Reckoning flood frequency and susceptibility area in the lower Brahmaputra floodplain using geospatial and hydrological approach

Climate change has remarkably intensified the occurrence of floods around the globe. Flooding causes loss of life and property. Flood frequency analysis (FFA) is an important investigation and plays a key role in flood‐related studies. Geographically, the study area is confined in the lower Brahmaputra floodplains, flat slope, and rivers are braided in nature. Because of Heavy rainfall, major rivers in the area carry huge influxes of surged water during the summer period. Hence, disastrous flooding can be seen every year in the study region. The present study aims to model flood frequency using the hydrological data to understand the effects within the area. FFA approaches like Gumbel, Log Pearson type 3 (LP‐3), and Log‐Normal (LN) were used, and comparative analyses were done using water level data for the Manas, Aie, and Brahmaputra Rivers. Moreover, remote sensing and the geographic information system (GIS) environment were used to generate FFA‐based flood predictive inundation map at 5, 10, 50, 100, and 200 years of return periods. Here, Gumbel's distribution has found the best fit for all the rivers among the three. The distribution reveals that at a 200‐year return period, the highest water level would be increased by 1.45, 2.41, and 4 m for the Manas, Aie, and Brahmaputra Rivers, respectively. The study shows that almost 493.54 and 673.72 km2 of areas are expected to be submerged at 5 and 200‐year return periods according to Gumbel's distribution; LP‐3 distribution predicted 493.01 and 555.66 km2, and the log‐normal distribution method predicted 432.51 and 555.74 km2 of flood‐sensitive areas at 5 and 200‐year return periods, respectively. The FFA highlighted spatio‐temporal effects on the expansion of submerged areas. We hope that the findings of the present study will aid in the different flood hazard management strategies for future endeavors.


