Modeling metamorphosis of the Old Brahmaputra River and associated impacts on landscapes in the Central Bengal Basin, Bangladesh

Abstract

This study explores causal mechanisms of river metamorphosis and its impacts on regional landscapes. The study also investigates the implications of metamorphosis on associated ecological resources. Advanced GIS and remote sensing technologies were used to delineate morphological parameters describing metamorphosis of the Old Brahmaputra River from historical maps (i.e., Rannell's Map in 1776, Tassin's Map in 1840, Topographic Survey Map in 1943) and remotely sensed optical satellite imagery Sentinel-2 in 2022. Flood frequencies were investigated for different periods by applying Gumbel’s Analytical Method (GAM), Log-Pearson Type III, and Log-Normal Method to estimate probability of flood vulnerability and impacts of flooding on morphodynamics in the central Bengal Basin. During the periods between 1776 and 2022, the area of sedimentation (77,999.43 ha) was greater than the eroded area (2983.29 ha).This difference was attributed to siltation of the channel bed morphology and corresponding accelerated flood vulnerability that accompanied river metamorphosis. Hydrological variables particularly annual average discharge significantly declined from 22 to ~ 18 m³/s per year during the period from 1965 to 2020. The study results demonstrated that the log-normal methods significantly overestimated peak flood discharge compared to Log-Pearson methods and Gumbel’s probability model. The extrapolation of the discharge for the 100-year flood by applying the three methods produced values of 712.66 m³/s, 1750.26 m³/s, and 2462.92 m³/s. Differences of these magnitudes may be critical for planning purposes because these differences in results will generate large-scale projected impacts on morphodynamics of the central Bengal Basin.

Appendices

Appendix 1. Details linear-log Regression Method

The results are routinely expressed using a graphical presentation where the log-normal values of the recurrence interval (T) define the independent variable and corresponding maximum flood peak discharges are the dependent variable (Eq. 4). In general, the method is applied by considering a numeric constant value a, which is derived from Eq. (5) and is the ratio of the total of the discrepancy between the 2nd and 1st highest peak flood discharge, the 3rd and 2nd highest flood discharge, and continuing through the flood data series, divided by the natural log of the second highest exceedance probability values separated from the preceding first value, and so on, for all the data values (Ramasamy et al. 2022).

$$Q_{p} = a*\ln \left[ T \right] + b.,$$

(3)

where $Q_{p}$ is displayed as the peak flood discharge; ln is the natural logarithm function, and whereas a and b are constants.

$$T = \frac{n + 1}{m},$$

(4)

where T is defined as the recurrence interval of flood peak discharge usually in years; N defines the number of years of flood peak data series; m is the rank of maximum peak flood discharge of the flood data series, whereas, rank 1 demonstrated the highest flood peak discharge for the study period, and subsequently the rankings follow in a descending order.

$$a = \frac{{\mathop \sum \nolimits_{i = 0}^{n} \left( {Q_{pi + 1} - Q_{pi} } \right)}}{{\mathop \sum \nolimits_{i = 0}^{n} \left( {\ln T_{i + 1 - } \ln T_{i} } \right)}} ,$$

(5)

where, a defines a slope coefficient, which is generally expressed as the change in the ratio of the flood peak discharge ($Q_{p}$) and relative change of recurrence interval (T).

$$R^{2} = 1 - \left[ {\frac{{\sum \left( {Q_{pi} - a\ln T_{i} + b} \right)}}{{\sum \left( {Q_{pi} - \overline{Q}_{p} } \right)^{2} }}} \right],$$

(6)

where $R^{2}$ is the coefficient of determination, and n = (1, 2, 3…….0.53) and i = (1, 2, 3, 4 …… 53).

Appendix 2. Details of Gumbel’s Analytical Method (GAM)

According to Gumbel's model of extreme value calculation, the probability that an occurrence will happen that is equal to or greater than a value (${Q}_{p0}$) is determined by Eq. (7). Based on the recorded annual daily discharge values of the river, the maximum discharge value of 53 years was used to determine the mean flood peak $({\overline{Q} }_{p})$ by summing each year’s flood peak and dividing by total the number of events/year (N = 53) (Eq. 14). The standard deviation (${\sigma }_{n-1}$) of the flood peak of 53 years was computed using Eq. 15. The frequency factors (K) were calculated as the difference between the reduced variate (${y}_{T })$ and reduced mean (${y}_{n)}$ divided by the reduced standard deviation (${S}_{n}$) for future time dimension (Eq. 16) (see Appendix 7). The required flood magnitude (${Q}_{pT}$) by mean discharge $\left({\overline{Q} }_{p}\right)$ and the multiplication results of frequency factor (K) and standard deviation (${\sigma }_{n-1}$) were determined for various return periods Eq. (12).

