Efficient soil loss assessment for large basins using smart coded polygons

Soil erosion is a severe ecological problem. Most conventional methodologies for soil-erosion assessment are appropriate for small or medium river basins. This paper presents an approach to soil-erosion intensity assessment in large basins, utilizing coded polygons identified by spatially overlapping gradation levels of primary environmental factors. Efficient assessment of soil-erosion intensity is achieved by matching the coded polygons to selected polygons pre-assigned to reference groups. A case study is presented for the soil-erosion assessment of the Yellow River Basin. It is found that the calculated and observed soil-erosion intensities are in close agreement for 86% of the total area. Sensitivity analysis indicates that acceptable results are obtained using a 5% sample of the original 9,921 coded polygons, thus reducing substantially the computational load. Direct comparisons between the polygon codes in the reference and test groups show that uncertainty is reduced with respect to previous methods. This is confirmed by the reduction in information entropy from 7.49 to 1.33. The proposed approach should be of particular use in the cost-effective assessment of soil erosion in large basins.

[1]  P. Kinnell.  Event soil loss, runoff and the universal soil loss equation family of models: A review , 2010 .

[2]  A. Bronstert,et al.  GIS application of USLE and MUSLE to estimate erosion and suspended sediment load in experimental catchments, Valdivia, Chile , 2011 .

[3]  Gamini Herath,et al.  Soil erosion in developing countries: a socio-economic appraisal. , 2003, Journal of environmental management.

[4]  C. S. Wallace,et al.  An Information Measure for Hierarchic Classification , 1973, Comput. J..

[5]  W. G. Knisel,et al.  CREAMS: a field scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems [USA] , 1980 .

[6]  Rashmi Sharma,et al.  GIS versus CAD versus DBMS: What Are the Differences? , 2011 .

[7]  Rubén Prieto-Díaz.  Implementing faceted classification for software reuse , 1991, CACM.

[8]  Daniel C. Yoder,et al.  Enhancing RUSLE to include runoff‐driven phenomena , 2011 .

[9]  M. Stocking.  Soil erosion in developing countries: where geomorphology fears to tread! , 1995 .

[10]  L. R. Oldeman.  The Global Extent of Soil Degradation , 1992 .

[11]  Modeling Response of Soil Erosion and Runoff to Changes in Precipitation and Cover , 2005 .

[12]  Loredana Antronico,et al.  Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: An application model for Calabria (southern Italy) , 2009 .

[13]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[14]  D. Dowe,et al.  A conceptual model for integrating physical geography research and coastal wetland management, with an Australian example , 2010 .

[15]  Susan C. Herring,et al.  A Faceted Classification Scheme for Computer-Mediated Discourse , 2007 .

[16]  Andrew A. Millward,et al.  Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed , 1999 .

[17]  D. Greenland,et al.  Soil Resilience and Sustainable Land Use , 1994 .

[18]  Jian Peng,et al.  Land use change and soil erosion in the Maotiao River watershed of Guizhou Province , 2011 .

[19]  J. Poesen,et al.  Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates , 2003 .

[20]  De Graaff.  The costs of soil erosion , 2004 .

[21]  L. Stroosnijder,et al.  A tool for rapid assessment of erosion risk to support decision-making and policy development at the Ngenge watershed in Uganda , 2010 .

[22]  G. Jacks,et al.  Soil Conservation , 1940, Nature.

[23]  L. D. Meyer.  Evolution of the universal soil loss equation , 1984 .

[24]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[25]  John Boardman,et al.  Soil erosion science: Reflections on the limitations of current approaches ☆ , 2006 .

[26]  P. A. Burrough,et al.  Development of intelligent geographical information systems , 1992, Int. J. Geogr. Inf. Sci..

[27]  C. S. Wallace,et al.  Hierarchical Clusters of Vegetation Types. , 2005 .

[28]  M. Lesmez,et al.  Soil Erosion , 2021 .