| INTRODUCTION
An extreme hydrological event like flood is an unavoidable phenomenon and random in nature (Shiau, 2003).Various natural and anthropogenic factors lead to floods, which directly or indirectly affect people (Bhattachaiyya & Bora, 1997;Sarma, 2004).The flood caused death worldwide for 6.8 million of the population in the 20th century (Doocy et al., 2013).With the pace of climate change around the world, an extreme phenomena like flood disasters and its effect also been rapidly increasing.Unsettling thriving nature in the number of disaster victims has increased, where flood alone counts for the two-thirds ratio of affected people (UNISDR, 2002).Recently, flood-generating factors have intensified extreme weather events and are slated to rise in upcoming years (Tanoue et al., 2016).In the past 20 years, 400 million individuals have been affected due to floods each and every year (Bitch et al., 2011).Flooding is an inescapable peril that creates a high gamble over the planet earth, especially in low-slung areas.Coastal, deltaic, and riverine regions are more prone to flood than elevated regions in the world (Haigh et al., 2010;Shiau, 2003;Warner & Tissot, 2012).Flood risk management highly demands studying flood characteristics for both developed and developing nations to compensate from associated threats (Parry et al., 2007).Therefore, there is a need for scientific investigation to sustain significant natural, social, cultural, and monetary administration (Rao et al., 2020).In the real world, strategies and capacities have not been found beyond rescue, and relief operations, where applying scientific and traditional measures is also essential in the proper flood management process.
Flood-contributing factors are manifolds worldwide (UI Hassan et al., 2019).India is characterized by monsoon effects, and massive mesh of river systems spill over creates floods bringing tragic catastrophes in many dimensions.Rainfall is an influential factor in raising the extension of floods within the country (India), especially in the northeastern states (Bhattachaiyya & Bora, 1997).Conspicuous precipitation and subsequent flood bring abnormality to the quality of people's lives.Hence proper evaluation and investigation of the hydrological regime are of utmost requirement to reduce uncertainties (Manohar Reddy, 2022).It is generally seen in those watershed management case studies where underlying structural designs are most sweepingly taken as techniques for lessening the misfortunes related to flood hazards; wherein planning of hydraulic structure is dependent on the hydro-geomorphological behavior of a stream (Alexander & Wilson, 1995;Chow et al., 1988;Shammout, 2014;Teraguchi et al., 2011).In recent times, technological knowhow has been widely used in the flood study to improvise assessment levels at different scales, which supports an integrated approach (Plate, 2002;Pottier et al., 2005;Vojtek & Vojteková, 2016;Werritty, 2006).Scholars worldwide have used remote sensing techniques and historical data for flood risk analysis (Sanyal & Lu, 2006).Flood level data coupled with remote sensing data and GIS environment provide one of the best platforms to map Inundated areas and help to assess futuristic viewpoints (Bates et al., 1997;Dawod et al., 2011).Spatio-temporal Inundation area mapping in the flood study has become a magnificent output to identify the pattern of floods which would help for sustainable management and development of the riverine landscape (Huang et al., 2014;Odunuga & Raj, 2014;Borah et al., 2018).Research on paleo flood as specific water resources' subjectivity and lessens their collective damage potential (Stendiger & Baker, 1987).
Studies on flood dynamics have been successfully carried out by different scholars, followed by various techniques and methods.Flood frequency analysis (FFA) is a wellacknowledged and scientific strategy for flood study; it aims to reduce misfortunes, decision-making process, planning and development purpose (Blazkov & Beven, 1997;Burn, 1990;Burn & Whitfield, 2016;Cunderlik et al., 2004;Haddad & Rahman, 2008;Lam et al., 2017;Saf, 2008).This strategy plays a vital role in water resource management, where past hydrological records and probability distribution methods have been used to forecast the size of outrageous events to their recurrence incidents for the future portend (Benson, 1950;Bhat et al., 2019;Castellarin et al., 2001;Chow et al., 1988;Karmakar & Simonovic, 2008;Pandey & Nguyen, 1999;Stedinger & Baker, 1987;Stedinger & Cohn, 1986;Vogel et al., 1993).In the hydrological study, various probability distribution methods have been used to fit the effectiveness of risks associated with flood magnitude and proximity (Enzel et al., 1993;Fill & Stedinger, 1995;Pegram & Parak, 2004;Ramasamy et al., 2022).Gumbel's extreme value, Log-Normal, and Log-Pearson type III (LP-3) are well-known techniques used for evaluating flood recurrence investigation with the help of water level and river discharge data (UI Hassan et al., 2019;Zhang et al., 2017).Even though FFA has been toted in many scientific studies using several methods, it is not well defined as universally accepted (Law & Tasker, 2003).Many studies reveal that flood data (water level and discharge) acts as a primary input in probability estimation; considering 10-30 years of observed data ranges are preferable for better results (Bobee et al., 1993;Bobee & Robitaille, 1977;Odunuga & Raji, 2014;Saghafian et al., 2014;Sahoo et al., 2021;Singh, 1998).
Assam valley experienced regular floods because of the immense augmentation of the Brahmaputra stream framework, which inundates larger areas.The flood years of 1962 and 1988 were remarkably pathetic for Assamese dwellers (Bhattachaiyya & Bora, 1997).With increasing nature of anthropogenic causalities in the valley has expanded the flood's potential with high recurrence, size, and harm potential (Bhattachaiyya & Bora, 1997;Dubey & Singh, 2021).The Brahmaputra River and its tributaries overflow during monsoons with enormous storm surges, affecting nearby stream networks (Rao et al., 2020).
Frequent floods in the Brahmaputra plain pose havoc to people's lives within a riverine landscape.The flood's impact is crystal clear in the entire basin after each flood period.The flood phenomenon affects the lower part of the basin every year.In the present work, an attempt has been made to investigate flood characteristics of the Manas, Aie, and Brahmaputra Rivers using different approaches of FFA such as Gumbel, Log-Pearson, and Log-Normal probability distribution.Further, the study also seeks to estimate the spatio-temporal extent of submergence area and mapping based on different FFA predicted values for various return periods.The study's rationale is to find out the expected possible outrageous flood magnitude and to map the floodsensitive area which may help in the sustainable flood management of the region.With rigorous looking into the different works of literature, the study has been proposed conducted as no reliable scientific study has been found within the study area.The present work is confined within the lower Brahmaputra River plain with an administrative unit.

