Mapping of Water Erosion and Deposition Affected by Different LS Algorithms Using WaTEM/SEDEM Model

Document Type : Research Article

Authors

• H. Neisi
• A. Khademalrasoul
• H. Amerikhah

Soil Science Department, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

https://doi.org/10.22067/jsw.2020.14940.0

Abstract

Introduction: Soil erosion is one of the most important forms of soil degradation which topographical characteristics are effective on its occurrence and spatial distribution. Actually, soil erosion is one form of soil degradation that includes on-site and off-site effects and the off-site effect is deposition and sedimentation. In recent decades, the potential of soil erosion has been recognized as a serious threat against soil sustainability. Topographical attributes such as slope gradient (S) and slope length (L) are considered as the most important land surface properties which control energy fluxes, overland and intra-soil transport of water and sediment, and vegetation cover distribution within a landscape. The L and S are two main factors in the USLE equation which are meaningfully effective on soil erosion. The development of modern techniques such as geomorphometry has made it possible to quantify these attributes in GIS environments. Geomorphometry or terrain analysis is a computer technology-based science in which morphometric and hydrological attributes are calculated by a series of mathematical algorithms from a digital elevation model (DEM). WaTEM/SEDEM is water and tillage erosion model/sedimentation which is possible to estimate water erosion and also different forms of sediments in the watershed and hydrographical network. The accuracy of DEM in this model is really important and effective on the quality of model outputs. 
Material and Methods: Landscape planning tools might help simplify the complexity of soil erosional processes. Furthermore, using predictive tools open up for the possibilities to investigate the effectiveness of different management scenarios on soil erosional responses to make a decision for improving soil properties by application of BMPs. Soil erosion modelling as a landscape planning tool is an efficient way to investigate the on-site and off-site effects of erosion. At the same time this tool opens up for an opportunity to perform scenario analysis with the respect to the placement of structural BMPs such as buffer zones. The soil erosion model WaTEM has been used as a landscape planning tool. WaTEM is a spatially distributed empirical model to simulate both erosion and deposition by water explicitly in a two dimensional landscape. This soil erosion model has been used as a landscape planning tool. The Universal Soil Loss Equation (USLE) has been developed to predict sheet and rill erosion. Desmet and Govers (1996) showed that using the 2D-calculation of the LS-factor in WaTEM made it possible to predict rill, interrill, and ephemeral gully erosions. In this study the spatial distribution of soil erosion and deposition affected by different LS-factors were investigated using WaTEM/SEDEM model that including rainfall erosivity (R-factor), soil erodibility (K-factor), topography (LS-factor), crop cover (C-factor) and management (P-factor) as GIS layers (.rst format) in Zoji watershed located in Shush (Khuzestan province). The WaTEM/SEDEM includs three main input parts, the first part consist of DEM, parcel map and stream network. The second part is CP factor and the third part consist of LS algorithms. The variations of LS algorithms are a milestone of this model and provide the possibility to define different LS situations in the watershed. In order to evaluate the effectiveness of LS algorithms, in the simulation process Govers, McCool, Nearing and Wishmeier-Smith algorithms were defined for WaTEM/SEDEM model. 
Results and Discussion: Results of correlation (R=0.78) showed that topography had the highest effect on soil erosion distribution. Also our results illustrated that the amount of deposition in forms of total sediment production (TSP), total sediment deposition (TSD) and total sediment export (TSE) between different LS algorithms were disparate. Based on prediction of rill and interrill erosion, Nearing algorithm was the best LS algorithm and Govers algorithm was convenient in order to monitor and evaluate gully erosion. This study results showed that Govers algorithm estimated the highest amount of TSP because the Govers algorithm basically estimate the sheet, rill, interrill and gully erosion, therefore the amount of sediment in this algorithms is the highest one. For Govers algorithm the estimated TRE was the highest because the Gully erosion also was in the calculations and mostly the volume discharge originated from Gully was significantly higher than sheet and rill erosion. Therefore, regarding the types of prevailing erosion in each case the type of selected LS algorithm to simulate soil erosion and deposition distribution should be different. 
Conclusion: In general, WaTEM/SEDEM and its LS algorithms is a suitable tool to select and apply best management practices (BMPs) to control soil erosion at critical areas and hotspots. Our results confirmed that regarding the selection of each LS algorithm, the amount of sediment components and their distribution could be different.

Keywords

• Best management practices (BMPs)
• Rill and interrill erosion
• Simulation
• Specific surface area of watershed
• Topography factor

20.1001.1.20084757.1400.35.1.5.0

References

1- Alatorre L.C, Begueria S., Garcia-Ruiz J.M. 2010. Regional scale modeling of hillslope sediment delivery: a case study in the Barasona Reservoir watershed (Spain) using WaTEM/SEDEM. Journal of Hydrology 391:111–125.

2- Amsalu T., and Mengaw A. 2014.GIS based soil loss estimation using RUSLE model: The case of Jabi Tenan Woreda, ANRS, Ethiopia, Natural Resource 616-625.

3- Bouyoucos G.J. 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy Journal 56: 464-465.

4- Cobo J.G., Dercon G., Cadisch G. 2010. Nutrient balances in African land use systems across different spatial scales: A review of approaches, challenges and progress. Agr Ecosystem Environment 136:1-15.

5- Elizeu J.D., Paolo Gomes Minella J., Evrard O. 2017. Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil., Soil and Tillage Research 174: 24–33.

