. Because these locations are low terrain and subject to tidal surges, the reverse scenario occurs when sea levels rise.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Total different types of dry spells and their trend\u003c/h2\u003e \u003cp\u003eIn 67.65% of weather stations, more than eight short dry spells (2\u0026ndash;4 days long) and more than one medium dry spell (5\u0026ndash;7 days long) were observed yearly (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In 41.17% of weather stations, the mean long dry spell (more than 7 days long) is observed more than 0.5. In 55.88% of weather stations, more than 35 dry spell days were observed yearly. The height means short dry spells, medium dry spells, long dry spells, and total dry spell days were observed in Chuadanga (10.06), Rangpur (2.21), Rajshahi (1.75), and Syedpur (52.73) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These stations are located in the western and northern regions of Bangladesh. On the other hand, the lowest means of short dry spells, medium dry spells, long dry spells, and total dry spell days were observed in Sylhet (3.98), Teknaf (0.67), Teknaf (0.15), and Sylhet (15.76).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of dry spells and trend in dry spells during the summer monsoon in Bangladesh (1985\u0026ndash;2022).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWeather Station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMean of Dry Spell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTrend of Dry Spell (MK Z)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;4 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;7 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u0026thinsp;+\u0026thinsp;days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal dry spell days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026ndash;4 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u0026ndash;7 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u0026thinsp;+\u0026thinsp;days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal dry spell days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.537**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.591**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTangail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMymenshing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.737*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaridpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.306***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.496**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadaripur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSrimangal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSylhet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBogra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRajshahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.803**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.864***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIshurdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.502**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.17**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDinajpur\u003c/p\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.071**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.741*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.727***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSydpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003cp\u003e1.75*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCox's Bazar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.761*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeni\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.867*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHatiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.761*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKutubdia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM. court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.569**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.061**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangamati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.852*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.795*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandwip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSitakunda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatuakhali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e*** 99% Significant level; ** 95% Significant level; * 90% Significant level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost of the short-length dry spells occur in western and northern Bangladesh. The southwestern, northcentral, and southcentral regions have significantly higher short-length dry spells. Significantly less amount of this type of dry spell occurs in northeastern, southcentral, and southern regions. Very few short dry spells occur in the eastern and southcentral regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Medium-length dry spells mainly occur in the northern region, and almost a higher amount is observed in the northwestern region. The eastern and southern regions have very few medium-length dry spells. There are moderate medium-length dry periods in the remaining middle area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Similarly, extended dry spells are more common in Bangladesh's northern and northwest areas. The yearly mean number of dry spell days is mostly observed in the northern and northwestern regions, and moderately higher dry spell days are observed in the southwestern and central regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The northeastern, southeastern, and southern regions have moderately fewer, and the eastern region has very few yearly mean dry spell days (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e82.35% of weather stations are rising, and 17.65% have a declining trend in short dry spells (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the rising trends, 3.57% are high rising, 7.14% are moderate rising, and 89.29% are low rising trends. On the other hand, 16.67% are moderately declining, and 83.33% have a low declining trend. 73.53% of weather stations show a rising tendency for medium-length dry spells, while 26.47% have a declining trend. 8% are high rising trends, 36% are moderate, and 56% are low rising trends (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other side, all the declining trends are low. For long dry spells, half of the weather stations have a rising trend, and others have a declining trend. Among the rising trends, 17.64% are moderate rising, and 82.36% are low rising trends. On the other side, all the declining trends are low. For the total dry spell days, 79.41% of weather stations have a rising trend, and 20.59% have a declining trend. Among the rising trends, 18.51% are high rising, 40.74% are moderate rising, and 40.76% are low rising trends (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other side, all the declining trends are low.