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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (4): 500-514    DOI: 10.1007/s40333-025-0012-z    
Research article     
Erosion on marginal slopes of unpaved roads in semi-arid Brazil, and the role of Caatinga vegetation in sediment retention and disconnectivity
Teresa Raquel Lima FARIAS1,*(), Maria Thereza Rocha CHAVES1, Cicero Lima de ALMEIDA2, Pedro Henrique Augusto MEDEIROS1, José Carlos de ARAÚJO3, Joaquín NAVARRO-HEVIA4
1Federal Institute of Education, Science and Technology of Ceará, Department of Civil Construction, Fortaleza 60040531, Brazil
2Federal Institute of Education, Science and Technology of Ceará, Sobral 62042030, Brazil
3Federal University of Ceará, Department of Agricultural Engineering, Fortaleza 60450760, Brazil
4Agroforestry Department, University of Valladolid, Palencia 34004, Spain
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Abstract  

Vegetation plays a major role in soil protection against erosion effects, and studies have also highlighted its importance in retaining sediments from roadside slopes. Yet, hydro-sedimentological studies under natural precipitation conditions are still scarce in semi-arid areas due to difficulties in monitoring the few and very concentrated precipitation events. Quantifying sediment connectivity and yield at watershed scale, often highly impacted by the erosion of unpaved roads, is necessary for management plans. This study aims to evaluate the efficiency of native vegetation on roadside slope segments in Caatinga biome in retaining sediments and conserving the soil in a semi-arid area of Brazil. Surface runoff, sediment concentration, and yield measurements were measured from 34 natural precipitation events in four years on two slopes with and without vegetation. The runoff coefficients of the plot with no vegetation varied from 3.0% to 58.0%, while in the vegetated plot, they showed variation from 1.0% to 21.0%. The annual specific sediment yield ranged from 4.6 to 138.7 kg/(hm2•a) for the vegetated plot and from 34.9 to 608.5 kg/(hm2•a) for the unvegetated one. These results indicate a 4 to 12 times higher soil loss on the unvegetated slope in relation to the vegetated one and demonstrate that natural Caatinga vegetation acts as an effective barrier against surface-transported sediments. Moreover, natural Caatinga vegetation present on the slope plays an important role in breaking connectivity between sediment flows from unpaved roads and the watershed drainage system. These findings indicate that investments in unpaved road and roadside slope restoration, not only enhance road infrastructure but also promote environmental gains by reducing the impact of erosion.

Key words： erosion control      road erosion      road slopes      road impact      sediment retention      slope restoration     
Received: 27 July 2024      Published: 30 April 2025
Corresponding Authors: *Teresa Raquel Lima FARIAS (E-mail: teresafarias@ifce.edu.br)
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Teresa Raquel Lima FARIAS
Maria Thereza Rocha CHAVES
Cicero Lima de ALMEIDA
Pedro Henrique Augusto MEDEIROS
José Carlos de ARAÚJO
Joaquín NAVARRO-HEVIA
Cite this article:

Teresa Raquel Lima FARIAS, Maria Thereza Rocha CHAVES, Cicero Lima de ALMEIDA, Pedro Henrique Augusto MEDEIROS, José Carlos de ARAÚJO, Joaquín NAVARRO-HEVIA. Erosion on marginal slopes of unpaved roads in semi-arid Brazil, and the role of Caatinga vegetation in sediment retention and disconnectivity. Journal of Arid Land, 2025, 17(4): 500-514.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0012-z     OR     http://jal.xjegi.com/Y2025/V17/I4/500

Fig. 1 Location of the study area (a and b) in the semi-arid Brazil and the road (c) where the studied plots are located
Fig. 2 Roadside slope and plots with and without vegetation. (a), installation of the sediment monitoring systems in September 2012; (b), vegetation recolonization in February 2013; (c), vegetation recolonization in September 2014; (d) vegetation recolonization in January 2016.
Fig. 3 Precipitation histogram (a), box plots of annual precipitation (b), and monthly precipitation (c) distribution during 1994-2024. Boxes in Figure 3b and c indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Whisker is shown as the mean. Outliers are shown as circles. Bars extend to the most extreme value within 1.5×IQR. Green circles in Figure 3b represent the annual precipitation during monitoring years.
