[go: up one dir, main page]

Skip to main content

Advertisement

Log in

Effect of different topographic data sources on soil loss estimation for a mountainous watershed in Northern China

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

The effect of topography on soil loss can be characterized by slope length and slope steepness factors. These factors are commonly evaluated using digital elevation models (DEM) which can be generated from a large variety of topographic data. The purpose of this study was to evaluate the effect of topographic data source on estimated soil loss for a mountainous watershed. The Dongtaigou watershed (90 ha) is located in Huairou District, Beijing, China. Data sources included topographic maps at 1:2000, 1:10,000, and 1:50,000 scales, and 30-m DEM from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) dataset. The grid sizes considered were 2, 5, 10, 25, and 30 m. The results showed that the calculated average slope steepness and soil loss were reduced and the average slope length increased when the map scale was decreased. With results using 1:2000 map and 2-m grid as the reference, the land area with minimal soil loss (<2 t ha−1 year−1) was 5.7 ha, and the area with moderate soil loss (2–25 t ha−1 year−1) was 77.9 ha. Compared to these reference values, the 1:10,000 map would bring out a 42 % increase in the area with minimal soil loss, and a concurrent decrease of −8 % in the area with a moderate soil loss. For the 30-m ASTER GDEM V1, the calculated slope steepness is some 64 % lower than the reference value. The slope length, on the other hand, was increased by 265 % and soil loss decreased by 47 % in comparison with the reference values. For all map scales and grid sizes, the relative errors in the estimated soil loss were about 50 % of the relative errors in slope steepness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ahamed TRN, Rao KG, Murthy JSR (2000) Fuzzy class membership approach to soil erosion modelling. Agric Syst 63:97–110

    Article  Google Scholar 

  • Alexakis DD, Hadjimitsis DG, Agapiou A (2013) Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus. Atmos Res 131:108–124

    Article  Google Scholar 

  • Avanzi JC, Silva MLN, Curi N, Norton LD, Beskow S, Martins SG (2013) Spatial distribution of water erosion risk in a watershed with eucalyptus and Atlantic forest. Cienc Agrotec 37(5):427–434

    Article  Google Scholar 

  • Bahadur KCK (2009) Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environ Geol 57:695–705

    Article  Google Scholar 

  • Bartsch KP, Van Miegroet H, Boettinger J, Dobrowolski JP (2002) Using empirical erosion models and GIS to determine erosion risk at Camp Williams, Utah. J Soil Water Conserv 57(1):29–37

    Google Scholar 

  • Beskow S, Mello CR, Norton LD, Curi N, Viola MR, Avanzi JC (2009) Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling. Catena 79:49–59

    Article  Google Scholar 

  • Bewket W, Teferi E (2009) Assessment of soil erosion hazard and prioritization for treatment at the watershed level: case study in the Chemoga watershed, Blue Nile basin, Ethiopia. Land Degrad Develop 20:609–622

    Article  Google Scholar 

  • Bhattarai R, Dutta D (2007) Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resour Manag 21:1635–1647

    Article  Google Scholar 

  • Browne CL, Wilson L (2013) Evaluating inputs to models of hominin raw material selection: map resolution and path choices. J Archaeol Sci 40(11):3955–3962

    Article  Google Scholar 

  • Chatterjee S, Krishna AP, Sharma AP (2014) Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India. Environ Earth Sci 71:357–374

    Article  Google Scholar 

  • Chen N (2013) Influence of resolutions of DEM on the error of slope (in Chinese). Geomat Inf Sci Wuhan Univ 38(5):594–599

    Google Scholar 

  • Chen LG, Qian X, Shi Y (2011a) Critical area identification of potential soil loss in a typical watershed of the three gorges reservoir region. Water Resour Manag 25:3445–3463

    Article  Google Scholar 

  • Chen T, Niu RQ, Li PX, Zhang LP, Du B (2011b) Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China. Environ Earth Sci 63:533–541

    Article  Google Scholar 

  • China Nonferrous Metals Industry Association (2008) Code for engineering surveying (GB50026-2007)

