Elsevier

Ocean & Coastal Management

Volume 103, January 2015, Pages 14-24
Ocean & Coastal Management

Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed

https://doi.org/10.1016/j.ocecoaman.2014.10.007Get rights and content

Highlights

  • GWR reveals spatial variation in water pollution-land use linkages.

  • Water pollution is associated more with built-up than with cropland or forest.

  • More built-up is associated with more pollution for less urbanized sub-watersheds.

  • Forest has a stronger negative association with pollution in urban sub-watersheds.

  • Cropland has a weak association with water pollution among 21 sub-watersheds.

Abstract

Land use can influence river pollution and such relationships might or might not vary spatially. Conventional global statistics assume one relationship for the entire study extent, and are not designed to consider whether a relationship varies across space. We used geographically weighted regression to consider whether relationships between land use and water pollution vary spatially across a subtropical coastal watershed of Southeast China. Surface water samples of baseflow for seven pollutants were collected twelve times during 2010–2013 from headwater sub-watersheds. We computed 21 univariate regressions, which consisted of three regressions for each of the seven pollutants. Each of the three regressions considered one of three independent variables, i.e. the percent of the sub-watershed that was cropland, built-up, or forest. Cropland had a local R2 less than 0.2 for most pollutants, while it had a positive association with water pollution in the agricultural sub-watersheds and a negative association with water pollution in the non-agricultural sub-watersheds. Built-up had a positive association with all pollutants consistently across space, while the increase in pollution per increase in built-up density was largest in the sub-watersheds with low built-up density. The local R2 values were stronger with built-up than with cropland and forest. The local R2 values for built-up varied spatially, and the pattern of the spatial variation was not consistent among the seven pollutants. Forest had a negative association with most pollutants across space. Forest had a stronger negative association with water pollution in the urban sub-watersheds than in the agricultural sub-watersheds. This research provides an insight into land-water linkages, which we discuss with respect to other watersheds in the literature.

Introduction

River water pollution in watersheds is strongly related to increasing anthropological influences, such as urbanization, agriculture, industry and sewage (Li et al., 2009, Xu et al., 2009, Ye et al., 2009). These anthropological influences are part of the larger process of watershed land use and land cover change that can affect the water pollution of rivers, lakes, and downstream estuarine and coastal waters (Baker, 2003, Roberts and Prince, 2010). Watershed land use impacts water pollution through non-point sources, which are major contributors of contaminants to the catchment-coast continuum (Swaney et al., 2012, Huang et al., 2013b). Therefore, exploring the linkages between land use and water pollution is commonly recognized as an imperative step to forecast river water pollution in watersheds and to control land-based pollution in coastal bays.

Many studies address the general relationships between land use and water pollution. Generally, built-up land and agricultural land have significant positive correlations with water pollution, which are associated with point or non-point source pollution (Johnson et al., 1997, Sliva and Williams, 2001, Fedorko et al., 2005, Mehaffey et al., 2005, Stutter et al., 2007, Tu et al., 2007, Bahar et al., 2008, Tran et al., 2010, Pratt and Chang, 2012, Yang, 2012). Woodland is significantly negatively correlated with nutrients, due to the general understanding that forests can absorb nutrients (Osborne and Kovacic, 1993, Novotny, 2002, Galbraith and Burns, 2007, Bahar et al., 2008, Lopez et al., 2008).

However, the relationships between land use and water pollution can be inconsistent across time and space. Inconsistency is well documented in terms of seasonal or inter-annual variations (Sliva and Williams, 2001, Kaushal et al., 2008, Lee et al., 2009, Huang et al., 2013a), spatial scale effects (e.g. buffer versus entire watersheds) (Sliva and Williams, 2001, Uuemaa et al., 2005, Guo et al., 2010, Tran et al., 2010, Pratt and Chang, 2012) and watershed characteristics represented by various dominant land uses such as woodland, built-up, and mining (Mehaffey et al., 2005, Xiao and Ji, 2007, Bahar et al., 2008). The methods used in most of the above-mentioned studies are conventional global statistical methods, such as Pearson correlation analysis (Tong and Chen, 2002, Galbraith and Burns, 2007, Bahar et al., 2008, Lee et al., 2009, Sun et al., 2011b) and multiple regression (Sliva and Williams, 2001, Fedorko et al., 2005, Huang et al., 2013a, Yang, 2012). These global statistics are commonly used to analyze the overall association for the entire study area, and may hide some local relationships, especially among watersheds that are dominated by different uses, such as urban, forest or agriculture (Tu and Xia, 2008, Tu, 2011). Global statistics are not designed to explore spatial variations in relationships between land use and water pollution.

Some researchers have applied a statistical method named geographically weighted regression (GWR) to examine the spatially varying relationships between land use and water pollution (Brunsdon et al., 1998, Tu, 2011, Pratt and Chang, 2012). However, few studies have examined spatial variations in relationships between land use and water pollution in the watersheds of China. Such examinations are critical for China's watershed management, due to the fact that freshwater pollution is a prime concern, especially in the relatively developed regions such as the Eastern coastal areas of China (Huang et al., 2013a). More attempts need to be made to investigate relationships in these areas of China between land use and water pollution in the coastal watersheds with intensive human activities, great spatial variability of land use, and subsequent water pollution.

Our previous study used global multiple linear regression to show that land use, especially the percentage of built-up land, can be one of the most important predictors of water pollution in the Jiulong River Watershed (JRW), which is a typical medium-sized subtropical coastal watershed in China (Huang et al., 2013a). However, a better understanding concerning how land use impacts water pollution is critical for developing watershed management practices in such a coastal watershed. The objectives of this study are to explore the spatial variations in the relationships between land use and water pollution and to develop potential insights for watershed management in the JRW.