$$p\left( {Q_{p} \ge Q_{p0} } \right) = 1 - e^{{e^{ - y} }} ,$$

(7)

where y is a dimensional variable

$$y = \alpha \left( {Q_{p - } \beta } \right),$$

where $\beta = \overline{Q}_{p} - 0.45005\sigma_{n - 1}$

$$\alpha = \frac{1.285}{{\sigma_{n - 1} }}$$

$$y = \frac{{1.2825 \left( {Q_{p} - \overline{Q}_{p} } \right)}}{{\sigma_{n - 1} }} + 0.557$$

(8)

$$Q_{p} = \frac{{\sigma_{n - 1} \left( {y - 0.557} \right)}}{1.2825} + \overline{Q}_{p} ,$$

(9)

where ${\overline{Q} }_{p}$ is the mean of maximum flood discharge and ${\sigma }_{n-1}=$ Standard deviation of variate ${Q}_{p}$.the necessary value is the value of ${\overline{Q} }_{p}$ for a given probability (p), hence Eq. (5) is changed to read as ${y}_{(p)}$= value of variate y for a given probability (p).

$$= - \ln \left[ { - \ln \left( {1 - p} \right)} \right]$$

(10)

$$y_{T} = - \ln \left[ {\ln .\ln \frac{T}{T - 1}} \right]$$

(11)

$$y_{T} = - \left[ {0.834 + 2.303{\text{log log}}\frac{T}{T - 1}} \right]$$

(12)

$$Q_{pT} = \overline{Q}_{p} + K\sigma_{n - 1} ,$$

(13)

where ${Q}_{pT}$ is the maximum flood value for the return period T, ${\overline{Q} }_{p}$ is the mean peak flood discharge expressed by

$$\overline{Q}_{p} = \frac{{\sum Q_{p} }}{N},$$

(14)

$denotes$ Standard deviation which is calculated by

$$\sigma_{n - 1} { } = \sqrt {\frac{{\sum \left( {Q_{p} - \overline{Q}_{p} } \right)^{2} }}{N - 1}}$$

(15)

$$K = \frac{{\left( {y_{T} - 0.55} \right)}}{1.2825},$$

(16)

where Eq. (11) gives the value of $y_{T}$. Equation (15) is changed when N is less and has a finite value.

$$K = \frac{{\left[ {y_{T} - \overline{y}_{n} } \right]}}{{S_{n} }}$$

(17)

and, K is the frequency factor, $\overline{y}_{n} =$ A decreased mean, based on sample size N (See, Appendix 8), for $N \to \propto$, $y_{n} \to 0.557$, $\overline{y}_{n} =$ Standard deviation reduction, based on sample size N (see Appendix 9), $N\to \propto$ ${\overline{y} }_{n}\to$ 1.2825.

Appendix 3. Details of Log-Pearson Type III and Log-Normal Method

The mean $\left(\overline{Z }\right)$, standard deviation $\left({\sigma }_{z}\right)$, and coefficient of skewness (${C}_{s}$) of the base 10 logarithm of annual peak discharge were computed using Eq. (19). If the coefficient of skewness (${C}_{s}$) was not statistically different from zero at the 5% level of significance, the data set was assumed to have a log-normal distribution with a coefficient of skewness (${C}_{s}$) value equal to zero (Eq. 22). The flood discharge of z variate for various recurrence intervals (${Z}_{T}$) was calculated using frequency factor (${K}_{z}$), that is, ${K}_{z}$ is a function of recurrence interval and the coefficient of skewness $({C}_{s}$) and the variation of the ${K}_{z}=f({C}_{s}, {Z}_{T}$) (Appendix 5). Upon estimation of recurrence intervals (${Z}_{T}$) by following Eq. (20), the corresponding value of peak flood discharge $({Q}_{pT})$ was obtained by Eq. (23)

$$Z = {\text{log}}\left( {Q_{p} } \right)$$

(18)

$$\overline{Z} = \left( {\frac{{log \left( {Q_{p} } \right)}}{N}} \right)$$

(19)

$$\sigma_{z} = \sqrt {\frac{{\left( {z - \overline{z}} \right)^{2} }}{N}}$$

(20)

$$Z_{T} = \overline{z} + K_{Z} Q_{Z}$$

(21)

$$C_{s} = \frac{{N\sum \left( {z - \overline{z}} \right)^{3} }}{{\left( {N - 1} \right)\left( {N - 2} \right)\left( {\sigma_{z} } \right)^{3} }}$$

(22)

$$Q_{pT = } {\text{Anti}} - {\text{log }}\left( {Z_{T} } \right)$$

(23)