  • Chou WC (2010) Modelling watershed scale soil loss prediction and sediment yield estimation. Water Resour Manag 24:2075–2090

    Article  Google Scholar 

  • Cohen MJ, Shepherd KD, Walsh MG (2005) Empirical reformulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma 124:235–252

    Article  Google Scholar 

  • Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resour Manag 22:1783–1798

    Article  Google Scholar 

  • Das C, Capehart WJ, Mott HV, Zimmerman PR, Schumacher TE (2004) Assessing regional impacts of Conservation Reserve Program-type grass buffer strips on sediment load reduction from cultivated lands. J Soil Water Conserv 59(4):134–142

    Google Scholar 

  • Demirci A, Karaburun A (2012) Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed, northwest Turkey. Environ Earth Sci 66:903–913

    Article  Google Scholar 

  • Desmet PJJ, Govers G (1996) A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51(5):427–433

    Google Scholar 

  • Erdogan EH, Erpul G, Bayramin I (2007) Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed. Environ Monit Assess 131:153–161

    Article  Google Scholar 

  • Erkal T, Yildirim U (2012) Soil erosion risk assessment in the Sincanli sub-watershed of the Akarcay Basin (Afyonkarahisar, Turkey) using the Universal Soil Loss Equation (USLE). Ekoloji 21(84):18–29

    Article  Google Scholar 

  • Fu SH, Cao LX, Liu BY, Wu ZP, Savabi MR (2014) Effects of DEM grid size on predicting soil loss from small watersheds in China. Environ Earth Sci. doi:10.1007/s12665-014-3564-3

    Google Scholar 

  • Fu SH, Liu BY, Zhou GY, Sun ZX, Zhu XL (2015) Calculation tool of topographic factors (in Chinese). Sci Soil Water Conser 13(5):105–110

    Google Scholar 

  • Gaffer RL, Flanagan DC, Denight ML, Engel BA (2008) Geographical information system erosion assessment at a military training site. J Soil Water Conserv 63(1):1–10

    Article  Google Scholar 

  • Gao J (1997) Resolution and accuracy of terrain representation by grid DEMs at a micro-scale. Int J Geogr Inf Sci 11(2):199–212

    Article  Google Scholar 

  • Gao Y, Lv N, Xue CS, Ma HC (2007) Influence of digital elevation model and different scales to the graded intensity of soil erosion (in Chinese). Soil Water Conserv China 10:26–28

    Google Scholar 

  • Halim R, Clemente RS, Routray JK, Shrestha RP (2007) Integration of biophysical and socio-economic factors to assess soil erosion hazard in the upper Kaligarang watershed, Indonesia. Land Degrad Dev 18:453–469

    Article  Google Scholar 

  • Hutchinson MF (1989) New procedure f or gridding elevation and stream line data with automatic removal of spurious pits. J Hydrol 106(3–4):211–232

    Article  Google Scholar 

  • Jain MK, Das D (2010) Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resour Manag 24:2091–2112

    Article  Google Scholar 

  • Jain SK, Kumar S, Varghese J (2001) Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour Manag 15:41–54

    Article  Google Scholar 

  • Jain MK, Mishra SK, Shah RB (2010) Estimation of sediment yield and areas vulnerable to soil erosion and deposition in a Himalayan watershed using GIS. Curr Sci 98(2):213–221

    Google Scholar 

  • Kim SM, Jang TI, Kang MS, Im SJ, Park SW (2014) GIS-based lake sediment budget estimation taking into consideration land use change in an urbanizing catchment area. Environ Earth Sci 71:2155–2165

    Article  Google Scholar 

  • Krasa J, Dostal T, Vrana K, Plocek J (2010) Predicting spatial patterns of sediment delivery and impacts of land-use scenarios on sediment transport in Czech catchments. Land Degrad Dev 21:367–375

    Article  Google Scholar 

  • Li H, Chen XL, Lim KJ, Cai XB, Sagong M (2010) Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, using USLE, GIS, and RS. J Earth Sci 21(6):941–953

    Article  Google Scholar 

  • Lin CY, Lin WT, Chou WC (2002) Soil erosion prediction and sediment yield estimation: the Taiwan experience. Soil Tillage Res 68:143–152

