Evaluation of water erosion at a mountain catchment in Poland using the G2 model

Abstract The Western Carpathians region in southern Poland is characterized by high erosion risk due to steep slopes, flysch formation and intense precipitation with frequent storm events. The G2 model, based on the principles of the Universal Soil Loss Equation (USLE), was used to investigate soil erosion assessment, except that the yearly rainfall erosivity factor was substituted for by the monthly one. The plant cover factor was determined based on the CORINE land cover 2012 database and field observations of vegetation stages. Slope intercept was estimated by applying a Sobel filter. Terrain properties were calculated from a 5 m DEM of the area. Modeling investigations were carried out in the agricultural basin of the mountain catchment (1.47 km2) in the years 2011–2014. Rainfall data were collected from weather stations, and soil properties were measured in 43 locations. The G2 model estimated total annual soil loss as between 3.37 Mg ha−1 (2012) and 31.05 Mg ha−1 (2014). The erosive events that contributed most to yearly erosion occurred in May (2014: 80.40% of yearly total) and June (2013: 57.08%). Redundancy analysis based on land-use types provided factors affecting soil erosion by water. In conclusion, the G2 model was useful in erosion estimation in a steep-sloped agricultural basin with a variable hydrological regime.

[1]  L. M. Risse,et al.  USLE-M : Empirical modeling rainfall erosion through runoff and sediment concentration , 1998 .

[2]  M. Todorović,et al.  Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia , 2012, Environmental Earth Sciences.

[3]  Panos Panagos,et al.  Soil erodibility in Europe: a high-resolution dataset based on LUCAS. , 2014, The Science of the total environment.

[4]  THE DIMENSIONAL ANALYSIS OF THE USLE - MUSLE SOIL EROSION MODEL , 2010 .

[5]  T. Steenhuis,et al.  Spatial and Temporal Patterns of Soil Erosion in the Semi-humid Ethiopian Highlands: A Case Study of Debre Mawi Watershed , 2014 .

[6]  Dazhi Mao,et al.  Impacts of land-use change on hydrologic responses in the Great Lakes region. , 2009 .

[7]  Jan Lepš,et al.  Multivariate Analysis of Ecological Data using CANOCO , 2003 .

[8]  S. J. Smith,et al.  Prediction of sediment yield from southern plains grasslands with the modified universal soil loss equation. , 1984 .

[9]  Panos Panagos,et al.  The G2 erosion model: An algorithm for month-time step assessments , 2018, Environmental research.

[10]  S. Twardy,et al.  Wielkość erozji wodnej obliczona metodą USLE , 2012 .

[11]  Daniele de Rigo,et al.  Modelling soil erosion at European scale: towards harmonization and reproducibility , 2014 .

[12]  Ashish Pandey,et al.  Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing , 2008 .

[13]  G. Kirchner,et al.  Estimating short-term soil erosion rates after single and multiple rainfall events by modelling the vertical distribution of cosmogenic 7Be in soils , 2015 .

[14]  Panos Panagos,et al.  The new assessment of soil loss by water erosion in Europe , 2015 .

[15]  P. Burrough GIS and geostatistics: Essential partners for spatial analysis , 2001, Environmental and Ecological Statistics.

[16]  Tammo S. Steenhuis,et al.  Development and application of a physically based landscape water balance in the SWAT model , 2011 .

[17]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[18]  Christos G. Karydas,et al.  Monthly Time-Step Erosion Risk Monitoring of Ishmi-Erzeni Watershed, Albania, Using the G2 Model , 2015, Environmental Modeling & Assessment.

[19]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[20]  R. Marks,et al.  Anleitung zur Bewertung des Leistungsvermögens des Landschaftshaushaltes (BA LVL) , 1992 .

[21]  Panos Panagos,et al.  Mapping monthly rainfall erosivity in Europe , 2017, The Science of the total environment.

[22]  Panos Panagos,et al.  Modelling monthly soil losses and sediment yields in Cyprus , 2016, Int. J. Digit. Earth.

[23]  M. Coutinho,et al.  A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal , 2001 .

[24]  V. Pampalone,et al.  A new version of the USLE‐MM for predicting bare plot soil loss at the Sparacia (South Italy) experimental site , 2015 .

[25]  A. Zabaleta,et al.  Factors controlling suspended sediment yield during runoff events in small headwater catchments of the Basque Country , 2007 .

[26]  Panos Panagos,et al.  Seasonal monitoring of soil erosion at regional scale: An application of the G2 model in Crete focusing on agricultural land uses , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[27]  J. Latron,et al.  Relationships among rainfall, runoff, and suspended sediment in a small catchment with badlands , 2008 .

[28]  K. C. Krishna Bahadur,et al.  Mapping soil erosion susceptibility using remote sensing and GIS : a case of the Upper Nam Wa Watershed, Nan Province, Thailand , 2009 .

[29]  Kyoung Jae Lim,et al.  Soil erosion risk assessment of the Keiskamma catchment, South Africa using GIS and remote sensing , 2012, Environmental Earth Sciences.

[30]  K. Moore,et al.  Predicting saturation‐excess runoff distribution with a lumped hillslope model: SWAT‐HS , 2017 .

[31]  S. Kushwaha,et al.  Modelling soil erosion risk based on RUSLE-3D using GIS in a Shivalik sub-watershed , 2013, Journal of Earth System Science.

[32]  P. Kinnell Comparison between the USLE, the USLE-M and replicate plots to model rainfall erosion on bare fallow areas , 2016 .

[33]  Panos Panagos,et al.  A classification of water erosion models according to their geospatial characteristics , 2014, Int. J. Digit. Earth.

[34]  G. R. Foster,et al.  storm Erosivity Using Idealized Intensity Distributions , 1987 .

[35]  Panos Panagos,et al.  Rainfall erosivity in Europe. , 2015, The Science of the total environment.

[36]  In Ho Choi,et al.  Scaling effect for the quantification of soil loss using GIS spatial analysis , 2010 .

[37]  R. Sugumaran,et al.  Integration of modified universal soil loss equation (MUSLE) into a gis framework to assess soil erosion risk , 2009 .

[38]  J. Poesen,et al.  Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate , 2001 .

[39]  Panos Panagos,et al.  High resolution spatiotemporal analysis of erosion risk per land cover category in Korçe region, Albania , 2016, Earth Science Informatics.

[40]  Panos Panagos,et al.  Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European Scale , 2015 .

[41]  Chuluong Choi,et al.  Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation , 2011 .

[42]  Christian Valentin,et al.  Land-use impacts on surface runoff and soil detachment within agricultural sloping lands in Northern Vietnam , 2008 .

[43]  J. Arnold,et al.  Development of a grid‐based version of the SWAT landscape model , 2015 .

[44]  Panos Panagos,et al.  Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service , 2012, Int. J. Digit. Earth.

[45]  C. Yoshimura,et al.  Spatio-temporal patterns of soil erosion and suspended sediment dynamics in the Mekong River Basin. , 2016, The Science of the total environment.

[46]  E. Kruk,et al.  INFLUENCE OF DAILY PRECIPITATION ON YIELD OF ERODED SOIL IN MOUNTAIN BASIN USING THE MUSLE MODEL , 2017 .