| STUDY AREA
The present study area (Bongaigaon district) is confined within the lower Brahmaputra floodplain of Assam.The district accounted for 73,8,804 persons as per the 2011 census, with a density of 680/km 2 .The geographical extension of the site spreads between 26°09′52″N to 26°30'03″N latitudes and 89°28′E to 92°22′47″E longitudes, with an area of 1093 km 2 shown in Figure 1.Topographically, the study area belongs to mostly alluvial flood plain, whereas small sizes of isolated hills & hillocks are found within the district.The climatic condition in this region depends upon the monsoonal effects that allow heavy precipitation with an average range between 250 and 350 cm.
Being a part of the geo-environmentally sensitive zone of the Brahmaputra valley in Assam, the present study area occasionally encounters many hazardous issues.Flooding is a common natural disaster in the area among them.Every year, the presence of the Brahmaputra and its large tributaries, such as the Aie and Manas, causes chaos.The research region frequently experiences flooding and river bank erosion during the monsoonal wet season, with a wide variety of negative effects.As a result, a mass number of the human population, livestock, and other property fall into threat either directly or indirectly every year in the riverine tracks of the area.The increasing rate of dwellers in the riverine tracks and their associated activities such as unplanned land feeling, cutting of river bank and river bed sites for construction purposes, and so on makes the places more vulnerable to threats.According to DDMA (District Disaster Management Authority, Bongaigaon district) report it can be seen that flood ruins basic human lifestyle every year likewise food shortage, house displacement, an obstacle to proper education facility, the spread of illness and disease, forced migration which influences livelihood instability.Thus, the socioeconomic status of the Bongaigaon district finds obstacles to boost faster comparatively under these circumstances.In this regard, an appropriate assessment of flood hazard is necessary to understand the flood characteristics that prevail in the study area for better management purposes.

| DATA SOURCES AND METHODOLOGY
Flood frequency investigation is a quantitative measure that is being done to anticipate and evaluate the flood data, for example, water discharge and stage height at a specific stream area in the respective period.Though a set of probability distribution methods have been used to locate the different flood sizes but challenging to pick the recognized way (Dearman et al., 2017;Hosking & Wallis, 1997;Nwaigwe & Weli, 2019).Recorded sample data sets pose a critical input in the probability estimation, whereas standard norms can be considered 30 years; on the contrary, a lack of temporal data series leads to poor frequency results (Bobee & Robitaille, 1977;Saghafian et al., 2014).However, many studies around the world have been carried out with the probability methods to understand the flood hazard, especially in the data-scarce area (ungauged stations) or with at least 10 years or above data range (Odunuga & Raji, 2014;Samantaray et al., 2021).Different probability distribution approaches like Gumbel, Log-Pearson, and Log-Normal are widely applicable or suitable for frequency analysis at a particular-site (Hydrological station) or regional level frequency analysis (UI Hassan et al., 2019).Gumbel distribution provides the notion of extreme value of an event to be occurred in a particular year and it has potential applicability in various discipline such as hydrological parameter, financial risks assessment, and so on (Gilli & Këllezi, 2006;Kamal et al., 2017).Water resource council of the United States suggested LP-3 distribution method is a basic approach in probability estimation which enhance the accuracy of the outcome (Singh, 1998).This particular approach offers mean, standard deviation, and coefficient of skewness of sampled data and widely used for flood frequency assessment (Elsebaie, 2012).One of the prominent advantage and applicability of using Log-Normal distribution method is that it helps to study a long-term recurring intervals of frequency analysis (Ehiorobo & Izinyon, 2013).Considering the current study work, it was intended to estimate the recuring intervals from low to high range therefore combination of Gumbel, LP-3, and Log-Normal distribution approaches have been implemented in a detailed manner.Although, there are many machine learning models accessible for the concern study in recent days but Gumbel, Log-Pearson, and Log-Normal probability distributions are well-known and highly recommended basic statistical approaches that are still used by many expertise to study FFA around the globe (Farooq et al., 2018;UI Hassan et al., 2019;Shahid et al., 2023).Moreover, the study incorporated multiple approaches to check what extent the results deviate from each method.
3.1 | Methods used for assessment of FFA