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA high-rising trend for short dry spells is observed in the northern and northcentral regions. The rising trend is observed in the southwestern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The rising trend for medium-length dry spells is observed in the western and northern regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). On the other hand, the rising trend for long dry spells is observed in the northern, southwestern, and southcentral regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Also, a rising trend was observed from central to western and northern regions for the total dry spell days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The rising trend in all classes of dry spell and total dry spell days is observed in the northern and northwestern regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relationship between Indian Ocean Dipole (IOD) and summer monsoon rainfall in Bangladesh\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the total quantity of summer-monsoon rainfall in Bangladesh (1985\u0026ndash;2022) together with the yearly IOD indices. There is a statistically significant positive correlation coefficient between the IOD score and the country's total monsoon rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This correlation is ongoing through the month of September, beginning in the month of May. The correlation between monthly rainfall and DMI during summer monsoon is 0.39 for June, 0.31 for July, 0.33 for August, and 0.38 for September in Bangladesh. The standard deviation criterion's sigma was used to choose IOD occurrences. Nine positive (1993, 1994, 1997, 2006, 2007, 2015, 2018, 2019, 2020) and ten negative (1985, 1992, 2995, 1996, 1998, 2005, 2010, 2016, 2021, 2022) years were chosen from the IOD time series study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The majority of the years during the IOD index's positive phases revealed a positive correlation between Bangladesh's DMI index and monsoon rainfall, however one year revealed a negative correlation. The time series for the years 1992, 1994, 1996, 1997, 2005, 2010, 2012, 2015, 2016, 2020, and 2022 showed extremely distinct patterns: the amount of monsoon rainfall was higher when the IOD index was higher and lower when the IOD index was lower.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effect of SST anomaly on summer monsoon rainfall\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, there is a strong correlation between SST and summer monsoon rainfall anomalies (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The results of this study point to more rainfall under a changing climate with rising SSTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). However, these changes in SSTs affect the monsoon rainfall pattern. Most weather stations observed rising dry spells during monsoon according to the changes in SSTs. There is a pocket region near Bangladesh where SSTs are trending upward (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Throughout the research period, Bangladesh's monsoon rainfall pattern is directly impacted by the shifting SSTs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 ENSO effects on dry spells during summer monsoon\u003c/h2\u003e \u003cp\u003eSST changes and their persistence over the Indian Ocean are the basis for the operational definition of ENSO conditions. El Ni\u0026ntilde;o and La Ni\u0026ntilde;a were the two categories into which ENSO years were divided, with the other years falling into the neutral category. In El Ni\u0026ntilde;o years, the dry spell lasted an average of 35 days, ranging from 32 to 45 days. The dry period in the La Ni\u0026ntilde;a phase lasted 31 to 49 days, with an average of 41 days, whereas the dry spell in the neutral phase lasted 32 to 47 days, with an average of 38 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Zonal mean atmospheric circulation's sensitivity to SST anomalies\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the wind (anomaly) composites for the four deficient monsoon season years (1992, 2002, 2012, and 2022). The composites are shown for various pressure levels of UT/LS. It is also evident that during the deficient (dry) composites over the Indian subcontinent, there are significant changes in the circulation patterns in the high atmosphere (850 hPa) and lower troposphere (200 hPa). Wind circulation in the upper atmosphere, lower troposphere, and the SST anomalies all changes during the research period (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe maximum dry/wet spell duration has altered dramatically, according to a spatiotemporal analysis of Bangladesh's precipitation patterns, however, the total number of precipitation events from 1906 to 2010 showed very little change (Dash \u0026amp; Maity, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Monir et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The significance of the summer monsoon rains on the economy and civilization of the region has made it a key factor in climate change. The Indian Subcontinent has seen a dramatic decrease in rainfall, with an average of \u0026minus;\u0026thinsp;0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 mm/decade (Subrahmanyam et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The South Asian summer monsoon season accounts for 85% of the subcontinent's annual rainfall (Turner \u0026amp; Annamalai \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fahad et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Bangladesh, June and September received 57.6% of the country's annual rainfall (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Annual summer monsoon rainfall in Bangladesh ranges from around 1000 in the west to 4000 in the northeast (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In general, the western and northern regions receive less rainfall at this time of year; the southern region receives medium-level rainfall, while the northeastern region receives more (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Since 1951, the mean summer monsoon rainfall has been dropping at a substantial in the Indian subcontinent (Rawat et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verma et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, in Bangladesh, rainfall is dropping by 75% during the monsoon season (Monir et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). About 80% of the yearly precipitation across the Indian subcontinent falls during the Indian Summer Monsoon, which also saw unusual sub-seasonal changes in rainfall throughout the summer monsoon season, including a rapid change of 