Startseite Quantify distribution of topsoil erodibility factor for watersheds that feed the Al-Shewicha trough – Iraq using GIS
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Quantify distribution of topsoil erodibility factor for watersheds that feed the Al-Shewicha trough – Iraq using GIS

  • Saleem Mohmood Imariq , Ali A. Abdul-Sahib , Hiba D. Saleem und Mohammed S. Shamkhi EMAIL logo
Veröffentlicht/Copyright: 2. Dezember 2022
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Abstract

Soil functions such as water storage, soil filtration, and reducing reservoir life due to sediment load are directly affected by the process of “soil erosion.” The Universal Soil Loss Equation was adopted to predict the erodibility factor for watersheds feeding the Al-Shewicha trough. The K-average factor’s determined value was 0.156, indicating that the K-factor had substantial variability. The K-factor for erodibility values was linked to the high sand content in the topsoil of the study area because of the high sand content of the topsoil. The topsoil is coarse-textured and then permeable. ArcGIS (geographic information system) was used to obtain the distribution of soil types as well as the erodibility factor distribution.

Keywords: erodibility; factor; watershed; Al-Shewicha; trough

1 Introduction

Erosion of the topsoil layer has adverse effects. The loss of soil loss in the catchment is of great importance, being a significant threat to physical resources. Soil erosion poses threats to other soil functions such as water storage, soil filtration and transformation, and agricultural productivity [1,2]. Also, the negative impact of soil erodibility is to increase the sedimentary load of floodwater, which causes the reservoir’s life to be reduced and retrogression of downstream water quality [3,4,5]. Based on the preceding, the need arose to focus land management on controlling the soil loss of watersheds by preventing unscientific deforestation and agricultural practices. The soil’s natural susceptibility to being detached and translocated by erosion processes like splash erosion, surface runoff, or both is regarded as resistivity of the soil to rainfall and runoff erosivity. It is also known as the K-factor (soil erodibility factor) [6,7,8]. Because a wide range of physical factors influence soil erosion susceptibility, chemical and mechanical soil qualities, and hydrological processes, the idea of how to quantify the K-factor of such parameters is challenging [6,9,10,11]. Due to the importance of the topic, many researchers have studied soil erosion. Much previous research has addressed using a rainfall simulator to explain the relationship between precipitation and soil erosion [12,13,14]. This research aimed to determine erodible soil constant for watersheds feeding the Al-Shewicha trough (Figure 1) as a preliminary step to study sediment discharge of these watersheds.

Figure 1 Seasonal collected water in the Al-Shewicha trough after sitting of sediment load, Wasit Province, Iraq.
Figure 1

Seasonal collected water in the Al-Shewicha trough after sitting of sediment load, Wasit Province, Iraq.

2 Study area

The study area site is located between “509374 E and 659374 E, and between 3603825 N and 3803825 N.” The area is described as plain land, mountains, and hilly regions. The Al-Shewicha trough is a flat area having a minimum elevation of +10.0 m from m.s.l. Six basins flow into the Al-Shewicha trough, as shown in Figures 2 and 3 [Shamkhi et al. [15]]. The drainage basins had varying topographic relief. In the Al-Shewicha marsh, the mean elevation of all watersheds ranges from 1,837 to 10 m. The basin's area is equal to 13,290 km2. These watersheds are shared between Iraq and Iran’s Islamic Republic, which is considered a humid area for the winter season (Figure 2).

Figure 2 Location map of the study area with stream networks [15].
Figure 2

Location map of the study area with stream networks [15].

Figure 3 The six basins flow into the Al-Shewicha trough [15].
Figure 3

The six basins flow into the Al-Shewicha trough [15].

The research area’s average annual rainfall was calculated to be around “325 and 233 mm, respectively; these basins “are categorized as semi-rainfall zones. The maximum annual runoff for basins 1 and 2 were 330.5 and 271.2 mm, respectively, as shown in Figures 4 and 5 [16].

Figure 4 Annual runoff of basin 1 [16].
Figure 4

Annual runoff of basin 1 [16].

Figure 5 Annual runoff of basin 2 [16].
Figure 5

Annual runoff of basin 2 [16].

3 Methods and data

3.1 Data sources

The elevation model in digital form of the research area from the Shuttle Radar Topographic Mission was used in this work, with a horizontal spatial resolution of 30 m. It is a raster representation of the elevation of the surface topography in XYZ directions. The image was re-projected in a regional format (WGS 1984 UTM Zone 38). The Food and Agriculture Organization (FAO) global soil map of the world was used to classify the soils. The national land cover database 2011 was used to differentiate the land cover of watersheds.

