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Use of spatially explicit physicochemical data to measure downstream impacts of headwater stream disturbance
Author(s) -
Johnson B. R.,
Haas A.,
Fritz K. M.
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2009wr008417
Subject(s) - streams , hydrology (agriculture) , watershed , environmental science , downstream (manufacturing) , confluence , sinuosity , water quality , ecology , geology , geomorphology , computer network , operations management , geotechnical engineering , machine learning , computer science , economics , biology , programming language
Regulatory agencies need methods to quantify the influence of headwater streams on downstream water quality as a result of litigation surrounding jurisdictional criteria and the influence of mountaintop removal coal mining activities. We collected comprehensive, spatially referenced physicochemical data (pH, dissolved oxygen, temperature, and specific conductance) from the partially mined Buckhorn Creek, KY, watershed in summer 2005 ( n = 239 sites) and spring 2006 ( n = 494 sites). We found conductivity was >10X higher in mined streams than in forested streams. Semivariograms, which quantify the degree of spatial dependence in chemistry values, indicated summer temperatures in both mined and unmined portions of the watershed had similar lag distances (approximately 5 km). Data for other parameters and seasons, however, violated model assumptions because of strong confluence effects in headwaters. We therefore developed a post hoc predictive model for water physicochemistry downstream of confluences using watershed areas as weighting factors. This weighted average model accurately predicted downstream conductivity (mean absolute error, MAE = 55.34 μ S cm −1 ), pH (MAE = 0.16 units), and temperature (MAE = 0.41°C) for confluences in Buckhorn Creek and two additional watersheds with headwater disturbance in West Virginia and Ohio. Use of semivariograms or predictive confluence models can help regulatory agents identify downstream influence of headwater streams and presence of a “significant nexus” with downstream waters.