Brahmaputra
6.2 Synthesis of observations and interpretation from different experiments and test results
Among the analyzed geochemical parameters pH, OC, CC, CEC, ESP, d10, d50 and d90,
were more fluctuating in erosion sites than those of non-erosion sites, as revealed from range and standard deviation values (Table 6.1 and Table 6.2).
Q K
Q
M Q Q Q
0 100 200 300 400 500 600 700 800
5 10 15 20 25 30 35 40 45 50 55 60 65 70
Intensity (CPS)
2 theta (degrees)
Table 6.1 Descriptive statistics of different parameters of soil samples from erosion sites (N = 36)
Range Minimum Maximum Mean SD
pH 3.17 4.93 8.10 6.81 .93
OC 6.48 .12 6.60 1.74 1.75
CC 5.45 .22 5.67 1.30 1.15
SAR 3.90 .26 4.16 1.12 .765
CEC 3.97 .24 4.22 1.32 .96
ESP 14.41 1.26 15.67 4.15 3.15
d10 343.10 1.90 345.00 35.22 75.35
d50 605.00 15.00 620.00 105.98 133.65
d90 1340.90 59.10 1400.00 343.21 346.81
Table 6.2 Descriptive statistics of different parameters of samples from non-erosion sites (N = 34)
Range Minimum Maximum Mean SD
pH 2.13 6.67 8.80 7.34 .38
OC 5.50 1.10 6.60 2.49 1.13
CC 2.32 .68 3.00 1.82 .69
SAR 6.85 .75 7.60 2.61 1.62
CEC 3.54 .64 4.19 1.48 .71
ESP 4.42 1.47 5.89 3.22 1.25
d10 32.10 2.10 34.20 7.25 6.23
d50 84.08 10.72 94.80 36.78 17.43
d90 272.62 34.38 307.00 110.17 54.96
Bank materials of erosion site E had the lowest pH (acidic, mean value 5.3). Less amount of exchangeable Na, K, Ca and Mg was observed in all locations including non-erosion site of D, which resulted in very low value of CEC in all locations. This may be attributed to less amount of clay and high amount of silt and sand particles in soil. Comparatively, more varied as well as high values of Ca and Mg were observed in erosion site of C. Increasing soluble Ca and Mg improves aggregate stability in soils.
However, impact of high Ca and Mg was not observed in loose unconsolidated bank materials of C location in spite of having clay pockets. This may be attributed to high amount of silt particles (mean value 71%) in the bank materials. Low amount of exchangeable Na (8%) in bank materials of erosion site C is one geochemical factor contributing to loose structure of bank.
Low value of SAR in erosion sites could be due to low available Na in soils. Although comparatively high values of SAR were observed in non-erosion sites of D, only 4 samples out of 12 samples had higher SAR values than the mean value. High variation of SAR with depth along with high clay content and less variation of sand particles have been observed as significant geochemical properties contributing to non-erosion in the locations of D.
Erosion site A had comparatively high sand content and the lowest value of minimum clay content (0.3%) among the study areas. Erosion sites of B, D, E and F also had high sand content (52%, 44%, 36% and 31% respectively) with very low amount of clay sized particles (3 – 8%). These two factors including low organic content are likely to be considered as significant geochemical factors for low cohesion in the studied locations as revealed from direct shear test results (Figure 6.11).
y = 0.8909x + 0.1525 R² = 0.9973
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
0 0.5 1 1.5
Shear stress (kg/cm2)
Normal stress (kg/cm2) Sample: Location A
c = 0.15 kg/cm2 ϕ = 41º
y = 0.8105x + 0.0181 R² = 0.9998
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0 0.5 1 1.5
Shear stress (kg/cm2)
Normal stress (kg/cm2) Sample: Location B
c = 0.02 kg/cm2 ϕ = 39º
Figure 6.11 Direct shear test results of bank materials
High angle of internal friction of samples in spite of low cohesion could be explained by aggregation of soil particles (Lebert and Horn, 1991). The aggregates were strong
y = 0.6771x + 0.1084 R² = 0.9989
0 0.2 0.4 0.6 0.8 1 1.2
0 0.5 1 1.5
Shear stress (kg/cm2)
Normal stress (kg/cm2) Sample: Location C
c = 0.1 kg/cm2 ϕ = 34º
y = 0.8641x + 0.0558 R² = 0.9869
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
0 0.5 1 1.5
Shear stress (kg/cm2)
Normal stress (kg/cm2) Sample: Location D1
c = 0.05 kg/cm2 ϕ = 40º
y = 0.8528x + 0.1 R² = 0.9987
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
0 0.5 1 1.5
Shear stress (kg/cm2)
Normal stress (kg/cm2) Sample: Location F
c = 0.1 kg/cm2 ϕ = 40º
enough to resist the stresses and the soil acted like very dense sand with high angle of internal friction (Lebert and Horn, 1991). Lohnes and Handy (1968) suggested that unconsolidated sediments with low cohesion can stand temporarily at steep angles.