| Gumbel distribution
The present study has adopted specific statistical techniques to calculate the hydrological frequency analysis, which holds the base for the Gumbel distribution method (Chow, 1951).
where X T is the value of flood for the given return period, x is the mean value, K is the frequency factor, and σ is the standard deviation.
To find out the value of X T using Equation (1), specific mathematical steps have been carried out: Step 1.The mean value of X (water level) finds out using the following equation: where Σx is the sum of all observations and N is the number of observations.
Step 2. The standard deviation of X (water level) was calculated using the following equation: .
n 1 2 (3) Step 3. Determining the Y n and S n from the constant Gumbel's reduced mean table.
Step 4. Reduced variantY T for a given T period is found using the following equation: where Y T is the reduced variant of data series, T is the return period, ln is the natural log.
Step 5. Frequency factor (K ) for Gumbel distribution achieved using the following equation: where Y n is the mean reduced variant, S n is the standard deviation reduced variant.
It is noteworthy that the value of X T (Equation 1) in the Gumbel method was obtained from the thorough mathematical procedures used in Equations ( 2), ( 3), (4), and (5).

| LP-3 probability distribution
LP-3 frequency distribution method has been widely used in the probability investigation (McMahon & Srikanthan, 1981;Subramanya, 1994), for which a base statistical formula is shown below: where Z is the observation value, Z is the mean of Z, K Z is the frequency factor based on C s , and σ Z is the standard deviation of Z.
To find out the value of Z T using Equation ( 6), the following mathematical steps have been done so far: Step 1.The z value is calculated using the following equation: Step 2. The mean value of Z was assessed using the following equation: Step 3. The standard deviation of the Z value estimated using the following equation: Step 4. To get the K z value, the period and value of C s have been considered from Log Pearson constant value table, and to get the C s value, Equation (10) has been applied.
where C s is the coefficient of skew and N is the sample size.
Step 5. Based on the nature of the data, for some cases, the C s value lies in the range of two constant C s values mentioned in the Log Pearson's K Z frequency table.In this regard, K the Z value for the return period is located with the help of the linear interpolation technique.
Step 7. To find out the X T (value of return period) using the following equation: In Log standard distribution method, to get the Z T and X T value, Equations ( 7), ( 8), ( 9), and (11) have been rigorously followed; where to get C s value Equation ( 10) was not considered, instead "0" has been considered as default C s value, and K Z was found according to desired return period.

| Chi-square (C-S) test for goodness of fit
C-S is a statistically defined nonparametric measure used to evaluate the values that support the goodness of fit of each distribution for three major rivers in this study.Testing distributions with the help of a C-S measure provides integral club and similarity in the desired research (Odunuga & Raji, 2014;Opere et al., 2006).To find out the C-S value in the present study, Equation ( 12) has been applied: where X 2 is the chi-square, O is the observed data (X T ), and ; where n = 5.
After finding the C-S values, all data were fitted on the probable degree of freedom 4 and proved their significance (α) at 1% or 0.99 level.Data applied in the C-S test are found themselves are significant in their group.To calculate the degree of freedom (df), methods, for example, 13 has been applied.
where c is the no. of column, r is the no. of row, and 1 is the constant value.

