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International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 5, May 2017, pp. 1445–1459, Article ID: IJCIET_08_05_155 Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=5 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 © IAEME Publication Scopus Indexed

APPROXIMATING SOIL PHYSICAL PROPERTIES USING GEO-STATISTICAL

MODELS IN LOWER KOSI BASIN, OF GANGA RIVER SYSTEM, INDIA PRONE TO FLOOD

INUNDATION R. S. Meena

Ph.D. Student, Department of Civil Engineering, National Institute of Technology, Patna, Bihar, India

R. Jha Professor, Department of Civil Engineering, National Institute of Technology,

Patna, Bihar, India

ABSTRACT The knowledge of the spatial distribution of soil texture and soil water content at

field capacity and the wilting point is important, mainly, for flood inundation modelling, crop water requirement, environmental and hydrological modeling. Soil water content plays a crucial role in partitioning the rainfall into infiltration and surface runoff. In this study, the approximation of physical properties has been done using geostatistical techniques in lower Kosi Basin, of the Ganga river system, India, which is prone to flood inundation. To test the validity of the geostatistical models, soil samples from twenty-nine different locations of the Kosi basin have been collected and are analyzed to determine various properties of soil. Exhaustive testing in the laboratory has been done to estimate bulk density, the percentage of sand, silt, clay, and organic carbon in each soil sample, and soil water content at different pressures (-33, -100, -500, -1500kPa). For geostatistical analysis, all the observed data have been used in Circular, Spherical, Exponential, and Gaussian semivariogram models of Ordinary Kriging. In addition, Co-Kriging, Block Kriging, Inverse Distance Weighted and Spline models are tested for their applicability but failed to provide good results. The Mean Multiplicative Error (MME), Normalized Mean Error (NME) and Standard Errors (SE) have been used to decide the most suitable model in Ordinary Kriging for the approximation of soil physical properties in the lower Kosi basin. MME is found to be the only unbiased technique for estimating errors and is used in the present study to obtain the best model. Key words: Field capacity, flood inundation, geostatistical, wilting point

R. S. Meena and R. Jha


Cite this Article: R. S. Meena and R. Jha. Approximating Soil Physical Properties Using Geo-Statistical Models in Lower Kosi Basin, of Ganga River System, India Prone to Flood Inundation. International Journal of Civil Engineering and Technology, 8(5), 2017, pp. 1445–1459. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=5

1. INTRODUCTION Soil texture and soil water content are the important soil physical properties. These two variables play a crucial role in various hydrological, environmental and agricultural applications. These soil properties control the proportion of rainfall that infiltrates into the soil and causes runoff over the land surface. The knowledge of the spatial distribution of soil moisture is necessary for estimating runoff at the catchment scale [1]–[3], for the design of irrigation planning[4], [5]and in modeling of temporal evolution of spatial patterns in soil moisture content[3], [6], [7]. Soil texture and soil water content also change with and space, like other environmental elements. These spatiotemporal variability of soil texture and soil water content may lead to structural differences in soil quality [8] and hydrological cycle [9]. For site-specific management, the detailed information about the soil quality factors and their spatial distribution patterns in field scale is an essential requirement[10].

Direct measurement of these two soil physical properties at multiple locations and development of continuous surface map is difficult, time-consuming and costly. Alternately, these properties can be estimated from classical statistics and geostatistical analysis. Geostatistics provides a set of statistical tools for incorporating spatial and temporal coordinates of observations in data processing to produce a thematic map of the soil properties[11]–[13]. Various geostatistical methods have been used by the researchers, depending upon the requirements and situations of field experiments to develop the spatial variability maps of soil properties[10], [14]–[19].However, these methods do not consider basin scale and scope limited to field experiments at the local level. Moreover, such models have not used most commonly used geostatistical model for their applicability in a lower Kosi basin of the Ganga river system of India, which is prone to flooding. Keeping this in view, a geostatistical study is done in the lower Kosi basin, India, which would help in the assessment of rainfall-runoff modelling, real-time flood inundation modelling, and in the design of agricultural scheduling of the study region.