Event Date
(dd/mm/yyyy)		 P (mm) Slope with vegetation (WV) Slope with no vegetation (NV)
CR (%) CS (mg/L) SY (kg/hm2) CR (%) CS (mg/L) SY (kg/hm2)
1 04/01/2013 29 5 653 10.1 11 690 21.4
2 16/02/2013 53 6 43 1.3 6 1327 41.1
3 26/03/2013 42 7 172 5.3 7 648 98.7
4 04/04/2013 11 7 428 6.6 14 1506 11.7
5 01/05/2013 19 16 404 12.2 58 395 43.8
6 23/06/2013 13 6 117 1.8 12 3010 23.3
7 09/09/2013 29 16 65 3.0 45 145 19.1
8 20/12/2013 64 1 162 1.3 5 250 8.7
9 23/12/2013 50 3 62 1.0 43 342 78.0
10 13/01/2014 23 15 79 2.7 45 378 37.5
11 31/01/2014 12 6 173 1.3 7 2506 19.4
12 04/02/2014 31 10 49 1.5 17 2083 105.8
13 11/02/2014 44 2 32 0.2 23 42 3.2
14 19/02/2014 10 8 59 0.5 8 82 0.6
15 06/03/2014 10 8 102 0.8 8 224 1.7
16 18/03/2014 15 5 35 0.3 35 87 4.4
17 28/03/2014 17 4 94 0.7 5 205 1.6
18 31/03/2014 5 15 60 0.5 15 258 2.0
19 02/04/2014 37 2 31 0.2 8 110 3.2
20 19/09/2014 7 16 241 2.6 17 315 3.4
21 17/11/2014 18 4 48 0.4 19 80 2.7
22 24/11/2014 32 2 670 5.2 11 852 29.0
23 18/01/2015 6 13 205 1.6 13 245 1.9
24 04/02/2015 142 2 29 1.0 6 130 11.1
25 20/02/2015 31 2 76 0.6 3 80 0.6
26 23/02/2015 35 2 44 0.3 57 75 15.0
27 02/03/2015 49 3 32 0.5 11 47 2.4
28 06/03/2015 27 3 36 0.3 3 53 0.4
29 20/03/2015 26 3 38 0.3 3 445 3.5
30 14/01/2016 79 4 139 4.9 13 390 39.6
31 21/01/2016 88 19 510 85.5 30 1835 489.2
32 25/01/2016 42 4 9 0.2 8 13 0.5
33 18/03/2016 95 21 238 47.7 23 249 54.1
34 12/05/2016 12 13 26 0.4 36 573 25.1
Minimum 5 1 9 0.2 3 13 0.4
Maximum 142 21 670 86.0 58 3010 489.0
Median 29 6 71 1.2 13 254 11.4
Mean 35 7 152 6.0 18 579 35.0
Standard deviation 30 6 177 16.0 16 758 85.0
Table S1 Values for precipitation (P), runoff coefficient (CR), sediment concentration (CS), and sediment yield (SY) on the slopes with and without vegetation during 2013-2016
Year Test Surface runoff Sediment concentration Sediment yield
WV NV WV NV WV NV
2013 Shapiro-Wilk test 0.06 0.02 0.06 0.02 0.08 0.08
Hypothesis test 0.04 0.01 0.01
2014 Shapiro-Wilk test 0.06 0.02 <0.01 <0.01 <0.01 <0.01
Hypothesis test <0.01 <0.01 <0.01
2015 Shapiro-Wilk test <0.01 <0.01 <0.01 0.02 0.04 0.04
Hypothesis test 0.03 0.02 0.03
2016 Shapiro-Wilk test 0.23 0.80 0.33 0.10 0.09 <0.01
Hypothesis test 0.15 0.23 0.42
Table 1 Normalization test and hypothesis for surface runoff, sediment concentration, and sediment yield for samples from slopes with (WV) and without vegetation (NV)
Year Pannual (mm) Pac (mm) Paverage (mm) Number of P events Average runoff coefficient (%) Total sediment yield (kg/(hm2•a))
WV NV NV/WV WV NV NV/WV
2013 575 310 34.4 9 6.1 19.7 3.2 42.6 345.8 8.1
2014 382 261 20.1 13 5.8 17.7 3.0 16.9 214.5 12.7
2015 398 316 45.1 7 2.5 11.8 4.6 4.6 34.9 7.6
2016 367 316 63.2 5 13.6 20.9 1.5 138.7 608.5 21.9
Table 2 Characteristics of the monitored events in the slopes with vegetation (WV) and without vegetation (NV) of the unpaved road
Fig. 4 Variability of runoff coefficient (a), sediment concentration (b), and sediment yield (c) in the slopes with vegetation (WV) and without vegetation (NV) during 2013-2016. Boxes indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Whisker is shown as the mean. Outliers are shown as circles. Bars extend to the most extreme value within 1.5×IQR.