[29]  H. Aksoy,et al.  A review of hillslope and watershed scale erosion and sediment transport models , 2005 .

[30]  J. Poesen,et al.  Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models , 2005 .

[31]  G. R. Foster,et al.  A Process-Based Soil Erosion Model for USDA-Water Erosion Prediction Project Technology , 1989 .

[32]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[33]  T. Tokola,et al.  Effect of vegetation cover on soil erosion in a mountainous watershed , 2008 .

[34]  Z. Shia,et al.  Soil conservation planning at the small watershed level using RUSLE with GIS : a case study in the Three Gorge Area of China , 2003 .

[35]  Jan Nyssen,et al.  Specific sediment yield in Tigray-Northern Ethiopia: Assessment and semi-quantitative modelling , 2005 .

[36]  A. Vrieling.  Satellite remote sensing for water erosion assessment: A review , 2006 .

[37]  Kyoung Jae Lim,et al.  Assessment of soil erosion and sediment yield in Liao watershed, Jiangxi Province, China, Using USLE, GIS, and RS , 2010 .

[38]  Li Rui.  A Review On the Research of Regional Soil Erosion and Environment in China , 2006 .

[39]  M. F. Guimarães,et al.  The costs of soil erosion , 2011 .

[40]  Donald Gabriëls,et al.  Extending the RUSLE with the Monte Carlo error propagation technique to predict long term average off-site sediment accumulation. , 2000 .

[41]  Geert Sterk,et al.  Erosion risk mapping : a methodological case study in the Colombian Eastern Plains , 2002 .

[42]  D. K. Borah,et al.  WATERSHED-SCALE HYDROLOGIC AND NONPOINT-SOURCE POLLUTION MODELS: REVIEW OF MATHEMATICAL BASES , 2003 .

[43]  L. Ci,et al.  Desertification assessment in China: An overview , 2005 .

[44]  R. Young,et al.  AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds , 1989 .

[45]  Bo Du,et al.  Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China , 2011 .

[46]  Li De,et al.  Artificial Intelligence with Uncertainty , 2004 .

[47]  L. K. Felker,et al.  Quantitative determination of , 2003 .

[48]  L. F. Huggins,et al.  ANSWERS: A Model for Watershed Planning , 1980 .

[49]  W. H. Wischmeier.  Use and misuse of the universal soil loss equation. , 1976 .

[50]  Hubert Gulinck,et al.  Temporal change in land use and its relationship to slope degree and soil type in a small catchment on the Loess Plateau of China , 2006 .

[51]  W. H. Wischmeier,et al.  Predicting rainfall-erosion losses from cropland east of the Rocky Mountains , 1965 .

[52]  J. Dijkshoorn,et al.  Soil and landform properties for LADA partner countries (Argentina, China, Cuba, Senegal, South Africa and Tunisia) , 2008 .

[53]  Alistair G.L. Borthwick,et al.  Soil erosion assessment based on minimum polygons in the Yellow River basin, China , 2008 .

[54]  Fangju Wang.  A parallel intersection algorithm for vector polygon overlay , 1993, IEEE Computer Graphics and Applications.

[55]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[56]  D. Pimentel,et al.  Environmental and Economic Costs of Soil Erosion and Conservation Benefits , 1995, Science.

[57]  Zhonglu Guo,et al.  The effects of rainfall regimes and land use changes on runoff and soil loss in a small mountainous watershed , 2012 .

[58]  Anton Van Rompaey,et al.  Predicting catchment sediment yield in Mediterranean environments: the importance of sediment sources and connectivity in Italian drainage basins , 2006 .

[59]  Zijian Zheng,et al.  Constructing X-of-N Attributes for Decision Tree Learning , 2000, Machine Learning.

[60]  Y. Lü,et al.  Hydrological responses and soil erosion potential of abandoned cropland in the Loess Plateau, China , 2012 .

[61]  A. W. Zingg,et al.  Degree and length of land slope as it affects soil loss in run-off. , 1940 .