2. MATERIAL AND METHODS

2.1. Study Area The study was carried out for the alluvial soils of the lower Kosi floodplain of the Ganga river system, India. The Kosi basin is an important sub-basin of the Ganga basin. The upper catchment of the Kosi basin liesat great heights of the Himalayan range in Nepal and Tibet. The total drainage area of the Kosi River is 74,030 km2 out of which 11,410 km2 lies in India and the rest 62,620 km2 lies in Tibet and Nepal (FMIS, 2013). The topography of the basin is very steep in the upper reaches and mild in lower reaches. The geographical location of the lower Kosi basin in India lies between 86°22’24’’- 87°37’40” East longitude and 25°19’25’’- 26°35’16’’ North latitude. Most of the rainfall (80 to 90%) is received during the monsoon season from mid-June to mid-October, the mean annual rainfall in the study area is about 1450 mm. The landuse/landcover pattern in the study area is the majority of agricultural area (about 76%) and associated fallow lands (about 19%) and water bodies (about 5%). The flood plains of the lower Kosi basin are regarded as a large inland delta formed by the huge sandy deposit of the Kosi river and its tributaries. Figure 1 shows the location map of Kosi basin

Approximating Soil Physical Properties Using Geo-Statistical Models in Lower Kosi Basin, of Ganga River System, India Prone to Flood Inundation


situated in India, Nepal and Tibet, main Kosi River and its tributaries extracted from Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) 30-meter resolution.

(a)

(b)

Figure 1 Location map of (a) study area and (b) soil sampling sites in lower Kosi Basin, India



2.2. Methods

2.2.1. Soil Sampling and Laboratory Analysis Disturbed and undisturbed grab soil samples are collected from 14 different locations of the lower Kosi basin, India (up to the depth of 100cm) were analyzed. The geographical location of all sampling locations are measured with the help of hand handled global positioning system (GPS). The soil samples are analysed to estimate the bulk density (BD), fraction of sand, silt, clay, organic carbon, and soil water content at different pressures (-33, -100, -500, -1500 kPa).

Soil samples are air-dried and ground to pass through a 2-mm sieve. Sieve analysis and hydrometer methods are used to determine the particle size distribution. Fractions are separated according to clay (<0.002 mm), silt (0.002-0.05 mm) and sand (0.05-2 mm) and soil textural classification were done using United State Department of Agricultural (USDA) textural classification. Two classes of soil texture sandy loam and loamy sand were found in the study region.

The bulk density of the soil samples is determined using undisturbed soil samples collected from the field. Soil organic carbon content is determined by the chromic oxidation equivalent of soil organic matter proposed by [20].

Soil water contents at 0 kPa (saturation), -33 (field capacity), -100, -500 and -1500 kPa (wilting point) are measured using pressure plate apparatus (IS 14765: 2000) method at Indian Council of Agricultural Research (ICAR) Patna and in National Institute of Technology, Patna. Soil water content at saturation (0 kPa) is measured using the gravitational method. Available water (AW) content is determined as the difference of field capacity (FC) and wilting point (WP). Volumetric water content is determined by multiplying the gravimetric water content with bulk density.

2.2.2. Statistical and Geostatistical Analysis of Soil Physical Properties The soil physical properties obtained through the laboratory analysis are subjected to descriptive statistical analysis. Descriptive statistics like minimum, maximum, mean, median, standard deviation (SD), skewness, kurtosis, and coefficient of variation (CV) are obtained for all soil physical properties using Microsoft Excel.

The geostatistical analysis that employs the use of sample points at different locations in the study region to create (interpolate or approximate) spatial distribution maps. The sample points are the measurements of some phenomenon, such as soil water content, soil texture, or elevation heights. The analysis derives a surface using the values from the measured locations to predict values for each location in the landscape. For geostatistical analysis, Circular, Spherical, Exponential, and Gaussian semivariogram models of Ordinary Kriging have been used. In addition, Co-Kriging, Block Kriging, Inverse Distance Weighted (IDW) and Thin Plate Smoothing Spline models and failed to provide good results. All the methods are available in the literature. However, only Ordinary Kriging method, which provides the best results, is discussed.

The Kriging Method Kriging is most appropriate when we know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology. Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values.



( ) = ( ) (1)

where Z(Si) is the measured value at the ith location, λi is an unknown weight for the measured value at the ith location S0 is the prediction location, N is the number of measured values.