Index Slope with vegetation Slope with no vegetation
CR CS SY CR CS SY
Mann-Kendall statistics 64 -31 -144 -162 -18 -152
Test statistics -0.44 -2.12 -2.39 -0.25 -2.29 -1.63
P-value 0.65 0.03 0.01 0.80 0.02 0.10
Sen's slope 0.00 -2.12 -1.70×10-5 0.00 -13.05 -3.80×10-4
Trend Not significant Decreasing Decreasing Not significant Decreasing Not significant
Table 3 Results of the Mann-Kendall trend and Sen's slope analysis for runoff coefficient (CR), sediment concentration (CS), and sediment yield (SY)
Fig. 5 Variation in sediment concentration on the slopes with (WV) and without (NV) vegetation after precipitation events during 2013-2016. (a), precipitation; (b), sediment concentration.
Fig. 6 Variation in sediment yield on the slopes with (WV) and without (NV) vegetation after precipitation events during 2013-2016
Fig. 7 Spearman correlation among precipitation (P), surface runoff coefficient (CR), sediment concentration (CS), and yield (SY) of the slopes with (WV) and without (NV) vegetation.*, P<0.05 level; **, P<0.01 level.
[1]   Ai N, Wei T X, Zhu Q, et al. 2017. Impacts of land disturbance and restoration on runoff production and sediment yield in the Chinese Loess Plateau. Journal of Arid Land, 9(1): 76-86.
[2]   Almeida C L D, Araújo J C D, Costa M C G, et al. 2017. Fallow reduces soil losses and increases carbon stock in Caatinga. Floresta E Ambiente, 24: 017516, doi: 10.1590/2179-8087.017516.
[3]   Bai L L, Shi P, Li Z B, et al. 2023. Synergistic effects of vegetation restoration and check dams on water erosion in a slope-gully system. Land Degradation & Development, 34(12): 3581-3592.
[4]   Brasil J B, Andrade E M D, Queiroz Palácio H A D, et al. 2020. Temporal variability of throughfall as a function of the canopy development stage: From seasonal to intra-event scale. Hydrological Sciences Journal, 65(10): 1640-1651.
[5]   Brasil J B, Andrade E M D, Queiroz Palácio H A D, et al. 2022. Canopy effects on rainfall partition and throughfall drop size distribution in a tropical dry forest. Atmosphere, 13(7): 1126, doi: 10.3390/atmos13071126.
[6]   Cao L X, Zhang K L, Dai H L, et al. 2015. Modeling interrill erosion on unpaved roads in the Loess Plateau of China. Land Degradation & Development, 26(8): 825-832.
[7]   Chang E H, Li P, Li Z B, et al. 2019. The impact of vegetation successional status on slope runoff erosion in the Loess Plateau of China. Water, 11(12): 2614, doi: 10.3390/w11122614.
[8]   Costa J A, Navarro-Hevia J, Costa C A G, et al. 2021. Temporal dynamics of evapotranspiration in semiarid native forests in Brazil and Spain using remote sensing. Hydrological Processes, 35(3): e14070, doi: 10.1002/hyp.14070.
[9]   Cunha M C, Thomaz E L. 2015. Is it possible to reduce water turbidity in a rural basin through the implementation of infiltration boxes? Brazilian Journal of Geomorphology, 16(4): 725, doi: 10.20502/rbg.v16i4.725. (in Portuguese)
[10]   Ehsan Bhuiyan M A, Nikolopoulos E I, Anagnostou E N, et al. 2019. Assessment of precipitation error propagation in multi-model global water resource reanalysis. Hydrology and Earth System Sciences, 23(4): 1973-1994.
doi: 10.5194/hess-23-1973-2019
[11]   Farias T R L, Medeiros P H A, Navarro-Hevia J, et al. 2019. Unpaved rural roads as source areas of sediment in a watershed of the Brazilian semi-arid region. International Journal of Sediment Research, 34(5): 475-485.
