Geo-cognitive computing method for identifying “source-sink” landscape patterns of river basin non-point source pollution

Zhang Xin, Cui Jintian, Liu Yuqi, Wang Lei


The aim of this study was to quantitatively evaluate the influences of landscape composition and spatial structure on the transmission process of non-point source pollutants in different regions. The location-weighted landscape contrast index, using the hydrological response unit (HRULCI) as the minimum research unit, was proposed in this paper. Through the description of the endemic landscape types and various geographical factors in the basin, the index calculation can reflect the impact of the “source-sink” landscape structure on the non-point source pollution in different regions and quantitatively evaluate the contribution of different landscape types and geographical factors to non-point source pollution. This study constructed a method of geo-cognitive computing for identifying “source-sink” landscape patterns of river basin non-point source pollution at two levels. 1) The basin level: the spatial distribution and landscape combination of the entire basin are identified, and the crucial “source” and “sink” landscape types are obtained to measure the differences in the non-point source pollutant transmission processes between the “source” and “sink” landscapes in the different watersheds. 2) The landscape level: HRULCI is calculated based on multiple geographical correction weighting factors. By using the idea of intersecting geographic information system (GIS) and landscape ecology, the landscape spatial pattern and ecological processes are linked. Compared with the traditional method for studying landscape patterns, the calculation of HRULCI makes the proposed method more ecologically significant. Lastly, a case study was evaluated to verify the significance of the proposed research method by taking the Yanshi River basin, a sub-basin belonging to the Jiulong River basin located in Fujian Province, China, as the experimental study zone. The results showed that this method can reflect the spatial distribution characteristics of the “source-sink” types and their relationship with non-point source pollution. By comparing the resulting calculation based on HRULCI, the risk of nutrient loss and the influence of landscape patterns and ecological processes on non-point pollution in different catchments can be obtained.
Keywords: non-point source pollution, “source-sink” landscape pattern, remote sensing, hydrological response unit, quantitative calculation
DOI: 10.25165/j.ijabe.20171005.3272

Citation: Zhang X, Cui J T, Liu Y Q, Wang L. Geo-cognitive computing method for identifying “source-sink” landscape patterns of river basin non-point source pollution. Int J Agric & Biol Eng, 2017; 10(5): 55–68.


non-point source pollution, “source-sink” landscape pattern, remote sensing, hydrological response unit, quantitative calculation


Munafò M, Cecchi G, Baiocco F, Mancini L. River pollution from non-point sources: a new simplified method of assessment. Journal of Environmental Management, 2005; 77: 93–98.

Corwin D L, Vaughan P J, Loague K. Modeling non-point source pollutants in the vadose zone using GIS. Environmental Science and Technology, 1997; 31(8): 2157–2175.

United States Environmental Protection Agency. National Water Quality Inventory, USA: 1998 Report to Congress, 2000.

Boers P C M. Nutrient emissions from agriculture in the Netherlands, causes and remedies. Water Science and Technology, 1996; 4-5: 183–189.

Cheng H F, Hu Y A, Zhao J F. Meeting China’s water shortage crisis: Current practices and challenges. Environmental Science and Technology, 2009; 43: 240–244.

Liu S L, Fu B J. Application of landscape ecology principle in soil science. The Journal of Soil and Water Conservation, 2001; 15(3): 102–106.

Verburg P H, Steeg J V D, Veldkamp A, Willemen L. From land cover change to land function dynamics: a major challenge to improve land characterization. Journal of Environmental Management, 2009; 90(3): 1327–1335.

Kibena J, Nhapi I, Gumindoga W. Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River, Zimbabwe. Physics and Chemistry of the Earth, 2014; 67-69: 153–163.

Rajaei F, Sari A E, Salmanmahiny A, Delavar M, Bavani A R M, Srinivasan R. Surface drainage nitrate loading estimate from agriculture fields and its relationship with landscape metrics in Tajan watershed. Paddy and Water Environment, 2017; 15(3): 541–552.