| Methodology used for flood inundation mapping
Flood inundation modeling or mapping is perhaps a critical evaluation that helps to assess the risks associated with the flood (Chen et al., 2009).In the view of recent times, the implication of the GIS platform plays a vital role in mapping flood-liable areas from the local to the global level and acts as a database for decision-makers and policymakers (Teng et al., 2017).In the proposed study, computed average water level (X T ) value of Gumbel, Log Pearson, and Log-Normal and surface height value has been employed to produce predictive flood inundation mapping.In the context of flood mapping, a two-dimensional model has been incorporated for each 5, 10, 50, 100, and 200 return period.
Though no weightage of C-S values has been taken as essential input, a holistic approach has been applied to make the flood inundation area.Remotely sensed Shuttle Radar Topography Mission (SRTM) DEM with cell sizes 0.00027777778 and 0.00027777778 have been used to assess the study area's surface height (meter).The GIS environment has been incorporated over the two data sets, for example, average water level and surface height.GIS Arc Tool, like Spatial Analyst in ArcGIS 10.7.1, has been implanted to process the input data.To get the raster output (inundation map), ArcGIS-enabled raster mathematical tools such as logical mathematics and equal or less than equivalent operations have been utilized.The raster images are reclassified to a polygon with the help of the conversion tool in the Arc toolbox.Furthermore, inundated and noninundated areas of each flood map have been calculated using raster calculator in the GIS environment.The flow chart of the adopted methodology is given in Figure 2. provides the R 2 value 0.9999 as the best-fit line plotted against between expected water level and return period in Figure 3a.Manas River in the Gumbel distribution follows the frequency of flood levels of 46.62, 46.90, 47.54, 47.81, and 48.07 m respectively based on 5, 10, 50, 100, and 200 years of returning period (  3c shows the value of R 2 is 0.9999 for the Brahmaputra River based on Gumbel's method, it denotes the Coefficient of determination of the distribution fitted by computed water level value against the respective flood return period.Against the return period of 5, 10, 50, 100, and 200 years, the flood level of Brahmaputra River at the Goalpara gauge station is expected to lie at 34.18, 34.95, 36.64, 37.35, and 38.06 m accordingly (Table 4).Almost 4 m of flood water level have been anticipated to increase at the gauge site in the upcoming 200 Years.

| LP-3 distribution
The resultant value in the LP-3 distribution method for Manas River was drawn from the mean ( ̅ z) 1.6649, Standard deviation of (z) 0.003580, and Coefficient of skewness (C s ) 0.26.The best-fit line gives the R 2 value as 0.9955 shown in Figure 3a against the calculated value and T period.The frequency factor based on the C s value in this distribution follows as 0.828, 1.304, 2.180, 2.500, and 2.800 for each T period shown in Table 2.The expected flood frequency level for the return period of 5, 10, 50, 100, and 200 years is found at 46.53, 46.71, 47.06, 47.18, and 47.30 m correspondingly for the Manas River at the NH road crossing gauge station.A bit of increase in the water level, for example, 0.77 m, is found with this distribution.Considering the Aie River, at the NH Rly.Crossing gauge station anticipated flood stage in respect of different T years has been seen as 51.47, 51.65, 51.91, 52.00, and 52.06 m against the corresponding 5, 10, 50, 100, and 200 recurrence intervals (Table 3).Here, the mean of (z) is found from the handed data set as 1.7070, where the SD of (z) goes with 0.005404 and the C s value lies on −0.9 followed by the best-fit line R 2 value is 0.9793 (Figure 3b).The frequency factor in this distribution ranges according to return periods like 0.854, 1.147, 1.549, 1.66, and 1.749 respectively as per 5, 10, 50, 100, and 200 years.In respect of the Brahmaputra River, calculated water level has been found using the LP-3 technique against the many reoccurrence interims of 5, 10, 50, 100, and 200 years which stands for the resultant value of X T 33.88, 34.04, 35.48, 35.89, and 36.30m at the Goalpara gauge station (Table 4).The frequency factor of respected T periods relays on 0.80, 1.32, 2.34, 2.72, and 3.09; whereas the mean of (z) goes with 1.52, SD of (z) is 0.013, and the R 2 value is 0.9853 shown in Figure 3c.Almost 3 m of flood stage at the gauge station were found to be increased in the coming 200 years.