Appendix 4. Geology, physiography, and soil texture of the Central Bengal Basin

	Sl. no	Class	Area (ha)	Area (%)
Geological Formation	1	Alluvial sand	52,495.3	3.29
	2	Alluvial silt	642,691.9	40.29
	3	Alluvial silt and clay	322,688.2	20.23
	4	Chandina alluvium	130,935	8.21
	5	Dihing and Dupi Tila formations	76.32	0.01
	6	Madhupur clay residuum	348,715.5	21.86
	7	Marsh clay and peat	97,343.2	6.1
	8	Young gravelly	49.47	0.01
	Grand Total		1,594,995	100
Physiographic Unit	1	Active Brahmaputra–Jamuna Floodplain	113,080.7	7.22
	2	Active Ganges Floodplain	14,341.4	0.92
	3	Arial Beel	15,169.83	0.97
	4	Low Ganges River Floodplain	61,375.35	3.92
	5	Madhupur Tract	412,082.4	26.32
	6	Middle Meghna River Floodplain	5643.21	0.36
	7	Northern and Eastern Hills	39.36	0
	8	Northern and Eastern Piedmont Plains	2291.99	0.15
	9	Old Brahmaputra Floodplain	379,131.4	24.21
	10	Old Meghna Estuarine Floodplain	44,645.74	2.85
	11	Young Brahmaputra and Jamuna Floodplain	494,577.6	31.58
	12	Urban Area and Others	23,536.46	1.5
	Grand Total		1,565,915	100
Soil Texture	1	Clay	274,889.65	17.24
	2	Clay Loam	577,508.814	36.21
	3	Clay Loam/Clay	5692.1	0.36
	4	Clay Loam/Loam	116.95	0.01
	5	Clay Loam/Sandy Loam	1725.2	0.11
	6	Clay/Clay Loam	3234.85	0.2
	7	Loam	414,739.18	26
	8	Loam/Sandy Loam	9946.86	0.62
	9	River	39,731.32	2.49
	10	Sand	27,579.5	1.73
	11	Sandy Loam	21,993.24	1.38
	12	Sandy Loam/Loam	956.89	0.06
	13	Settlement	135,286.03	8.48
	14	Waterbodies	12,617.74	0.79
	15	Char	4387.22	0.28
	16	No Data	64,443.14	4.04
	Grand Total		1,594,848.8	100

Appendix 5. Metadata Profile of historical image of the Old Brahmaputra River

Year	Historical Maps	Representative Fraction (R.F)	Atlas	Publisher	Publishing Year and Location	References
1776	Rannell's Map	1:316,800	Bengal Atlas	East India Company	Survey of India Offices, 1780	Rennell (1780)
1840	Tassin's Map	1: 1: 253,000	Tassin's Atlas of the Delta, 1840	Oriental Lithographic Press in Calcutta	Calcutta, 1841	Tassin (1841)
1943	Topographic Survey Map	1: 1: 253,000	Army Map Service (NSS & H)	Corps of Engineers, U.S. Army	Washington, D.C, USA in 1943	US Army (1943)

Appendix 6. Sources of secondary data and description

S.l. No	Data Type	Description	Source
01	Geology	The geological formation of the earth’s crust is vector data that disclose sediment deposits and their geologic history	Geological Survey of Bangladesh (GSB)
02	Physiography	Physiographic unit of Bangladesh with 30 classes in vector file format	CEGIS Archive
03	Discharge (m3/s)	Tabulated data of discharge of the Old Brahmaputra River at the Mymensingh Gaugin station during the years from 1965 to 2022	CEGIS archive
04	Rainfall Data	Monthly average rainfall data for30 years from 1987 to 2017	Bangladesh Metrological Department (BMD)
05	Soil Texture	Soil texture vector data for the year 1995. Soil classified on the basis of soil texture classes	Soil Resource Development Institute (SRDI) of Bangladesh
06	Digital Elevation Model (DEM)	Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM 003). (spatial resolution 30 m)	EARTHDATA(https://earthdata.nasa.gov)
07	Satellite 2 Image (2022)	Sentinel-2 satellite Image is an optical high-resolution satellite Image of European Commission and the European Space Agency (spatial resolution 10 m). Generally, the Sentinel-2 data were acquired, processed, and generated by the European Space Agency (ESA) and repackaged by USGS into tile-based bundles	https://scihub.copernicus.eu/dhus/#/home

Appendix 7. Computation of return period and exceedance probability of flood peak discharge of the Old Brahmaputra River (1965–2020)