3.2 Soil erodibility factor (K)

Soil erodibility expresses the resistance of soil to both detachment and transport. The “Universal Soil Loss Equation” (USLE) is an empirical equation to predict the longtime erosion of average yearly soil loss due to runoff.

(1) A = R . K . L S . C . P .

Where A is the estimated rate of soil erosion loss per year, R is the rainfall-runoff erosivity factor, K is the soil erodibility factor, LS is length of the slope and the steepness factor, C vegetation cover and P support practice factor [17]. The factor of erodibility of the soil (K) is the criterion of soil susceptibility to runoff erosion [18]. The K-factor is dependent on many soil properties such as soil texture, permeability, and organic matter content, which reverberate the soil impedance to erosion [10]. It is extracted depending on the soil properties using the universal equation for soil loss USLE monograph, which was notified by Wischmeier and Smith in 1978 [17], then amended by ref. [19] and [20]. The monograph shown in Figure 6 includes the percentages of silts, very fine sand, sand, organic materials, soil structure, and permeability.

Figure 6 The monograph to extract soil erodibility factor [21].
Figure 6

The monograph to extract soil erodibility factor [21].

The silt percentage content in topsoil is the most influential factor in predicting soil erodibility due to high runoff. There is an inverse relation between clay percentage content in topsoil. The erodibility factor of the soil ranged from 0.05 to 0.2. The erodibility factor of the clay soil is ranged from 0.05 to 0.2. In general, the soil’s erodibility factor ranges from 0.02 to 0.69 [5,6,21,22].

The K-factor can be predicted in the SI unit using equation (1).

(2) K = f csand × f cl − si × f orgc × f hisand ,

where K is the USLE soil erodibility factor.

(3) f csand = 0 . 2 + 0 . 3 × exp ⁡ − 0 . 256 × m s × 1 − m silt 100 ,

(4) f cl − si = ( m silt m c ÷ + m silt ) 0 . 3 ,

(5) f orgc = 1 − 0 . 25 × orgC orgC + exp ⁡ [ 3 . 75 − 2 . 95 × orgC ] ,

(6) f hisand = 1 − 0 . 7 × ( 1 − m s 100 ) 1 − m s 100 + exp ⁡ [ − 5 . 51 + 22 . 9 × 1 − m s 100 ,

where m s is the percentage of sand content in topsoil; m silt is the percentage of silt content in topsoil; m c is the percentage of clay content in topsoil; and orgC is the percentage of organic content in topsoil.

4 Results and analysis

Soil erodibility is the sign of the ingrained resistance of soil particles to the segregating and transporting power of rainfall. The sand, silt, clay, and organic matter percentages data for the watershed were obtained depending on United Nations-FAO (UN-FAO, 2007) data to classify the topsoil type of study area utilizing ArcGIS (geographic information system) 10.2 software, as demonstrated in Table 1 and Figures 7–10.

Table 1

The percentage of various types of study area topsoil

Symbols Sand % topsoil Silt% topsoil Clay % topsoil OC % topsoil
I 58.7 16.13 24.2 0.97
Jc 39.35 39.54 20.46 0.65
Rc 63.5 18.44 17.3 0.76
Xk 48.7 29.06 21.6 0.64
Yk 63.5 17.54 18.7 0.26
Yy 49 10.57 40.3 0.13
Zo 43.2 24 32.4 0.4
Figure 7 Sand percentage distribution of study area topsoil.
Figure 7

Sand percentage distribution of study area topsoil.

Figure 8 Silt percentage distribution of study area topsoil.
Figure 8

Silt percentage distribution of study area topsoil.

Figure 9 Clay percentage distribution of study area topsoil.
Figure 9

Clay percentage distribution of study area topsoil.

Figure 10 Organic matter content percentage distribution of study area topsoil.
Figure 10

Organic matter content percentage distribution of study area topsoil.

Erodibility K-factor for the topsoil of the study was calculated using equations (2)–(6), as presented in Table 2. Table 3 shows the percentage area of each type of topsoil. The resulting data from Table 2 in addition to data from Table 3 were utilized to produce a spatial distribution map of erodibility K-factor as demonstrated in Figure 11 for the study area using ArcGIS. The K-factor varied from 0.125 to 0.177, with an average of 0.156. The erodibility K-factor values are reasonable, reflecting the high percentage of sand in the topsoil of the study area. So, the topsoil is coarse-textured and then permeable. The results coincided with [15].

