Advertisement

Water Conservation Science and Engineering

, Volume 4, Issue 4, pp 187–200 | Cite as

Quantitative Estimation of Soil Erosion Using Open-Access Earth Observation Data Sets and Erosion Potential Model

  • Edon Maliqi
  • Sudhir Kumar SinghEmail author
Original Paper

Abstract

The soil erosion is a physical process and considered as worldwide environmental problem. The present study aims to investigate spatial-temporal distribution of soil loss using Erosion Potential Model (EPM) in Mitrovica region (Kosova) in last 18 years (2000–2018). The precipitation, temperature, topography, geology, and land use/land cover layers were considered as input into the model. The model was implemented on annual basis for years 2000 and 2018. The hypsometric analysis was performed using Calhypso pulgin available in QGIS in order to understand the different stages of the watershed. In addition, the water retention curve of the region was also developed to understand the potential of soil to store the water. The validation was made through the regular field checks and surveys. In year 2000, the minimum and maximum soil loss was observed as 100 and 2000 m3/km2/year, respectively. In year 2018, the minimum and maximum detachment of soil volume was reported as 150 and 2200 m3/km2/year, respectively. Furthermore, the present study demonstrates the potential application of EPM in data scare regions like Mitrovica region.

Keywords

Erosion Potential Model Soil erosion Sediment yeild Topography Mitrovica 

Notes

Acknowledgments

The author Edon Maliqi’s grateful to the Prof. Dr. Petar Penev for supervising his Ph.D at the University of Architecture, Civil Engineering and Geodesy; Department of Photogrametry and Cartography; Sofia, Bulgaria. As well as, Mr. Maliqi would like to thank to Prof.Dr. Ivica Milevski from Ss. Cyrill and Methodius University, Skopje, North Macedonia.