Year	Peak Flood Discharge (QP) (m)3/s	Rank	Return period (Tr) [(n + 1)/m]	Exceedance PRobability (1/Tr)
1988	4890	1	54.00	0.02
1984	4750	2	27.00	0.04
1974	3820	3	18.00	0.06
1977	3556	4	13.50	0.07
1966	3490	5	10.80	0.09
1980	3340	6	9.00	0.11
1970	3250	7	7.71	0.13
1965	3230	8	6.75	0.15
1976	3210	9	6.00	0.17
1987	3210	10	5.40	0.19
2004	3178.69	11	4.91	0.20
1985	3070	12	4.50	0.22
1975	3060	13	4.15	0.24
1967	3000	14	3.86	0.26
1981	2990	15	3.60	0.28
1968	2900	16	3.38	0.30
1991	2890	17	3.18	0.31
2002	2793.43	18	3.00	0.33
1969	2770	19	2.84	0.35
1978	2770	20	2.70	0.37
1996	2639.39	21	2.57	0.39
1979	2630	22	2.45	0.41
1998	2558.1	23	2.35	0.43
1982	2470	24	2.25	0.44
2020	2431	25	2.16	0.46
2003	2420.37	26	2.08	0.48
2016	2368.37	27	2.00	0.50
1983	2360	28	1.93	0.52
1989	2160	29	1.86	0.54
1999	2089.74	30	1.80	0.56
2019	2088.9	31	1.74	0.57
1990	2050	32	1.69	0.59
1993	2050	33	1.64	0.61
2000	2029.13	34	1.59	0.63
2017	2018.81	35	1.54	0.65
1997	2013.72	36	1.50	0.67
1986	1930	37	1.46	0.69
1995	1611.28	38	1.42	0.70
1992	1480	39	1.38	0.72
2010	1425.41	40	1.35	0.74
2005	1379.37	41	1.32	0.76
2008	1221.59	42	1.29	0.78
2001	1141.9	43	1.26	0.80
2009	1089.21	44	1.23	0.81
2015	1061.92	45	1.20	0.83
2011	940.27	46	1.17	0.85
2018	875.75	47	1.15	0.87
1994	809	48	1.13	0.89
2012	737.45	49	1.10	0.91
2006	626.44	50	1.08	0.93
2013	508.74	51	1.06	0.94
2014	494.85	52	1.04	0.96
2007	487.27	53	1.02	0.98
Average	2271.058491

Appendix 8. Frequency Factors K for Gamma and log-Pearson Type III Distributions (Haan, 1977, Table 7.7)