| Log-Normal distribution
Tables 1, 2, and 3 show the common frequency factor, for example, 0.842, 1.282, 2.054, 2.326, and 2.576 for all the rivers taken for study using the Log-Normal distribution method.The Coefficient of skewness for all the rivers has been considered for '0' throughout the calculation.The crossing gauge site is to be followed by 5, 10, 50, 100, and 200 years of the interval with an increasing rate of 1.14 m of flood height (Table 3).Figure 3b inferred the R 2 value of the best-fit line for river Aie is 0.9958.Considering the Brahmaputra River, Table 4 shows shown expected flood levels at the 5, 10, 50, 100, and 200 of T period are to reach 33.95, 34.40, 35.21, 35.49, and 36.00 m, and 0.9950 is the best-fit value against the computed water level and return period shown in Figure 3c.Approximately 2.05 m of flood stage in the next 200 years will be enriched at the Goalpara gauge station of the river Brahmaputra.

| The goodness of fit (GOF) analysis
GOF tests have been carried out on handy water level data sets for three rivers, for example, Manas, Aie, and the Brahmaputra shown in Table 5. C-S statistics test is being tested at a degree of freedom (df = 4) with a significant level (α) at 1% or 0.99.C-S returned the critical value of 0.297 for all the data sets.

| Inundation area and mapping (spatio-temporal effects)
The classified return period and calculated average water level X ( ) for different rivers are shown in Table 6.Generalized calculated results X ( ) T 44.14,44.65,45.80,46.25,and 47.00 m for Gumbel;44.00,44.13,44.81,45.02,44.30,44.81,45.06,and 45.27 m for Log-Normal are used as a critical input to prepare flood inundation mapping.
The spatial and temporal extension of the inundated and noninundated areas during different periods were T A B L E 2 Flood frequency estimation using Gumbel, LP-3, and Log-Normal distribution for Manas River.

Return period (Tr)
Gumbel distribution

LP-3 distribution
Log-Normal distribution Exceedance probability  Expected inundated areas followed through the Gumbel method are 493.54, 494.34, 556.66, 616.69, and 673.72 km 2 at various time intervals of 5, 10, 50, 100 and 200 years; where corresponding expected non-inundated area covers with 646.76, 645.96, 583.64, 523.61, and 466.58 km 2 at a same time interval.Results based on the Log-Pearson distribution show the district will face inundation areas of 493.01, 493.51, 499.38, 555.48, and 555.66 km 2 , whereas the section will remain untouched with flood water of 647.29, 646.79, 640.92, 584.82, and 584.64 km 2 at each 5, 10, 50, 100 and 200 years of the return period.
Thus, the inundation area to the district's total size is expected to increase with a temporal variation.Results stated from the Gumbel distribution method that about 15.8% of the total area is slated to be under flood water level; where LP-3 method follows 5.49% of increasing inundation area and the Log-Normal distribution method shows the 10.8% of the site shall come under the flood water in the upcoming years (Table 7).The geoenvironmental conditions of the study area have been influenced by the nature of the Brahmaputra River and its tributaries since it is a vital component of the Brahmaputra River basin.Environmental quality damage factors like Global warming, Greenhouse gases, and so on, have been significantly influencing the quality of climatic parameters which have been turned into effects of climate change around the globe (Stern & Kaufmann, 2014).Impacts of Global climatic change, such as rapid ice-melting in mountainous regions (especially the Himalayan region), intensification of rainfall rate, and so on, are become leading causes to rise in water levels and are responsible for the rise of floods peak in the Brahmaputra basin (Apurv et al., 2015;Xu et al., 2009).In addition, unforeseen human actions including deforestation, cutting river banks, feeling of the depressional area, sand mining, and so on, are also prevalent for an increase of intensity and flood liable areas in the region.