The weights are based not only on the distance between the measured points and the prediction location but also on the overall spatial arrangement of the measured points.

Ordinary Kriging is the most general and widely used of the Kriging methods and is the default. It assumes the constant mean is unknown. Ordinary Kriging assumes the model

( ) = + ( ) (2) where µ is an unknown constant. in Kriging, a semivariogram model is used to define the

weights of the function [13] and the semivariance is an autocorrelation statistic defined as follows [21].

Semi-variogram A plot of the calculated semivariance values against the distance (lags) is known as a semivariogram. Spatial variability of any variables described by a semivariogram [16], [22], [23]. It is calculated as half of the average squared difference between paired data values. It is a graphical representation of spatial self-correlation by plotting the semi-variance against several distance intervals. The general expression to estimate the semivariogram is given as:

(ℎ) =1

2 (ℎ) [ ( ) − ( + ℎ)]( )

(3)

where, γ(h) is the estimated semivariogram, N(h) is the number of pairs of the sample points [z(xi), z(xi+h)] separated by a distance or lag vector h [24].

Four commonly used semivariogram models circular, spherical, exponential, and Gaussian were fitted for each soil property. Best-fit model with minimum root mean square error (RMSE) were selected for each soil property. Expressions for different semivariogram models used in this study are given below.

Circular model

(ℎ) =2 + ℎ

1 −ℎ

+ℎ

(4)

Exponential model

(ℎ) = + 1 − −ℎ

for h ≥ 0 (5)

Gaussian model

(ℎ) = + 1 − −ℎ

for h ≥ 0 (6)



Spherical model

(ℎ) = + 1.5ℎ

− 0.5ℎ

when 0 < ℎ ≤ a

= + when h > (7) In all these semivariogram models C0 is the nugget,C0 + C1 is the sill or total

semivariance and a is the range. The relative nugget effect (RNE %) was calculated to see the spatial dependencies of the soil variables.

% = + ∗ 100 (8)

2.2.3. Performance Evaluation The performance of each model was validated to measure the accuracy of the generated maps showing the spatial distribution of soil variables. Various error statistics based on the differential errors standard error (SE), normalized mean error (NME), mean multiplicative errors (MME) and a correlation coefficient (r) are used for performance evaluation. The SE and NME are can determined using the following equations:

=( − )

⁄

(9)

=100% −

(10)

Where N is the number of measurements and E and M are the estimated and measured values.

The MME is considered to provide a better basis for assessing the impact of inaccuracies in predicting [25], the equation is defined as:

=∑ | ( ⁄ ) |

(11)

When the values of E and M are very close, the model produces good results. Therefore, the value of MME will be close to unity. Note that the statistical criteria described above are independent of each other[26]. The RMSE uses the difference in magnitude of the observed and predicted values, while MME uses their ratios. The MME was also used herein to compare the performance of various approaches. Since it was used in a seminal paper by[25], the results can be readily compared. Various researchers have been used these approaches [27]–[30]and found MME to provide un-bias results for smaller as well as larger values.

3. RESULTS AND DISCUSSION

3.1. Analysis of Soil Texture and Soil Physical Properties Soil textural classification obtained in the laboratory tests indicates the prominence of loamy sand and sandy loam in the study region as illustrated in Figure 2.



Figure 2 Soil textural classification of the study area based on USDA classification

Soil physical properties, i.e. bulk density (BD), particle size distribution, organic carbon (OC), and soil water content at different pressures are analyzed in the laboratory. Soil physical properties for each of 29 locations, including its textural properties are shown in Figure 3. It has been observed that sand fraction is in decreasing order from high elevation to low elevation, whereas the silt fraction is found inverse to the sand fraction. The presence of clay fraction is found low in the study area. Soil water content (volumetric water content) is found higher in the center of the study area, where silt and clay fraction are higher.