    Article  Google Scholar 

  • Lin WT, Tsai JS, Lin CY, Huang PH (2008) Assessing reforestation placement and benefit for erosion control: a case study on the Chi-Jia-Wan Stream, Taiwan. Ecol Model 211:444–452

    Article  Google Scholar 

  • Liu BY, KL Zhang, Y Xie (2002) An empirical soil loss equation. In: Proceedings 12th international soil conservation organization conference (Vol 2, p 15), Vol. III. Tsinghua University Press. Beijing, China

  • Liu BY, Bi XG, Fu SH (2010) Beijing soil loss equation (in Chinese). Scientific Publishing House Press, Beijing

    Google Scholar 

  • Lufafa A, Tenywa MM, Isabirye M, Majaliwa MJG, Woomer PL (2003) Prediction of soil erosion in a Lake Victoria basin catchment using a GIS-based Universal Soil Loss model. Agric Syst 76:883–894

    Article  Google Scholar 

  • Ma JW, Xue Y (2003) A data fusion approach for soil erosion monitoring in the Upper Yangtze River Basin of China based on Universal Soil Loss Equation (USLE) model. Int J Remote Sens 24(23):4777–4789

    Article  Google Scholar 

  • Mellerowicz KT, Rees HW, Chow TL, Ghanem I (1994) Soil conservation planning at the watershed level using the Universal Soil Loss Equation with GIS and microcomputer technologies: a case study. J Soil Water Conserv 49(2):194–200

    Google Scholar 

  • Millward AA, Mersey JE (1999) Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38:109–129

    Article  Google Scholar 

  • Molnar D, Julien P (1998) Estimation of upland erosion using GIS. Comput Geosci 24(2):183–192

    Article  Google Scholar 

  • Naqvi HR, Mallick J, Devi LM, Siddiqui MA (2013) Multi-temporal annual soil loss risk mapping employing Revised Universal Soil Loss Equation (RUSLE) model in Nun Nadi Watershed, Uttrakhand (India). Arab J Geosci 6:4045–4056

    Article  Google Scholar 

  • Neigh CSR, Nelson RF, Ranson KJ, Margolis HA, Montesano PM, Sun G, Kharuk V, Næsset E, Wulder MA, Andersen H (2013) Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR. Remote Sens Environ 137:274–287

    Article  Google Scholar 

  • Ozsoy G, Aksoy E, Dirim MS, Tumsavas Z (2012) Determination of Soil Erosion Risk in the Mustafakemalpasa River Basin, Turkey, Using the Revised Universal Soil Loss Equation, Geographic Information System, and Remote Sensing. Environ Manag 50:679–694

    Article  Google Scholar 

  • Pan JH, Wen Y (2014) Estimation of soil erosion using RUSLE in Caijiamiao watershed, China. Nat Hazards 71:2187–2205

    Article  Google Scholar 

  • Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour Manag 21:729–746

    Article  Google Scholar 

  • Perović V, Životić L, Kadović R, Đorđević A, Jaramaz D, Mrvić V, Todorović M (2013) Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia. Environ Earth Sci 68:115–128

    Article  Google Scholar 

  • Rao E, Ouyang ZY, Yu XX, Xiao Y (2014) Spatial patterns and impacts of soil conservation service in China. Geomorphology 207:64–70

    Article  Google Scholar 

  • Ren SM, Liang Y, Sun B (2011) Research on sensitivity for soil erosion evaluation from DEM and remote sensing data source of different map scales and image resolutions. Proc Environ Sci 10:1753–1760

    Article  Google Scholar 

  • Sharma A, Tiwari KN, Bhadoria PBS (2011) Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environ Monit Assess 173:789–801

    Article  Google Scholar 

  • Shinde V, Sharma A, Tiwari KN, Singh M (2011) Quantitative determination of soil erosion and prioritization of micro-watersheds using remote sensing and GIS. J Indian Soc Remote Sens 39(2):181–192