Compliance with Ethical Standards

There is no compliance with ethical standards.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Adhikary PP, Chakraborty D, Kalra N, Sachdev C, Patra A, Kumar S, Tomar R, Chandna P, Raghav D, Agrawal K (2008) Pedotransfer functions for predicting the hydraulic properties of Indian soils. Soil Research 46:476–484CrossRefGoogle Scholar
  2. 2.
    Anache AAJ, Flanagan CD, Srivastava A, Wendland EC (2018) Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado. Sci Total Environ 622–623:140–151.  https://doi.org/10.1016/j.scitotenv.2017.11.257 CrossRefGoogle Scholar
  3. 3.
    Anees MT, Abdullah K, Nawawi MNM, Norulaini NAN, Syakir MI, Omar AKM (2018) Soil erosion analysis by RUSLE and sediment yield models using remote sensing and GIS in Kelantan state, Peninsular Malaysia. Soil Res 56(4):356–372CrossRefGoogle Scholar
  4. 4.
    Avdan U and Jovanovska E (2016) Algorithm for automated mapping of land surface temperature using Landsat 8 satellite data. Hindawi Publishing Corporation. Journal of SensorsGoogle Scholar
  5. 5.
    Blinkov I (2012) An approach for conversion of erosion data produced by EPM method inweight measure. In International Conference On Land Conservation–LandconGoogle Scholar
  6. 6.
    Choudhari PP, Nigam GK, Singh SK, Thakur S (2018) Morphometric based prioritization of watershed for groundwater potential of Mula river basin, Maharashtra, India. Geology, Ecology, and Landscapes 2(4):256–267.  https://doi.org/10.1080/24749508.2018.1452482 CrossRefGoogle Scholar
  7. 7.
    De Cesare G, Beyer Portner N, Boillat J and Scleiss A (1998) Modelling of erosion andsedimentation based on field investigation in Alpine reservoirs of hydropower schemes. German Coastal Engineering Research Council, parallel session 34Google Scholar
  8. 8.
    De Vente J, Poesen J (2005) Predicting soil erosion and sediment yield at the basin scale: scale issue and semiquantitative models. Earth Sci Rev 71:95–125CrossRefGoogle Scholar
  9. 9.
    De Vente J, Poesen J, Bazzzoffi B, Van Rompaey A, Verstraeten G (2006) Predicting catchment sediment yield in Mediterranean environments: the importance of sediment sources and connectivity in Italian drainage basins. Earth Surf Process Landf 31:1017–1034CrossRefGoogle Scholar
  10. 10.
    Dragicevic N (2016) Model for erosion intensity and sediment production assesment based on erosion potential method modification. University of Rijeka. Faculty of Civil EngineeringGoogle Scholar
  11. 11.
    Efthimiou N, Lykoudi E, Panagoulia D and Karavitis C (2016) Assessment of soilsusceptibility to erosion using EPM and RUSLE models: the case of Venetikos river catchment. Global NEST Journal. Vol 18Google Scholar
  12. 12.
    Elhag M, Kojchevska T, Boteva S (2019) EPM for soil loss estimation in different geomorphologic conditions and data conversion by using GIS. World Multidisciplinary Earth Science Symposium. IOP Conference Series: Earth and Environmental Science 221CrossRefGoogle Scholar
  13. 13.
    Emmanouloudis D and Filippidis E (2002) A quantitative estimation model of mountainous watershed degradation. Kick-off workshop on IAHS, Decade of prediction in ungaugedbasins (PUB) - Hydrological Science on Mission. BrazilGoogle Scholar
  14. 14.
    Emmanouloudis D, Christou O and Filippidis E (2003) Quantitative estimation of degradation in the Aliakmon river basin using GIS. Erosion Prediction in Ungauged Basins: Integrating Methods and Techniques. IAHS Publication, 279, 234Google Scholar
  15. 15.
    Eskelmann W, Baritz R, Bialousz S, Bielek P, Carre F, Houskova B, Jones RJA, Kibblewhite MG, Kozak J, Le Bas C, Toth G, Toth T, Varallyay G, Yli Halla M, Zupan M (2006) Coomon criteria for risk area identification according to soil threats. EUR 22185 EN. Office for Official Publications of the European Communities, LuxemburgGoogle Scholar
  16. 16.
    Fannetti D, Vezzoli L (2007) Sediment input and evolution of lacustrine deltas: The Breggia and Greggio rivers case study (Lake Como, Italy). Quat Int 173-174(SUPPL):113–124CrossRefGoogle Scholar
  17. 17.
    Gavrilovic S (1962) A method for estimating the average annual quantity of sedimentsaccording to the potency of erosion. Fac For 26:151–168 (In Serbian)Google Scholar
  18. 18.
    Gavrilovic, S. 1970. Modern ways of calculating the torrential sediment and erosion mapping. In: Erosion, Torrents and Alluvial Deposits. Yugoslav Committee for International Hydrological decade, Belgrade. p. 85–100. (In Serbian).Google Scholar
  19. 19.
    Gavrilovic S (1972) Engineering of torrens and erosion. Journal of Construction (Special Issue), Belgrade, Yugoslavia (In Serbian)Google Scholar
  20. 20.
    Gavrilovic Z (1988) The use of empirical method (Erosion Potential Method) for calculating sediment production and transportation in unstudied or torrential streams. In: White WR (ed) International Conference on River Regime. John Wiley & Sons, Chichester, pp 411–422Google Scholar
  21. 21.
    Globevnik L, Holjevic D, Petkovesk G, Rubinic J (2003) Applicability of the Gavrilovic method in erosion calculation using spatial data manipulation. Erosion Prediction in Ungaunged Basins; Integrating Methods and Techniques. IAHS Publication 279. pp 334–233Google Scholar