Weighted	Recurrence Interval In Years
	1.0101	2	5	10	25	50	100	200
Skew coefficient	Percent Chance (> =) = 1-F
Cs	99	50	20	10	4	2	1	0.5
3	– 0.667	– 0.396	0.42	1.18	2.278	3.152	4.051	4.97
2.9	– 0.69	– 0.39	0.44	1.195	2.277	3.134	4.013	4.904
2.8	– 0.714	– 0.384	0.46	1.21	2.275	3.114	3.973	4.847
2.7	– 0.74	– 0.376	0.479	1.224	2.272	3.093	3.932	4.783
2.6	– 0.769	– 0.368	0.499	1.238	2.267	3.071	3.889	4.718
2.5	– 0.799	– 0.36	0.518	1.25	2.262	3.048	3.845	4.652
2.4	– 0.832	– 0.351	0.537	1.262	2.256	3.023	3.8	4.584
2.3	– 0.867	– 0.341	0.555	1.274	2.248	2.997	3.753	4.515
2.2	– 0.905	– 0.33	0.574	1.284	2.24	2.97	3.705	4.444
2.1	– 0.946	– 0.319	0.592	1.294	2.23	2.942	3.656	4.372
2	– 0.99	– 0.307	0.609	1.302	2.219	2.912	3.605	4.298
1.9	– 1.037	– 0.294	0.627	1.31	2.207	2.881	3.553	4.223
1.8	– 1.087	– 0.282	0.643	1.318	2.193	2.848	3.499	4.147
1.7	– 1.14	– 0.268	0.66	1.324	2.179	2.815	3.444	4.069
1.6	– 1.197	– 0.254	0.675	1.329	2.163	2.78	3.388	3.99
1.5	– 1.256	– 0.24	0.69	1.333	2.146	2.743	3.33	3.91
1.4	– 1.318	– 0.225	0.705	1.337	2.128	2.706	3.271	3.828
1.3	– 1.383	– 0.21	0.719	1.339	2.108	2.666	3.211	3.745
1.2	– 1.449	– 0.195	0.732	1.34	2.087	2.626	3.149	3.661
1.1	– 1.518	– 0.18	0.745	1.341	2.066	2.585	3.087	3.575
1	– 1.588	– 0.164	0.758	1.34	2.043	2.542	3.022	3.489
0.9	– 1.66	– 0.148	0.769	1.339	2.018	2.498	2.957	3.401
0.8	– 1.733	– 0.132	0.78	1.336	1.993	2.453	2.891	3.312
0.7	– 1.806	– 0.116	0.79	1.333	1.967	2.407	2.824	3.223
0.6	– 1.88	– 0.099	0.8	1.328	1.939	2.359	2.755	3.132
0.5	– 1.955	– 0.083	0.808	1.323	1.91	2.311	2.686	3.041
0.4	– 2.029	– 0.066	0.816	1.317	1.88	2.261	2.615	2.949
0.3	– 2.104	– 0.05	0.824	1.309	1.849	2.211	2.544	2.856
0.2	– 2.178	– 0.033	0.83	1.301	1.818	2.159	2.472	2.763
0.1	– 2.252	– 0.017	0.836	1.292	1.785	2.107	2.4	2.67
0	– 2.326	0	0.842	1.282	1.751	2.054	2.326	2.576
– 0.1	– 2.4	0.017	0.846	1.27	1.716	2	2.252	2.482
– 0.2	– 2.472	0.033	0.85	1.258	1.68	1.945	2.178	2.388
– 0.3	– 2.544	0.05	0.853	1.245	1.643	1.89	2.104	2.294
– 0.4	– 2.615	0.066	0.855	1.231	1.606	1.834	2.029	2.201
– 0.5	– 2.686	0.083	0.856	1.216	1.567	1.777	1.955	2.108
– 0.6	– 2.755	0.099	0.857	1.2	1.528	1.72	1.88	2.016
– 0.7	– 2.824	0.116	0.857	1.183	1.488	1.663	1.806	1.926
– 0.8	– 2.891	0.132	0.856	1.166	1.448	1.606	1.733	1.837
– 0.9	– 2.957	0.148	0.854	1.147	1.407	1.549	1.66	1.749
– 1	– 3.022	0.164	0.852	1.128	1.366	1.492	1.588	1.664
– 1.1	– 3.087	0.18	0.848	1.107	1.324	1.435	1.518	1.581
– 1.2	– 3.149	0.195	0.844	1.086	1.282	1.379	1.449	1.501
– 1.3	– 3.211	0.21	0.838	1.064	1.24	1.324	1.383	1.424
– 1.4	– 3.271	0.225	0.832	1.041	1.198	1.27	1.318	1.351
– 1.5	– 3.33	0.24	0.825	1.018	1.157	1.217	1.256	1.282
– 1.6	– 3.88	0.254	0.817	0.994	1.116	1.166	1.197	1.216
– 1.7	– 3.444	0.268	0.808	0.97	1.075	1.116	1.14	1.155
– 1.8	– 3.499	0.282	0.799	0.945	1.035	1.069	1.087	1.097
– 1.9	– 3.553	0.294	0.788	0.92	0.996	1.023	1.037	1.044
– 2	– 3.605	0.307	0.777	0.895	0.959	0.98	0.99	0.995
– 2.1	– 3.656	0.319	0.765	0.869	0.923	0.939	0.946	0.949
– 2.2	– 3.705	0.33	0.752	0.844	0.888	0.9	0.905	0.907
– 2.3	– 3.753	0.341	0.739	0.819	0.855	0.864	0.867	0.869
– 2.4	– 3.8	0.351	0.725	0.795	0.823	0.83	0.832	0.833
– 2.5	– 3.845	0.36	0.711	0.711	0.793	0.798	0.799	0.8
– 2.6	– 3.899	0.368	0.696	0.747	0.764	0.768	0.769	0.769
– 2.7	– 3.932	0.376	0.681	0.724	0.738	0.74	0.74	0.741
– 2.8	– 3.973	0.384	0.666	0.702	0.712	0.714	0.714	0.714
– 2.9	– 4.013	0.39	0.651	0.681	0.683	0.689	0.69	0.69
– 3	– 4.051	0.396	0.636	0.66	0.666	0.666	0.667	0.667

Appendix 9. Frequency factor (K) for Gumbel’s method

Sample size (N) in years	RP(T) in years
	5	10	15	20	25	30	50	60	75	100	1000
15	0.967	1.703	2.117	2.41	2.632	2.823	3.321	3.501	3.721	4.005	6.27
20	0.919	1.625	2.023	2.302	2.517	2.69	3.179	3.352	3.563	3.836	6.01
25	0.888	1.575	1.963	2.235	2.444	2.614	3.088	3.257	3.463	3.729	5.85
30	0.866	1.541	1.922	2.188	2.393	2.56	3.026	3.191	3.393	3.653	5.73
35	0.851	1.516	1.891	2.152	2.354	2.52	2.979	3.142	3.341	3.598
40	0.838	1.495	1.866	2.126	2.326	2.489	2.943	3.104	3.301	3.554	5.58
45	0.829	1.478	1.847	2.104	2.303	2.464	2.913	3.078	3.268	3.52
50	0.82	1.466	1.831	2.086	2.283	2.443	2.889	3.048	3.241	3.491	5.48
55	0.813	1.455	1.818	2.071	2.267	2.426	2.869	3.027	3.219	3.467
60	0.807	1.446	1.806	2.059	2.253	2.411	2.852	3.008	3.2	3.446
65	0.801	1.437	1.796	2.048	2.243	2.398	2.837	2.992	3.183	3.429
70	0.797	1.43	1.788	2.038	2.23	2.387	2.824	2.979	3.169	3.413	5.36
75	0.792	1.423	1.78	2.029	2.22	2.377	2.812	2.967	3.155	3.4
80	0.788	1.417	1.773	2.02	2.212	2.368	2.802	2.956	3.145	3.387
85	0.785	1.413	1.767	2.013	2.205	2.361	2.793	2.946	3.135	3.376
90	0.782	1.409	1.762	2.007	2.198	2.353	2.785	2.938	3.125	3.367
95	0.78	1.405	1.757	2.002	2.193	2.347	2.777	2.93	3.116	3.357
100	0.779	1.401	1.752	1.993	2.187	2.341	2.77	2.922	3.109	3.349	2.61