Steep river banks in erosion sites of Brahmaputra might be due to high angle of internal friction resulting from aggregation of cohesion less soil particles.
Brahmaputra at erosion sites A, B and F is extremely braided. So, section average velocity at A, B and F are comparatively low than that of single channel in other location. However, the average velocities corresponding to a pre-monsoon discharge and peak monsoon discharge are two to hundred folds higher than that required for threshold movement of sediment (Table 6.3), as obtained from Hjulström diagram.
Table 6.3 Particle size and corresponding velocities for threshold movement of sediments
Location B
Pre-monsoon discharge = 15000 m3/s Monsoon peak discharge = 64619 m3/s Pre monsoon velocity = 0.82 m/s Monsoon velocity = 1.01 m/s
Location F
Pre-monsoon discharge = 15000 m3/s Monsoon peak discharge = 76303 m3/s Pre monsoon velocity = 0.80 m/s Monsoon velocity = 0.86 m/s
d50 TV d90 TV d50 TV d90 TV
0.06 0.07 0.16 0.40 0.01 0.009 0.03 0.03
0.06 0.07 0.24 0.50 0.02 0.02 0.07 0.09
0.06 0.07 1.40 3 0.02 0.02 0.10 0.35
0.11 0.35 1.10 2.7 0.02 0.02 0.06 0.07
0.62 0.80 1.10 2.7 0.02 0.02 0.12 0.38
TV: velocities for threshold movement of sediments
6.2.1 Role of geochemical properties of bank materials in erosion
The first preliminary step was to see potential correlation between predictor variables (different geochemical properties of bank materials) and the outcome, i.e., erosion.
Correlation matrices (Table 6.4) showed that pH, organic content, carbonate content, SAR had negative correlation with erosion and particle size (d50 and d90) had positive correlation with erosion.
Table 6.4 Correlation matrices of different parameters of all samples
Erosion pH OC CC SAR CEC ESP d10 d50 d90
Erosion 1
pH -.35** 1
OC -.25* .07 1
CC -.27* .05 .51** 1
SAR -.52** .27* .21 .29* 1
CEC -.09 .17 .34** .47** .13 1
ESP .19 -.48** -.24* -.38** -.10 -.56** 1
d10 .25 .21 -.27* -.22 -.15 -.15 .13 1
d50 .34** .07 -.22 -.26* -.21 -.20 .22 .93** 1 d90 .43** .04 -.21 -.13 -.22 .01 .10 .63** .68** 1
** correlation is significant at 0.01 level
* correlation is significant at 0.05 level
Among different geochemical properties, negative correlation (0.48) was observed between pH and ESP. Increasing pH of soil will lead to decrease in ESP and decreasing ESP will lead to increase in CEC (Sumner, 1993; Rengasamy and Churchman, 1999;
Quirk, 2001). OC had significant positive correlation with carbonate content (.51) and CEC (0.34) and negative correlation with particle size, i.e., d50 and d90. SAR had positive correlation with carbonate content (0.29). d10, d50, and d90 are strongly correlated to each other. d10 had significant positive correlation with d50 (0.93) and d90
(0.63). With increase in finer fraction of soil particles, there was increase of coarse particles in the study area. Presence of coarse particles was dominant in erosion sites, although a few sites had clay pockets.
6.2.2 Results of analysis of data using logistic regression in SPSS
To evaluate contribution of selected geochemical properties to erosion event, binary logistic regression in SPSS was used with ‘Erosion’ (‘Yes’ or ‘No’) as dependent variable. Two sets of potential predictor variables were considered from correlation matrices:
i) OC, SAR, d50
ii) OC, SAR, d90
Outputs of the Binary logistic model for the first set of predictor variables (OC, SAR and d50)are shown in Table 6.5.