54% in summer monsoon rainfall (Vibhute et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Surface Water Occurrence Change Intensity (1985\u0026ndash;2022) shows that surface water levels are decreasing in Bangladesh's center and northeastern regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), where dry spells increase during the summer monsoon. A total of around 90,000 sq km of permanent surface water vanished between 1984 and 2015, While in other places, new, permanent bodies of water spanning 184,000 square km appeared (Pekel et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, more than eight short dry spells and more than one medium dry spell were seen each year in 67.65% of weather stations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the present study, 82.35% of weather stations are rising for short dry spells, 73.53% in medium-length dry spells, and 50% in long dry spells. Also, since the 1970s, the worldwide mean dry spell length (DSL) has grown by 0.46 days each decade (He et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Dry season spells across South America and southern Africa are getting longer, up to about two days every decade, while those over West Africa are getting shorter, around the same length (Wainwright et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOceanic moisture sources dominate the Indian Subcontinent\u0026rsquo;s summer monsoon rains. However, land surface processes play a substantial role in precipitation formation across the Indian subcontinent. Evapotranspiration over a region adds moisture to the atmosphere, which may result in precipitation in the same location (Pathak et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The IOD score has a statistically significant positive correlation with the country's total monsoon rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The correlation coefficient between monthly rainfall and DMI during the summer monsoon is 0.39 in June, 0.31 in July, 0.33 in August, and 0.38 in September. This conclusion is consistent with earlier research showing that seasonal rainfall in Northeast India (May\u0026ndash;September) is negatively impacted by greater positive IOD (Sarkar et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While some research (Ashok et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) find a favorable link between IODM and monsoon rainfall over the Indian subcontinent, others (Saji \u0026amp; Yamagata, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) report a weak and inconsequential relationship. Therefore, it is still unclear how the IODM and monsoon rainfall are related. There is a larger correlation between East Asia and the monsoon than there is with South Asia, according to a recent study (Kripalani et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It makes sense to look at how the monsoon affects the dipole since the primary Indian monsoon peaks in the boreal summer, June through September, while the IOD develops in the summer and peaks in the autumn. The sliding correlations between monsoon rainfall and the dipole mode index suggest that the impact of monsoon over dipole is weakening after the 1960s. This weakening relationship has been evidenced by the composites of sea-surface temperature anomalies and circulation patterns (Kulkarni et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis present study found a substantial association between SST and summer monsoon rainfall anomalies (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The findings of this study hint at increased rainfall in a changing climate with rising SSTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Despite prior research indicating that the summer monsoon variability is not actively influenced by SST, the mean SSTs over the Asian monsoon basins (Indian Ocean and north-west Pacific) are primarily over the threshold. It also suggests that rising sea surface temperatures brought on by climate change may not always translate into more monsoon precipitation (Roxy, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For the past three decades, it has been maintained that there is a threshold of 28\u0026ndash;29.5\u0026deg;C (Rajendran et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) at which there is no longer any meaningful relationship between precipitation and SST (Sabin et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SST is a slave over the region and has no active influence on the summer monsoon's variability since the mean SSTs over the monsoon basins (the Bay of Bengal) are generally higher than the threshold (Gadgil et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). However, the present study found a pocket region near Bangladesh where SSTs are trending upward (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Throughout the research period, Bangladesh's monsoon rainfall pattern is directly impacted by the shifting SSTs.\u003c/p\u003e \u003cp\u003eAccording to the present study, the dry spell lasted in El Ni\u0026ntilde;o years an average of 35 days, in the La Ni\u0026ntilde;a phase 41 days, and in the neutral phase 38 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Previously it has been discovered that the East Asian summer monsoon is significantly influenced by the ENSO (Xie et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a predominant interannual fluctuation climatic pattern in the tropical Pacific (Wu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current analysis indicates that there are notable changes in the lower troposphere (200 hPa) circulation patterns and upper atmosphere (850 hPa) during the deficient (dry) composites over the Indian subcontinent (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). From 10\u0026deg; N to 30\u0026deg; N, there are positive westerly wind anomalies, with a noticeable anomaly of the westerly wind over Southeast Asia and the Bay of Bengal. This indicates that, in comparison to years with negative IOD, there are stronger westerly winds over Southeast Asia and the Bay of Bengal in positive IOD years (Saji \u0026amp; Yamagata, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, during the summer, from high pressure zones wind are originate and moisture is transported from the Arabian Sea in a southeasterly direction, this wind direction mechanism is entirely reversed, leading to more rainfall across the Indian region (Hussain et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the trend in historical dry spell trend (1985\u0026ndash;2022) during the summer monsoon and how different ocean factors such as IOD, SST anomalies, ENSO conditions, and the zonal wind circulation affect the summer monsoon rainfall pattern in Bangladesh were explored. The following is a summary of the primary findings from this study:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGenerally speaking, the western and northern areas have less rainfall during summer monsoon. Rainfall in the southern and southeast was relatively more significant for a few years, including 2001\u0026ndash;2002, 2006\u0026ndash;07, 2012\u0026ndash;13, and 2015\u0026ndash;18. The center and northeastern regions are experiencing a decrease in surface water levels.