[12]   Farias T R L, Medeiros P H A, de Araújo J C, et al. 2021. The role of unpaved roads in the sediment budget of a semiarid mesoscale catchment. Land Degradation & Development, 32(18): 5443-5454.
[13]   Feng J, Wei W, Pan D L. 2020. Effects of rainfall and terracing-vegetation combinations on water erosion in a loess hilly area, China. Journal of Environmental Management, 261: 110247, doi: 10.1016/j.jenvman.2020.110247.
[14]   Feng T J, Wei W, Chen L D, et al. 2018. Assessment of the impact of different vegetation patterns on soil erosion processes on semiarid loess slopes. Earth Surface Processes and Landforms, 43(9): 1860-1870.
[15]   Figueiredo J V, de Araújo J C, Medeiros P H A, et al. 2016. Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil. Hydrological Processes, 30(13): 2390-2400.
[16]   Gerlach T. 1967. Hillslope troughs for measuring sediment movement. In: TricartJ, Se Macar P. 1967. Field Methods for the Study of Slope and Fluvial Processes. Belguim: The Comission on Applied Geomprphology International Geographical Union, 173.
[17]   Griebeler N P, Pruski F F, Silva J M A. 2009. Erosion control on unpaved roads. In: PruskiF F. Federal University de Viçosa, Soil and Water Conservation: Mechanical Practices for Water Erosion Control (2nd ed.). Viçosa: 166-215. (in Portuguese)
[18]   Gu C J, Mu X M, Gao P, et al. 2020. Distinguishing the effects of vegetation restoration on runoff and sediment generation on simulated rainfall on the hillslopes of the Loess Plateau of China. Plant and Soil, 447: 393-412.
[19]   Gumus V, Avsaroglu Y, Simsek O. 2022. Streamflow trends in the Tigris River basin using Mann-Kendall and innovative trend analysis methods. Journal of Earth System Science, 131(1): 34, doi: 10.1007/s12040-021-01770-4.
[20]   Han D D, Deng J C, Gu C J, et al. 2021. Effect of shrub-grass vegetation coverage and slope gradient on runoff and sediment yield under simulated rainfall. International Journal of Sediment Research, 36(1): 29-37.
[21]   Jeung S J, Lim G K, Kim B S. 2020. Analysis of statistical characteristics of annual precipitation in Korea using data screeening technique. Journal of Korean Society of Disaster and Security, 13(3): 15-28.
[22]   Linh N T M, MacDonald L H, Gomi T, et al. 2024. Runoff and erosion from three unpaved road segments in northern Vietnam. Journal of Hydrology: Regional Studies, 51: 101625, doi: 10.1016/j.ejrh.2023.101625.
[23]   Medeiros P H A, de Araújo J C, Mamede G L, et al. 2014. Connectivity of sediment transport in a semiarid environment: A synthesis for the Upper Jaguaribe Basin, Brazil. Journal of Soils and Sediments, 14: 1938-1948.
[24]   National Transport Confederation. 2024. Roadway Research CNT 2024. Brasília. [2024-06-28]. https://pesquisarodovias.cnt.org.br/. (in Portuguese)
[25]   National Water and Sanitation Agency. 2025. Portal Hidro Web. [2024-05-07]. https://www.snirh.gov.br/hidroweb/serieshistoricas.
[26]   Navarro-Hevia J. 2002. Erosion control in clearings caused by road infrastructure projects:Application to the area around Palencia City. PhD Dissertation. Madrid: Technical University of Madrid. (in Spanish)
[27]   Negishi J N, Sidle R C, Ziegler A D, et al. 2008. Contribution of intercepted subsurface flow to road runoff and sediment transport in a logging-disturbed tropical catchment. Earth Surface Processes and Landforms, 33(8): 1174-1191.
[28]   Ochoa P A A, Fries A, Mejía D, et al. 2016. Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes. Catena, 140: 31-42.