    Article  Google Scholar 

  • Silva RMD, Montenegro SMGL, Santos CAG (2012) Integration of GIS and remote sensing for estimation of soil loss and prioritization of critical sub-catchments: a case study of Tapacura catchment. Nat Hazards 62:953–970

    Article  Google Scholar 

  • Singh R, Phadke VS (2006) Assessing soil loss by water erosion in Jamni River Basin, Bundelkhand region, India, adopting universal soil loss equation using GIS. Curr Sci 90(10):1431–1435

    Google Scholar 

  • Tang GA, Yang QK, Zhang Y, Liu YM, Liu XH (2001a) Research on accuracy of slope derived from DEMs of different map scales (in Chinese). Bull Soil Water Conserv 21(1):53–56

    Google Scholar 

  • Tang GA, Chen N, Liu YM, Zhang YS, Chen ZJ (2001b) A comparison on digital terrain models of different scales in Loess hill and gully area (in Chinese). Bull Soil Water Conserv 21(2):34–36

    Google Scholar 

  • Thomas J, Prasannakumar V, Vineetha P (2015) Suitability of space borne digital elevation models of different scales in topographic analysis: an example from Kerala, India. Environ Earth Sci 73:1245–1263

    Article  Google Scholar 

  • Uuemaa E, Roosaare J, Mander Ü (2005) Scale dependence of landscape metrics and their indicatory value for nutrient and organic matter losses from catchments. Ecol Ind 5(4):350–369

    Article  Google Scholar 

  • Vezina K, Bonn F, Van CP (2006) Agricultural land-use patterns and soil erosion vulnerability of watershed units in Vietnam’s northern highlands. Landscape Ecol 21:1311–1325

    Article  Google Scholar 

  • Wang GQ, Hapuarachchi P, Ishidaira H, Kiem AS, Takeuchi K (2009) Estimation of soil erosion and sediment yield during individual rainstorms at catchment scale. Water Resour Manag 23:1447–1465

    Article  Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses, a guide to conservation planning. In: Handb USDA (ed) 537. U.S. Gov. Print. Off, Washington, DC

    Google Scholar 

  • Wolock DM, Price CV (1994) Effects of digital elevation model map scale and data resolution on a topography-based watershed model. Water Resour Res 30(11):3041–3052

    Article  Google Scholar 

  • Wu S, Li J, Huang G (2005) An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models. Environ Model Assess 10:33–42

    Article  Google Scholar 

  • Yang QK, Li R, Liang W (2006a) Cartographic analysis on terrain factors for regional soil erosion modeling (in Chinese). Res Soil Water Conserv 13(1):56–58

    Google Scholar 

  • Yang QK, McVicar TR, Van Niel TG, Li LT (2006b) Comparison of hydro-geomorphology representing between DEMs by TIN and ANUDEM approaches (in Chinese). Bull Soil Water Conserv 26(6):84–88

    Google Scholar 

  • Yoshino K, Ishioka Y (2005) Guidelines for soil conservation towards integrated basin management for sustainable development: a new approach based on the assessment of soil loss risk using remote sensing and GIS. Paddy Water Environ, 3:235–247

    Article  Google Scholar 

  • Zhang HM, Yang QK, Li R, Liu QR, Moore D, He P, Ritsema CJ, Geissen V (2013) Extension of a GIS procedure for calculating the RUSLE equation LS factor. Comput Geosci 52:177–188

    Article  Google Scholar 

  • Životić L, Perović V, Jaramaz D, Đorđević A, Petrović R, Todorović M (2012) Application of USLE, GIS, and Remote Sensing in the assessment of soil erosion rates in Southeastern Serbia. J Environ Stud 21(6):1929–1935

    Google Scholar 

Download references

Acknowledgments

Research for this paper was funded by National Natural Science Foundation of China (No. 41571259) and the State Key Program of National Natural Science Foundation of China (No. 41530858).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suhua Fu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Zhu, X., Zhang, W. et al. Effect of different topographic data sources on soil loss estimation for a mountainous watershed in Northern China. Environ Earth Sci 75, 1382 (2016). https://doi.org/10.1007/s12665-016-6130-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-016-6130-3

Keywords

Navigation