Section snippets

Study area

The JRW covers about 14,700 sq km in the eastern coastal area of China (from 116°46′55″E to 118°02′17″E, and from 24°23′53″ to 25°53′38″N) and consists mainly of eight counties/districts: Zhangzhou, Xinlou, Zhangping, Hua’an, Changtai, Pinghe, Longhai and Nangjing (Fig. 1). The JRW is situated in a subtropical zone with a monsoon climate: annual average temperature is 19–21 °C, and annual precipitation averages 1400–1800 mm, of which 70% occurs between April and September. The watershed

Water pollutants

Surface water samples of base flow were collected from 21 sampling sites at twelve time points. Each time point was during a particular season of a particular year. There were four years from 2010 to 2013, and three seasons per year. The flood season is in August, the dry season is in November, and the transition season is in March. The samples were kept at 4 °C and transported to the laboratory for analysis. Seven pollutants were analyzed following standard methods (SEPAC, 2002) and completed

Spatial variation in land use classes

Fig. 2 shows the land use composition for each of the 21 sub-watersheds along with the three sub-watershed groups. The group of agricultural sub-watersheds with IDs from A1 to A12 have more than 10% cropland; A1, A8, A11 and A12 have over 20% cropland. The sub-watersheds with IDs U1, U2, and U3 are in the urban group, and have 27%, 9% and 14% Built-up. The natural group of sub-watersheds with IDs from N1 to N6 have over 80% forest.

Global correlation between land use and water pollution

Table 1 shows the Pearson correlations between water pollution

Relationships between land use types and water pollution indicators

The relationships between water pollutants and land use revealed multiple influences on water pollution sources. The percentage of cropland was significantly positively correlated with NO3–N in this study (Table 1), which is understandable because the NO3–N is likely to come from fertilizers used on the agricultural land (Cao et al., 2006, Shen et al., 2011). Others have reported that salinity tends to increase in the non-forest areas, especially the urban areas (de Souza et al., 2013). We

Conclusions

Globally, the association between water pollution was stronger with built-up percent than with cropland percent or forest percent. Cropland percent had a weak association, forest percent had a negative association, while built-up percent had a positive association with water pollution. Local R2 values are all less 0.4 for cropland percent. All the sub-watersheds with a high built-up percent have high pollution concentrations; and an increase in built-up percent is associated with a noticeable

Acknowledgments

This study was supported by the Natural National Science Foundation of China (Grant No. 40901100 and Grant No. 41471154) and the National Science and Technology Support Program (Grant No. 2013BAC06B01). Anonymous reviewers supplied constructive feedback that helped to improve this paper.

References (63)

  • B. Pratt et al.

    Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales

    J. Hazard. Mater.

    (2012)
  • E.R. Porter-Goff et al.

    Changes in riverine benthic diatom community structure along a chloride gradient

    Ecol. Indic.

    (2013)
  • A.D. Roberts et al.

    Effects of urban and non-urban land cover on nitrogen and phosphorous runoff to Chesapeake Bay

    Ecol. Indic.

    (2010)
  • Y.N. Shen et al.

    Response of stream pollution characteristics to catchment land cover in Cao-E River Basin, China

    Pedosphere

    (2011)
  • N.T. Skoulikidis et al.

    Analysis of factors driving stream water composition and synthesis of management tools—A case study on small/medium Greek catchments

    Sci. Total Environ.

    (2006)
  • L. Sliva et al.

    Buffer zone versus whole catchment approaches to studying land use impact on river water quality

    Water Res.

    (2001)
  • M.I. Stutter et al.

    River sediments provide a link between catchment pressure and ecological status in a mixed land use Scottish River system

    Water Res.

    (2007)
  • D.P. Swaney et al.

    Five critical questions of scale for the coastal zone

    Estuar. Coast. Shelf Sci.

    (2012)
  • S.T.Y. Tong et al.

    Modeling the relationship between land use and surface water quality

    J. Environ. Manag.

    (2002)
  • C.P. Tran et al.

    Land-use proximity as basis for assessing stream water quality in New York State (USA)

    Ecol. Indic.

    (2010)
  • M.P. Tripathi et al.

    Identification and prioritization of sub-watersheds for soil conservation management using SWAT model

    Biosyst. Eng.

    (2003)
  • J. Tu

    Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression

    Appl. Geogr.

    (2011)
  • J. Tu et al.

    Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation

    Sci. Total Environ.

    (2008)
  • E. Uuemaa et al.

    Scale dependence of landscape metrics and their indicatory value for nutrient and organic matter losses from catchments

    Ecol. Indic.

    (2005)
  • K.P. Woli et al.

    Evaluating river water quality through land use analysis and N budget approaches in livestock farming areas

    Sci. Total Environ.

    (2004)
  • H. Xu et al.

    Anthropogenic impact on surface water quality in Taihu Lake region, China

    Pedosphere

    (2009)
  • X.J. Yang

    An assessment of landscape characteristics affecting estuarine nitrogen loading in an urban watershed

    J. Environ. Manag.

    (2012)
  • M.M. Bahar et al.

    Relationship between river water quality and land use in a small river basin running through the urbanizing area of Central Japan

    Limnology

    (2008)
  • A. Baker

    Land use and water quality

    Hydrol. Process.

    (2003)
  • C. Brunsdon et al.

    Geographically weighted regression-modeling spatial non-stationarity

    Statistician

    (1998)
  • W.Z. Cao et al.

    Anthropogenic nitrogen sources and export in a village-scale catchment in Southeast China

    Environ. Geochem. Health

    (2006)
  • Cited by (0)

    View full text