Table 2

Calculation of erodibility factor for case study topsoil

Symbols Sand % topsoil Silt % topsoil Clay % topsoil Organic matter % topsoil FCS and f ci–si f orge f hisand K-factor
I 58.7 16.13 24.2 0.97 0.2 0.76 0.993 0.994 0.15
Jc 39.35 39.54 20.46 0.65 0.201 0.882 0.996 1.00 0.177
Rc 63.5 18.44 17.3 0.76 0.20 0.82 0.996 0.986 0.161
Xk 48.7 29.06 21.6 0.64 0.203 0.846 0.998 1.00 0.169
Yk 63.5 17.54 18.7 0.26 0.2 0.804 1.00 0.986 0.1585
Yy 49 10.57 40.3 0.13 0.20 0.624 1.00 1.00 0.127
Zo 43.2 24 32.4 0.4 0.20 0.774 1.00 1.00 0.155
Table 3

Percntage area of each type of topsoil

Soil unit symbol Area (km2) % of area
I 3607.8 27.15
Jc 1,138 8.56
Rc 3,175 23.89
Wr 14 0.11
Xk 33.8 0.25
Yk 2118.7 15.94
Yy 372.7 2.80
Zo 2,830 21.29
Figure 11 Erodibility K-factor distribution of study area.
Figure 11

Erodibility K-factor distribution of study area.

5 Conclusion

Calculating the K-factor is crucial. The K-spatial factor’s distribution for six watersheds supplying the Al-Shewicha trough was successfully estimated in this study. The K-factor had an average computed value of 0.156, indicating that the K-factor had substantial variability. The high percentage of sand in the topsoil of the study area is reflected in the erodibility K-factor readings.

Acknowledgments

This research was supported by the University of Wasit, Iraq.

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Received: 2022-03-10
Revised: 2022-04-13
Accepted: 2022-05-06
Published Online: 2022-12-02