[29]   Parsakhoo A, Hosseini S A. 2023. Effect of bioengineering treatments on reduction of soil erosion from road cut slope and fill slope. Journal of Forest Science, 69(9): 367-376.
[30]   Ramos-Scharrón C E, MacDonald L H. 2007. Measurement and prediction of natural and anthropogenic sediment sources, St. John, U.S. Virgin Islands. CATENA, 71(2): 250-266.
[31]   Ramos-Scharrón C E. 2010. Sediment production from unpaved roads in a sub-tropical dry setting-Southwestern Puerto Rico. Catena, 82(3): 146-158.
[32]   Ramos-Scharrón C E, Alicea-Díaz E E, Figueroa-Sánchez Y A, et al. 2022. Road cutslope erosion and control treatments in an actively-cultivated tropical montane setting. Catena, 209(2): 105814, doi: 10.1016/j.catena.2021.105814.
[33]   Ramos-Scharrón C E, McLaughlin P, Figueroa-Sánchez Y. 2024. Impacts of unpaved roads on runoff and erosion in a dry tropical setting: Isla De Culebra, Puerto Rico. Journal of Soils and Sediments, 24: 1420-1430.
[34]   Rijsdijk A, Bruijnzeel L S, Sutoto C K. 2007. Runoff and sediment yield from rural roads, trails and settlements in the upper Konto catchment, East Java, Indonesia. Geomorphology, 87(1-2): 28-37.
[35]   Santos J C N D, de Andrade E M, Medeiros P H A, et al. 2017. Effect of rainfall characteristics on runoff and water erosion for different land uses in a tropical semiarid region. Water Resources Management, 31(1): 173-185.
[36]   Shi P, Li P, Li Z B, et al. 2022. Effects of grass vegetation coverage and position on runoff and sediment yields on the slope of Loess Plateau, China. Agricultural Water Management, 259: 107231, doi: 10.1016/j.agwat.2021.107231.
[37]   Silva C C, Minella P G, Schlesner A, et al. 2021. Unpaved road conservation planning at the catchment scale. Environmental Monitoring and Assessment, 193: 595, doi: 10.1007/s10661-021-09398-z.
pmid: 34426857
[38]   Soares N S, Costa C A G, de Lima J B C, et al. 2024. Method for identification of hydrological seasons in the semi-arid Caatinga biome, Brazil. Hydrological Sciences Journal, 69(3): 309-320.
[39]   Tong S C, Li G R, Li X L, et al. 2023. Soil water erosion and its hydrodynamic characteristics in degraded bald patches of alpine meadows in the Yellow River Source Area, western China. Sustainability, 15(10): 8165, doi: 10.3390/su15108165.
[40]   Wang B, Zhang G H. 2017. Quantifying the binding and bonding effects of plant roots on soil detachment by overland flow in 10 typical grasslands on the Loess Plateau. Soil Science Society of America Journal, 81(6): 1567-1576.
[41]   Wischmeier W H, Smith D D. 1978. Predicting Rainfall Erosion Losses:A Guide to Conservation Planning (Agriculture Handbook, No. 537). Washington DC: United States Department of Agriculture, Science and Education Administration.
[42]   Yue L, Jiao J Y, Tang B Z, et al. 2020. Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region, China. Journal of Hydrology, 584: 124694, doi: 10.1016/j.jhydrol.2020.124694.
[43]   Zhang H Y, Meng Q, You Q J, et al. 2022. Influence of vegetation filter strip on slope runoff, sediment yield and nutrient loss. Applied Sciences, 12(9): 4129, doi: 10.3390/app12094129.
[44]   Zhang L, Wang J M, Bai Z K, et al. 2015. Effects of vegetation on runoff and soil erosion on reclaimed land in an opencast coal-mine dump in a loess area. Catena, 128: 44-53.
[45]   Zhang X, Yu G Q, Li Z B, et al. 2014. Experimental study on slope runoff, erosion and sediment under different vegetation types. Water Resources Management, 28(9): 2415-2433.
[46]   Zhu W, Wang S T, Luo P P, et al. 2022. A quantitative analysis of the influence of temperature change on the extreme precipitation. Atmosphere, 13(4): 612, doi: 10.3390/atmos13040612.
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