| DISCUSSION
Brahmaputra River and its tributaries are highly exposed to flood hazards due to various circumferences of geoenvironmental settings.In virtue of floods in the Brahmaputra River basin are marked by widespread, extreme sizes, and havoc in nature (Bhattachaiyya & Bora, 1997).This genuine hazard accelerated with the addition of human T A B L E 4 Flood frequency estimation using Gumbel, LP-3, and Log-Normal distribution for Brahmaputra River.
Return period (Tr)   interruption throughout the basin (Talukdar & Kalita, 2005).The primary cause that contributes to the rise of frequent floods within the Bongaigaon district is the rising water level in the Brahmaputra, Manas, and Aie Rivers during the monsoon period.It has also been seen that other factors like deforestation, river bed aggradation, and the closeness of tributaries play an essential role in floods within the Bongaigaon district (Walia & Pal, 2014).The study discussed flood frequency levels depending on the methods adopted for various rivers.Gumbel distribution in this study stated that the high water level of large rivers denotes the increased risk that prevails in this region.However, Log Pearson and Log-Normal distribution demonstrated relatively lower risk associated.Regarding the findings of the results, comparatively, the flood water level of the Brahmaputra River is rising more than the other two critical rivers.Manas and Aie are flood-generating northern tributaries of the Brahmaputra River; hence the massive influx of water and sands carries and dumps into the Brahmaputra River, which leads to rise in water level, bed aggradation, and surface morphology (Sarma, 2014;Walia & Pal, 2014).Naturally, the Brahmaputra, Manas, and Aie Rivers are interconnected and hence frequently raise up the water level in monsoon season making the region vulnerable to flood.The result also reveals that there is an increasing trend in respect of water depth of different rivers over the stipulated return period which have significant effects on rising water level as well as on submergence area expansion process.Among all the methods, Gumbel distribution shows the high range of value in the context of depth of water variation.Recent flooding years of 2015,2016,2017,2018,2019,2020  water level and rising inundated areas over time.It has been found that the projected water levels (average) are actively liable for the submergence of the area within the district in future and havoc losses to the society is been expected.In recent times due to the inundation of extensive areas under the region's effects on settlement, loss of life, livestock, crop, and infrastructure are common damages in the area (DDMP, 2011(DDMP, -2012)).Forecasted inundated areas found from the results in this study can be helpful for the assessment of potential damage areas by flood water and to protect those sensitive areas for future risk reduction.In this article, three methods provided the different patterns of inundation areas to be followed within the district.Prediction of inundation area in the section was found to be highest in the Gumbel distribution method and considered significant as the C-S test had been found reliable to the data set.Flood occurrence is a natural process which is impossible to stop with modern technology, but risks associated with a flood can be lessened with the implication of proper management strategies.Controlling, adaptive capacity, and mitigation treatment for managing floods have many dimensions in different places (Thanvisitthpon et al., 2020).Flood prediction and the associated identity problems became an essential strategy for flood management in the past (Olanrewaju & Reddy, 2022).Although structural and nonstructural measures are also taken to manage floods, but not so successfully achieved the result.Assessment of flood water level encountered for future to understand flood behavior among specified rivers and can give light to the people dwelling within the flood proximity area of the Bongaigaon district.Moreover, the discussed data set can be further used by government officials in respect of hazard mitigation and management purpose to reduce the flood risks within the district.With rationale, remember that the prediction of flood level and inundation area has been taken as unavoidable consideration for sustainable flood risk management in the neighborhood.