(a) Sand, silt, clay fraction, organic carbon (OC) and bulk density (BD)

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Sand (%) Silt (%) Clay (%) OC (%) BD (g/cc)



(b) Volumetric water content at different pressures

Figure 3 Soil texture and physical properties of soil for all 29 locations

3.2. Statistical and Geostatistical Analysis

3.2.1. Descriptive Statistics Descriptive statistics like minimum, maximum, mean, median, standard deviation (SD), skewness, kurtosis, and coefficient of variation (CV) are also obtained for all soil physical properties (Table 1). According to[31] Sand, silt, OC, and water soil water content at all pressures showed the medium variability. The greatest variation has been observed in the clay fraction (52.04 %) whereas the smallest variation is in BD (4.31 %). The median of each soil properties except silt fraction is found equal or little lower than the mean, which indicates that the effects of abnormality in the data does not exist. Skewness values up to 0.5 suggest a specific attribute with a normal distribution. When the coefficient of skewness is greater than one, a logarithmic transformation is considered. In this study sand fraction, OC and BD showed higher positive skewness.

Table 1 Descriptive statistics of soil physical properties obtained in laboratory

Statistics Minimum Maximum Mean Median SD Skewness Kurtosis CV (%)

Sand (%) 48.93 86.42 64.19 62.44 8.92 0.99 0.81 13.89 Silt (%) 11.08 46.87 31.95 32.72 8.54 -0.82 0.66 26.71 Clay (%) 1.50 8.38 3.86 3.37 2.01 0.74 -0.33 52.04 OC (%) 0.14 0.47 0.26 0.26 0.07 0.95 1.33 27.57 BD (g/cc) 1.49 1.75 1.59 1.57 0.07 0.89 0.40 4.31 0 kPa 0.436 0.626 0.556 0.562 0.045 -1.19 1.42 8.04 33 kPa 0.230 0.460 0.351 0.351 0.052 -0.09 0.44 14.93 100 kPa 0.186 0.322 0.262 0.259 0.038 -0.18 -0.79 14.65 500 kPa 0.074 0.179 0.129 0.127 0.025 -0.08 0.10 19.17 1500 kPa 0.065 0.115 0.097 0.097 0.013 -0.71 0.25 13.80 AW 0.165 0.351 0.254 0.253 0.041 0.28 0.61 16.16

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Volumetric water content (θ)

0 kPa 33 kPa 100 kPa 500 kPa 1500 kPa AW



SD is the standard deviation, CV is the coefficient of variation, OC is the organic carbon, BD is the bulk density, and AW is the available water.

Although the results obtained from the laboratory and statistical analysis provide useful information about these soil physical properties. This analysis does not describe the reciprocal effects of the soil variables on soil behavior in a continuous way. Hence, the geostatistical analysis has been performed to achieve the continuous spatial information of the soil variables in the study area.

3.2.2. Geostatistical Analysis In this study, geostatistical analysis has been performed using Circular, Spherical, Exponential, and Gaussian semi-variogram models of Ordinary Kriging. In addition, Co-Kriging, Block Kriging, Inverse Distance Weighted (IDW) and Thin Plate Smoothing Spline models are also used but failed to provide good results. All the data analyzed in the laboratory have been used for the analysis. The four most commonly used semivariogram models of Ordinary Kriging, namely; Circular, Exponential, Gaussian, and Spherical provided better results. In the analysis, the anisotropic semi-variograms did not show any differences in spatial dependence based on direction, and therefore, isotropic semi-variograms are considered. The Exponential model provided better results in comparison to other models of Ordinary Kriging. The parameters used in the model are shown in Table 2.

Table 2 Geostatistical parameters of the exponential model for soil physical properties

Variables Nugget (Co)

Range (a) Partial Sill (C)

Lag Size Sill (C0+C)

RNE (%)

+

Spatial correlation

Sand (%) 0.000 37419.40 75.718 4677.43 75.718 0.00 Silt (%) 19.065 47625.05 45.913 5953.13 64.977 29.34 Moderate Clay (%) 1.939 19346.67 2.910 2418.33 4.850 39.99 Moderate OC (%) 0.005 27193.95 0.002 3399.24 0.006 71.15 Moderate BD (g/cc) 0.000 37419.41 0.005 4677.43 0.005 0.00 0 kPa 0.000 37419.41 0.002 4677.43 0.002 0.00 33 kPa 0.001 85806.61 0.002 10725.83 0.003 26.57 Moderate 100 kPa 0.000 70851.00 0.002 8856.38 0.002 6.10 Strong 500 kPa 0.000 85806.61 0.001 10725.83 0.001 21.10 Strong 1500 kPa 0.000 19623.08 0.000 2452.89 0.000 0.00 AW 0.001 106909.84 0.001 13363.73 0.002 50.70 Moderate OC is organic carbon, BD is bulk density, and AW is available water.