  22. 22.
    Kalinderis I, Sapountzis M, Stathis D, Tziaftani F, Kourakli P and Stefanidis P (2009) The risk of sedimentation of artificial lakes, following the soil loss and degradation process in the wider drainage basin. Artificial lake of Smokovo case study (Central Greece). International Conference LANDCON 0905 “Global Change-Challenges for soil management-from degradation-through soil and water conservation-to sustainable soil management”, Tara Mountain, SerbiaGoogle Scholar
  23. 23.
    Kirkby MJ, Irvine BJ, Jones RJA, Govers G, the PESERA Team (2008) The PESERA coarse scale erosion model for Europe. I.-Model rationale and implementation. Eur J Soil Sci 59:1293–1306CrossRefGoogle Scholar
  24. 24.
    Kostadinov K, Braunovci S, Gragicevic S, Zlatic M, Dragovic N, Rakonjac N (2018) Effects of erosion control work: case study – Grdelica Gorge, the South of Morava River (Serbia). WaterGoogle Scholar
  25. 25.
    Kumar N, Singh SK, Srivastava PK, Narsimlu B (2017) SWAT model calibration and uncertainty analysis for streamflow prediction of the Tons river basin, India, using sequential uncertainty fitting (SUFI-2) algorithm. Model Earth Syst Environ.  https://doi.org/10.1007/s40808-017-0306-z.s
  26. 26.
    Kumar N, Singh SK, Pandey HK (2018a) Drainage morphometric analysis using open access earth observation datasets in a drought-affected part of Bundelkhand, India. Appl Geomatics.  https://doi.org/10.1007/s12518-018-0218-2 CrossRefGoogle Scholar
  27. 27.
    Kumar N, Singh SK, Singh VG, Dzwairo B (2018b) Investigation of impacts of land use/land cover change on water availability of tons River Basin, Madhya Pradesh, India. Model Earth Syst Environ 4:295–310CrossRefGoogle Scholar
  28. 28.
    Kumar M, Denis DM, Singh SK, Szabo S, Suryavanshi S (2018c) Landscape metrics for assessment of land cover change and fragmentation of a heterogeneous watershed. Remote Sens Appl Soc Environ 10(2018):224–233Google Scholar
  29. 29.
    Lazarevic R (1968) Erosion in the Gvozdacka river basin – supplement to the instructions for erosion map elaboration. Bull Serbian Geogr Soc 49(2):75–98 (In Serbian)Google Scholar
  30. 30.
    Lazarevic R (1985) The new method for erosion coefficient determination – Z. Erosion Prof Factsheet 13:54–61 (In Serbian)Google Scholar
  31. 31.
    Luo W, Harlin JH (2003) Theoretical travel time based on watershed hypsometry. J Am Water Resour Assoc 39:785–792CrossRefGoogle Scholar
  32. 32.
    Maliqi E, Penev P (2018) Monitoring of vegetation change by using RS and GIS techniquesin Mitrovica, Kosovo. J Cartogr Geogr Inf Syst 1:1–13 Clausius Scientific Press, CanadaGoogle Scholar
  33. 33.
    Markose VJ, Jayappa KS (2011) Hypsometric analysis of Kali River Basin, Karnataka, India, using geographic information system. Geocarto Int 26:553–568.  https://doi.org/10.1080/10106049.2011.608438 CrossRefGoogle Scholar
  34. 34.
    Milevski I (2015) An approach of GIS based assessment of soil erosion rate on country level in the case of Macedonia. Physical Geography; Cartography; Geogrphic Information Systems & Spatial planing. pp 97–104Google Scholar
  35. 35.
    Milevski I, Ivanova E (2013) Erosion potential modeling of the territory of municipalities Pehchevo and Simitli using remote sensing data. 9th Scientific Conference with International Participation. 20 - 22 November. Sofia, BulgariaGoogle Scholar
  36. 36.
    Mondal A, Khare D, Kundu S, Meena PK, Mishra PK, Shukla R (2015) Impact of climate change on future soil erosion in different slope, land use, and soil-type conditions in a part of the Narmada River Basin, India. J Hydrol Eng 20(6):C5014003–1–12CrossRefGoogle Scholar
  37. 37.
    Murmu P, Kumar M, Lal D, Sonker I, Singh SK (2019) Delineation of groundwater potential zones using geospatial techniques and analytical hierarchy process in Dumka district, Jharkhand, India. Groundw Sustain Dev 9:100239CrossRefGoogle Scholar
  38. 38.
    Nearing MA, Pruski FF, O'Neal MR (2004) Expected climate change impacts on soil erosion rates: A review. J Soil Water Conserv 59(1):43–50Google Scholar
  39. 39.
    Panagos P, Borrelli P, Meusburgerb K, Alewellb K, Lugatoa E, Montanarellaa L (2015a) Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 48(2015):38–50CrossRefGoogle Scholar
  40. 40.
    Panagos P, Borrelli P, Poesen J, Ballabio C, Lugato E, Meusburger K, Montanarella L, Alewell C (2015b) The new assessment of soil loss by water erosion in. Eur Environ Sci Policy 54(2015):438–447CrossRefGoogle Scholar
  41. 41.
    Panagos P, Borrelli P, Poesen J (2019) Soil loss due to crop harvesting in the European Union: a first estimation of an underrated geomorphic process. Sci Total Environ 664(2019):487–498CrossRefGoogle Scholar
  42. 42.
    Petras J, Kuspilic N, Kunstek D (2005) Some experience on the prediction of suspended sediment concentrations and fluxes in Croatia. Proceedings of Symposium SI held during the Seventh IAHS Scientific Assembly at Foz do Igacu, Brazil. IAHS 292:179–184Google Scholar
  43. 43.