Appendix 10. Rate of change statistics of the geometry of the Old Brahmaptura River (1776–2022)

Geometry	Rate of Change
	1776–1840	1840–1943	1943–2022	1776–2022
	Time Interval (64)	Time Interval (103)	Time Interval (79)	Time Interval (246)
Channel Length (km)	0.13	0.20	0.26	0.26
Valley Length (km)	0.09	– 0.05	– 0.07	0.01
Bar Length (km)	1.15	– 1.52	– 1.98	– 0.45
Average Channel Width (m)	– 13.71	– 32.17	– 41.94	– 18.03
Channel Area (ha)	– 652.99	– 257.74	– 336.04	– 304.96
Bar or Char Area (ha)	233.87	– 263.33	– 343.33	– 69.49

Appendix 11. Calculation of flood discharge for different Return Periods (RP) using Gumbel’s Analytical Method (GAM)

RP	${\overline{Q} }_{p}$	${\sigma }_{n-1}$	K	GAM ${Q}_{p}={\overline{Q} }_{p}+K{Q}_{n-1}$
2	2271.06	1047.89	– 0.157	2106.4
5	2271.06	1047.89	0.8151	3125.19
10	2271.06	1047.89	1.4588	3799.72
25	2271.06	1047.89	2.27212	4651.99
50	2271.06	1047.89	2.87548	5284.25
100	2271.06	1047.89	3.47439	5911.84
200	2271.06	1047.89	4.07112	6537.15
1000	2271.06	1047.89	5.45338	7985.61

Appendix 12. Calculation of flood discharge for different Return Periods (RP) using the Log-Pearson Method

RP	$\overline{Z }$	$\sum {\left(z-\overline{z }\right)}^{3}$	${\sigma }_{z}$	${C}_{s}$	${K}_{z}$	${Z}_{T}$	Log-Pearson ${Q}_{p}=Antilog{Z}_{T}$
2	3.296	– 0.774	0.253	– 0.96	0.148	3.33	2155.71
5	3.296	– 0.774	0.253	– 0.96	0.854	3.51	3251.76
10	3.296	– 0.774	0.253	– 0.96	1.147	3.59	3856.65
25	3.296	– 0.774	0.253	– 0.96	1.407	3.65	4487.02
50	3.296	– 0.774	0.253	– 0.96	1.549	3.69	4873.78
100	3.296	– 0.774	0.253	– 0.96	1.66	3.72	5199.18
200	3.296	– 0.774	0.253	– 0.96	1.749	3.738	5475.707
1000	3.296	– 0.774	0.253	– 0.96	1.91	3.78	6013.85

Appendix 13. Calculation of flood discharge for different Return Periods (RP) using the Log-normal Method

RP	$\overline{Z }$	$\sum {\left(z-\overline{z }\right)}^{3}$	${\sigma }_{z}$	${C}_{s}$	${K}_{z}$	${Z}_{T}$	Log-normal ${Q}_{p}=Antilog{Z}_{T}$
2	3.296	– 0.774	0.253	0	0	3.30	1977.72
5	3.296	– 0.774	0.253	0	0.842	3.51	3229.12
10	3.296	– 0.774	0.253	0	1.282	3.62	4172.04
25	3.296	– 0.774	0.253	0	1.751	3.74	5482.09
50	3.296	– 0.774	0.253	0	2.054	3.82	6539.83
100	3.296	– 0.774	0.253	0	2.326	3.88	7662.10
200	3.296	– 0.774	0.253	0	2.576	3.95	8862.70
1000	3.296	– 0.774	0.253	0	3.090	4.08	11,954.81

Appendix 14. Comparison of flood discharge or different return periods with GAM, Log-Pearson and Log-Normal approach

RP	Differences in Discharge between GAM and Log-Pearson		Differences in Discharge between Log-normal and GAM		Differences in Discharge between Log-normal and Log-Pearson
	Discharge (m3/s)	%	Discharge (m3/s)	%	Discharge (m3/s)	%
2	– 49.31	– 2.28	– 128.68	– 6.109	– 177.99	– 8.25
5	– 126.57	– 3.89	103.93	3.32	– 22.64	– 0.69
10	– 56.93	– 1.47	372.32	9.79	315.39	8.177
25	164.97	3.67	830.1	17.84	995.07	22.17
50	410.47	8.422	1255.58	23.76	1666.05	34.18
100	712.66	13.70	1750.26	29.60	2462.92	47.37
200	1061.443	19.38	2325.55	35.57	3386.993	61.85
1000	1971.76	32.78	3969.2	49.70	5940.96	98.78