Relative Nugget Effect (RNE %) was used to define different classes of spatial dependence for the soil variables[32]. If the RNE ratio is less than 25%; the variable is considered to have a strong spatial dependence if the ratio is between 25 and 75%; has a moderate spatial dependence, otherwise, the variable is considered to have a weak spatial dependence. In this study silt, clay fraction, OC, and soil water content at 33, and AW were found moderate spatial dependent. Moderate spatial class for the soil particles has been found by some researchers [19], [33]. The range provides information about the size of a search window used in the spatial interpolation methods [34].The geospatial data generated by the Exponential semi-variogram model of Ordinary Kriging is shown in Figure 4.



(a) Sand (b) Silt

(c) Clay (d) Organic carbon

(e) Bulk density (f) Water content at 0 kPa



(g) Water content at 33 kPa (h) Water content at 100 kPa,

(i) Water content at 500 kPa (j) Water content at 1500 kPa

(k)AW = FC-WP (l) Soil textural classification

Figure 4 Spatial distribution maps of different soil physical properties in the study area

In Figure 4, the sand fraction was found higher in the higher elevation and the silt fraction was found higher in low-lying areas and approximately half of the study area ranged from 28 to 36 % silt fraction. Bulk density is found higher towards the higher fraction of sand and the saturation percentage is found higher in low-lying area Kosi basin, which is dominated by silt



and clay fraction. It is understood that higher the smaller size of the particles, higher the water-holding capacity of the soil. The study is very useful for assessment of flood-prone regions and is used as input to flood inundation modelling.

3.3. Performance Evaluation To test the validity of the model Standard Error (SE), Normalized Mean Error (NME) and most important unbiased error criteria i.e. Mean Multiplicative Error (MME) has been applied. As discussed in the previous section MME has been used to minimize the bias, if higher and lower values are considered. Various error statistics like SE, NME, and MME obtained using equations (9), (10) and (11), the plots are shown in Figure 5.

The SE values were found higher in sand, silt, clay fraction, and for other properties, it varied from 0.01 to 0.07. NME values were found higher for clay, silt, and OC, for other properties the NME values were very low. The results obtained from SE and NME are considered to be biased for larger values and squared errors, as these error statistics provide differential errors [25]. The MME value is considered to be the most accurate criteria for error estimation [27]. It has been observed that MME found maximum (1.55) for clay prediction and minimum (1.03) for bulk density (BD) prediction. MME values close to one are considered more accurate, in this study most of the values were found close to one.

(a) Standard error

(b) Normalized mean error

0.001.002.003.004.005.006.007.008.009.00

Sand (%)

Silt (%)

Clay (%)

OC (%)

BD (g/cc)

0 kPa 33 kPa

100 kPa

500 kPa

1500 kPa

AW

SE

0.000.050.100.150.200.250.30

Sand (%)

Silt (%)

Clay (%)

OC (%)

BD (g/cc)

0 kPa 33 kPa

100 kPa

500 kPa

1500 kPa

AW

NME



(c) Mean multiplicative error

Figure 5 Performance evaluation of the Exponential Semi-variogram model of Ordinary Kriging using SE, NME and MME

4. CONCLUSION Geostatistical methods are evaluated to predict the spatial variability and approximation of soil physical properties in Kosi floodplains of the Ganga river system, India. The Standard Errors (SE), Normalized Mean Error (NME), and Mean Multiplicative Error (MME), have been used to decide the most suitable model in Ordinary Kriging as the other models used for the analysis failed to perform well in present cases. The MME error statistics are found most suitable to evaluate the model performance and to predict soil texture and soil physical properties in Kosi floodplains.

The soil texture and soil water content are essential variables, which are used in rainfall-runoff modelling, flood inundation modelling, agricultural, and land-use management practices. The Exponential semivariogram model of an Ordinary Kriging estimator is found to be a most suitable model based on error criteria, specifically based on unbiased error statistics MME. Moreover, derivation the spatial pattern of soil texture and water content at different pressures from discrete sample points can be helpful as a profitable approach in various activities.

