Abstract
Geospatial analysis and statistical analysis are coupled in this study to determine the dynamic linkage between landscape characteristics and water quality for the years 1996, 2002, and 2007 in a subtropical coastal watershed of Southeast China. The landscape characteristics include Percent of Built (%BL), Percent of Agriculture, Percent of Natural, Patch Density and Shannon’s Diversity Index (SHDI), with water quality expressed in terms of CODMn and NH4 +–N. The %BL was consistently positively correlated with NH4 +–N and CODMn at time three points. SHDI is significantly positively correlated with CODMn in 2002. The relationship between NH4 +–N, CODMn and landscape variables in the wet precipitation year 2007 is stronger, with R2 = 0.892, than that in the dry precipitation years 1996 and 2002, which had R2 values of 0.712 and 0.455, respectively. Two empirical regression models constructed in this study proved more suitable for predicting CODMn than for predicting NH4 +–N concentration in the unmonitored watersheds that do not have wastewater treatment plants. The calibrated regression equations have a better predictive ability over space within the wet precipitation year of 2007 than over time during the dry precipitation years from 1996 to 2002. Results show clearly that climatic variability influences the linkage of water quality-landscape characteristics and the fit of empirical regression models.
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Acknowledgments
A preliminary form of this paper was presented at the Association of American Geographers (AAG) meeting on April 14–17, 2010 in Washington D.C. USA. This study is supported by Natural National Science Foundation of China (Grant No. 40810069004, Grant No. 40901100), and Natural Science Foundation of Fujian province, China (Grant No. 2009J01222). Anonymous reviewers supplied constructive feedback that helped to improve this paper.
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Huang, J., Li, Q., Pontius, R.G. et al. Detecting the Dynamic Linkage between Landscape Characteristics and Water Quality in a Subtropical Coastal Watershed, Southeast China. Environmental Management 51, 32–44 (2013). https://doi.org/10.1007/s00267-011-9793-2
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DOI: https://doi.org/10.1007/s00267-011-9793-2