    Pike RJ, Wilson SE (1971) Elevation-relief ratio, hypsometric integral and geomorphic area-altitude analysis. Geol Soc Am Bull 82:1079–1084CrossRefGoogle Scholar
  44. 44.
    Rafaelli S, Peviani M and Perez Ayala F (1998) Study of sediment yield on the mountain Cuence del Rio Iruya (Argentina). IARH AMH, Hydraulic XVIII Latin American Conference, Oaxaca, Mexico. (In Spanish)Google Scholar
  45. 45.
    Rawat KS, Singh SK (2018) Appraisal of soil conservation capacity using NDVI Model-based C Factor of RUSLE Model for a semi arid ungauged watershed: a case study. Water Conserv Sci Eng 3(1):47–58.  https://doi.org/10.1007/s41101-018-0042-x CrossRefGoogle Scholar
  46. 46.
    Rawat KS, Singh SK, Pal RK (2019) Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data. Remote Sensing Applications: Society and Environment.  https://doi.org/10.1016/j.rsase.2019.100250 CrossRefGoogle Scholar
  47. 47.
    Renard K, Foster G, Weesies G, McCool D, Yoder D (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE), Agricultural Handbook No. 703, 65–100.  https://doi.org/10.1201/9780203739358-5
  48. 48.
    Ritter DF, Kochel RC, Miller IR (2002) Process geomorphology. McGraw Hill, BostonGoogle Scholar
  49. 49.
    Sharma SK et al (2013) Use of geographical information system in hypsometric analysis of Kanhiya Nala watershed. Int J Remote Sens Geosci 2:30–35Google Scholar
  50. 50.
    Simunek J, Sejna M, Van Genuchten MT (1998) The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version2.0. IGWMC-TPS-70. Int. Ground Water Modeling Ctr., Colorado School of Mines, GoldenGoogle Scholar
  51. 51.
    Simunek J, Van Genuchten MT, Sejna M (2005) The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version3.0. HYDRUS Softw.Ser.1. Dep. Of Environ. Sci., Univ. of California, RiversideGoogle Scholar
  52. 52.
    Staut M (2004) Recent erosional processes in the catchment of the Dragonja river. Published thesis. Faculty of Arts. University of Ljubljana. (In Serbian)Google Scholar
  53. 53.
    Stefanidis P, Myronidis D, Sapountzis M, Stathis D (1998) The torrent “Sklitrho” in Florina. Torrential Environment and torrent control system. Scientific Annals. Department of forestry and natural environment. Aristot Univ Thessaloniki 41(2):1275Google Scholar
  54. 54.
    Strahler AN (1957) Quantitative analysis of watershed geomorphology. Trans Am Geophys Union 38:913–920CrossRefGoogle Scholar
  55. 55.
    Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks. In: Chow VT (ed) Handbook of applied hydrology. McGraw Hill, New York, pp 39–76Google Scholar
  56. 56.
    Sivakumar V et al (2011) Hypsometric analysis of Varattaru river basin of Harur taluk, Dharmapuri districts, Tamilnadu, India using geomatics technology. Int J Geomatics Geosci 2:241–247Google Scholar
  57. 57.
    Szabó G, Singh SK, Szabó S (2015) Slope angle and aspect as influencing factors on the accuracy of the SRTM and the ASTER GDEM databases. Phys Chem Earth, Parts A/B/C 83–84:137–145CrossRefGoogle Scholar
  58. 58.
    Thakur JK, Singh SK, Ekanthalu VS (2016) Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling. Appl Water Sci:1–14Google Scholar
  59. 59.
    Thieken H, Lucke A, Diekkruger B, Richter O (1999) Scaling input data by GIS forhydrological modelling. Hydrol Process 13(4):611–630CrossRefGoogle Scholar
  60. 60.
    Thiemann D (2006) Detection and assessment of erosion and soil erosion risk in the watershed of the Bilate river-southern Ethiopian rifft valley. Ph.D. Thesis. Freie University Berlin. Institute for Geographic Sciences. Berlin, GermanyGoogle Scholar
  61. 61.
    Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44:892–898CrossRefGoogle Scholar
  62. 62.
    Vogt V, Colombo R, Bertolo F (2003) Deriving drainage networks and catchmentboundaries; a new methodology combining digital elevation data and environmental characteristics. Geomorfology 53(3-4):281–298Google Scholar
  63. 63.
    Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. Agric Handb 537(537):285–291.  https://doi.org/10.1029/TR039i002p00285 CrossRefGoogle Scholar
  64. 64.
    Wosten J, Pachepsky YA, Rawls W (2001) Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J Hydrol 251:123–150CrossRefGoogle Scholar
  65. 65.
    Yadav SK, Singh SK, Gupta M, Srivastava PK (2014) Geocarto International. Morphometric analysis of Upper Tons basin from Northern Foreland of Peninsular India using CARTOSAT satellite and GIS, Geocarto International.  https://doi.org/10.1080/10106049.2013.868043 CrossRefGoogle Scholar
  66. 66.
    Yadav SK, Dubey A, Szilard S, Singh SK (2016) Prioritisation of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India. Geocarto Int 33(4):339–356CrossRefGoogle Scholar
  67. 67.
    Zemljic M (1971) Calculation of sediment load. Evaluation of vegetation as anti-erosive factor. Proceedings of the international symposium Interpraevent. Villach, Australia. (In French)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.UBT – Higher Education Institution, Faculty of Architecture and Spatial PlanningPristinaKosovo
  2. 2.K. Banerjee Centre of Atmospheric and Ocean StudiesUniversity of AllahabadPrayagrajIndia

Personalised recommendations