ACKNOWLEDGEMENT The authors would like to thank the National Institute of Technology, Patna for providing all the facilities and financial support for conducting this study.

REFERENCES [1] C. Fitzjohn, J. L. Ternan, and A. G. Williams, “Soil moisture variability in a semi-arid

gully catchment: Implications for runoff and erosion control,” Catena, vol. 32, no. 1, pp. 55–70, 1998.

[2] A. W. Western, R. B. Grayson, and T. R. Green, “The Tarrawarra project: high resolution spatial measurement, modelling and analysis of soil moisture and hydrological response,” Hydrol. Process., vol. 13, no. 5, pp. 633–652, 1999.

0.00

0.50

1.00

1.50

2.00

Sand (%)

Silt (%)

Clay (%)

OC (%)

BD (g/cc)

0 kPa 33 kPa

100 kPa

500 kPa

1500 kPa

AW

MME



[3] K. Kumar, M. K. Arora, K. S. Hariprasad, and M. K. A. K. S. Hariprasad, “Geostatistical analysis of soil moisture distribution in a part of Solani River catchment,” Appl. Water Sci., vol. 6, pp. 25–34, 2016.

[4] J. M. J. Blonquist, S. B. Jones, and D. A. Robinson, “Water Conservation from Precise Irrigation Scheduling Using a Subsurface Electromagnetic Soil Moisture Sensor.,” Ag. Water Manag., vol. 84, pp. 153–165, 2006.

[5] G. Vellidis, M. Tucker, C. Perry, C. Kvien, and C. Bednarz, “A real-time wireless smart sensor array for scheduling irrigation,” Comput. Electron. Agric., vol. 61, no. 1, pp. 44–50, 2008.

[6] A. Papritz and H. Fluhler, “Temporal change of spatially auticorrelated soil properties: optimal estimation by cokrigging,” Geoderma, vol. 62, pp. 29–43, 1994.

[7] G. B. M. Heuvelink, P. Musters, and E. J. Pebesma, “Spatio-temporal kriging of soil water content,” Geostatistics Wollongong ’96, Vols 1 2, vol. 8, no. 1–2, pp. 1020–1030, 1997.

[8] T. a. Kettler, J. W. Doran, and T. L. Gilbert, “Simplified Method for Soil Particle-Size Determination to Accompany Soil-Quality Analyses,” Soil Sci. Soc. Am. J., vol. 65, pp. 849–852, 2001.

[9] A. W. Western, R. B. Grayson, G. Blöschl, and D. J. Wilson, “Spatial variability of soil moisture and its implications for scaling,” Scaling methods soil Phys., pp. 119–142, 2003.

[10] M. Emadi, M. Baghernejad, M. Pakparvar, and S. Hashemi, “Assessment Of Spatial Variability Of Some Soil Properties In Salt And Sodic Affected Soils In Arsanjan Plain, Fars Province, Southern Iran,” Pakistan J. Biol., 2008.

[11] P. Goovaerts, Geostatistics for natural resources evaluation. 1997.

[12] P. Goovaerts, “Geostatistics in soil science: State-of-the-art and perspectives,” Geoderma, vol. 89, no. 1–2, pp. 1–45, 1999.

[13] R. Webster and M. A. Oliver, Geostatistics for Environmental Scientists: Second Edition. 2008.

[14] T. M. Burgess and R. Webster, “Optimal Interpolation and Isarithmic Mapping of Soil Properties: 2.Block Kriging,” J. Soil Sci., vol. 31, no. x, pp. 333–341, 1980.

[15] E. H. Isaaks and R. M. Srivastava, Applied Geostatistics, vol. 21. 1989.

[16] P. Goovaerts, “Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties,” Biol. Fertil. Soils, vol. 27, no. 4, pp. 315–334, 1998.

[17] R. M. Lark, “A comparison of some robust estimators of the variogram for use in soil survey,” Eur. J. Soil Sci., vol. 51, no. 1, pp. 137–157, 2000.

[18] P. Santra, K. Chopra, U., and D. Chakraborty, “Spatial variability of soil properties and its application in predicting surface map og hydraulic parameters in a agricultural farm,” Cyrrent Sci., vol. 95, no. 7, pp. 937–945, 2008.