6- Fu G.B., Chen S.L., and McCool D.K. 2006. Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS. Soil and Tillage Research 85: 38-49.

7- Gia Pham T., Degener J., and Kappas M. 2018. Integrated universal soil loss equation (USLE) and Geographical Information System (GIS) for soil erosion estimation in A Sap basin: Central Vietnam. International Soil and Water Conservation Research 6: 99–110.  

8- Govers G. 1991. Time-dependency of runoff velocity and erosion: the effect of the initial soil moisture profile. Earth surface processes and Landforms 16: 713-729.

9- Liu Y., and Fu B. 2016. Assessing Sedimentological  Connectivity  Using  WaTEM/SEDEM  Model in  A  Hilly  And  Gully  Watershed  of  The  Loess  Plateau, China. Ecological Indicators 66: 259-268.

10- McCool DK., Foster G.R., Mutchler C.K., and Meyer L.D. 1989b. Revised Slope Length factor in the Universal Soil Loss Equation. T. Am. Soc. Agr. Eng. 32: 1571–1576.

11- Nearing M.A., Nicks A.D. 1998. Evaluation of the Water Erosion Prediction Project model for hillslopes. In: Boardman J, Favis-Mortlock D (eds) Modelling soil erosion by water. Springer, Oxford, pp43-53.

12- Paul L., and Vlek G. 2011. Modeling soil erosion and reservoir sedimentation at hillslope and catchment scale in semi-arid Burkina Faso., Ecology and Development Series No. 80, 2011.

13- Renard K.G., Foster G.R., Weeies G.A., McCool D.K., and Yoder D.C. 1997. Predicting soil erosion by water; A guide to conservation planning with the Revised Universal Soil Loss Equation. U.S. Department of Agriculture, Agriculture Handbook No. 703.

14- Rezaei P., Faridi P., Ghorbani M., and Kazemi M. 2014. Estimation of soil erosion using RUSLE model and recongnizing of most effective factor in Gabric watershed-Southeast of Hormozgan Provine. Researches of Qualitative Geomorphology 3(1): 97-113.

15- Ro¨mkens M.J.M., Young R.A., Poesen J.W.A., McCool D.K., El-Swaify S.A., and Bradford J.M. 1997. Chapter 3. Soil erodibility factor (K). In K. G. Renard, G. R. Foster, G. A. Weesies, D. K. McCool and D. C. Yoder (eds.) Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook No 703. (Washington, DC: US Department of Agriculture), pp. 65_99.

16- Shi Z.H., Ai L., Fang N.F., and Zhu H.D. 2012. Modeling the impacts of integrated small watershed management on soil erosion and sediment delivery: A case study in the Three Gorges Area, China., Journal of Hydrology 438–439: 156–167.

17- Shirazi M.A., and Boersma L. 1984. A unifying quantitative analysis of soil texture, Soil Science Society of America Journal 48: 142-147.

 

18- Van Oost K., Govers G., and Desmet P. 2000. Evaluating the effects of changes in landscape structure on soil erosion by water and tillage .Landscape Ecology 15: 577–589.

19- Van Oost K. 2003. Spatial modelling of soil redistribution processes in agricultural landscapes. K.U.Leuven, Belgium.

20- Van Oost K., Govers G., Cerdan O., Van Rompaey D.A., Steegen A., Nachtergaele J., Takken I., and Poesen I. 2005. Spatially distributed data for erosion model calibration and validation: The Ganspoel and Kinderveld datasets. Catena 61: 105–12.

21- Van Rompaey A.J.J., Verstraeten G., Van Oost K., Govers G., and Poesen J. 2001. Modelling mean annual sediment yield using a distributed approach. Earth Surf Process Landf 26:1221–1236. doi:10. 1002/esp.275

22- Van Rompaey A.J.J., Govers G., and Puttemans C. 2002. Modelling land use changes and their impact on soil erosion and sediment supply to rivers. Earth Surf Process Landf 27:481–494. doi:10.1002/esp.335

23- Verstraeten G., Van Oost K., Van Rompaey A., Poesen J., and Govers G. 2002. Evaluating an integrated approach to catchment management to reduce soil loss and sediment pollution through modeling. Soil Use Manage 19: 386–394.

24- Van Rompaey A.J.J., Verstraeten G., Van Oost K., Govers G., and Poesen J. 2001. Modeling mean annual sediment yield using a distributed approach. Surf. Process. Landf.26: 1221–1236.

25- Van Rompaey A.J.J., and Govers G. 2002. Data quality and model complexity for regional scale soil erosion prediction. International Journal Geography Inf. Science 16: 663–680.

26- Wilson J.P., and Gallant J.C. 2000. Digital terrain analysis. In Wilson, JP and Gallant JC. (Eds). Terrain analysis: principles and applications. John Wiley & Sons, New York, PP: 1-22.

27- Wischmeier W.H., and Smith D.D. 1978. Predicting. Washington D.C. 58 pp. Rainfall Erosion Losses. Agricultural Handbook No. 537. USDA-Science and Education Administration.

28- Yitayew M., Pokrzywka S.J., and Renard K.G. 1999. Using GIS for facilitating erosion estimation. Applied Engineering in Agriculture 15: 295-301.

29- Zhang H.,YangQ., Li R., Liu Q., Moore D., He P., and Geissen V. 2013 .Extension of a GIS procedure for calculating the RUSLE equation LS factor. Journal of Computers and Geosciences 52: 177–188.