Liess A, Le Gros A, Wagenhoff A, Townsend C R, Matthaei C D. Landuse intensity in stream catchments affects the benthic food web: consequences for nutrient supply, periphyton C:nutrient ratios, and invertebrate richness and abundance. Freshwater Science, 2012; 31(3): 813–824.

Derek R. Implementing landscape indices to predict stream water quality in an agricultural setting: an assessment of the Lake and River Enhancement (LARE) protocol in the Mississinewa River watershed, East-Central Indiana. Ecological Indicators, 2010; 10: 1102–1110.

Chen L D, Fu B J, Xu J Y, Gong J. Location-weighted landscape contrast index: a scale independent approach for landscape pattern evaluation based on “Source-Sink” ecological processes. Acta Ecologica Sinica, 2003; 23(11): 2406–2413. (in Chinese)

Wang J L, Shao J A, Wang D, Ni J P, Xie D T. Identification of the “source” and “sink” patterns influencing non-point source pollution in the Three Gorges Reservoir Area. Journal of Geographical Sciences, 2016; 10: 1431–1448.

Bhaduri B, Harbor J, Engel B, Grove M. Assessing watershed-scale, long-term hydrologic impacts of land use change using a GIS-NPS model. Environment Management, 2000; 26(6): 643–658.

Li Z, Liu W Z, Zhang X C, Zheng F L. Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 2009; 377: 35–42.

Merz R, Blǒschl G, Parajka J. Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 2006; 331: 591–604.

Gergel S E.Spatial and non-spatial factors: When do they affect landscape indicators of watershed loading? Landscape Ecology, 2005; 20: 177–189.

Moreno J L, Navarro C, Heras J D L. Abiotic ecotypes in south-central Spanish rivers: Reference conditions and pollution.Environmental Pollution, 2006; 143: 388–396.

Basnyat P, Teeter L D, Flynn K M, Lockaby B G. Relationships between landscape characteristics and non-point source pollution inputs to coastal estuary. Environmental Management, 1999; 23(4): 539–549.

Wu J G. Paradigm shift in ecology: an overview. Acta Ecologica Sinica, 1996; 16(5): 449–460.

Shen Z Y, Hou X S, Li W, Aini G. Relating landscape characteristics to non-point source pollution in a typical urbanized watershed in the municipality of Beijing. Landscape and Urban Planning, 2014; 123: 96–107.

Chen L D, Tian H Y, Fu B J, Zhao X F. Development of a new index for integrating landscape patterns with ecological processes at watershed scale. Chinese Geographical Science, 2009; 19: 37–45.

Ouyang W, Skidmore A K, Toxopeus A G, Hao F H. Long-term vegetation landscape pattern with non-point source nutrient pollution in upper stream of Yellow River basin. Journal of Hydrology, 2010; 389(3-4): 373–380.

Tim U S. Coupling vadose zone models with GIS: Emerging trends and potential bottlenecks. Journal of Environmental Quality, 1996; 25(3): 535–544.

Ning J C, Gao Z Q, Lu Q S. Runoff simulation using a modified SWAT model with spatially continuous HRUs. Environmental Earth Sciences, 2015; 7: 5895–5905.

Lin W-T, Chou W-C, Lin C-Y, Huang P-H, Tsai J-S. Automated suitable drainage network extraction from digital elevation models in Taiwan’s upstream watersheds. Hydro Process, 2006; 20(2): 289–306.

Guo X D, Chen L D, Fu B J. Effects of land use / land cover changes on regional ecological environment. Chinese Journal of Environmental Engineering, 1999; 7(6): 66–75. (in Chinese)

Ersahin S, Brohi A R. Spatial variation of soil water content in topsoil and subsoil of a Typic Ustifluvent. Agricultural Water Management, 2006, 83(1-2): 79–86.

Western A W, Zhou S L, Grayson R B, McMahon T A, Bloöschl G, Wilson D. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology, 2004; 286(1): 113–134.

Blyth E M, Dolman A J, Wood N. Effective resistance to sensible-and latent-heat flux in heterogeneous terrain. Quarterly Journal of the Royal Meteorological Society, 1993; 119(511): 423–442.

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