[19] S. K. Reza, D. C. Nayak, T. Chattopadhyay, S. Mukhopadhyay, S. K. Singh, and R. Srinivasan, “Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach,” Arch. Agron. Soil Sci., pp. 1–10, 2015.

[20] A. Walkley and I. A. Black, “An examination of the Degtjareff methd for determining soil organic matter, and a proposed modification of the chromic acid titration method,” Soil Sci., vol. 37, no. 1, 1934.

[21] L. Mabit and C. Bernard, “Assessment of spatial distribution of fallout radionuclides through geostatistics concept,” J. Environ. Radioact., vol. 97, no. 2–3, pp. 206–219, 2007.



[22] A. B. McBratney and M. J. Pringle, “Spatial variability in soil—implications for precision agricultur,” Precis. Agric. ´97. Vol. 1 Spat. varibility soil Crop, pp. 3–31, 1997.

[23] R. J. Godwin and P. C. H. Miller, “A review of the technologies for mapping within-field variability,” Biosyst. Eng., vol. 84, no. 4, pp. 393–407, 2003.

[24] A. G. Journel and C. J. Huijbregts, “Mining geostatistics,” 1978.

[25] D. Moog and G. Jirka, “Analysis of reaeration equations using mean multipicative error,” Environ. Eng., vol. 124, no. 2, pp. 104–110, 1998.

[26] S. K. Jain and R. Jha, “Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d’estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d’eau,” Hydrol. Sci. J., vol. 50, no. 6, 2005.

[27] R. Jha, C. S. P. Ojha, and K. K. S. Bhatia, “Refinement a predictive reaeration equations for a typical Indian river,” Hydrol. Process., vol. 15, no. 6, pp. 1047–1060, 2001.

[28] R. Jha, C. S. P. Ojha, and K. K. S. Bhatia, “A supplimentary approach for estimating reaeraton rate coefficients,” Hydrol. Process., vol. 18, pp. 65–79, 2004.

[29] R. Jha, C. S. P. Ojha, and K. K. S. Bhatia, “Estimating nutrient outflow from argicultural watersheds to the River Kali in India,” J. Environ. Eng., vol. 131, no. 12, pp. 1706–1715, 2005.

[30] [30] R. Jha, C. Ojha, and K. Bhatia, “Development of refined BOD and DO models for highly polluted Kali River in India,” J. Environ. Eng., 2007.

[31] L. P. Wilding, “Spatial variability: its documentation, accomodation and implication to soil surveys,” Soil Spat. Var., pp. 166–194, 1985.

[32] C. Cambardella and T. Moorman, “Field-scale variability of soil properties in central Iowa soils,” Soil Sci. Soc., 1994.

[33] J. Iqbal, J. A. Thomasson, J. N. Jenkins, P. R. Owens, and F. D. Whisler, “Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils,” Soil Sci .Soc.Am.J., vol. 69, pp. 1–13, 2005.

[34] P. Burrough and R. McDonnell, “Creating continuous surfaces from point data,” Princ. Geogr. Inf. Syst. Oxford, 1998.

[35] Rituparna Ch oudhury, B.M. Patil, Vipin Chandra, Uday B. Patil and T. Nagendra, Assessment of Flood Mitigation Measure For Mithi River – A Case Study, International Journal of Civil Engineering and Technology, 7(3), 2016, pp. 56–66.

[36] Kalpalatha.Ganamala and P. Sundar Kumar, A Case Study on Flood Frequency Analysis, International Journal of Civil Engineering and Technology, 8(4), 2017, pp. 1762-176

[37] Ch. Nithin Kumar Reddy, SS. Asadi and A.V.S. Prasad , Evaluation of Flood Management For Krishna River Bank Stretch of Andhra Pradesh State. International Journal of Civil Engineering and Technology, 8(1), 2017, pp. 302–306.

[38] A. K. Sahoo, Dipak Sarkar and K. S. Gajbhiye (2002), "Soil Series of Bihar" NBSS Pub. No. 98, NBSS & LUP (lCAR), Nagpur, 289p.

approximating soil p hysical properties using geo ......basin of the ganga river system of india,...

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