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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (11): 1269-1289    DOI: 10.1007/s40333-023-0110-8     CSTR: 32276.14.s40333-023-0110-8
Research article     
Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia
Olfa TERWAYET BAYOULI1,2, ZHANG Wanchang1,2,*(), Houssem TERWAYET BAYOULI3
1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3National Agronomic Institute of Tunisia (INAT), Tunis 1082, Tunisia
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Abstract  

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands, leading to detrimental environmental impacts, such as degraded lands, deteriorated ecosystem services and biodiversity, and increased greenhouse gas emissions. In response to the current lack of studies combining drought conditions and soil erosion processes, in this study, we developed a comprehensive Geographic Information System (GIS)-based approach to assess soil erosion and droughts, thereby revealing the relationship between soil erosion and droughts under an arid climate. The vegetation condition index (VCI) and temperature condition index (TCI) derived respectively from the enhanced vegetation index (EVI) MOD13A2 and land surface temperature (LST) MOD11A2 products were combined to generate the vegetation health index (VHI). The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed, southeastern Tunisia. The revised universal soil loss equation (RUSLE) model was applied to quantitatively estimate soil erosion. The relationship between soil erosion and droughts was investigated through Pearson correlation. Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000-2016. The average soil erosion rate was determined to be 1.8 t/(hm2·a). The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts. The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion. The relationship between droughts and soil erosion had a positive correlation (r=0.3); however, the correlation was highly varied spatially across the watershed. Drought was linked to soil erosion in the Negueb watershed. The current study provides insight for natural disaster risk assessment, land managers, and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Key words： droughts      soil erosion      vegetation health index (VHI)      revised universal soil loss equation (RUSLE) model      southeastern Tunisia     
Received: 05 December 2022      Published: 30 November 2023
Corresponding Authors: * Wanchang Zhang (E-mail: zhangwc@radi.ac.cn)
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Olfa TERWAYET BAYOULI
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Olfa TERWAYET BAYOULI, ZHANG Wanchang, Houssem TERWAYET BAYOULI. Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia. Journal of Arid Land, 2023, 15(11): 1269-1289.

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http://jal.xjegi.com/10.1007/s40333-023-0110-8     OR     http://jal.xjegi.com/Y2023/V15/I11/1269

Fig. 1 Overview of the study area
Drought parameter Equation Symbol and unit
Area ${{D}_{a}}=\frac{\sum\limits_{i=1}^{n}{da}}{n}\times 100 \%$ Da is the drought area (%); da is the number of pixels with the vegetation health index (VHI) greater than 40; and n is the total number of pixels.
Duration $D=\frac{\sum\limits_{i=1}^{n}{di}}{m}$ D is the drought duration (month); di is the duration of ith drought event; and m is the total number of drought events.
Frequency $F=\frac{{{n}_{m}}}{{{N}_{m}}}\times 100 \%$ F is the drought frequency (%); nm is the number of drought months (month); and Nm is the total number of months (month).
Intensity $I=\left| \frac{1}{{{N}_{m}}}\sum\limits_{i=1}^{z}{\text{VH}{{\text{I}}_{i}}} \right|$ I is the drought intensity; Nm is the total number of months (month); z is the number of drought occurrences in months with the VHI greater than 40; and VHIi is the VHI value below the threshold (40).
Table 1 Drought characteristics used in this study
VHI value Drought condition
<10 Extreme drought
10-20 Severe drought
20-30 Moderate drought
30-40 Mild drought
>40 No drought
Table 2 Classification of the vegetation health index (VHI)
Fig. 2 Flowchart of the methodology used in this study. VHI, vegetation health index; TCI, temperature condition index; VCI, vegetation condition index; EVI, enhanced vegetation index; LST, land surface temperature; CV, coefficient of variation; SWC, soil and water conservation; A, the average annual soil loss per unit area; R factor, rainfall erosivity factor; K factor, soil erosivity factor; LS factor, slope length and steepness factor; C factor, cover management factor; P factor, conservation support practice factor; MODIS, moderate resolution imaging spectroradiometer; RUSLE, revised universal soil loss equation; DEM, digital elevation model; SRTM, shuttle radar topography mission; LULC, land use/land cover.
Model Reference Model Reference
$R=0.264 \times \mathrm{MFI}^{1.5}$ Arnoldus (1977) $R=96.46+1.84 \times \mathrm{MFI}-0.158 \times \mathrm{P}$ Ferro et al. (1991)
$R-587.9-1.219 \times \mathrm{P}+0.004105 \times \mathrm{P}^{2}$
for P>850 mm/a		 Renard and Freimund (1994) $R=b_{1}+b_{1} \times P+b_{1} \times \mathrm{P}^{2}$ for P>850 mm/a, where b0=0.0483 MJ/(hm2•h)); b1=1.610; and b2=0.004105. Yang et al. (2003)
$R-0.87-0.037 \times \mathrm{P}+0.016 \times \mathrm{P}^{2}$ Pepin and Hammouda (2012) $R=b_{b} \times P^{2} \times \sqrt{P} \times\left(a \times b_{1} \times L\right) $, where α=2; b0=0.117; b1= –0.015. Diodato and Bellocchi (2010)
$R=168.42+9.27 \times \mathrm{MFI}$ Roose (1977) R-1.042 \times $\mathrm{MFI}^{1.59}$ Belaid and Habaieb (2015)
$R=15.485+0.602 \times \mathrm{MFI}$ DVWK (1990)
Table 3 Calculation of the rainfall erosivity factor (R factor)
Soil type K factor (t•hm2•h/(hm2•MJ•mm)) Soil type K factor (t•hm2•h/(hm2•MJ•mm))
Raw mineral soils (Lithosols or Regosols) 0.036 Isohumic soils 0.054
Slightly evolved alluvial soils 0.080 Composed soils 0.050
Table 4 Soil erodibility factor (K factor) of different soil types in the Negueb watershed
LULC type C facor LULC type C facor
Croplands behind Jessours 0.18 Bedrocks 0.90
Croplands behind Tabias 0.18 Bare soils 1.00
Rangelands 0.55
Table 5 Classification of land use/land cover (LULC) types and cover management factor (C factor)
Anti-erosion practice P factor Anti-erosion practice P factor
Bench terraces with or without plantations (5%-10%) 0.10 Retention bench terraces (0%-5%) 0.10
Bench terraces with or without plantations (15%-20%) 0.16 Retention bench terraces (5%-15%) 0.12
Bench terraces with or without plantations (20%-30%) 0.18 Retention bench terraces (15%-25%) 0.16
Table 6 Conservation support practice factor (P factor) for different anti-erosion practices with slope ranges
Fig. 3 Spatiotemporal drought severity in the study area during 2000-2016 (a-q)
Fig. 4 Maps of coefficient of variation (CV; a) and mean (b) of VHI and drought frequency (c)
Fig. 5 Soil erosion map (a) with R factor (b), LS factor (c), P factor (d), K factor (e), C factor (f)
Fig. 6 Correlation coefficient (r) between soil loss and droughts
Fig. 7 Identification of areas affected by severe water erosion through Google Earth and TerraIncognita software
Fig. 8 Visited sites affected by different water erosion types in the Negueb watershed. (a and b), gully erosion; (c, d, and e), rill erosion; (f and g), sheet erosion; and (h and i), sedimentation.
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