Table 6.5a Variables in the equation (for the first set of variables)
B S.E. Wald Sig. Exp(B)
OC -.049 .192 .065 .799 .952
SAR -1.344 .461 8.515 .004 .261
d50 .021 .011 3.410 .065 1.021
Constant 1.283 .939 1.867 .172 3.607
Table 6.5b Model Summary (for the first set of variables)
-2 Log likelihood Cox & Snell R Square Nagelkerke R Square
62.276a .391 .521
a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.
Nagelkerke’s R2 of .521 (Table 6.5b) indicated a moderate relationship between prediction and grouping. The Wald criterion demonstrated that only SAR made a significant contribution to prediction (p = .004).
Outputs of the Binary logistic model for the second set of predictor variables (OC, SAR and d90)are shown in Table 6.6.
Table 6.6a Variables in the equation (for the second set of variables)
B S.E. Wald Sig. Exp(B)
OC -.073 .199 .135 .713 .929
SAR -1.321 .485 7.405 .007 .267
d90 .010 .004 5.888 .015 1.010
Constant .796 .951 .701 .402 2.217
Table 6.6b Model Summary (for the second set of variables)
-2 Log likelihood Cox & Snell R Square Nagelkerke R Square
56.520a .439 .586
a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.
Nagelkerke’s R2 of .586 (Table 6.6b) indicated a moderate relationship between prediction and grouping. The Wald criterion demonstrated that SAR and d90 made a significant contribution to prediction (p = .007 and .015 respectively).
Higher value of Nagelkerke’s R2 of .586 for the second set of predictor variables (OC, SAR and d90) suggested the variables more suitable for analysis. The odd ratio was .929 for an additional unit in OC, which suggested that for one unit increase of OC, the odds of erosion was lowered by a factor of 0.929. For one unit increase of OC, the odds of erosion was lowered by 7% (=.929×100 – 100).
The odd ratio was .267 for an additional unit in SAR, which suggested that for one unit increase of SAR, the odds of erosion was lowered by a factor of 0.267. For one unit increase of SAR, the odds of erosion was lowered by 73% (=.267×100 – 100). Brady and Weil (2002) suggested that SAR and erodibility were positively correlated for clay soils. The anomaly in our study might be due to abundance of sand content as clay size particles were responsible for expansion and dispersion at high SAR.
The odd ratio was 1.010 for an additional unit in d90 values, which suggested that for one unit increase of d90, the odds of erosion was increased by a factor of 1.010. For one unit increase of d90, the odds of erosion was increased by 1% (=1.010×100 – 100). The resistance of a bank to fluvial erosion and mass failures tends to increase with increasing silt–clay content (Thorne and Tovey, 1981; Osman and Thorne, 1988), i.e., decrease in particle size. Thus, with increase in particle size, susceptibility of erosion increases. Now, considering geochemical properties of bank materials, probability of erosion (P) can be measured as follows (using equation 3 from Chapter 3):
P = 𝑒{0.796 + (−.073)OC + (−1.321)SAR + (. 010)d90}
1 + 𝑒{0.796 + (−.073)OC + (−1.321)SAR + (. 010)d90}
For example, in a site with soil OC= 2.4, SAR= 1.7 and d90= 200µm, probability of bank erosion,
P = 𝑒{0.796 + (−.073)2.4 + (−1.321)1.7 + (. 010)200}
1 + 𝑒{0.796 + (−.073)2.4 + (−1.321)1.7 + (. 010)200}
= 𝑒{0.796 − 0.175 − 2.246 + 2}
1 + 𝑒{0.796 − 0.175 − 2.246 + 2}
= 𝑒{0.375}
1 + 𝑒{0.375}
= 1.455 2.455 = 0.593
Hence, probability of erosion in a location with soil OC = 2.4, SAR = 1.7 and d90 = 200µm is 59%.
Geochemical evaluation of bank materials of Brahmaputra
Particle size and OC were the most important geochemical factors of river bank erosion in Brahmaputra, holding other parameters constant. Role of particle size (Wolman, 1959; Schumm, 1960; Walker et al., 1987; Dade et al., 1992) and OC (Wischmeier and Mannering, 1969; Robinson and Phillips, 2001; Brady and Weil, 2002; Morgan, 2005) in erosion was already documented. The above equation can be used for rapid assessment of vulnerability to bank erosion in alluvial rivers like Brahmaputra.
Geochemical evaluation of bank materials of Brahmaputra