Appendix 15. Present status of Ecological diversity of the Old Brahmaputra River

Sl No	Ecological bio-diversity	Types	Local Bengali Name	English Name	Scientific Name	Family	Current Status	Causes of decline
1	Fauna	Large-size Fish	Boaal	Wallago	Wallago attu	Siluridae	Vulnerable (VU)	Water scarcity and siltation on the river bed
2	Fauna	Large-size Fish	Silond	Silond Catfish	Silondia Silondia	Schilbeidae	Least Concern (LC)	Water scarcity and siltation on the river bed
3	Fauna	Large-size Fish	Pangas	Yellowtail catfish	Pangasius pangasius	Pangasiidae	Endangered (EN)	Habitat destruction
4	Fauna	Large-size Fish	Aire	Long-Whiskered Catfish	Mystus aor	Bagridae	Vulnerable (VU)	Water scarcity and siltation on the river bed
5	Fauna	Large-size Fish	Baghaire	Devil catfish	Bagarius	Sisoridae	Critically Endangered (CR)	Water scarcity and siltation on river bed
6	Fauna	Large-size Fish	Rita	Rita	Rita Rita	Bagridae	Endangered (EN)	Water scarcity and siltation on river bed
7	Fauna	Large-size Fish	Chital	Clown Knifefish	Notopterus chitala	Notopteridae	Endangered (EN)	Over fishing and habitat reduce
8	Fauna	Large-size Fish	Shol	Striped snakehead	Channa striatus	Channidae	Least Concern (LC)	Habitat reduce
9	Fauna	Large-size Fish	Gazar	Bullseye snakehead	Channa marulius	Channidae	Endangered (EN)	Habitat reduce and water scarcity
10	Fauna	Large-size Fish	Rui	Rohu	Labeo rohita	Cyprinidae	Least Concern (LC)	Over fishing and anthropogenic pollution
11	Fauna	Large-size Fish	Catla	Catla	Catla catla	Cyprinidae	Least Concern (LC)	Over fishing and Over fishing and water scarcity
12	Fauna	Large-size Fish	Mrigal	Mrigal Carp	Cirrhinus cirrhosus	Cyprinidae	Non-threatened	Habitat destruction and water scarcity
13	Fauna	Large-size Fish	Kalibaush	Orange Fin Labeo	Labeo calbasu	Cyprinidae	Least Concern (LC)	Habitat destruction and water scarcity
14	Fauna	Small-size Fish	Singi	Fossil cat	Heteropneustes fossilis	Heteropneustidae	Least Concern (LC)	Over fishing and water scarcity
15	Fauna	Small-size Fish	Tara Baim	Spiny eel	Macrognathus aculeatus	Mastacembelidae	Near Threatened (NT)	Over fishing
16	Fauna	Small-size Fish	Baim/Sal Baim	Tire-track spiny eel	Mastacembelus armatus	Mastacembelidae	Endangered (EN)	Over fishing and water scarcity
17	Fauna	Small-size Fish	Pholi	Bronze featherback	Notopterus notopterus	Notopteridae	Vulnerable (VU)	Over fishing and water scarcity
18	Fauna	Small-size Fish	Magur	Walking catfis	Clarias batrachus	Clariidae	Least Concern (LC)	Over fishing and water scarcity
19	Fauna	Small-size Fish	Koi	Climbing gourami	Anabas cobojius	Anabantidae	Least Concern (LC)	Habitat reduce
20	Fauna	Small-size Fish	Chanda	Elongate glassy perchlet	Chanda nama	Centropomidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
21	Fauna	Small-size Fish	Ranga Chanda	Indian glassy fish	Parambassis ranga	Ambassidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
22	Fauna	Small-size Fish	Bata	Mackerel	Labeo bata	Cyprinidae	Least Concern (LC)	Over fishing and water scarcity
23	Fauna	Small-size Fish	Kalabata	Gangetic latia	Crossocheilus latius	Cyprinidae	Critically Endangered (CR)	Over fishing and water scarcity
24	Fauna	Small-size Fish	Jat Punti	Pool Barb	Puntius sophore	Cyprinidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
25	Fauna	Small-size Fish	Tit punti	Ticto barb	Puntius ticto	Cyprinidae	Vulnerable (VU)	Habitat reduce and anthropogenic pollution
26	Fauna	Small-size Fish	Punti	Swamp barb	Puntius chola	Cyprinidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
27	Fauna	Small-size Fish	Sarpunti	Olive barb	Systomus sarana	Cyprinidae	Near Threatened (NT)	Anthropogenic pollution and water scarcity
28	Fauna	Small-size Fish	Chela	Silver razorbelly minnow	Salmophasia acinaces	Cyprinidae	Least Concern (LC)	Anthropogenic pollution and Habitat reduce
29	Fauna	Small-size Fish	Darkina	Indian flying barb)	Esomus danricus	Cyprinidae	Least Concern (LC)	Anthropogenic pollution and Habitat reduce
30	Fauna	Small-size Fish	Ghonia	Boggut Labeo	Labeo boggut	Cyprinidae	Vulnerable (VU)	Anthropogenic pollution and Habitat reduce
31	Fauna	Small-size Fish	Jaya	Jaya	Aspidoparia jaya	Cyprinidae	Least Concern (LC)	Over fishing and water scarcity
32	Fauna	Small-size Fish	Morar	Aspidoparia	Aspidoparia morar	Cyprinidae	Vulnerable (VU)	Over fishing and water scarcity
33	Fauna	Small-size Fish	Along	Bengala barb	Megarasbora elanga	Cyprinidae	Endangered (EN)	Over fishing and water scarcity
34	Fauna	Small-size Fish	Koksa	Shacra baril	Barilius shacra	Cyprinidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
35	Fauna	Small-size Fish	Barali	Barred Baril	Barilius barila	Cyprinidae	Endangered (EN)	Over fishing and water scarcity
36	Fauna	Small-size Fish	Chapchela	Indian glass barb	Laubuka laubuca	Cyprinidae	Least Concern (LC)	Habitat reduce
37	Fauna	Small-size Fish	Anju	Zebra danio	Danio rerio	Cyprinidae	Near Threatened (NT)	Habitat reduce and anthropogenic pollution
38	Fauna	Small-size Fish	Mola	Indian Carplet	Amblypharyngodon microlepis	Cyprinidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
39	Fauna	Small-size Fish	Lohasura	Coito	Osteobrama cotio	Cyprinidae	Near Threatened (NT)	Water scarcity and siltation on the river bed
40	Fauna	Small-size Fish	Bou Machh	Bengal loach	Botia dario	Botiidae	Endangered (EN)	Habitat reduce
41	Fauna	Small-size Fish	Gutum	Guntea loach	Lepidocephalichthys guntea	Cobitidae	Least Concern (LC)	Habitat reduce and anthropogenic pollution
42	Fauna	Small-size Fish	Lal Kholisha	Dwarf gourami	Trichogaster lalius	Osphronemidae	Least Concern (LC)	Over fishing and water scarcity
43	Fauna	Small-size Fish	Kholisha	Banded Gourami	Trichogaster fasciata	Osphronemidae	Least Concern (LC)	Over fishing and water scarcity