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe short-length, medium-length, and extended dry spells are more common in Bangladesh's northern and northwest areas, and these dry spells increase in 82.35%, 73.53%, and 50% of weather stations respectively.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe total amount of monsoon rainfall is positively correlated (statistically significant) with the IOD score and DMI and the monthly rainfall correlation coefficient is 0.39 in June, 0.31 in July, 0.33 in August, and 0.39 in September.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA strong correlation between summer monsoon rainfall and SST anomalies, SSTs rise and the climate changes, there may be more rainfall.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe dry spell lasted El Ni\u0026ntilde;o years an average of 35 days, in the La Ni\u0026ntilde;a phase 41 days, and in the neutral phase lasted 38 days.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFrom 10\u0026deg; N to 30\u0026deg; N, there are positive westerly wind anomalies, with a notable westerly wind anomaly over Southeast Asia and the Bay of Bengal.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe study's conclusions imply that the timing of rainfall is changing. Over the past few decades, Bangladesh's annual average rainfall has been relatively constant; however, during the summer monsoon, when the majority of rainfall typically falls, there have been more dry spells. Understanding the spatiotemporal variance of compound dry-hot extremes is made easier by looking at the temporal evolutions of dry spells. The increased likelihood of drought and crop failure suggests that more urgent work needs to be done on developing better, immediately transferable plans for managing climate risk. However, there are certain limits to the results of this study, and more research will help to achieve the objective of comprehending how Pakistan's precipitation is affected by air circulation and how it is physically related to the IOD mode. The current study recommends that future researchers focus on dry and rainy spells concurrently for different seasons.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to constraints imposed by the BMD authority, the rainfall datasets analyzed during the current work are not publicly available. However, the accompanying author is willing to provide them upon reasonable request. Other data sets are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonir, M.M.:\u003c/strong\u003e Conceptualization, Methodology, Software, Data curation, Visualization, and Writing- Original draft. \u003cstrong\u003eSarker, S.C.:\u003c/strong\u003e Conceptualization, Supervision, Validation, Reviewing and Editing. \u003cstrong\u003eRipon, M.M.:\u003c/strong\u003e Reviewing. \u003cstrong\u003eIslam, M.N.:\u003c/strong\u003e Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that they have no known financial or interpersonal conflicts that would have seemed to have an impact on the research presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully thank the Tokyo Climate Center/WMO Regional Climate Centre in RA II (Asia), the NOAA Physical Sciences Laboratory (PSL), the Bangladesh Meteorological Department, and the JRC Global Surface Water Mapping Layers v1.3 (FAO, UN) for providing the data utilized in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgbazo, M.N., Ad\u0026eacute;chinan, J.A., N\u0026rsquo;gobi, G.K., Bessou, J. 2021. 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Lett., 30: 1063. https://doi.org/10.1029/2002GL015639.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rainfall, Dry Spell, Indian Ocean Dipole, Sea Surface Temperature, ENSO, Zonal wind","lastPublishedDoi":"10.21203/rs.3.rs-4368007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4368007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo assess drought risk, susceptibility to food security, and water resource utilization, it is crucial to comprehend dry spell patterns from a hydrological perspective. Some regional studies have noted an extension of dry spells on a global and regional scale, but it is still unclear how often dry spells occur during the summer monsoon season, which is dominated by rainfall. This study uses the Mann-Kendall trend test to examine the trend of dry spells during Bangladesh's summer monsoon from 1985 to 2022 to close this gap. Using the Frontier Atmospheric General Circulation model and remote sensing methods to examine the effects of ocean elements such as Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), El Ni\u0026ntilde;o-Southern Oscillation (ENSO) conditions, and the zonal wind. Daily rainfall data for 34 weather stations were obtained from the Bangladesh Meteorological Department, while surface water occurrence and change intensity data were retrieved from the JRC Global Surface Water Mapping Layers, v1.3 (FAO, UN). The NOAA Physical Sciences Laboratory (PSL) and the Tokyo Climate Center/WMO Regional Climate Centre in RA II (Asia) provided the IOD, SST, ENSO, and zonal wind data. A notable dry spell anomaly over Bangladesh was also noted in this research, with the short, medium-length, and long dry spells increasing in 82.35%, 73.53%, and 50% of weather stations. When El Ni\u0026ntilde;o was present, there was less of a dry spell and more during La Ni\u0026ntilde;a. The climatic variability of IOD events and SST anomalies in the eastern and western tropical Indian Ocean were also noted by this study to be connected to these anomalous events. The correlation coefficient between summer monsoon rainfall and DMI is 0.34. Throughout the study period, there were changes in the upper atmosphere's and lower troposphere's wind circulation. The study allows the prioritization of regions for drought, effective water resource management, and food scarcity preparedness.\u003c/p\u003e","manuscriptTitle":"Analysis of the trend of dry spells and how ocean factors affect its patterns during the summer monsoon in Bangladesh using the Mann-Kendall and Frontier Atmospheric General Circulation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 14:23:44","doi":"10.21203/rs.3.rs-4368007/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9db6da31-8b84-416d-aad7-042fa999ab12","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-22T15:30:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 14:23:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4368007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4368007","identity":"rs-4368007","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}