| CONCLUSION
In this study, monthly average water level data for the last three decades (in between 1990s and 2020s) has been considered to analyze the flood frequency scenario in the Bongaigaon district of the lower Brahmaputra plain.With the widely popular probability distribution approach like Gumbel, LP-3, and Log-Normal, the current study sought to estimate flood frequency for the Brahmaputra, Manas, and Aie Rivers at NH. Road crossing, Rly Bridge crossing, and Goalpara gauging sites respectively.Recurrence periods of 5, 10, 50, 100, and 200 were considered for calculating the expected flood level at each gauge station.Rivers are found to increase water levels near the time and liable to expand extensive inundated area within the study boundary.Among all the rivers, the Brahmaputra River recorded the highest increasing rate of water level; however, Manas and Aie also parallelly initiated with increasing nature.As water levels in  the rivers have increased over time, accordingly their depth of water also has been seen in an increased manner so far.Results from each method deployed for the frequency study for each river were also summarized to get the average water level which helped to identify the inundated and noninundated areas at respective years of the return period.The result of the study indicates the emerging nature of flood liable areas with the increasing water level of tested rivers.It seems that the region is heading toward a more vulnerable place in the future than today.The study shall bring highlight the nature of the flood scenario and shall contribute to enhancing different approaches to flood management systems in the district.Further, the findings of the study can help decision-makers (Government or nongovernmental) in the context of structural constructions likewise, embankments, bridges, roads, industrial areas, and so on.Moreover, the result also inferred flooded areas which significantly determines the vulnerable places in the district.It can be helpful to locate and the establishment of flood shelters and relief distribution centers in the region for better flood mitigation plans.Besides, in the flood management process, it is humbly recommended to strengthen and execute different flood mitigation or management measure like the construction of elevated land, high embankments, the concrete road alongside rivers, proper land use and land cover planning near the rivers, practices of afforestation next to rivers shall suit for the study area.The final outputs of the study are at hoped to be helpful for the decision-making process and flood management plan in the Bongaigaon district of the lower Brahmaputra reach.
| FFA 4.1.1| Gumbel distribution Based on the computation of the water level for Manas River at the NH road crossing site, the Gumbel distribution F I G U R E 2 Flow chart of the detail methodology used in the study.DUTTA and DEKA | 389

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I G U R E 3 (a) Composite flood frequency estimation for Manas River using Gumbel, LP3, and Log-Normal distribution; (b) Composite flood frequency estimation for Aie River using Gumbel, LP3, and Log-Normal distribution and (c) Composite flood frequency estimation for Brahmaputra River using Gumbel, LP3, and Log-Normal distribution consequently.
U R E 4 (a) Composite Projected depth of water of Manas River; (b) Composite Projected depth of water of Aie River, and (c) Composite Projected depth of water of Brahmaputra River using Gumbel, Log Pearson type 3 (LP-3), and Log-Normal distribution, respectively.
, and 2021 inferred that rising water levels in the mentioned rivers destroyed land holdings, houses, livestock, and so on, so forth in the floodplain area of the lower Brahmaputra reach of the district.As per the government report of Assam, revenue villages of the Bongaigaon (pt) circle, Srijangram circle, Boitamari circle, Sidli (pt) circle, and Bijni (pt) circle are experienced inundation by flood water every year.Results showed the uncertainty in the

T
A B L E 7 Inundated and noninundated area and percentage share based on different probability distribution method.
Table2).The lowest expected value in the next 5 years of the flood is 46.62, and the highest follows 48.07 in the next 200 years of return periods.Gumbel distribution for Manas River found an increasing trend regarding flood water level.Approximately 1.45 m of flood water level is slated to rise of river Manas in the coming 200 years at the NH road crossing site.Computed water level data based on the Gumbel distribution method for the Aie River gives the Coefficient of determination R 2 is 0.9999 in Figure3b.The result shows (Table3) that 51.63 m of water level is expected to occur in the next 5-year reoccurrence period and 54.04 m of flood water is found to appear in the 200 years of the return period.The flood frequency level of the river Aie in the Gumbel's method lies at 51.63, 52.10, 53.15, 53.59, and 54.04 m, respectively,  based on 5, 10, 50, 100, and 200years of returning period.About 2.41 m of flood water level has been expected to hike the Aie River in the next 200 years at the NH Rly crossing gauge station.Figure

Table 5
Details of the data used in this study.
T A B L E 1 Normal distribution.Respective inundation map of the study area for each 5, 10, 50, 100, and 200 years for Gumbel, Log-Pearson, and Log-Normal distributions have shown in Figures 5, 6, and 7.
Chi-square test for Manas, Aie, and Brahmaputra River for Gumbel, LP-3, and Log-Normal distribution.
Computed average water level ( XT ) for Flood Inundation mapping.