1. Source: Field Survey (2022), Islam and Kitazawa (2013), Bashar et al. (2020), Ahmed et al. (2013), Raushon et al. (2017)

Appendix 16. Local Boro Rice Cultivation area and production statistics during the time periods 2011–2012 and 2020–2021 in the Old Brahmaputra River Basin districts.

District	2011–2012		2020–2021		Change Statistics of Boro Crops (2011–2012) and (2020–2021)
	Boro Rice Cultivated Area (Ha)	Boro Rice Production (M. Ton	Boro Rice Cultivated Area (Ha)	Boro Rice Production (M. Ton)	Boro Rice Cultivated Area Loss /Increase (ha)	Boro Rice Cultivated Area Loss /Increase (%)	Boro Rice Cultivated Area Loss /Increase Rate (Ha/yr)	Boro Rice Production Change (M. Ton)	Boro Rice Production Change (%)	Boro Rice Production Change (M. Ton/Yr)
Dhaka	328	593	380	869	276	15.85	30.67	276	46.543	30.67
Gazipur	275	434	157	281	– 153	– 42.91	– 17.00	– 153	– 35.253	– 17.00
Jamalpur	1248	1248	397	620	– 628	– 68.19	– 69.78	– 628	– 50.321	– 69.78
Kishoreganj	663	943	437	859	– 84	– 34.09	– 9.33	– 84	– 8.908	– 9.33
Manikganj	1609	2649	688	1158	– 1491	– 57.24	– 165.67	– 1491	– 56.285	– 165.67
Munshiganj	408	610	65	103	– 507	– 84.07	– 56.33	– 507	– 83.115	– 56.33
Mymensingh	670	1330	116	251	– 1079	– 82.69	– 119.89	– 1079	– 81.128	– 119.89
Narayanganj	268	505	70	65	– 440	– 73.88	– 48.89	– 440	– 87.129	– 48.89
Narsingdi	272	530	160	310	– 220	– 41.18	– 24.44	– 220	– 41.509	– 24.44
Sherpur	266	555	52	93	– 462	– 80.45	– 51.33	– 462	– 83.243	– 51.33
Tangail	2107	1750	676	1168	– 582	– 67.92	– 64.67	– 582	– 33.257	– 64.67
	8114		3198

1. Source: BBS (Bangladesh Bureau of Statistics) (2012, 2022)