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2022-0328
Schlagwörter für diesen Artikel
erodibility; factor; watershed; Al-Shewicha; trough
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  43. Study of emitted gases from incinerator of Al-Sadr hospital in Najaf city
  44. Experimentally investigating comparison between the behavior of fibrous concrete slabs with steel stiffeners and reinforced concrete slabs under dynamic–static loads
  45. ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region
  46. Future short-term estimation of flowrate of the Euphrates river catchment located in Al-Najaf Governorate, Iraq through using weather data and statistical downscaling model
  47. Utilization of ANN technique to estimate the discharge coefficient for trapezoidal weir-gate
  48. Experimental study to enhance the productivity of single-slope single-basin solar still
  49. An empirical formula development to predict suspended sediment load for Khour Al-Zubair port, South of Iraq
  50. A model for variation with time of flexiblepavement temperature
  51. Analytical and numerical investigation of free vibration for stepped beam with different materials
  52. Identifying the reasons for the prolongation of school construction projects in Najaf
  53. Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland
  54. Flow parameters effect on water hammer stability in hydraulic system by using state-space method
  55. Experimental study of the behaviour and failure modes of tapered castellated steel beams
  56. Water hammer phenomenon in pumping stations: A stability investigation based on root locus
  57. Mechanical properties and freeze-thaw resistance of lightweight aggregate concrete using artificial clay aggregate
  58. Compatibility between delay functions and highway capacity manual on Iraqi highways
  59. The effect of expanded polystyrene beads (EPS) on the physical and mechanical properties of aerated concrete
  60. The effect of cutoff angle on the head pressure underneath dams constructed on soils having rectangular void
  61. An experimental study on vibration isolation by open and in-filled trenches
  62. Designing a 3D virtual test platform for evaluating prosthetic knee joint performance during the walking cycle
  63. Special Issue: AESMT-2 - Part I
  64. Optimization process of resistance spot welding for high-strength low-alloy steel using Taguchi method
  65. Cyclic performance of moment connections with reduced beam sections using different cut-flange profiles
  66. Time overruns in the construction projects in Iraq: Case study on investigating and analyzing the root causes
  67. Contribution of lift-to-drag ratio on power coefficient of HAWT blade for different cross-sections
  68. Geotechnical correlations of soil properties in Hilla City – Iraq
  69. Improve the performance of solar thermal collectors by varying the concentration and nanoparticles diameter of silicon dioxide
  70. Enhancement of evaporative cooling system in a green-house by geothermal energy
  71. Destructive and nondestructive tests formulation for concrete containing polyolefin fibers
  72. Quantify distribution of topsoil erodibility factor for watersheds that feed the Al-Shewicha trough – Iraq using GIS
  73. Seamless geospatial data methodology for topographic map: A case study on Baghdad
  74. Mechanical properties investigation of composite FGM fabricated from Al/Zn
  75. Causes of change orders in the cycle of construction project: A case study in Al-Najaf province
  76. Optimum hydraulic investigation of pipe aqueduct by MATLAB software and Newton–Raphson method
  77. Numerical analysis of high-strength reinforcing steel with conventional strength in reinforced concrete beams under monotonic loading
  78. Deriving rainfall intensity–duration–frequency (IDF) curves and testing the best distribution using EasyFit software 5.5 for Kut city, Iraq
  79. Designing of a dual-functional XOR block in QCA technology
  80. Producing low-cost self-consolidation concrete using sustainable material
  81. Performance of the anaerobic baffled reactor for primary treatment of rural domestic wastewater in Iraq
  82. Enhancement isolation antenna to multi-port for wireless communication
  83. A comparative study of different coagulants used in treatment of turbid water
  84. Field tests of grouted ground anchors in the sandy soil of Najaf, Iraq
  85. New methodology to reduce power by using smart street lighting system
  86. Optimization of the synergistic effect of micro silica and fly ash on the behavior of concrete using response surface method
  87. Ergodic capacity of correlated multiple-input–multiple-output channel with impact of transmitter impairments
  88. Numerical studies of the simultaneous development of forced convective laminar flow with heat transfer inside a microtube at a uniform temperature
  89. Enhancement of heat transfer from solar thermal collector using nanofluid
  90. Improvement of permeable asphalt pavement by adding crumb rubber waste
  91. Study the effect of adding zirconia particles to nickel–phosphorus electroless coatings as product innovation on stainless steel substrate
  92. Waste aggregate concrete properties using waste tiles as coarse aggregate and modified with PC superplasticizer
  93. CuO–Cu/water hybrid nonofluid potentials in impingement jet
  94. Satellite vibration effects on communication quality of OISN system
  95. Special Issue: Annual Engineering and Vocational Education Conference - Part III
  96. Mechanical and thermal properties of recycled high-density polyethylene/bamboo with different fiber loadings
  97. Special Issue: Advanced Energy Storage
  98. Cu-foil modification for anode-free lithium-ion battery from electronic cable waste
  99. Review of various sulfide electrolyte types for solid-state lithium-ion batteries
  100. Optimization type of filler on electrochemical and thermal properties of gel polymer electrolytes membranes for safety lithium-ion batteries
  101. Pr-doped BiFeO3 thin films growth on quartz using chemical solution deposition
  102. An environmentally friendly hydrometallurgy process for the recovery and reuse of metals from spent lithium-ion batteries, using organic acid
  103. Production of nickel-rich LiNi0.89Co0.08Al0.03O2 cathode material for high capacity NCA/graphite secondary battery fabrication
  104. Special Issue: Sustainable Materials Production and Processes
  105. Corrosion polarization and passivation behavior of selected stainless steel alloys and Ti6Al4V titanium in elevated temperature acid-chloride electrolytes
  106. Special Issue: Modern Scientific Problems in Civil Engineering - Part II
  107. The modelling of railway subgrade strengthening foundation on weak soils
  108. Special Issue: Automation in Finland 2021 - Part II
  109. Manufacturing operations as services by robots with skills
  110. Foundations and case studies on the scalable intelligence in AIoT domains
  111. Safety risk sources of autonomous mobile machines
  112. Special Issue: 49th KKBN - Part I
  113. Residual magnetic field as a source of information about steel wire rope technical condition
  114. Monitoring the boundary of an adhesive coating to a steel substrate with an ultrasonic Rayleigh wave
  115. Detection of early stage of ductile and fatigue damage presented in Inconel 718 alloy using instrumented indentation technique
  116. Identification and characterization of the grinding burns by eddy current method
  117. Special Issue: ICIMECE 2020 - Part II
  118. Selection of MR damper model suitable for SMC applied to semi-active suspension system by using similarity measures
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Heruntergeladen am 20.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/eng-2022-0328/html
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